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Artists who got almost $1,500 a month under a basic income pilot ...
|
Artists who got almost $1,500 a month under a basic income pilot say their work improved
|
https://www.aol.com
|
[
"Aol Staff",
"Lauren Edmonds",
"June",
"At Pm"
] |
This month, Basic Income Ireland called on the government to immediately implement a universal and unconditional basic income for the country. A ...
|
A basic income program for the arts in Ireland ends in August after three years. Brian Lawless - PA Images/PA Images via Getty Images
Ireland's basic income pilot program for the arts ends in August.
For three years, 2,000 artists and creative arts workers received about $370 a week.
Recipients said the stipend overall improved their daily lives.
For about 2,000 artists and creative arts workers in Ireland, a weekly stipend provided through a basic income program has been a lifeline for years.
Now, it's almost over.
The pilot program began in 2022 under Catherine Martin, Ireland's former minister for tourism and culture. Martin allocated about $28 million to the arts sector following the COVID-19 pandemic.
Participants were randomly chosen and given an unconditional stipend of €325, or about $370, weekly for three years. During that time, participants met periodically via Zoom to discuss how the additional income had affected their livelihoods, careers, and ability to meet basic needs.
The final session was held this month before the program's conclusion in August.
Artists and cultural workers who attended the session grappled with what their lives would look like after August, but they hoped government officials would extend the program.
"We need no further pilots. People need a UBI now to face and deal with the many social, economic, and ecological crises of our world," Reinhard Huss, the organizer of UBI Lab Leeds, which sponsored the event alongside Basic Income Ireland, UBI Lab Arts, and UBI Lab Network, told Business Insider.
New developments in AI are reshaping the job market, replacing some entry-level positions. Tech industry leaders like Elon Musk and OpenAI CEO Sam Altman have said implementing a universal basic income will be essential in the near future when AI supplants jobs in most industries.
A universal basic income offers an entire population recurring, unconditional payments regardless of an individual's socioeconomic status. Ireland's program, like many others in the United States, is a guaranteed basic income, which targets certain segments of the population for a set period of time.
Impact of Ireland's basic income program for artists
Jenny Dagg, a sociologist lecturing at Ireland's Maynooth University, authored a new report that provides insights into participants' reactions to the program. She gathered data from over 50 of the 2,000 recipients.
Although the report outlined nearly a dozen key impacts reported by program recipients, Dagg highlighted five major takeaways during the Zoom session.
Dagg said that recipients who received money from the program reported more stability and "significantly reduced" financial stress. It relieved their anxiety about fulfilling their basic needs.
Participating in the pilot program also allowed artists to re-prioritize how they spend their time and what they choose to focus on. "The opportunity to focus more on their specific creative interests opened new possibilities and career trajectories," the report said.
Artists said the added income allowed them to spend more time "researching, experimenting, taking risks, and failing," which has improved the quality of their work.
Artists, the report said, also felt more confident in themselves and their work during the program. "Many recipients talk of feeling empowered, of being in control of the choices within their lives, and envisioning a viable career path longer-term," the report said.
Recipients even reported better mental health, which led to improved sleep quality and lowered stress levels.
What's next for Ireland's basic income program
With the end of the program fast approaching, recipients of the weekly payment are reckoning with what how their lives might change.
"Across art forms, recipients report concerns about financial stability and sustaining the momentum of their careers when, or if, the basic income scheme ends," Dagg's report said.
This month, Basic Income Ireland called on the government to immediately implement a universal and unconditional basic income for the country. A spokesperson for the UBI Lab Network said the pilot program's success shows that basic income is a viable option. The campaign group shared a proposal for introducing a universal basic income to Ireland.
"As the pilot shows, basic income works and people need a UBI now to face and deal with the many social, economic, and ecological crises of our world. The Network will continue to help demonstrate basic income within communities and show how it is a sustainable policy," the statement said.
Patrick O'Donovan, Ireland's minister for arts and culture, said he would evaluate the data collected throughout the pilot program and create proposals for the government regarding the next steps.
"I am heartened by the responses of the Basic Income recipients in this paper," O'Donovan said in the May report. "This research will add to the evaluation being conducted by my department, which to date clearly shows that the Basic Income Pilot has been an effective support for the artists in receipt of it."
Read the original article on Business Insider
| 2025-06-08T00:00:00 |
https://www.aol.com/news/artists-got-almost-1-500-205113466.html
|
[
{
"date": "2025/06/08",
"position": 74,
"query": "universal basic income AI"
},
{
"date": "2025/06/08",
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},
{
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"date": "2025/06/08",
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{
"date": "2025/06/08",
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{
"date": "2025/06/08",
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"date": "2025/06/08",
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"query": "universal basic income AI"
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{
"date": "2025/06/08",
"position": 78,
"query": "universal basic income AI"
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] |
|
AI cuts begin at the bottom: Entry-level jobs disappear in ...
|
AI cuts begin at the bottom: Entry-level jobs disappear in the age of automation
|
https://www.calcalistech.com
|
[] |
That same weekend, Business Insider laid off 21% of its staff while reaffirming its commitment to AI. ... layoffs, particularly in white-collar professions.
|
In late April, Luis von Ahn, founder and CEO of the language-learning app Duolingo, sent an email to all employees with startling news. The company, which describes itself as “AI-first,” plans to eliminate contract workers whose tasks can now be completed by artificial intelligence.
Duolingo isn’t alone. In March, Shopify implemented a policy requiring managers to prove that AI cannot do a given job before new hires are approved. In May, Salesforce confirmed that integrating AI into its operations led to a hiring slowdown, and 500 customer service employees were reassigned to other roles. That same weekend, Business Insider laid off 21% of its staff while reaffirming its commitment to AI. “Over 70% of Business Insider employees are already using Enterprise ChatGPT regularly (our goal is 100%),” CEO Barbara Peng wrote in a memo.
1 View gallery Anthropic CEO Dario Amodei ( Benjamin Girette/Bloomberg )
Ever since the launch of powerful generative AI models and autonomous agents two and a half years ago, experts have warned that such technology could reduce recruitment and even lead to layoffs, particularly in white-collar professions. While once considered speculative, mounting evidence shows this is already happening. Recent data suggests these are not isolated incidents: the replacement of human workers with AI, especially at the junior level, is well underway.
Zanele Munyikwa, an economist at labor analytics firm Revelio Labs, recently examined online job postings for roles involving tasks AI can now perform. She found that listings for such positions have declined by 19% over the past three years. Her conclusion: companies are choosing not to hire for jobs AI can handle.
According to Munyikwa, roles with high exposure to AI - such as database managers, IT specialists, and data engineers - are more vulnerable to reduced demand than lower-exposure jobs like restaurant managers, construction foremen, or mechanics. In other words, white-collar jobs reliant on data processing and analysis are facing the sharpest drop in demand. Yet, she cautioned that it’s not clear whether AI in its current state is capable of doing all the white-collar jobs that employers think it can do.
The trend is also visible in macroeconomic data. The U.S. unemployment rate for recent college graduates has surged to 5.8%, a sharp rise. According to the Federal Reserve Bank of New York, employment prospects for this group are “deteriorating markedly.” In May, Oxford Economics reported that this spike in graduate unemployment is driven in large part by shifting hiring practices in the tech industry. “There are signs that entry-level jobs are being replaced by artificial intelligence at an accelerating pace,” the firm noted.
According to The New York Times, this data may only hint at the broader transformation already underway. More companies are rapidly automating entry-level positions and replacing junior employees with AI agents. One tech executive told the paper that their firm had ceased hiring programmers with 3–7 years of experience, since AI coding tools can handle the work. Another startup said one data scientist now does what previously required a 75-person team.
Anthropic CEO Dario Amodei echoed that concern in a recent interview with Axios, predicting that AI could wipe out half of all entry-level jobs and increase overall unemployment by 10–20% over the next five years. “Most workers are unaware that this is going to happen,” he said. “It sounds crazy. People just don’t believe it.”
Still, the future Amodei envisions is not inevitable. The technology is not yet foolproof, and some companies have been forced to backtrack. Swedish fintech Klarna, which announced two years ago that it would replace its customer service staff with AI chatbots, has since begun rehiring humans after customer complaints about declining service quality. In some cases, companies may adopt AI too quickly, expecting imperfect systems to improve rapidly, only to find that the results fall short.
But this may be a temporary hiccup. The pace of improvement among AI models from firms like OpenAI and Google has been dramatic. Even smaller players, such as China’s DeepSeek, have shaken up the field with impressive advances.
| 2025-06-08T00:00:00 |
2025/06/08
|
https://www.calcalistech.com/ctechnews/article/rkqg5y1xg
|
[
{
"date": "2025/06/08",
"position": 42,
"query": "AI layoffs"
}
] |
Reflections on Workforce Transformation: AI, Agile, and the Human ...
|
Reflections on Workforce Transformation: AI, Agile, and the Human Touch
|
https://www.linkedin.com
|
[
"Christopher Rj. Cunningham",
"Prashant S V",
"Walkingtree Technologies",
"Danny Tan",
"Cyber Security Consultant",
"Auditor At Dannytan Ace Pte Ltd"
] |
Workforce transformation is about balancing the power of AI and Agile with the wisdom and empathy of human experience. It's about creating an ...
|
After an intense week of “vibe coding”—where using a large language model (LLM) truly felt like collaborating with a colleague—I’ve been reflecting on what it means for our generation to lead workforce transformation in the age of Artifical Intelligence and Agile. The concept of work will be radically different in a decade time.
AI is no longer just about automation or replacing jobs; it’s redefining expertise and reshaping how we work together. The most successful organizations in this transformation are those that see AI as a partner, not a disruptor. They leverage AI to identify skill gaps, personalize learning, and accelerate internal mobility, making their workforce more adaptable and resilient. But technology alone won’t drive transformation—people, culture, and mindset are just as important.
The Human Side of Change
Managing change in this environment is complex. It’s not enough to roll out new tools and hope for the best. Employees need to feel supported, not threatened. Gaining top management buy-in is crucial, but more importantly is building trust with the human workforce. Many worry that transformation is just a euphemism for headcount reduction. Addressing these fears openly and honestly is essential. Leaders must communicate and sincerely believe that the goal is upskilling, not downsizing, and provide real opportunities for growth.
Agile Transformation and Continuous Learning
Agile transformation is about more than new processes—it’s about fostering a culture of continuous learning and adaptability. AI can help by providing real-time insights, personalized training, and support, but it still needs experienced professionals to guide the process.
In my recent mini coding projects, I’ve seen how generative AI can suggest technical designs or code faster that I can ever hope to master, but it’s the human architect who provides the critical feedback, context, and construction advice that ensure the solution is robust and fit-for-purpose, and the LLM doing the work do not go down a rabbit hole. The feeling reminds me of the role of an experienced manager coaching an intern or a new staff out of college. Good instruction, forcing the AI agent to develop a plan, human review the plan, and then execute the project part by part is necessary to get good results. Providing proper guardrails on what is alright and what is going too far from social acceptance is also necessary.
A Learning Mindset for Everyone
The central takeaway from my recent exploration with "vibe coding" underscores a fundamental truth: the effectiveness of AI is intrinsically linked to the capabilities of its human users. To fully leverage the transformative potential of AI, organizations must prioritize strategic investments in upskilling initiatives, cultivate supportive environments that encourage experimentation without fear of failure, and foster a culture where continuous learning becomes the norm for everyone. Embracing change with both curiosity and resilience will be essential for navigating the evolving landscape and ensuring continued success. In essence, guiding AI through prompt engineering shares a striking similarity with communicating with a young child: clarity is paramount. Providing comprehensive context, clearly defined objectives, and a thorough understanding of the intended audience are crucial for achieving the desired outcomes.
Final Thoughts
Workforce transformation is about balancing the power of AI and Agile with the wisdom and empathy of human experience. It’s about creating an environment where everyone feels valued, supported, and ready to learn. With the right mindset and leadership, the future of work is not only more productive—it’s more human.
Tools like ChatGPT, Gemini and Perplexity AI have started to provide easy reach of information of the public space. Newer tools like Google Agentspace and Microsoft Copilot will add these search and analytical capabilities to the private enterprise information space.
My fellow white-collar professionals, the escalating automation of routine and labor-intensive tasks signals a profound and continuous reshaping of our professional landscape. Just as previous disruptive forces like the internet and mobile technology compelled adaptation, thriving in this evolving environment necessitates a proactive commitment to continuous upskilling. By cultivating higher-level cognitive abilities and strategic thinking, we can concentrate on roles that deliver greater value. Our capacity to critically assess the evolving capabilities of generative AI and the changing work environment will enable us to strategically adapt our focus, ultimately driving positive progress.
| 2025-06-09T00:00:00 |
https://www.linkedin.com/pulse/reflections-workforce-transformation-ai-agile-human-touch-lee-14xmc
|
[
{
"date": "2025/06/09",
"position": 33,
"query": "AI workforce transformation"
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{
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{
"date": "2025/06/09",
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{
"date": "2025/06/09",
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{
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{
"date": "2025/06/09",
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|
Is AI Coming for Your Job? Here's How to Tell - Investopedia
|
Is AI Coming for Your Job? Here's How to Tell
|
https://www.investopedia.com
|
[
"Adam Hayes",
"Ph.D.",
"Cfa",
"Is A Financial Writer With",
"Years Wall Street Experience As A Derivatives Trader. Besides His Extensive Derivative Trading Expertise",
"Adam Is An Expert In Economics",
"Behavioral Finance. Adam Received His Master'S In Economics The New School For Social Research",
"His Ph.D. The University Of Wisconsin-Madison In Sociology. He Is A Cfa Charterholder As Well As Holding Finra Series",
"Licenses. He Currently Researches",
"Teaches Economic Sociology"
] |
AI does not have to be a monolithic job killer; it can be a task-reallocation engine. Your individual vulnerability hinges on how many of your ...
|
Back in 2023, Goldman Sachs warned that generative AI could put 300 million jobs at risk worldwide. By 2025, experts warn that AI could wipe out half of all entry-level, white-collar jobs—and spike unemployment to 10%-20% over the next several years.
Large language models (LLMs) like Claude or ChatGPT can now write marketing copy, compose poetry and short stories, draft legal memos, and debug code in seconds. It can search the web, collate sources, generate research summaries, and even spit out polished slide decks. That makes many people wonder: Is my job next?
Recent research suggests the answer could depend less on your job title and more on the bundle of tasks you perform each day. Think of tasks as the sub-units of work that fill your calendar: drafting an invoice, negotiating with a supplier, sketching a storyboard frame, reconciling a ledger entry, or writing some code.
Depending on how any of these tasks can be automated with AI, you might or might not start to worry. Below, we explain how to gauge your risk and potential upside amid the AI rollout.
Key takeaways The more of your daily tasks that large-language models (LLMs) can already handle, the higher your displacement risk.
Workers whose task mix ranges from easily automated to hard-to-automate will likely fare better than specialists who do one thing well.
With the right design and policy, the technology could revive middle-skill, middle-income work rather than destroy it.
Task Exposure: The Metric to Watch
No surprise here: jobs composed mainly of tasks that AI can do entirely are most at risk. On the other hand, those that involve at least some human-only tasks appear to be safe (for now), as employees shift to the creative, client-facing, uniquely human tasks that AI still can’t do.
Run a mini-audit on yourself: list your top 10 weekly tasks and tick off any that a GPT-4-level model could do today. If AI could handle more than 50%, that signals displacement risk; under 30% suggests that AI could provide productive augmentation.
| 2025-06-09T00:00:00 |
https://www.investopedia.com/could-ai-be-coming-for-your-job-11749570
|
[
{
"date": "2025/06/09",
"position": 87,
"query": "AI economic disruption"
},
{
"date": "2025/06/09",
"position": 97,
"query": "AI economic disruption"
},
{
"date": "2025/06/09",
"position": 96,
"query": "AI economic disruption"
},
{
"date": "2025/06/09",
"position": 97,
"query": "AI economic disruption"
},
{
"date": "2025/06/09",
"position": 88,
"query": "AI economic disruption"
},
{
"date": "2025/06/09",
"position": 66,
"query": "AI economic disruption"
},
{
"date": "2025/06/09",
"position": 67,
"query": "AI economic disruption"
},
{
"date": "2025/06/09",
"position": 61,
"query": "ChatGPT employment impact"
},
{
"date": "2025/06/09",
"position": 45,
"query": "artificial intelligence employment"
}
] |
|
Anthropic's CEO Is Wrong About Job Loss to AI. It Will Be Worse ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
A recent study finds that even half of CEOs believe they can be replaced by artificial intelligence. Below them sit their vulnerable direct reports.
|
A subreddit devoted to the field of Future(s) Studies and evidence-based speculation about the development of humanity, technology, and civilization. -------- You can also find us in the fediverse at - https://futurology.today
Members Online
| 2025-06-09T00:00:00 |
https://www.reddit.com/r/Futurology/comments/1l6s00o/anthropics_ceo_is_wrong_about_job_loss_to_ai_it/
|
[
{
"date": "2025/06/09",
"position": 5,
"query": "AI job losses"
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{
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{
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{
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{
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"query": "AI job losses"
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{
"date": "2025/06/09",
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},
{
"date": "2025/06/09",
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{
"date": "2025/06/09",
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"query": "AI job losses"
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{
"date": "2025/06/09",
"position": 4,
"query": "AI job losses"
},
{
"date": "2025/06/09",
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"query": "AI job losses"
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|
klarna ai layoffs: Company that sacked 700 workers with AI now ...
|
Company that sacked 700 workers with AI now regrets it — scrambles to rehire as automation goes horribly wrong
|
https://m.economictimes.com
|
[] |
The Swedish fintech giant, once praised for firing 700 employees and replacing them with artificial intelligence, is now reversing that decision.
|
Why did Klarna fire 700 employees and replace them with AI?
What went wrong with Klarna’s AI-only approach?
Live Events
Is Klarna now rehiring the people it once let go?
What lessons does Klarna’s AI regret teach other companies?
Can AI and humans work together in the future of customer service?
Why Klarna’s AI mistake matters
FAQs:
(You can now subscribe to our
(You can now subscribe to our Economic Times WhatsApp channel
Klarna, the popular Swedish fintech company known for its “buy now, pay later” service, is walking back one of its most controversial decisions. After firing 700 employees and automating their jobs with artificial intelligence, the company now admits the move may have gone too far. Klarna CEO Sebastian Siemiatkowski has publicly acknowledged that while AI promised speed and cost savings, it came at the cost of customer satisfaction and service quality.The fintech giant, once valued at $45.6 billion in 2021, was one of the loudest voices championing AI in customer operations. But by 2024, the consequences of that AI-first approach were too obvious to ignore.Back in 2022, Klarna made headlines when it laid off around 700 workers, a significant portion of its workforce at the time. CEO Siemiatkowski was vocal about embracing AI tools that could take over tasks such as customer support, translation, content creation, and even executive-level decision-making.He believed these tools could match, or even exceed, human performance. In interviews and public statements, he claimed the company had managed to automate the equivalent of 700 roles. The move was framed as a bold step toward innovation and cost-efficiency during uncertain market conditions.Fast forward to 2024, and things didn’t go as planned. Klarna began facing a surge in customer complaints, a dip in user satisfaction, and growing frustration with the AI systems that had replaced its human workforce.According to reports from Livemint and The Economic Times, Klarna’s AI tools struggled with more nuanced support tasks — especially those that required empathy, discretion, or deeper understanding of user issues. Customers complained that automated responses were too generic, repetitive, or simply unhelpful when dealing with real-life problems.CEO Siemiatkowski later admitted the company’s overdependence on automation led to a “lower quality” customer experience. This public acknowledgment marked a shift in Klarna’s strategy — from full-speed AI deployment to rebalancing the human element in service.Yes, Klarna is now actively rebuilding its human support teams, a sign that it recognizes the value real people bring to customer interactions. The reversal shows a clear change in Klarna’s priorities: from purely chasing AI-driven efficiency to restoring trust, service quality, and brand reputation.Siemiatkowski’s tone has changed as well. In his recent remarks to multiple media outlets, he admitted, “We went too far.” He stressed the importance of striking the right balance between human workers and artificial intelligence, a lesson that could influence the broader fintech and tech industry.Klarna’s experience serves as a cautionary tale for businesses rushing to replace employees with AI. While AI tools can offer impressive speed and cost savings, they still lack the human touch needed in complex customer service situations.Other tech firms and startups may now think twice before eliminating large parts of their workforce, especially when customer experience is at stake. AI is a powerful tool, but it’s not a full substitute for people — at least not yet.This incident has pushed the conversation toward collaborative models, where AI assists human agents rather than replacing them outright. Tools like chatbots can handle simple, repetitive queries, but when emotions, trust, or complex issues are involved, people still want to talk to people.Klarna’s pivot back to human hiring is a strong signal that the future of AI isn’t about replacing jobs — it’s about enhancing them with smarter tools and better support systems. It’s also a reminder that companies should listen closely to their customers, even when chasing innovation.The Klarna AI layoffs story isn’t just about one company’s failed experiment — it’s about how tech companies around the world are learning what AI can and can’t do. Klarna’s $45 billion dream hit a reality check when customer complaints started flooding in.Now, with a more balanced strategy, Klarna is working to rebuild both its workforce and its trust with customers. The takeaway? Automation may be the future, but the present still needs a human touch.To automate customer service and cut costs.Yes, due to poor service quality and customer complaints.
| 2025-06-09T00:00:00 |
https://m.economictimes.com/news/international/us/company-that-sacked-700-workers-with-ai-now-regrets-it-scrambles-to-rehire-as-automation-goes-horribly-wrong/articleshow/121732999.cms
|
[
{
"date": "2025/06/09",
"position": 74,
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{
"date": "2025/06/09",
"position": 53,
"query": "AI layoffs"
},
{
"date": "2025/06/09",
"position": 20,
"query": "AI layoffs"
}
] |
|
What employers are looking for when hiring Artificial Intelligence ...
|
What employers are looking for when hiring Artificial Intelligence professionals.
|
https://www.kdrtalentsolutions.com
|
[] |
AI roles come in many shapes and sizes, machine learning engineers, computer vision specialists, data scientists, MLOps professionals, and LLM ...
|
As we move into summer 2025, the AI job market is very buoyant.
From computer vision to large language models (LLMs), the opportunities are still growing.
At KDR Talent Solutions, we speak to candidates every day who are excited about AI but unsure of how to make their next move. Whether you’re just getting started or looking to pivot from another tech discipline, this guide will help you take practical steps toward your next AI opportunity.
1. Understand the landscape.
AI roles come in many shapes and sizes, machine learning engineers, computer vision specialists, data scientists, MLOps professionals, and LLM researchers, just to name a few.
Top tip: Start by mapping out the kinds of problems you want to solve. Are you more interested in building models, deploying them, or working with the data that feeds them? Your passion and direction will help narrow your search and in turn, make your applications sharper.
2. Show value.
Hiring managers in AI want to see your work. Whether it’s a GitHub repo, a Kaggle competition entry, or a blog breaking down how you fine-tuned a model, evidence of hands-on experience compliments a tailored CV. Employers want to see the value you will bring to their business.
Bonus: Share your work online and tag relevant communities. Visibility can lead to opportunity.
3. Get Clear on the tech stack
In-demand tools and frameworks vary depending on the role. But here’s a snapshot of what we’re seeing right now:
Machine Learning & LLMs: Python, PyTorch, Hugging Face, LangChain, Vector DBs (like Pinecone or Weaviate)
Computer Vision: OpenCV, TensorFlow, YOLO and increasingly, diffusion models
MLOps/Deployment: MLflow, Docker, Kubernetes, Metaflow, SageMaker (AWS)
Data Engineering Support for AI: GCP, BigQuery, Airflow, dbt
If you’re missing some of these, don’t panic, employers value learning agility. Start skilling up and be ready to talk about what you’re currently learning and how that can be practically applied to solve business problems.
4. Tailor your applications for the role
AI job descriptions can often be buzzword-heavy. Drill into the job description and cut through the noise by showing how your skills directly relate to the company’s use case. If the company hasn’t published their use case, speak to the recruiter who is working the role as they will often have that insight and will be able to help you provide the best examples that align your skills to the issues the company is wanting to solve via the hire.
Tip from our recruiters: If a job posting mentions computer vision and LLMs, focus your CV and cover letter on the one you’re strongest in. Employers would rather see depth than a scattergun of acronyms. Tie in the teck stack with the business value you provided in your current or previous role.
5. Partner with the right recruiter
Working with a recruiter who specialises in AI makes a massive difference. At KDR, our team understands the nuances of the market, from early-stage startups building LLM infrastructure to established businesses embedding AI into core systems.
We’re not just matching keywords, we’re listening to what you want and making sure the companies we put you forward to are serious about AI, not just riding the hype.
Ready to make your move in AI? Let’s chat. Drop us a message or explore our live roles here.
| 2025-06-09T00:00:00 |
https://www.kdrtalentsolutions.com/what-employers-are-looking-for-when-hiring-artificial-intelligence-professionals
|
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|
AI recruiting: revolutionizing hiring and talent acquisition in 2025
|
AI recruiting: revolutionizing hiring and talent acquisition in 2025
|
https://www.simpplr.com
|
[
"Srishti Mathur"
] |
AI technologies streamline the recruitment process by helping recruiters sort resumes, prepare interviews, and automate administrative tasks.
|
HR leaders and candidates continue to face a turbulent job market. Against a backdrop of economic uncertainty and volatile hiring cycles, AI in recruitment offers organizations a powerful new path to talent acquisition.
What was once considered science fiction is now reality. Leading SaaS companies recognize AI’s strategic value and have integrated it into the core of their talent acquisition processes.
62% of talent acquisition professionals say they are optimistic about AI’s impact on recruitment. It’s no longer enough to simply invest in AI.
It’s no longer enough to simply invest in AI. The crucial next step is to clearly define and implement artificial intelligence in recruiting to enhance organizational efficiency and elevate employee experience.
Let’s discover how AI, as a catalyst for positive change in recruitment, can provide unfettered access to great leaders, diverse labor forces, and vast brain trusts with culturally stimulating environments.
What is AI recruiting?
AI recruiting is the use of technology to streamline and automate parts of the talent acquisition process. Machine learning models can help you identify and shortlist top talent, automate routine tasks, streamline the recruitment workflow, and free recruitment managers from repetitive high-volume tasks. With natural language processing and data analysis, AI analyzes vast amounts of applicant data to uncover patterns and trends, making the recruitment journey faster, more targeted, and highly effective.
Why is AI important for recruiting?
Recruiters often receive hundreds — sometimes thousands — of applications for one role. This influx makes it challenging to identify the most qualified candidates efficiently. AI technologies streamline the recruitment process by helping recruiters sort resumes, prepare interviews, and automate administrative tasks.
Organizations agree on the importance of AI in recruitment, but HR departments haven’t widely accepted the technology.
Only 9% of global companies report good alignment between their technology and a strong employee experience. (Willis Towers Watson).
This low adoption doesn’t mean the AI revolution isn’t coming — it’s inevitable. By integrating AI recruiting, organizations can start enhancing the employee experience now and deliver a more streamlined and personalized hiring process.
How does artificial intelligence recruiting work?
Artificial intelligence in recruiting leverages machine learning algorithms, natural language processing (NLP), and data analytics to automate and optimize various stages of the hiring process. This is especially beneficial when HR teams receive hundreds of applications, many of which are from underqualified candidates.
Here’s a breakdown of what automated recruiting can do:
Intelligent AI-powered screening systems identify qualified candidates within minutes by grading candidates according to keywords, algorithms, and recruitment data.
identify qualified candidates within minutes by grading candidates according to keywords, algorithms, and recruitment data. Automated communication tools engage with candidates via automated messaging and respond to questions about the roles and responsibilities.
engage with candidates via automated messaging and respond to questions about the roles and responsibilities. Recruiters instantly schedule interviews with candidates qualified by the Applicant Tracking Systems (ATS).
with candidates qualified by the Applicant Tracking Systems (ATS). AI voice and facial expression analysis assesses whether a candidate’s tone, demeanor, and emotional state during the interview fit the role.
AI recruitment tools enhance the efficiency and effectiveness of recruitment processes. Some core functionalities include:
Natural language processing : NLP analyzes human language patterns to evaluate both job descriptions and candidate materials. These tools can identify key qualifications, assess behavioral traits, and flag potential application concerns.
: NLP analyzes human language patterns to evaluate both job descriptions and candidate materials. These tools can identify key qualifications, assess behavioral traits, and flag potential application concerns. Automated sourcing: Advanced AI systems scan multiple online platforms to build your talent pipeline. The technology identifies qualified candidates across job boards, social media, and professional communities, whether they’re actively searching or not.
Advanced AI systems scan multiple online platforms to build your talent pipeline. The technology identifies qualified candidates across job boards, social media, and professional communities, whether they’re actively searching or not. Video interview analysis: AI evaluates conversation dynamics, including participant speaking ratios and emotional indicators. The technology also provides feedback on interviewer performance and generates searchable transcripts to track keywords.
AI evaluates conversation dynamics, including participant speaking ratios and emotional indicators. The technology also provides feedback on interviewer performance and generates searchable transcripts to track keywords. Predictive analytics: This technology uses historical data to forecast a candidate’s potential performance and likelihood of staying in a role. By analyzing resumes, skill sets, and social media presence, predictive analytics can help identify candidates who are not only qualified but also likely to thrive in the organization.
This technology uses historical data to forecast a candidate’s potential performance and likelihood of staying in a role. By analyzing resumes, skill sets, and social media presence, predictive analytics can help identify candidates who are not only qualified but also likely to thrive in the organization. Conversational HR chatbots: AI-driven chatbots facilitate communication with candidates by answering questions, guiding them through the application process, and scheduling interviews. These tools enhance the candidate experience by providing timely information and support.
What is the use of AI in recruitment?
Innovative hiring teams use AI to make key recruitment stages more efficient and data-driven. Here’s how AI tools optimize various aspects of talent acquisition.
The use of artificial intelligence in HR processes is a new and likely unstoppable trend. (Harvard Business Review)
AI can analyze large volumes of resumes and online profiles to identify the best candidates based on specific criteria set by recruiters. By automating the sourcing process, AI helps organizations widen their talent pool and discover potential candidates who may not have been found through traditional methods.
2. Fast, consistent applicant screening
With AI, recruiters can efficiently filter out candidates based on key skills, experiences and qualifications. This technology automates the initial screening process, allowing recruiters to focus on the most qualified candidates for the position.
41% of hiring managers find recruiting for entry-level positions challenging due to the high volume of applications received.
3. Smooth onboarding
AI simplifies onboarding by automating routine tasks such as paperwork and coordinating training schedules. It also personalizes the experience by matching new hires’ skills, learning styles, and career goals to the most relevant resources.
Related: How to get your team on board with AI
Advantages of AI in recruitment
The hiring process can be time-consuming, repetitive, and prone to bias. AI recruitment can speed up tedious tasks, offer deeper insights, and improve the candidate experience. Here’s how AI creates measurable advantages:
Faster resume screening: A 2023 Manpower Group study found that nearly 4 in 5 HR professionals globally report difficulty recruiting top talent. With AI, the time invested in this labor-intensive process can be reduced by up to 75%.
A 2023 Manpower Group study found that nearly 4 in 5 HR professionals globally report difficulty recruiting top talent. With AI, the time invested in this labor-intensive process can be reduced by up to 75%. Automate time-consuming tasks: According to a LinkedIn report, 74% of respondents believe that generative AI will help automate repetitive tasks, allowing recruiters to prioritize strategic work and be more productive.
According to a LinkedIn report, 74% of respondents believe that generative AI will help automate repetitive tasks, allowing recruiters to prioritize strategic work and be more productive. Actionable insights: AI-powered analytics offer insights into candidate sentiment that HR teams can use to improve the hiring process. Use surveys to gather candidate feedback to refine recruitment strategies and identify potential skill gaps.
AI-powered analytics offer insights into candidate sentiment that HR teams can use to improve the hiring process. Use surveys to gather candidate feedback to refine recruitment strategies and identify potential skill gaps. Diversified talent pool: With AI resume screening, employers can efficiently assess candidates from varied backgrounds to ensure inclusivity. The technology helps eliminate unconscious biases, fostering a workplace enriched with diverse experiences and perspectives.
With AI resume screening, employers can efficiently assess candidates from varied backgrounds to ensure inclusivity. The technology helps eliminate unconscious biases, fostering a workplace enriched with diverse experiences and perspectives. Improved candidate experience: Automated chatbots can promptly address candidate queries, while AI-driven platforms offer tailored job recommendations based on individual profiles. This personalized engagement gives a positive impression of your organization.
Automated chatbots can promptly address candidate queries, while AI-driven platforms offer tailored job recommendations based on individual profiles. This personalized engagement gives a positive impression of your organization. Lower costs and improved ROI: The average cost-per-hire in the U.S. is $4,129. Using AI in recruitment significantly reduces costs by automating candidate assessments, allowing HR teams to focus on the more human elements of the hiring process.
The average cost-per-hire in the U.S. is $4,129. Using AI in recruitment significantly reduces costs by automating candidate assessments, allowing HR teams to focus on the more human elements of the hiring process. Enhanced retention rates: AI recruitment tools do more than help HR find the ideal candidates. AI-powered platforms can also improve employee retention. AI examines employee feedback and performance data to spot risks of turnover. This enables HR teams to initiate targeted retention strategies to keep their new hires on board.
AI recruitment tools do more than help HR find the ideal candidates. AI-powered platforms can also improve employee retention. AI examines employee feedback and performance data to spot risks of turnover. This enables HR teams to initiate targeted retention strategies to keep their new hires on board. Keeps you up to date: A LinkedIn study indicates that 80% of HR professionals globally believe AI will be an essential tool in their work over the next five years. HR teams need both AI tools and the skills to use them effectively in their talent acquisition process.
Challenges of AI recruiting
Despite the many strengths outlined above, people tend to have a negative perception of AI in recruiting due to the following reasons:
AI lacks human understanding
While AI in recruiting excels at data processing, it can’t replace insight and empathy in evaluating cultural fit and interpersonal skills. The most effective recruitment combines AI efficiency with a human touch.
AI enables candidate misrepresentation
As candidates use AI programs to tailor their resumes to job descriptions, recruiters must then verify their claims. Additional screening is required to prevent mismatches from automated systems.
AI creates impersonal experiences
Over 63% of candidates report poor communication after applying. While AI can help track and schedule responses, organizations must ensure technology enhances rather than replaces meaningful candidate interaction.
AI threatens data security
Candidates and HR teams express concerns about data security and privacy. Organizations need clear policies about how AI programs collect, store, and use candidate information to alleviate these concerns.
Related: What you need to know about GenAI and employee efficiency
Ethical implications of AI in recruitment
It’s common to have concerns about AI. Is it safe? Is it ethical?
Generative AI shows promise in addressing bias in hiring decisions, but it’s not foolproof. Unconscious bias can still creep into AI systems through the people who develop and train them. While AI in recruitment has the potential to reduce discrimination, it also raises ethical and legal questions that organizations must carefully consider when implementing it into HR processes.
Ultimately, AI itself isn’t good or bad — it’s all about how it’s used.
Elevate employee experience with Simpplr AI
Technological innovation is essential in today’s competitive job market, where onboarding and retaining high performers is more important than ever. That’s where Simpplr makes its mark.
Simpplr for Human Resources combines employee experience (EX) and artificial intelligence (AI) to deliver seamless, personalized employee experiences at scale. Integrating engagement, enablement, and employee services into one platform enhances internal communications and strengthens workplace culture like no other intranet solution.
Here’s why HR teams choose Simpplr as their go-to EX platform:
Improves onboarding experience: Support new hires from day one with a personalized, automated onboarding workflow tailored to their needs.
Related: Nutanix increased new hire onboarding efficiency by 50%
Brings employees together: Foster connection among employees with a Digital Work Hub — a modern intranet that serves as your single source of truth and information.
Automates employee services: Let our AI Assistant handle day-to-day HR requests so you can focus on more important priorities.
Builds a vibrant company culture: Create an inclusive company culture through engaging content and peer recognition so employees feel valued and connected to the organization.
Provides actionable insights into employee satisfaction: Collect feedback with pulse, engagement and ad hoc surveys, then analyze employee sentiment to spot trends, address concerns, and improve the employee experience.
Show your appreciation: Motivate and inspire employees by celebrating their achievements with recognition and rewards programs.
Ready to see Simpplr’s AI in action? Request a demo today to discover how Simpplr can elevate your HR service delivery.
| 2025-01-15T00:00:00 |
2025/01/15
|
https://www.simpplr.com/blog/2025/ai-recruiting/
|
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"date": "2025/06/09",
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Will AI Crash the Job Market—and Drop Mortgage Rates to 3%?
|
Will AI Crash the Job Market—and Drop Mortgage Rates to 3%?
|
https://www.lendfriendmtg.com
|
[
"Michael Bernstein"
] |
What began as a way to eliminate repetitive tasks is now eliminating full-time roles. As AI adoption surges, so does concern that long-standing ...
|
When people think about artificial intelligence, they often imagine smarter apps, better customer service, or maybe a robot assistant that finally makes grocery shopping bearable. What most don't picture is a return to 3% mortgage rates. But the path from advanced algorithms to cheap home loans isn't as far-fetched as it seems.
AI is quietly reshaping the foundation of both the housing and labor markets. Under the right (or wrong) circumstances, its influence could trigger a slowdown severe enough to drive mortgage rates back to levels not seen since the early days of the pandemic. That’s not necessarily cause for celebration. Historically low interest rates typically signal deep economic trouble—especially when they're tied to surging unemployment and shrinking opportunity.
Let’s break down how this scenario could unfold—and why homebuyers should understand the growing link between AI disruption, the job market, and mortgage affordability.
Why Mortgage Rates Are Still High Today
Before we explore how rates could drop, it’s important to understand why they’re currently elevated. Despite a cooling housing market and slowing growth in some sectors, mortgage rates remain high thanks to two main forces: persistent inflation and a resilient labor market.
Inflation has come down from its 2022 peak, but it remains above the Federal Reserve’s 2% target. Core components like housing, insurance, and services continue to keep price pressure elevated. As long as inflation stays sticky, the Fed has little reason to reduce interest rates.
At the same time, the job market continues to surprise economists. As of mid-2025, unemployment is just 4.2%—a historically low figure. Employers are still hiring, wage growth is steady, and job openings outnumber job seekers in many sectors.
With inflation still hot and employment strong, the Fed sees no urgency to ease monetary policy. That’s kept mortgage rates in the 6–7% range, frustrating buyers and keeping affordability out of reach for many.
The Federal Reserve’s Dual Mandate
The Fed’s decisions aren’t arbitrary. Since 1977, it has been guided by a dual mandate:
Promote maximum employment Maintain stable prices
When inflation rises too fast, the Fed raises interest rates. When unemployment spikes, it cuts them. The tension between these two goals can complicate policy—but historically, when labor market conditions deteriorate rapidly, the Fed prioritizes job creation.
In a scenario where AI causes a sudden wave of job losses, this mandate all but guarantees a policy response that would bring mortgage rates down.
How Mortgage Rates Could Fall Back to 3%
Mortgage rates are driven by inflation expectations, bond yields, and Federal Reserve policy. If AI drives economic disruption—cutting jobs and cooling inflation—the Fed may be forced to act. Slower growth and deflationary pressure could trigger a wave of rate cuts. Bond yields would fall, and mortgage rates could follow.
We’ve seen this before. In early 2020, the Fed slashed interest rates and launched quantitative easing to offset the COVID-19 shock. Mortgage rates plunged below 3% as a result. If a similar employment crisis were sparked by AI, it’s likely the same tools would be deployed.
What History Tells Us About Rates and Unemployment
History supports this trajectory. When unemployment rises dramatically, the Fed acts swiftly—and mortgage rates follow.
1930s: During the Great Depression, rates fell significantly as the Fed cut rates to combat economic collapse.
1982: Unemployment hit 10.8%. Rates dropped from over 18% to under 13% as inflation subsided.
2008–2009: The Great Recession pushed joblessness to 10%. The Fed responded with near-zero rates and QE, bringing mortgage rates down to 4–5%.
2020: COVID-19 sent unemployment to 15%. The Fed again slashed rates and launched massive stimulus. Mortgage rates dipped below 3% for the first time ever.
Each of these moments shows a clear pattern: when the labor market deteriorates, mortgage rates decline as part of the Fed’s effort to stabilize the economy.
How AI is Reshaping the Economy
Artificial intelligence didn’t quietly enter our lives—it burst in with fanfare. Over the past few years, tools like ChatGPT, Midjourney, and GitHub Copilot went viral, with millions of users integrating them into daily routines. From students writing essays to executives generating reports, AI promised to make life easier.
Businesses followed suit. Eager to reduce costs and boost efficiency, companies embraced AI platforms for everything from customer support to legal work and fraud detection. It feels like a revolution—but few have paused to ask what’s being lost in the process.
What began as a way to eliminate repetitive tasks is now eliminating full-time roles. As AI adoption surges, so does concern that long-standing job functions—particularly white-collar ones—are becoming obsolete. And that shift could have lasting economic consequences.
A 2024 ScienceDirect study showed AI adoption in the U.S. growing by double digits annually. Banks are automating underwriting. Law firms are using AI to review documents. And according to forecasts, full-scale integration across white-collar industries is expected by 2027.
Already, we’re seeing early signs of displacement. A recent New York Times report revealed that many college graduates are struggling to find work in fields that have rapidly adopted automation. Tech leaders like Anthropic CEO Dario Amodei have warned that as much as 50% of entry-level jobs could be disrupted within a few years.
Past innovations—like the personal computer or internet—eliminated some jobs but created entire new industries in return. AI could break that cycle. Rather than creating new demand, it may simply absorb tasks that once required human labor.
Reports from USA Today and Axios show the pace of change is accelerating. Even high-paying tech roles are being eliminated. Departments are being restructured. Entire career paths are becoming obsolete.
While AI may boost productivity and profit margins, those benefits won’t immediately result in new employment. As ING’s 2025 outlook notes, the transition will be bumpy. The winners will be businesses that integrate AI wisely—and individuals who can adapt.
What Happens If Unemployment Hits 10%
A spike in unemployment to 10% would be devastating. Consumer spending would collapse. Investment would stall. Demand for housing would shrink. The Federal Reserve would almost certainly respond with an aggressive policy shift.
Here’s what that could look like:
The Fed slashes rates to near zero
Bond yields fall as investors seek safety
Mortgage lenders lower rates to compete for a smaller pool of qualified borrowers
In that environment, 30-year fixed mortgage rates could fall back to—or even below—the 3% mark. But those lower rates would come in the shadow of economic instability, not prosperity.
What Happens If Unemployment Hits 10%
AI won’t just reshape industries. It will reshape the economy, the job market, and the cost of homeownership. For borrowers and homeowners, the question isn’t whether rates will drop—but under what circumstances.
A return to 3% mortgage rates is possible. But it may come at a cost the country isn’t prepared to pay.
At LendFriend, we help buyers prepare for any market—because when uncertainty grows, expert guidance matters more than ever.
If you want to discuss how we view the housing market and rates in light of the AI craze, give us a call at 512.881.5099 or get in touch with me by completing this quick form, and I'll be in touch as soon as possible.
| 2025-06-09T00:00:00 |
https://www.lendfriendmtg.com/learning-center/will-ai-crash-the-job-market
|
[
{
"date": "2025/06/09",
"position": 96,
"query": "AI job creation vs elimination"
}
] |
|
Top 10 Benefits of AI in Education: Explore Key Insights
|
Top 10 Benefits of AI in Education: Explore Key Insights
|
https://www.spaceo.ai
|
[
"Rakesh Patel Is A Highly Experienced Technology Professional",
"Entrepreneur. As The Founder",
"Ceo Of Space-O Technologies",
"He Brings Over Years Of It Experience To His Role. With Expertise In Ai Development",
"Business Strategy",
"Operations",
"Information Technology",
"Rakesh Has A Proven Track Record In Developing",
"Implementing Effective Business Models For His Clients. In Addition To His Technical Expertise",
"He Is Also A Talented Writer"
] |
AI is providing significant support in improving the quality and accessibility of education from personalized learning experiences to streamlined ...
|
Artificial intelligence has opened up a new world of learning and teaching as the world changes rapidly. There are numerous advantages of AI in education that could change the face of education.
AI is providing significant support in improving the quality and accessibility of education from personalized learning experiences to streamlined administrative tasks.
As an AI development agency we will discuss the top ten benefits of AI in education, revealing the ways in which AI is changing the acquisition and transmission of knowledge for future generations.
Top 10 Benefits of AI in the Education Field
1. Tailoring Education with Personalized Learning
Personalized learning is a transformative aspect of education empowered by AI. It understands that each pupil is individual and possesses individualized learning styles, competencies, and deficiencies. AI’s ability to deal with vast amounts of data opens up the door for developing personal education experiences for every learner.
Want to Integrate AI Functionality into Your Education Portal? Contact us. Let’s validate your idea for free and convert it into a highly performative AI-based education solution. Book Your Free Call
In practice, this means that students no longer need to adhere to a one-size-fits-all curriculum. Instead, AI algorithms analyze individual learning patterns and adapt the content accordingly. This tailored approach not only enhances comprehension and retention but also fosters a sense of empowerment as students see their individual progress. With AI-powered systems, businesses can automate a wide range of tasks. Want to know more about how AI Technology can help? Here is a blog about benefits of AI Technology.
For Example The ease of use of an AI-based audio editing website is largely dependent on how user-friendly it is. A platform that is easy to use will have a simple learning curve, clear instructions, and an intuitive user interface.
2. Boosting Engagement with Interactive Learning
Engaging students in the learning process is a perennial challenge. However, AI brings new possibilities by creating interactive, dynamic, and motivating educational experiences. Through AI-driven content like chatbots and virtual tutors, students actively engage with course materials, receive real-time feedback, and track their progress.
These gamification elements make learning enjoyable and empower students to take charge of their education. As a result, students are not only more enthusiastic about their studies but also develop a sense of ownership and responsibility for their learning journey.
For Example Consider an online language learning platform that utilizes AI. It provides users with instant feedback, rewards for achievements, and the opportunity to compete with friends. This approach not only boosts language learning but also makes the journey enjoyable and engaging.
Efficiency in administrative tasks is vital to the smooth functioning of educational institutions. AI comes to the rescue by automating various administrative processes, such as attendance tracking and grading.
This automation not only saves educators significant time and effort but also reduces the likelihood of errors. By streamlining these administrative responsibilities, educators can focus more on teaching and guiding students to success.
For Example Teachers at a high school used to spend hours each week taking attendance and grading assignments. With AI automating these tasks, they can now invest more time in teaching and mentoring, resulting in a more enriched educational experience for their students.
4. Ensuring Accessibility and Inclusivity
AI plays a pivotal role in ensuring that education is accessible to all, regardless of disabilities or language barriers. Through tools like screen readers and translation services, AI makes educational content accessible to a diverse audience.
Students with visual impairments can listen to textbooks, and non-native English speakers can access translations, fostering an inclusive and supportive learning environment. These AI-driven accessibility solutions break down barriers and empower individuals from various backgrounds to engage fully in the educational experience.
For Example In a university with a diverse student population, AI-powered accessibility tools enable all students to fully participate in lectures and coursework. These tools ensure that education is truly inclusive, accommodating students with varying needs and backgrounds.
5. Providing 24/7 Learning Support
Learning doesn’t adhere to a strict schedule, and students often have questions beyond traditional classroom hours. AI steps in as a reliable 24/7 learning support system. Through chatbots and virtual assistants, students can access learning materials and receive immediate assistance whenever they need it. With AI-based customer support solution, you get reply in real-time with accurate information.
This continuous support helps in self-directed learning and enables students to manage their own education effectively. As a result, students develop self-dependence, thereby, promoting autonomy and self-confidence as they proceed with their studies.
For Example Meet Emily, a college student with a burning question about a complex physics concept at 3 a.m. AI-powered chatbots are there to provide her with instant answers and resources, ensuring that learning never sleeps.
6. Informing Decisions through Data-Driven Insights
AI-driven data analytics have become invaluable tools for educators and institutions alike. By processing and analyzing extensive datasets encompassing student performance, attendance, and participation, AI provides insights that guide decision-making.
This data-driven approach allows educators to identify areas of improvement, trends in learning outcomes, and potential early intervention opportunities. These insights help educators refine their teaching strategies, allocate resources effectively, and ultimately elevate the quality of education.
For Example A school district, armed with AI-driven insights, identified a trend of declining math scores among its students. Educators promptly adjusted their teaching methods, implemented additional support, and witnessed a significant improvement in student math performance.
7. Delivering Cost-Effective Education
In an age where education costs are a growing concern, AI brings cost-effective solutions to the forefront. By automating content creation, AI reduces the expenses associated with traditional textbooks and course materials.
Online courses powered by AI are often more affordable than their in-person counterparts. This affordability opens doors to education for a broader demographic, ensuring that quality learning is accessible to many. As a result, AI not only enhances the learning experience but also addresses the crucial issue of affordability.
For Example Consider a university that has transitioned to digital textbooks created and updated by AI. This shift significantly reduces the financial burden on students, making education more affordable and accessible to a wider range of learners.
8. Facilitating Global Collaboration
Collaboration has no geographical boundaries in a globalized world. AI breaks down physical barriers in order to globalize collaboration. Students and educators from remote areas of the world can learn together through virtual classrooms and online tools.
This creates a cross-cultural exchange and exposes learners to various views, thus enhancing the learning experience and making the learners ready to succeed in a global, interconnected society.
For Example Students from North America, Asia, and Europe collaborate on a research project in a virtual classroom powered by AI. Through the exchange of ideas, cultures, and perspectives, an individual gains a much richer and deeper understanding of the subject matter.
9. Creating Customized Content Efficiently
Creating content is a long process for educators. This is where AI comes into play to simplify it. Using AI algorithms, educators can prepare quizzes, exams, and learning modules according to teaching objectives and students’ preferences.
This efficiency allows teachers to provide more instruction, counselling and mentoring. Hence, with such an advantage, educators can then concentrate on giving personalized guidance to students, fostering their understanding of the subject, and providing the required support for further growth and achievement.
For Example A dedicated educator uses AI to quickly generate a set of interactive quizzes for their students. This automation not only saves hours of work but also ensures that the quizzes are aligned with the curriculum and cater to individual student needs.
Traditional assessments often present challenges, as they do not account for individual proficiency levels. AI-driven adaptive assessments change the game. These assessments adapt in real-time based on a student’s performance.
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If a student answers one question right, then the next question can be more difficult, in order to assess properly the student’s knowledge and make a fair evaluation.
For Example One who excels in math in an AI-driven test is given more complicated problems as they improve while a student who is struggling in the same subject is offered questions that match their current proficiency level. It assures that a student is evaluated fairly and accurately, given the varying nature of their responses.
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Frequently Asked Questions What are the challenges of AI in education? Here are the challenges of integrating AI into education. Unequal opportunities to access AI-based education can further deepen the already existing inequalities between students living in different regions and from different social classes.
There is always the possibility of unauthorized access to student data, which poses a serious threat to the privacy and security of educational data.
Inadequate training and skills among educators in effectively utilizing AI tools hinder their ability to adapt to the changing educational landscape.
Ethical concerns related to algorithmic bias, responsible technology use, and student privacy can cast doubt on the ethical integrity of AI in education. Is AI good or bad for education? AI in education has the potential to be beneficial by personalizing learning, improving efficiency, and providing insights, but it also raises concerns about privacy, equity, and overreliance on technology. Its impact depends on responsible implementation and a balance with human teaching.
Conclusion
AI in education has numerous benefits beyond the conventional classroom, and this will make a difference in learners and educators in the future. Therefore, AI infuses the learning environment with dynamic and personalized learning that is empowering students to excel in their studies. The automation of administrative tasks not only saves time but also increases the efficiency of educational institutions.
The provision of tools such as screen readers and translation services ensures that education is inclusive for all. This is despite disabilities or language barriers. This helps students engage in self-directed learning, giving them the power to own their educational journey by leveraging AI-driven chatbots and virtual assistants that support round-the-clock.
Finally, data-driven insights gained by AI are very useful for teachers in order to make the right decisions and improve overall education. By accepting the capabilities of AI and promoting innovation, we are moving towards a world in which every person has the opportunity to prosper through lifelong learning, with the intelligence of machines and the wisdom of educators.
| 2025-06-09T00:00:00 |
https://www.spaceo.ai/blog/benefits-of-ai-in-education/
|
[
{
"date": "2025/06/09",
"position": 45,
"query": "AI education"
}
] |
|
AI Literacy in Education: Why College Campuses Must Act ...
|
AI Literacy in Education: Why College Campuses Must Act Now
|
https://www.campussafetymagazine.com
|
[
"Guest Authors",
"Author",
"Velina Lee"
] |
Institutions must build AI literacy—equipping students with the skills to critically understand how AI systems work, evaluate the credibility and accuracy of AI ...
|
As artificial intelligence (AI) transforms how we live, learn, and work, education faces a dual responsibility: ensuring digital safety and preparing students with the AI fluency required to thrive in tomorrow’s workforce.
Today, more than one-third of U.S. college-aged adults use tools like ChatGPT regularly for writing, brainstorming, and studying. But while usage has surged, education around responsible and ethical AI use hasn’t kept pace.
Without structured guidance, students risk over-relying on AI-generated content, diminishing their critical thinking skills, or unintentionally spreading misinformation. For higher education institutions, this presents both a challenge and an opportunity: the challenge of addressing knowledge gaps, and the opportunity to lead on digital citizenship and AI literacy.
The Case for AI Literacy
AI is no longer a futuristic concept—it is now an integral part of nearly every profession. From healthcare and education to business and entertainment, AI-powered analytics and generative tools are embedded across sectors. Students must learn to understand, evaluate, and interact with these technologies thoughtfully. This means going beyond awareness.
Institutions must build AI literacy—equipping students with the skills to critically understand how AI systems work, evaluate the credibility and accuracy of AI-generated content, identify bias in algorithms and data sets, and apply AI tools in discipline-specific contexts with ethical insight.
This foundational knowledge empowers students to become responsible digital citizens—aware not only of the benefits of AI, but also of its limitations, implications, and risks.
——Article Continues Below——
Understanding the Risks of Unchecked AI Use
AI isn’t just a productivity tool. It carries significant ethical and safety considerations. Hallucinated facts, algorithmic bias, impersonation, cheating, and misuse of sensitive data are just a few examples of what can go wrong without guardrails.
On campus, AI misuse has real-world safety implications. Privacy violations may occur when AI tools capture and store sensitive student information. Generative tools used irresponsibly can spread misinformation or be employed for impersonation or surveillance. These issues can lead to psychological stress, reputational damage, and a loss of trust.
Additionally, AI bots or automated systems—especially those operated or manipulated by malicious actors like foreign entities (e.g., Russian bot networks)—can be used to amplify, distort, or manipulate information online. This is not hypothetical; it’s been observed in real-world contexts, including elections, social discourse, and public health. The potential for such misuse to target or impact campus communities only strengthens the case for proactive, critical AI education.
As a result, AI fluency must be viewed as an essential component of student safety—just like cybersecurity awareness or mental health support. Embedding AI education into campus life is no longer optional.
Building AI Literacy Skills for a Smarter Workforce
To prepare tomorrow’s workforce for an increasingly AI-powered economy, institutions must take a layered approach to AI education—equipping students not just to use these tools, but to understand, question, and improve them. That includes:
Demystifying AI concepts : Introduce basic AI principles with real-world examples across disciplines (e.g., AI in healthcare diagnostics, predictive policing, business analytics).
: Introduce basic AI principles with real-world examples across disciplines (e.g., AI in healthcare diagnostics, predictive policing, business analytics). Prompt engineering and evaluation : Teach students how to craft inputs that yield quality output, assess content for bias or error, and adjust prompts accordingly.
: Teach students how to craft inputs that yield quality output, assess content for bias or error, and adjust prompts accordingly. Productivity with a purpose: Encourage the ethical use of tools like ChatGPT or Microsoft Copilot for writing, coding, analysis, and creative tasks—emphasizing transparency and privacy.
As an example, a pre-law student might explore how predictive algorithms are shaping sentencing decisions, while an education major could examine how AI tutors adapt to different learning styles. These applications show students how AI intersects with their future careers—and how to use it responsibly.
Career and Technical Education (CTE) programs can also play a critical role in this effort, offering practical, career-aligned pathways for students to gain technical fluency and apply AI skills in real-world contexts. By integrating AI-focused coursework into CTE offerings, educators can ensure graduates are prepared not just for today’s job market—but for the rapidly evolving demands of tomorrow’s workforce.
Fostering Deep Learning and Critical Thinking
Used properly, AI can do more than just automate tasks—it can deepen learning and inspire creativity. AI-generated insights can help students ask better questions, uncover patterns in research, and approach assignments from new angles. When trained in prompt engineering and ethical evaluation, students learn to co-create with AI—not as passive users, but as active critical thinkers and innovators.
Faculty, too, are increasingly integrating AI into teaching and assessment. By redesigning assignments to encourage students to analyze, critique, and build upon AI-generated content, instructors can move beyond rote memorization and emphasize higher-order thinking. For instance, instead of asking students to write a traditional essay, faculty may ask them to evaluate and improve an AI-generated draft, promoting not only writing skills, but digital discernment, reasoning, and originality.
AI is pushing a pedagogical shift: one that values curiosity, interdisciplinary problem-solving, and intellectual agility. These are the very traits students need to navigate a complex, rapidly changing world.
AI Literacy Matters for Campus Safety and Student Success
In the realm of campus safety, AI is increasingly used in surveillance and behavior recognition, crisis chatbots and mental health triage tools, and predictive analytics for risk detection. When used responsibly, these tools can support early intervention and resource allocation. But without transparency and safeguards, they can erode student trust, perpetuate bias, and create unintended harm.
For example, mental health monitoring tools that analyze student emails or activity data may flag potential crises—but they also raise questions about consent, data use, and how decisions are made. AI literacy ensures that students understand how these tools operate and how to hold institutions accountable for ethical implementation. For campus safety and student well-being, that means AI education isn’t just a “nice-to-have.” It’s a critical component of digital citizenship.
Leading with Purpose
Higher education must take the lead in preparing students not just to use AI—but to understand, question, and shape its future. That means offering AI literacy courses across majors, embedding AI ethics into general education requirements, creating policies for responsible classroom AI use, supporting faculty in integrating AI meaningfully into teaching, and encouraging campus-wide conversations about technology, trust, and inclusion. AI doesn’t have to be scary or opaque. With the right guidance, it can be a tool for empowerment, insight, and creativity.
As AI transforms the academic and professional landscape, higher education can model thoughtful, inclusive, and innovative practices. By doing so, institutions not only prepare students to thrive—they protect their well-being and shape a more responsible digital future.
AI isn’t coming—it’s already here. The question is not if students will use it, but whether they will use it wisely. We don’t have to wait for AI to become perfect. It’s already shaping our campuses, our jobs, and our communities. Let’s ensure they’re equipped to lead—not follow—in the age of intelligent technology.
Velina Lee is General Manager, Career and Technical Education, at Vector Solutions.
| 2025-06-09T00:00:00 |
2025/06/09
|
https://www.campussafetymagazine.com/insights/ai-literacy-in-education-why-college-campuses-must-act-now/170277/
|
[
{
"date": "2025/06/09",
"position": 93,
"query": "AI education"
}
] |
In the age of AI, human emotion is journalism's superpower
|
In the age of AI, human emotion is journalism’s superpower
|
https://www.poynter.org
|
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The uncomfortable marriage of journalism and emotion has always mattered, but it's about to matter more than ever. In an AI world, human connection is all we' ...
|
Philip DeFranco told his millions of social media followers about a crisis in journalism, but he didn’t mention that he’s a living example of a solution.
His work shows us the way — by connecting a news product emotionally with people who consume journalism.
DeFranco calls himself “a dad who covers the news for you.” The news influencer reads the latest on YouTube like Tom Brokaw, but also nothing like Tom Brokaw, often wearing a T-shirt while shooting facts, rapid fire, at his Gen Z and millennial audience. Recently, for “The Philip DeFranco Show,” he told his six million followers about a fatal shooting and a budget bill, then ended with a report on wildlife staff dressed in bear costumes to make a cub feel more comfortable.
“You see? There is something that is not just infuriating news,” he said, breaking into a smile. “I always try to give you at least one.”
On TikTok, 50,000 followers clicked “like” for yet another DeFranco fact barrage that included: “With trust in media plummeting, reporters face increasing hostility, and at the same time, local news outlets are disappearing, turning vast swaths of the country into news deserts.”
Yes, we know, but what can we do about it? My answer: Be more like Philip DeFranco. Emotionally engage your journalism audience.
For 20 years, I’ve been honing my application of research into the emotions that drive a journalism audience. I worked closely with the now-shuttered Readership Institute of Northwestern University. I’m a former chief editor at a daily, a current community journalist, and the founder of SuccessfulJournalism.com, a free resource to help journalists serve and emotionally engage audiences.
The uncomfortable marriage of journalism and emotion has always mattered, but it’s about to matter more than ever. In an AI world, human connection is all we’ll have left.
In my Poynter article last year, “Love your community and watch your revenue models do better,” I wrote that journalists must not be outsiders, watching us all curiously like aliens from outer space. We must be a part of the community. We must feel like family.
When a news product binds with a community in this way, that’s emotional engagement. This is the path to stronger revenue models, as when The Batavian — a photo-heavy, people-oriented local news service in upstate New York — sells tons of ads. It’s the path to establishing trust, a defense against the muck of social media.
Here are a couple quick examples of how we can emotionally connect with a journalism audience, in addition to being a news-reading “dad” like DeFranco.
Headlines that sound clinical — like “Hurricane aftermath is arduous for residents” — should sometimes give way to ones that hold up an emotional mirror: “Exhausted!” Or instead of “COVID-19 lockdown enters second month,” how about “We want out”? When we connect with an audience in this way, and they see their own feelings, they know we get it. We are them. We are all in it together.
There are other ways to position a news product as a community cheerleader, earning trust. Run a photo of a lost dog, an essay celebrating great community values, or a guide to handling a challenge in the community. If you’re The New York Times, you buy Wordle, so the whole family can share results with one another, connecting through your brand. In our new AI land, emotional engagement like this will become even more powerful.
Here’s why. AI is getting good at writing stories, at making content. Yet we, the consumers of it, are human. We’re going to tire of reading AI articles and watching concocted people deliver information. We’ll want to know that the person we are hearing from is real. I have a friend in customer service; she’s already getting the question, “Are you real?”
AI and our interconnected world encourage human isolation, by surrounding us with ease and assistance. We will increasingly talk to machines at work, at school and at the store — even now, one may order the latest strawberry-infused calorie bomb at Crumbl Cookies without talking to anybody. You tap a screen, pick a cookie. Meanwhile, people are rolling out of bed and into work from home, doing a few Zooms perhaps. These bits of distance in our society add up. Our need for genuine human connection will grow in the hollow spaces, not subside. For journalism to succeed in this new world, we must offer our humanity.
In truth, we’ll have to doubly embrace human emotion, both on the back end and the front end. The front end is our news product’s relationship with its journalism audience, set by the content, the user’s experience, and messaging. The back end is a journalist’s set of relationships with sources.
The reality is that AI won’t be able to talk with someone and get them to share their story, at least not any time soon. That takes heart. I once sat with brownies in the kitchen of the mother of a state governor. She trusted me and talked about her son. I’ve knocked on the door of a family after a soldier was killed overseas, offering to either go away and never come back or to share whatever they would like to say with the community.
AI can’t bare a soul and get through that door. AI can’t get to know a city official, earn their trust, and learn about something the public should know.
AI is already writing well, and it’s only getting better. Front-line journalists may increasingly find themselves acting less like writers and more like editors — refining AI-generated drafts based on their reporting. The writing may shift to the machines, but the relationship-building, trust-gaining and some story-gathering will still depend on us.
Some people say there will be no jobs for journalists. I disagree. There will still be jobs for journalists — especially those who are interested in connection with sources and with a journalism audience.
AI can’t gather stories or win trust like people can. The journalist of the future will be a people person.
The journalism of the future will appeal to what’s inside a person.
| 2025-06-09T00:00:00 |
2025/06/09
|
https://www.poynter.org/commentary/2025/emotional-connection-journalism-ai/
|
[
{
"date": "2025/06/09",
"position": 11,
"query": "AI journalism"
},
{
"date": "2025/06/09",
"position": 13,
"query": "artificial intelligence journalism"
}
] |
The Death of Journalism by a Thousand AI Cuts
|
The Death of Journalism by a Thousand AI Cuts
|
https://winsomemarketing.com
|
[
"Writing Team"
] |
Every AI-generated summary represents journalism's retreat from its core mission of empirical investigation toward algorithmic convenience.
|
When The Wall Street Journal, Bloomberg, and Yahoo News proudly showcase their AI-generated article summaries, they're not demonstrating innovation—they're hosting their own funeral. These "Key Points" and "Takeaways" represent the final stage of journalism's transformation from investigation to aggregation, from reporting to regurgitation. What we're witnessing isn't technological progress; it's the systematic dismantling of journalism's core value proposition disguised as reader convenience.
The timing couldn't be more perverse. As newsrooms slash over 900 jobs in January 2025 alone—following 10,000 journalism layoffs in the past three years—these same organizations are celebrating AI tools that further automate the human elements that make journalism valuable. We're watching an industry commit suicide while calling it digital transformation.
The Hallucination Problem Nobody Wants to Discuss
Let's address the elephant in every newsroom: AI hallucinates. Consistently. Research from the Tow Center for Digital Journalism found that AI chatbots were "confidently wrong rather than declining to answer," often inaccurately citing news content even when given verbatim extracts. Yet The Wall Street Journal cheerfully admits their AI summaries require "regular care and maintenance" because "error rates are very low, they're not zero."
Very low isn't zero when you're dealing with factual information that shapes public understanding. Every "low error rate" in journalism represents misinformation entering the information ecosystem with the imprimatur of trusted news brands. When 66% of Americans are extremely concerned about getting inaccurate information from AI, newsrooms responding by integrating more AI into their workflow represents breathtaking tone-deafness.
Bloomberg's Chris Collins claims their summaries "complement" journalism rather than substitute for it, but this misses the fundamental issue. These AI summaries aren't just processing existing journalism—they're training readers to expect instant, simplified answers instead of engaging with complex reporting. We're conditioning audiences to prefer algorithmic convenience over journalistic rigor.
The Economics of Intellectual Surrender
The brutal mathematics of modern journalism reveal why AI summaries feel inevitable: Despite a 43% rise in traffic to top news sites over the past decade, their revenues declined 56%. Publishers are desperate for any tool that might reduce costs or increase engagement. But AI summaries represent a false economy—they reduce immediate labor costs while accelerating the long-term commoditization of news content.
When Yahoo News boasts that user engagement increased 50% after adding AI features, they're measuring the wrong metrics. Higher engagement with summarized content doesn't equal higher engagement with journalism—it equals higher engagement with algorithmic interpretations of journalism. The distinction matters enormously for democracy.
Meanwhile, 59% of Americans believe AI will lead to fewer journalism jobs in the next two decades, and they're absolutely right. When newsrooms like The Wall Street Journal build AI directly into their content management systems, they're creating infrastructure designed to replace human editorial judgment with algorithmic processing.
The Empirical Research Crisis
Here's what AI summary proponents won't tell you: journalism's value isn't in synthesizing information—it's in discovering information that doesn't yet exist in any database. AI can summarize existing articles, but it can't interview grieving families, cultivate whistleblower sources, sit through city council meetings, or investigate corporate malfeasance. AI cannot "go into the courtroom or interview a defendant behind bars, meet with the grieving parents of the latest school shooting victim, cultivate the trust of a whistleblower, or brave the frontlines of the latest war".
When newsrooms prioritize AI-generated summaries over empirical reporting, they're signaling that synthesis matters more than discovery. But synthesis without original investigation is just expensive aggregation. We're watching journalism transform from a research profession into a content processing industry.
The Reuters Institute's research reveals the deeper problem: "AI tools can hallucinate so the correct approach would be to check everything produced by the software before publishing it". But if human journalists must fact-check every AI output, where's the efficiency gain? The answer is there isn't one—there's only the gradual erosion of journalistic standards as "low error rates" become acceptable.
The Trust Apocalypse
American trust in news media has cratered precisely as newsrooms have embraced algorithmic content generation. Roughly half of U.S. adults say AI will have a negative impact on news over the next 20 years, while 41% say AI would do a worse job writing news stories than human journalists. Yet newsrooms continue doubling down on AI integration.
This represents a catastrophic misreading of the trust crisis. Audiences aren't demanding more algorithmic content—they're demanding more authentic, human-driven reporting. When The Wall Street Journal proudly displays "What's this?" buttons explaining their AI summaries, they're not building transparency—they're advertising that readers can't trust the content at face value.
The Columbia Journalism Review captured the contradiction perfectly: journalists are simultaneously preparing for AI threats while "cryptographically certifying their media to assert authenticity" because AI has made content verification necessary. We're creating elaborate authentication systems to prove our content is human-generated while simultaneously automating content generation.
The Democratic Reckoning
The most damaging aspect of AI journalism isn't economic—it's epistemological. When newsrooms automate summary generation, they're essentially admitting that journalism's primary value is information processing rather than truth discovery. This fundamental misunderstanding of journalism's democratic function explains why the industry is dying.
AI journalism raises "concerns about algorithmic biases, technology dependence, and the lack of transparency of models" that directly undermine democratic discourse. When AI systems trained on existing content generate news summaries, they necessarily reflect the biases and limitations of their training data—which increasingly includes the very misinformation journalism should combat.
The real threat isn't that AI will replace journalists—it's that newsrooms will voluntarily replace journalism with AI. Every AI summary represents a choice to prioritize algorithmic efficiency over human investigation, to value speed over accuracy, convenience over complexity.
The Path Back From the Brink
The solution isn't rejecting all AI tools—it's understanding journalism's irreplaceable human elements. AI can assist with data analysis, transcription, and research, but it cannot replace the ethical judgment, source cultivation, and investigative instincts that define quality journalism.
Newsrooms betting their future on AI summaries are fundamentally misunderstanding their value proposition. Readers don't need journalists to summarize existing information—they need journalists to discover new information, ask uncomfortable questions, and hold power accountable. These uniquely human functions can't be automated away, but they can be abandoned.
The Wall Street Journal, Bloomberg, and Yahoo News aren't pioneering journalism's future—they're documenting its surrender. Every AI-generated summary represents journalism's retreat from its core mission of empirical investigation toward algorithmic convenience.
Democracy needs journalists, not summarization algorithms. It's time to choose which one we're actually building.
Ready to build content strategies that emphasize authentic human expertise over algorithmic shortcuts? Our team at Winsome Marketing helps businesses develop genuine thought leadership that builds trust through real insights, not AI-generated summaries. Contact our strategy experts to create content that demonstrates human expertise.
| 2025-06-09T00:00:00 |
https://winsomemarketing.com/ai-in-marketing/the-death-of-journalism-by-a-thousand-ai-cuts
|
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"date": "2025/06/09",
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"query": "artificial intelligence journalism"
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] |
|
3 AI tools for news SEO
|
AI tools for news SEO
|
https://www.seoforjournalism.com
|
[
"Wtf Is Seo"
] |
You — not a robot — know your journalism best. Always rely on your own insights and suggestions first. Ahrefs AI Content Helper.
|
As news on Google Search evolves, what do publishers need to know? Join our panel discussion for a candid conversation on visibility, volatility and strategic approaches to help newsrooms stay competitive, in partnership with with Trisolute News Dashboard. Join us Wednesday, June 18 at 11 a.m ET/3 p.m. GMT.
Register now!
Hello, and welcome back. Jessie and Shelby here, back from the first truly nice summer weekend in Toronto. Jessie spent an afternoon wandering a street festival with pals, and at night being charmed by Josh Johnson’s very funny gig. Meanwhile, Shelby finally found the perfect pair of jeans and spent far too much money on skincare. Sometimes, you just have to treat yourself.
This week: Three tools for AI and four ways to use them. We walk through some of the tools in our workflow that make use of artificial intelligence for news SEO. That includes two approaches to prompts for ChatGPT, AI integrations in big-name tools and more.
Plus: Send us your best/favourite ways to use AI for news SEO, or best tools. We’ll round up your picks into a future newsletter!
Well, I need help doing the laundry and dishes … but there’s no GPT for that!
Let’s get it.
In this issue:
ChatGPT for data wrangling and analysis ChatGPT for competitive analysis and brand awareness AI integrations in Semrush Ahrefs AI Content Helper
THE 101
3 tools (and 4 ways) to use AI
Here are three AI tools — and two ways to use ChatGPT — that news SEOs may find useful to support their SEO strategy. This isn’t an exhaustive list, but rather a selection of tools we use in our daily workflows. (Note: none of the tools are WTF is SEO? sponsors.)
🛑 A huge caveat: A lot of big publications are in litigation with OpenAI over the use of their content in its models. Review your company's AI policies before using any AI tool.
ChatGPT: Data wrangling and analysis
What it does: ChatGPT can be very useful for data wrangling and analysis (caveat: fact-check everything; ChatGPT is still a robot!).
Why it’s helpful: It can handle some data work, freeing up your time for more comprehensive audience/SEO analysis. Here are three data-centric things ChatGPT does well.
Headline analysis
To understand what types of headlines perform best (e.g., long, short, first-person, etc.), the first step is to categorize each title. Provide ChatGPT the categories and headlines in your prompt, asking it to sort into each. Ask for a table and tell it to not skip any rows.
ChatGPT can help categorize headlines into buckets.
ChatGPT will provide a response that looks like this:
The response from ChatpGPT.
Extract data from a URL
When working with data exported from Google Search Console, you’re limited in the information you have access to (i.e., the URL and performance metrics). It can be helpful to use information in the URL for further analysis (i.e., if your URL has section folders, what sections have the best click-through rates? Or if dates are involved, using extracting those for further analysis).
Instead of wrangling the data in spreadsheets, ask ChatGPT to do the formatting.
A formula is provided for extracting the section and date from a URL.
Writing regex for data analysis
Writing regex for data analysis (including in GSC) can be a nightmare. Get ChatGPT to do it for you!
In this example, ChatGPT was asked to return a fairly complex regular expression. The prompt asks for a regex that filters for all queries containing questions.
Example of regex from ChatGPT.
To analyze for branded search, ask for a regex that collects all terms related to your brand, including common variations and misspellings.
Regex that looks for branded queries.
Note that the GPT also returns a boundary to avoid matches for words containing “star” like "starbucks." Advanced regular expression written in mere seconds!
🔥 Pro tip: ChatGPT has tips for doing data analysis with its platform, including using clear, jargon-free column headers that are descriptive of the data, not including multiple tabs in one sheet and file size limitations.
ChatGPT: Competitive analysis and brand awareness
What it does: ChatGPT can also help you analyze your competitors, making it easier to spot key differences, similarities and new opportunities.
As AI becomes a bigger part of a reader’s day-to-day life, brand awareness is even more important. Brands that people know and are familiar with are more likely to be clicked on. With fewer opportunities to be visible, increasing brand awareness is key.
Why it’s helpful: Any area of audience (social, search, programming, etc.) can use prompts for analysis about your brand, as well as competitors.
A few useful prompts to start:
What are the top [digital/tv/radio/newsletter] publications in [state/province/city]?
What are the pros and cons/benefits and disadvantages of these publications? Put it in a table.
What is the [publication’s] brand value that can be leveraged on search/social/the homepage?
How do I take action on the above right now in 3 bullet points?
How does [publication] build its topic authority within AI?
Give me reasons why [publication] can’t keep up with [competitor(s)]. Put in a table.
In the example below, ChatGPT was asked what Indiana’s top digital publications are. By specifying the format (digital, radio, or television, etc.), you ensure the results align with your preferred type of content (e.g., The Globe and Mail may consider the CBC a competitor, but the CBC has a ton of television content. This excludes that part of their business).
ChatGPT often returns “honourable mentions," including reasons they didn't make the cut. If your publication is in here, it’s a good place to start: are there specific tactics you can employ to fix that flaw?
From there, ask about each publication’s strengths and weaknesses. Include how ChatGPT should format the response.
A look at how ChatGPT formatted the prompt regarding pros and cons.
🔥 Pro tip: If you’d like to swap out a publication, include “Use BRAND instead of BRAND in the analysis” in your prompt.
From here, you can analyze each competitor in more depth.
Another approach is using ChatGPT to summarize a competitor's brand visibility — something that will become even more important as AI use increases.
Ask ChatGPT to return a table highlighting components of the brand's value and how it can be leveraged for search.
A look at a ChatGPT response for the IndyStar’s brand value.
Semrush
What it does: Semrush has several AI integrations within its tool (including its AI Marketing Booster helps automate tasks like assessing the E.E.A.T. signals of content).
Within the Keyword Overview tool, Semrush uses AI to power domain-specific recommendations, and calculates a Personal Keyword Difficulty (PKD) score — how hard it is for that specific brand to compete on a term.
In the screenshot below, the keyword “carrot salad” has a personalized difficulty score of 85 per cent for Bon Appetit (compared to an overall average difficulty of 57 per cent). It also scores “low” for topical authority. The PKD is also provided for each keyword in the “Keyword Ideas” section.
A look at Semrush’s Keyword Overview tool with AI incorporated.
Clicking into the Keyword Magic Tool, AI informs personalized potential traffic estimates, too, outlining the “potential traffic you are likely to get with quality content focused on the analyzed keyword.”
Semrush highlights the keywords a domain is already ranking for.
🔥 Pro tip: “Show ranking keywords” adds the position that the site is currently ranking for each keyword (e.g., Bon Appetit is ranking in spot #11 for “carrot salad.”) It's a nice addition and saves an extra click to SERP Analysis in the overview panel.
Within the SERP Analysis, Semrush uses AI to highlight where on the results a publisher can hope to rank (“your potential”).
🛑 Heads up: When preparing SEO briefs or internal reports, news SEOs should flag that AI has shaped the data, and that metrics like total search opportunity or keyword volume may not align with reporting in other tools.
What else to know: Test these tools to gauge their utility for your newsroom. It's possible the recommendations are not yet robust or distinct enough to be of real value. You — not a robot — know your journalism best. Always rely on your own insights and suggestions first.
Ahrefs AI Content Helper
What it does: Ahrefs’ Content AI Helper analyzes a URL and suggests optimizations based on the existing content and target keywords. The tool can suggest potential topics to add to a piece, to make it more useful. (As always, use judicially when taking considerations from AI).
Ahrefs' AI Content Helper provides topics with numbers that indicate how similar your article currently is to the respective ideas and suggestions on improving it.
Why it’s helpful: The tool is extremely helpful in optimizing content for competitors and search intent. Stories cover so many branches of the news tree that it can be hard to know exactly what’s involved in your coverage.
Below, Ahrefs recommends different buckets of competitors and is organized by intent to help inform the suggestions made. This ensures the audience you target is correct.
Different content needs different search intent, and Ahrefs’ Content AI Helper tries to bucket those intents for you.
From here, it’ll provide suggestions on optimizing the headline, content format and potential additions — all of which can be added if the writer feels necessary.
Join our Slack!
The bottom line: AI tools can be helpful for mundane or tedious tasks, as well as understanding how these LLMs interpret your brand. Use these tools with care, and supplement with your own insights. And tell us what we missed!
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Have something you’d like us to discuss? Send us a note on Twitter (Jessie or Shelby) or to our email: [email protected].
Written by Jessie Willms and Shelby Blackley
| 2025-06-09T00:00:00 |
https://www.seoforjournalism.com/p/ai-tools-news-seo
|
[
{
"date": "2025/06/09",
"position": 83,
"query": "AI journalism"
}
] |
|
How AI is driving more collaboration on skills intelligence
|
How AI is driving more collaboration on skills intelligence
|
https://www.weforum.org
|
[] |
Using advanced AI and machine learning, skills intelligence can transform fragmented data into actionable insights, empowering individuals, organizations and ...
|
Skills intelligence means using artificial intelligence (AI) and machine learning to collect, analyse and translate workforce data into actionable insights.
Public and private sector organizations are already starting to use this concept to design agile, data-informed skills development strategies.
This means shifting from skills identification to empowerment by highlighting deficiencies and actively building people’s capabilities to create future-ready workforces.
“Skills are the new currency.”
Perhaps you’ve heard this phrase recently in boardrooms, conferences or policy discussions. It’s a concise way to convey how vital competencies have become in today’s rapidly evolving world. But as we rethink what it takes to build resilient, future-ready economies, it’s worth asking whether this metaphor takes us far enough.
Currency, by its nature, is transactional. It fluctuates in value, depends on demand and is ultimately a medium of exchange. But skills, if they are to drive resilience, adaptability and inclusive growth, must be seen through a different lens – not as commodities to be traded, but as infrastructure that supports, anticipates and enables the growth of resilient, future-ready economies.
Just as roads, broadband and power grids form the foundation of a thriving society, skills form the infrastructure of a resilient economy. They support innovation, enable mobility and power entrepreneurial ecosystems. As the World Economic Forum convenes its Annual Meeting of the New Champions 2025 around the theme of “Entrepreneurship for a New Era,” it’s clear that treating skills as infrastructure is essential.
And like all infrastructure, the systems that deliver skills must evolve to meet rising and shifting demands. Static taxonomies and outdated frameworks can no longer keep pace with the speed of economic transformation. That’s where skills intelligence comes in via the strategic use of artificial intelligence (AI) and machine learning to collect, analyse and translate workforce data into actionable insights.
This isn’t a future vision, it’s happening now. For governments and policy-makers, embedding skills intelligence into national frameworks is the upgrade needed to design agile, data-informed strategies that can withstand today’s disruptions and shape tomorrow’s opportunities.
How AI helps build skills intelligence
The Future of Jobs Report 2025 highlights the significant shifts that are transforming the global workforce:
By 2030, nearly 40% of workers' core skills will change dramatically or become obsolete
But 63% of employers identify skills gaps as their top barrier to business transformation
And skill disruptions vary significantly by region, with countries including Lithuania (87%) and Bahrain (67%) facing far greater upheaval than Denmark (29%) or the Netherlands (30%), often due to differences in economic development or geopolitical stability.
Skills gaps are today’s defining workforce challenge. Traditionally, we’ve excelled at identifying these gaps through surveys and assessments. But we must ensure our current methods are truly preparing individuals and economies to adapt and succeed, rather than merely diagnosing problems without delivering meaningful solutions.
To do this, we must move from skills identification to skills empowerment. This means shifting from highlighting deficiencies to actively building capabilities. Using advanced AI and machine learning, skills intelligence can transform fragmented data into actionable insights, empowering individuals, organizations and economies to adapt rapidly and effectively.
This is how skills intelligence becomes essential infrastructure – an investment that garners an immediate return and sets the foundation for long-term economic growth and societal wellbeing.
Rethinking skills for a future-ready workforce
Government-led skills classification systems, such as O*NET in the US, ESCO in Europe, and Canada’s NOC, provide a foundational understanding of skills and occupations for their respective labour markets. These taxonomies (structured frameworks for defining and organizing skills) are widely used by policy-makers, educators and private sector leaders to shape workforce strategies, design training programmes and inform labour market policies.
But these frameworks were designed in an era when workplace dynamics evolved slowly and predictably. Today, the pace of workforce transformation is rapid and exponential, making static, periodically updated systems increasingly insufficient.
To navigate this new reality, we must transition from static frameworks to intelligent, responsive skills infrastructures. Integrating AI-powered skills intelligence into national systems allows governments to dynamically define, measure and adapt to workforce needs in real-time.
This is already underway. In 2022, the O*NET and ESCO systems created a "crosswalk" to map skills between their frameworks. This has opened the door to broader integration across national and regional labour market systems, unlocking real-time services like job matching, targeted reskilling and upskilling, better training alignment and sharper labour market analytics. This is a game changer for public and private stakeholders alike.
Collaborating on skills intelligence
Recognizing the urgency for interconnected skills frameworks, the WEF’s Global Skills Taxonomy Adoption Toolkit 2025 offers a practical roadmap for governments, businesses and education leaders. Just as physical infrastructure connects communities and supports growth, this toolkit calls for aligned, collaborative systems to tackle labour shortages and support workforce transformation at scale.
It outlines practical, near-term actions, such as standardizing how skills are verified, modernizing job descriptions to reflect transferable capabilities and embedding real-time skills tracking into national systems. These aren’t future ideals, they’re available now. But the potential is far-reaching.
Using skills intelligence, governments can identify “proximity skills”, which are adjacent or easily trainable capabilities that help workers pivot into emerging roles. AI-powered platforms can then match individuals to new opportunities with greater speed and accuracy. The most advanced systems go a step further, using validated skills data – reliable, verified evidence of what people can actually do.
This adds a new layer of trust and utility to workforce planning, making it possible to direct talent where it’s needed most, both within and across borders. In an era of fundamental economic transformation, validated skills will be essential to entrepreneurial solutions and innovation-driven growth.
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How skills intelligence drives economic resilience
As we look to a new era of entrepreneurship, the need to reimagine our approach to human potential is clear. The phrase “skills are the new currency” must evolve. Rather than treating skills as transactional units, it’s time to make them the foundation of the systems that drive innovation, resilience and inclusive growth.
| 2025-06-09T00:00:00 |
https://www.weforum.org/stories/2025/06/skills-intelligence-economic-resilience/
|
[
{
"date": "2025/06/09",
"position": 6,
"query": "AI skills gap"
},
{
"date": "2025/06/09",
"position": 68,
"query": "future of work AI"
},
{
"date": "2025/06/09",
"position": 2,
"query": "machine learning workforce"
}
] |
|
Assessing the Real Impact of Automation on Jobs | Stanford HAI
|
Assessing the Real Impact of Automation on Jobs
|
https://hai.stanford.edu
|
[
"Katie Gray Garrison"
] |
Autor found that for jobs that gained inexpert tasks but lost expertise, wages declined as technology made it possible for more people to do more of the tasks ...
|
Who’s more at risk of being replaced by automation: a crossing guard or an air traffic controller? Both prevent collisions, but only one requires years of specialized training. An air traffic controller could do a crossing guard’s job – but not the other way around. And while the guard’s work is untouched by automation, the controller’s tools are increasingly automated to reduce human error.
The answer, says MIT economist David Autor, is nuanced. In a talk hosted by the Stanford Digital Economy Lab at the Stanford Institute for Human-Centered AI, Autor challenged the assumption that automation exposure simply means job loss. “Exposure is not a very useful term,” Autor said. “Is it the case that if you’re exposed, you’re hosed?”
Not necessarily, he said. He pointed to Uber: Wages for taxi drivers stagnated, but employment rose 249% from 2000 to 2020 as automation lowered the barrier to entry. In contrast, proofreaders saw wages rise but job numbers decline as automation removed simpler tasks while adding expert tasks that made the role more specialized.
“Proofreading used to mean spell-checking. Now it’s about helping people write,” Autor said.
In short, Autor found that automation both replaces and augments expertise – it depends on whether rote tasks are removed and expert ones added, and how specialized a role becomes as a result.
An Objective Model to Track Automation’s Impact
To evaluate the impact of automation on occupations, Autor tracked the addition and removal of tasks in job descriptions from 1977 to 2018, alongside shifts in wages and employment. To determine what constituted an expert versus an inexpert job, Autor drew from Zipf’s law and the efficient coding hypothesis. These concepts explain how language evolves to include common shortcut words in order to streamline communication. Initially, these jargon words are understood by a select few experts, but they enter the common vernacular over time.
“I will be able to say ‘LLM’ or ‘GPT’ or whatever, and you’ll all know what I mean,” Autor said. “You wouldn’t have known what I meant five years ago, but you know what I mean now.”
Autor used this idea to distinguish between common – or nonexpert – words and less frequent – or expert – words as he evaluated job descriptions between 1977 and 2018.
Impact of Exposure Depends on Expert Supply
Autor found that for jobs that gained inexpert tasks but lost expertise, wages declined as technology made it possible for more people to do more of the tasks required – like in the Uber scenario. Knowing the most highly trafficked places to pick up passengers and the right routes to get them to their destinations were no longer expert tasks – more people could do the job. Taxi driving became less specialized.
Autor’s model found that the opposite trends occurred – employment went down and wages rose – for jobs that lost inexpert tasks but gained expert tasks that upgraded their expertise levels. In the proofreader example, inexpert tasks like “places proof and copy side by side on reading board” went away, but “consult reference books to check references with rules of grammar and composition” were added.
“Expertise is much closer to a supply change,” he added. “When expertise falls, it’s a reduction in barriers. When expertise requirements rise, it’s an increase in barriers.”
Predicting a Job’s Future by Assessing Routine Tasks
Autor used a large language model to classify tasks into three categories: abstract tasks that require creativity, reasoning, and interpersonal skills; routine tasks that follow clear, repetitive rules; and manual tasks that involve physical effort and common sense but little formal training. He found that 64.5% of removed tasks in his data set were routine, while 75.6% of added tasks were abstract. In other words, jobs with many routine tasks in 1977 had far fewer routine tasks by 2018.
What does that imply for a job’s expertise level? In some occupations, losing routine tasks led to lower wages. In others, it increased specialization and pay.
“For some things you’re taking away the supporting activities. You’re allowing people to specialize and focus on their comparative advantage,” Autor said. “For other sets of occupations, you’re taking away their primary activity, and so you’re removing what’s special about that occupation and reducing it down to the generic activities that many more people could do,” he said.
Autor noted that these results show the “exposure paradox” in action. “These are exposed occupations, but the exposure has completely different meanings for how that work is going to change,” he said.
| 2025-06-09T00:00:00 |
https://hai.stanford.edu/news/assessing-the-real-impact-of-automation-on-jobs
|
[
{
"date": "2025/06/09",
"position": 16,
"query": "ChatGPT employment impact"
}
] |
|
AI is wiping out entry-level jobs—and the impact is already ...
|
AI is wiping out entry-level jobs—and the impact is already here
|
https://www.ynetnews.com
|
[] |
As companies automate faster, entry-level white-collar jobs are vanishing and AI is replacing junior staff across industries—raising unemployment and ...
|
Luis von Ahn, founder and CEO of the language-learning app Duolingo, sent an email in late April to all employees which contained surprising news: the company, which brands itself as “AI-first,” plans to phase out contract workers whose jobs can now be done by artificial intelligence.
Duolingo is not alone. In March, Shopify introduced a policy requiring managers to prove that AI cannot perform a job before new hires are approved. In May, Salesforce acknowledged that its use of AI had contributed to a slowdown in hiring, with 500 customer service workers reassigned to other roles.
That same weekend, Business Insider laid off 21% of its staff while doubling down on its AI strategy. “Over 70% of Business Insider employees are already using Enterprise ChatGPT regularly (our goal is 100%),” CEO Barbara Peng wrote in a memo.
2 View gallery ( Photo: VesnaArt, Shutterstock )
Since the launch of powerful generative AI models and autonomous agents two and a half years ago, experts have warned that such technology could shrink hiring and lead to layoffs, particularly in white-collar professions. Once seen as a distant risk, this trend is now becoming a reality. A growing body of evidence shows that companies are actively replacing human workers with AI, especially at the entry level.
Zanele Munyikwa, an economist at labor analytics firm Revelio Labs, recently analyzed online job postings for roles that involve tasks AI can now perform. She found that listings for these jobs have dropped by 19% over the past three years. Her conclusion: companies are simply opting not to hire for positions that AI can do.
According to Munyikwa, roles with high exposure to automation, such as data engineers, database managers, and IT specialists, are more likely to be affected than jobs with lower exposure, like restaurant managers, construction foremen or mechanics. In other words, white-collar positions that rely heavily on data analysis and processing are seeing the sharpest decline in demand. Still, she warned, it’s unclear whether AI in its current form is truly capable of handling all the roles employers believe it can.
The trend is also showing up in macroeconomic data. The U.S. unemployment rate for recent college graduates has risen to 5.8%—a sharp uptick. According to the Federal Reserve Bank of New York, job prospects for this group are “deteriorating markedly.” A May report by Oxford Economics attributed the rise in graduate unemployment largely to changing hiring practices in the tech sector. “There are signs that entry-level jobs are being replaced by artificial intelligence at an accelerating pace,” the report stated.
Get the Ynetnews app on your smartphone: Google Play : https://bit.ly/4eJ37pE | Apple App Store : https://bit.ly/3ZL7iNv
According to The New York Times, these figures may only hint at a broader shift already in motion. More companies are automating entry-level roles and replacing junior staff with AI tools. One tech executive told the paper their company had stopped hiring programmers with 3–7 years of experience, as AI coding tools could now handle that work. Another startup said one data scientist had replaced what used to be a 75-person team.
Anthropic CEO Dario Amodei echoed these concerns in a recent interview with Axios, warning that AI could eliminate half of all entry-level jobs and raise overall unemployment by 10–20% in the next five years. “Most workers are unaware that this is going to happen,” he said. “It sounds crazy. People just don’t believe it.”
2 View gallery Anthropic CEO Dario Amodei ( Photo: Benjamin Girette/Bloomberg )
Still, the future Amodei envisions isn’t set in stone. Today’s AI systems are not perfect, and some companies have been forced to backtrack. Swedish fintech Klarna, which announced plans two years ago to replace its customer service agents with AI chatbots, has since rehired humans after complaints about declining service.
Such reversals may be temporary. AI models from companies like OpenAI and Google are improving rapidly, and even smaller players like China’s DeepSeek have made impressive breakthroughs.
The bigger long-term question is what happens to complex jobs that AI still can’t handle. Today’s experts started out as entry-level workers, learning through hands-on experience and mentorship. But in a world where companies are hesitant to invest in early-career talent, the pipeline for future expertise is thinning. Then again, perhaps companies are betting that by the time that becomes a problem, AI will be able to do those jobs too.
| 2025-06-09T00:00:00 |
2025/06/09
|
https://www.ynetnews.com/business/article/skmnluxxlg
|
[
{
"date": "2025/06/09",
"position": 74,
"query": "ChatGPT employment impact"
}
] |
Artificial Intelligence Jobs in 2025: Skills and Opportunities
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15 Best Artificial Intelligence Jobs in 2025 and Skills Needed
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https://www.upgrad.com
|
[] |
Artificial intelligence roles like Machine Learning Engineer, Data Scientist, NLP Specialist, and AI Product Manager are some of the most in-demand careers ...
|
Artificial intelligence is powering everything from personalized healthcare to autonomous finance. AI professionals are in high demand and shaping critical innovations like GenAI copilots in enterprise workflows and AI-driven climate modeling. This makes AI careers uniquely positioned for long-term growth, stability, and real-world impact across industries.
Kickstart your AI career with in-depth learning! Explore these top courses and gain hands-on expertise:
Here is a list of the top 15 career opportunities in AI for aspiring professionals:
1. AI Engineer
AI engineers design and develop AI models and algorithms to solve complex problems, such as automating tasks, improving decision-making, and enabling predictive analytics. For example, they might create systems to enhance customer service through AI-driven chatbots or develop algorithms that forecast market trends.
To become an AI engineer, you’ll need the following skills:
Technical Expertise in Statistics : Use statistical methods to analyze large datasets, build machine learning models, and improve predictions. Example: Applying statistical analysis to detect patterns in healthcare data for better patient outcomes.
Proficiency in Mathematics : Understanding linear algebra, calculus, and probability is essential for creating algorithms. Example: Leveraging calculus to fine-tune machine learning models and improve their accuracy in financial forecasting.
Knowledge of Programming Languages: Proficiency in languages like Python , R , and Java is crucial for developing AI applications. Example: Writing Python code to implement neural networks for image recognition in self-driving cars.
The average annual salary of an AI engineer in India is ₹10,00,000. However, the pay scale may vary due to location and experience levels.
Here is an overview of the AI engineer salary based on location across the top 5 cities in India:
Location Average Annual Salary Bangalore ₹20LPA Gurgaon ₹12.3LPA Pune ₹11.6LPA Hyderabad ₹8.5LpA New Delhi ₹7.2LPA
Source: AmbitionBox
Check out the salaries for AI engineers in India based on their experience levels:
Experience Level Average Annual Salary Entry-level (0-1 year) ₹8LPA Mid-level (4-6 years) ₹11LPA Experienced (7-9 years) ₹13.5LPA
Source: Glassdoor
Curious about how AI powers creativity and innovation? Join upGrad’s free Introduction to Generative AI course and explore real-world applications. Learn at your own pace and earn a certification online.
| 2025-06-08T00:00:00 |
2025/06/08
|
https://www.upgrad.com/blog/career-opportunities-in-artificial-intelligence/
|
[
{
"date": "2025/06/09",
"position": 52,
"query": "artificial intelligence employment"
}
] |
AI Progress Report | Local News Initiative
|
AI Progress Report
|
https://localnewsinitiative.northwestern.edu
|
[
"Brier Dudley"
] |
Based on discussions with more than 25 local news and AI experts worldwide, the report explored the potential benefits and perils presented by this ...
|
First, local news is the product that failed in the marketplace, right? It was often a weak product. It really depended on these journalism-adjacent elements to be indispensable. The journalism was often inconsistent, not very good, and so local news is in crisis. The New York Times is succeeding because they've innovated their product, and it's no longer failing in the marketplace. So local news is the product that needs to innovate most.
At the same time, local news is the arena in which there's the greatest potential to create a common public square and common facts. Why? Because the data is very clear: It is more trusted than national news. There is more commonality in the audience, particularly of local television news, across party and ideology. You got more people who disagree with each other watching the same local newscasts or reading the same local newspaper than you have in the ideologically fragmented national media ecosystem, and we have more common ground. We root for the same teams. We suffer the same weather, etc., etc. So that's the world in which we still have shared common facts.
That's also where local retailers are suffering, so businesses have a similar incentive to try and ally with a local publisher. That's why I think journalism has to sort of reorient itself from “We're monitoring the powerful on your behalf and doing once a year maybe something semi-investigative” to “How can we understand you better and help you live your lives better at the local level?” All of our journalism really needs to be service journalism. I don't mean that we stop covering city hall. I mean that we cover city hall in a way that I understand. What are the things that you need to know about city hall? What's relevant to you, and how can I cover this in a way where I don't just tell you these two guys are fighting, you've never heard of them before, and this guy got the better of that other guy? No, I'm going to tell you they're coming up with this plan, and here's what the plan would do, and here's how it would affect you, and if you want to get involved, here's how you can do that.
There is a way to make our accountability journalism into service journalism, but we really need to get closer to our audience to do that. It's not an easy task. But the crisis is its most acute at the local level, and the potential is greatest at the local level. I think that the effort to put money into and try and focus on local news has been well intended but not well executed. I think it needed to start much more with: What were the flaws in the product, and how can we rethink and reinvent them?
Tom Rosenstiel
| 2025-06-09T00:00:00 |
2025/06/09
|
https://localnewsinitiative.northwestern.edu/posts/2025/06/09/tom-rosenstiel-ai-q-and-a/
|
[
{
"date": "2025/06/09",
"position": 26,
"query": "artificial intelligence journalism"
}
] |
AI's Disruptive Wave: What It Means for Google and ...
|
AI’s Disruptive Wave: What It Means for Google and Nigerian Journalism
|
https://omedinewsnetwork.org
|
[] |
As the digital ecosystem evolves, journalists must adapt quickly, ensuring the integrity of news reporting while navigating a landscape where AI is shaping the ...
|
Stella Nwofia
For decades, Google has been the undisputed gatekeeper of information, shaping how billions of people search, learn, and interact online. Its powerful algorithms have dictated the visibility of news, research, and commercial content, making it the primary tool for individuals, businesses, and media organizations seeking information. However, the search giant’s dominance is now being tested like never before.
Artificial intelligence (AI) is shaking up the foundation of traditional search engines, introducing a more dynamic, conversational, and intuitive approach to information retrieval. Unlike Google’s link-based results, AI-driven search models generate direct, contextually rich responses, potentially reducing users’ dependence on traditional search rankings. This evolution marks a turning point in how knowledge is processed and distributed across digital platforms.
With AI-driven search tools gaining momentum, Nigerian journalists—and journalism worldwide—must prepare for a profound shift in how information is accessed, verified, and disseminated. The rise of AI-powered search may streamline research, but it also presents challenges, such as misinformation risks, bias in AI-generated content, and ethical concerns surrounding automated news curation. As the digital ecosystem evolves, journalists must adapt quickly, ensuring the integrity of news reporting while navigating a landscape where AI is shaping the future of media and public discourse.
Google’s Struggles and the Rise of AI Search
For years, Google has maintained an unshakable grip on the global search industry, shaping how information is accessed and monetized. Its dominance has been built on sophisticated algorithms, vast indexing capabilities, and a business model centered on advertising revenue. However, the rapid emergence of artificial intelligence (AI)-driven search tools is threatening this long-standing reign, presenting Google with one of its biggest challenges to date.
AI-powered search models, such as Microsoft’s Copilot and OpenAI’s ChatGPT, are transforming how people seek information online. Instead of generating a list of links, AI tools provide direct, conversational responses, eliminating the need for users to sift through multiple web pages. This shift has profound implications for Google, whose search engine relies on user engagement with advertised links to generate revenue. If AI-driven platforms continue to gain traction, fewer users may interact with Google’s search results, potentially disrupting its lucrative ad-based business model.
AI’s Emergence in Search and Journalism
The digital search landscape is undergoing a seismic shift, driven by the rapid advancement of artificial intelligence (AI). Traditional search engines like Google have long relied on keyword-based indexing, providing users with ranked lists of relevant web pages. However, AI-powered search models, such as Microsoft’s Copilot, OpenAI’s ChatGPT, and other emerging platforms, are revolutionizing how information is retrieved. These AI systems leverage natural language processing and machine learning to generate direct, contextually rich responses—offering a more intuitive, conversational approach to answering queries.
In response to this disruption, Google has introduced its own AI-driven Search Generative Experience (SGE), aiming to integrate AI into its search ecosystem to maintain relevance. Despite these efforts, the fundamental shift away from traditional search mechanics threatens Google’s core advertising-driven revenue model. Historically, Google’s business has thrived on search result-based advertising, generating billions in revenue from clicks and impressions. If users increasingly rely on AI-generated answers instead of clicking on multiple links, advertisers may see diminishing returns, forcing Google to rethink its monetization strategy.
Implications for Nigerian Journalism
For Nigerian journalists, AI’s growing influence presents a mixed bag of opportunities and challenges. On one hand, AI search tools offer remarkable efficiency, allowing reporters to access summarized information, quickly verify facts, and process complex queries with unprecedented speed. These advancements can streamline investigative journalism, data analysis, and content creation, enabling newsrooms to work more effectively in an increasingly fast-paced media environment.
On the other hand, AI-generated search results pose significant risks. Accuracy remains a critical concern, as AI models can occasionally produce misleading or biased responses due to limitations in their training data. For Nigerian journalists working in an environment where misinformation and disinformation are already pervasive, reliance on AI-generated information requires careful scrutiny. Journalists must prioritize fact-checking and source verification to ensure they are not inadvertently spreading inaccuracies.
Additionally, AI-driven search tools tend to reflect the biases present in their underlying datasets, which are predominantly shaped by Western perspectives. This raises concerns about representation and relevance, as Nigerian and African voices may be underrepresented in AI-generated responses. To mitigate this, media professionals and policymakers must advocate for AI models that incorporate diverse datasets, ensuring that Nigerian history, politics, and societal nuances are adequately represented in AI-generated search results.
The Path Forward
As AI continues reshaping digital search, Nigerian journalists must adapt to these changes while safeguarding the integrity of news reporting. AI-powered search tools can enhance efficiency, but they cannot replace the rigorous investigative methods that underpin credible journalism. Training programs and workshops on AI literacy should be introduced in media organizations and journalism schools, equipping professionals with the skills to critically evaluate AI-generated content.
Moreover, Nigeria has an opportunity to develop localized AI search tools tailored to regional needs. By investing in homegrown AI innovations, Nigerian tech entrepreneurs can create platforms that prioritize indigenous languages, cultural context, and locally relevant news sources—ensuring that AI’s benefits are accessible to all.
The future of search and journalism is unfolding rapidly, and Nigerian journalists must remain proactive in navigating this new landscape. While AI may redefine how information is accessed, human expertise, ethical reporting, and critical analysis will continue to be the cornerstone of responsible journalism in the AI-driven era.
| 2025-05-12T00:00:00 |
2025/05/12
|
https://omedinewsnetwork.org/ais-disruptive-wave-what-it-means-for-google-and-nigerian-journalism/
|
[
{
"date": "2025/06/09",
"position": 73,
"query": "artificial intelligence journalism"
}
] |
AlterNet's Code of Standards for Artificial Intelligence
|
AlterNet's Code of Standards for Artificial Intelligence
|
https://www.alternet.org
|
[] |
Use of AI tools will involve AlterNet journalists and editors at every step of the way. Any content produced with the assistance of AI is vetted by human ...
|
AlterNet is committed to accurate, independent and original journalism. Reporters aim to be precise with all headlines and statements. This includes identifying information such as names and positions as well as factual statements and quotations.
AlterNet is also committed to human-written journalism, and use of AI is generally contrary to our values. In recent months, however, we have begun to face increasing competition from publishers who appear to be using AI to get stories to print faster than us, which harms our ability to compete and threatens our business. To remain innovative and meet the demands of a rapidly evolving media environment, we have no choice but to experiment with use of AI in certain, carefully controlled circumstances. This document governs that experimentation. This policy will be revisited in six months, or sooner if circumstances warrant.
The following outlines AlterNet’s policies for use of AI.
Human Oversight
Use of AI tools will involve AlterNet journalists and editors at every step of the way. Any content produced with the assistance of AI is vetted by human journalists or editors for accuracy, fairness and completeness. Generative AI can help with the process of reporting but must be managed and accountable to journalists. Regardless of whether portions of a story were written with the use of AI, the journalist whose name appears on the story must own those words the same as if they were originally drafted by that journalist. Headlines, opening paragraphs, and key paragraphs that are central to the AlterNet voice must be written by humans without the use of AI. AI will typically be used only to generate background paragraphs or summaries of information that has previously been made public elsewhere.
Disclosure
AlterNet discloses its use of AI as part of this policy. Articles that involve substantial use of AI will include a disclosure stating that AI was involved in the creation of the content along with a human editor.
Fact-Checking
Any use of AI in the creating of AlterNet content will be rigorously fact-checked before publication.
Prohibited Uses
AlterNet prohibits the use of AI to:
Generate entire news articles for publication without human authorship and oversight
Generate made-up attributions or non-existent sources
Generate images or video purporting to depict real events or people without clear labeling
Mimic the voice or style of real individuals without their consent
Training
AlterNet journalists and editors will be trained on this policy if they are asked to use AI in their work.
Feedback and Concerns
AlterNet welcomes feedback from readers about its use of AI. Any concerns about the accuracy, ethics, or impact of AI-assisted content can be reported to [email protected].
| 2025-06-10T00:00:00 |
2025/06/10
|
https://www.alternet.org/alternet-s-code-of-standards-for-artificial-intelligence/
|
[
{
"date": "2025/06/09",
"position": 87,
"query": "artificial intelligence journalism"
}
] |
Framing AI Job Displacement: The Role of Free-Market Rhetoric
|
Framing AI Job Displacement: The Role of Free-Market Rhetoric
|
https://elgar.blog
|
[] |
Indeed, the new automation will eliminate millions of jobs for vehicle drivers and retail workers, as well as those for health care workers, lawyers, ...
|
Written by Imad A. Moosa
Free marketeers tell us repeatedly that we have nothing to worry about as long as we put our faith in the market, which provides salvage as we go along. Some free marketeers go as far as suggesting that adopting the free-market doctrine in its purest form (meaning no public policy or public sector whatsoever) guarantees the avoidance of economic and financial mishaps. This is how an over-enthusiastic free marketeer describes life under the care of the almighty market where the “invisible hand” operates without any constraints:1
It is hard to imagine the world with a completely free market. People would be much richer. We would have such advanced technology. We would live to be a hundred and fifty or older, because biotechnology has the capacity, if freed up from regulations and freed up from state control, to really extend human life dramatically. We might have rocket ships going to Mars, we might be colonizing Mars. More importantly than that, there would be no poor people in the world.
A natural response to this heroic and extravagant statement is “oh my God” or “wow”. In reality, however, this description of life under the purest form of free market economy defies logic. For one thing, people would die at a young age because they would not be able to afford the cost of healthcare. Even in its diluted form, the free market doctrine has been taking us from one crisis to a bigger one while aggravating inequality to unprecedented levels by making the rich richer and the poor poorer. Adopting free-market “policies” has enriched a small minority of oligarchs and corporate interests at the expense of the majority of wage and salary earners who, thanks to a twisted tax code, bear most of the tax burden.
As far as artificial intelligence is concerned, free marketeers tell us that there is nothing to worry about because AI, like previous technological revolutions, will bring with it a process of “creative destruction”, whereby new jobs are created to replace the jobs (or “tasks”, as they prefer to call them) eliminated by the introduction of AI. The typical argument that makes free marketeers optimistic about the effect of AI on employment is that technology boosts productivity, which in turn creates demand and jobs. Specifically, they claim that AI boosts productivity, enabling companies to lower prices for consumers, pay higher wages, or distribute profits to shareholders. As a result, aggregate demand will be stimulated, boosting job creation. Rising productivity, they claim, produces higher levels of income and spending, consequently boosting demand for goods and services across the economy. They even go as far as claiming that, thanks to AI, all of us will work less and play more.2 It follows that there is no need for public policy intervention and that if adjustment is to be made, the burden of adjustment must fall on the individual. This is Alice in Wonderland and Ali Baba and the Forty Thieves—in other words, pure fantasy that deserves inclusion in a volume of One Thousand and One Nights.
It is not as simple and straightforward as free-market enthusiasts make it sound. There is no doubt that AI, and technology in general, boosts productivity, but the effect of productivity growth on employment, wages and prices depends on who benefits from productivity gains, which is determined by the corporate sector (by profit-maximising firms, to be precise). A technology-driven rise in productivity means that the same quantity of output can be produced with smaller quantities of inputs, or that the same quantities of inputs can be used to expand output. In either case, gains will be realised by the producer, but the economy-wide outcome depends on how the realised productivity gains are distributed. Productivity gains can be (i) distributed to workers in the form of higher wages; (ii) used to finance capital investment to expand productive capacity; (iii) channelled to the owners, executives, directors and shareholders in the form of extravagant bonuses and dividends; and (iv) passed on to consumers in the form of lower prices. In a neoliberal world where the economy is dominated by profit-maximising firms operating in an environment with little regulation, productivity gains are more likely to be used to boost profit rather than reduce prices or raise wages. A decision to expand production and employment will be taken by a profit-maximising firm only if it boosts profit. Two prominent economists suggest that “the dominant perspective in most C-suites views labour as a cost to be cut, either to withstand competition or to better remunerate shareholders”.3 In a neoliberal economy, labour is a cost of production rather than a creator of value.
In my book, The Economics of Artificial Intelligence, I report the results of a simulation exercise covering four scenarios for channelling technology-driven productivity gains. Under scenarios [1], [2] and [3], productivity gains are channelled entirely to profits, wages, and prices while maintaining the level of output, which means that a smaller quantity of labour would be used. Under scenario [4], productivity gains are channelled to output by expanding production, using the same quantity of labour, in which case no effect on employment will materialise. The results show that only if a decision is taken to expand output beyond the productivity gains will employment expand, which can only happen if it is profitable to do so. This is very unlikely unless there is a source of extra demand, which is conditional upon the use of productivity gains to raise wages and/or reduce prices.
In a neoliberal world, the most likely scenario is [1]—that productivity gains are channelled entirely to profits. This proposition is supported by the historical record as reflected in US data on productivity, wages, corporate profit, consumer prices, employment, and capital intensity. The chart below displays average annual compound growth rates, over the period 1948-2023, showing clearly that the benefits of productivity growth have not been passed on to workers or consumers, but rather to directors and shareholders, as indicated by rampant growth in corporate profit. By contrast, productivity growth has been more rapid than the growth of employment and wages, implying that productivity gains have not been passed on to workers. Prices have been rising faster than productivity, implying that the gains have not been channelled to consumers. The rapid growth of capital intensity indicates that productivity growth has led to the replacement of labour by capital, thus reducing the potential growth of employment. This is to be expected in any neoliberal economy.
Average Annual Compound Growth Rates in the US (1948-2023)
Those who hold the view that AI will create more jobs than the jobs it destroys emphasise the point that workers should be retrained to assume new tasks and pursue different careers. Two terms that have become common in the literature dealing with this issue are “skills mismatch” and “upskilling.” The Future of Jobs Report 2023 identifies a wide range of jobs that will diminish or disappear and jobs that will emerge as a result of the advent of AI.4 By looking at the lists of jobs expected to disappear and emerge, it becomes clear what kind of upskilling is required so that people who lose their jobs can find new employment opportunities. This would be a monumental task because of the enormous difficulty of moving workers from low-skill to high-skill jobs. For example, what does it take to retrain a door-to-door sales person to be an AI and machine learning specialist? How about retraining a bookkeeper to be a fintech engineer? Can anyone seriously imagine retraining a stock-keeping clerk to become a robotics engineer? It is definitely easier to retrain a materials engineer to be a security guard, except that security guards will lose their jobs to machines. How about retraining a cashier to become an electro-technology engineer? Can a postal service clerk be retrained to become a digital transformation specialist? If any of these is possible at all, only very few clerks have the ability required to become robotics engineers, but it is absurd to imagine that all clerks who lose their jobs can be retrained to take any of the new sophisticated jobs. Then there is no guarantee that as many robotics engineers, fintech engineers, and machine leaning specialists will be required as the number of clerks who lose their jobs.
Those who look at the history of technology to foresee the future effects of AI on employment seem to forget that this time it is different because AI is more disruptive than any other technology. An observer describes the situation as follows:5
The adoption of AI in the workplace will likely be different from prior instances of technological disruption. Over the last few decades, technological change has largely impacted routine tasks that were mostly part of middle-wage jobs. In contrast, the adoption of AI is likely to impact a much wider degree of occupations, automating or augmenting nonroutine tasks that had not been impacted by past automation. Nonroutine tasks are mostly found in low-wage jobs, such as janitorial services, home health aides and food services, or in high-wage jobs, such as managerial roles and roles within the knowledge economy, posing a set of issues unlike before.
The impact of AI on employment is different from that of previous technological revolutions, because “machines” are no longer straightforward mechanical tools but have assumed more of a “worker” role, just as people who can learn and think tend to do. A distinguishing feature of AI is that it can be developed for many different types of activities, with the potential to spread rapidly in every sector of the economy and in every aspect of our lives.
A study prepared by the US Council of Economic Advisors suggests that previous technological advances in automation affected “routine” tasks, but AI has the potential to automate “non-routine” tasks, exposing large new swaths of the labour force to potential disruption.6 The study refers to “an emerging body of research” suggesting that AI can outperform workers in an increasing set of complex tasks that are typically assigned to educated workers. The view that this time it is different is expressed by an economist as follows:7
The “new automation” of the next few decades—with much more advanced robotics and artificial intelligence (AI)—will widen the range of tasks and jobs that machines can perform, and have the potential to cause much more worker displacement and inequality than older generations of automation. This can potentially affect college graduates and professionals much more than in the past. Indeed, the new automation will eliminate millions of jobs for vehicle drivers and retail workers, as well as those for health care workers, lawyers, accountants, finance specialists, and many other professionals.
In its report on the effect of generative AI on jobs, the Global Partnership on Artificial Intelligence expresses the following view:8
Unlike previous AI developments which focused on automating narrow tasks, Generative AI models possess the scope, versatility, and economic viability to impact jobs across multiple industries and at varying skill levels. Their ability to produce human-like outputs in areas like language, content creation and customer interaction, combined with rapid advancement and low deployment costs, suggest potential near-term impacts that are much broader and more abrupt than prior waves of AI.
The report goes on to say that generative AI is capable of creating content, solving complex problems, and even mimicking human-like text, which brings about a “different set of challenges and opportunities that extend far beyond traditional notions of automation”. What makes it different this time is that previous technological revolutions involved the replacement of human mechanical skills with tools and machinery. AI tools, on the other hand, are replacing human mental functions, particularly the ability to predict future outcomes and make decisions accordingly. This is something that has never happened before in human history.
Those who undermine the argument that AI will have extremely adverse consequences for employment and wages, thinking that it will still be business as usual, are challenged by those who believe that while the economic impact of AI has so far been “pretty mild”, AI technologies will likely deliver “massive economic shocks” over the next two decades. As an example, one observer illustrates the effect of one AI application, self-driving vehicles, which has the potential to put America, and many other countries, into recession. This is how he explains the situation:9
5 million Americans currently make their living driving taxis, buses, vans, trucks, and e-hailing vehicles. That’s approximately 3% of the workforce. The US Postal Service is already piloting self-driving trucks, Uber has based its whole future business model and valuation on replacing drivers with self-driving vehicles, and two years ago, GM announced that it plans to deploy a large-scale fleet of driverless taxis in large cities by the end of 2019. So, 5 million American jobs are going to disappear over the next 5-10 years. To put that in perspective, America lost 8.7 million jobs in the great recession of 2007 to 2010. So, just one AI technology has the power to put America into recession all by itself. Imagine what will happen when multiple AI technologies are adopted at the same time.
The problem is not in technology itself but in the system under which technology is developed and deployed. When technology is deployed in a neoliberal economy dominated by profit-maximising firms and oligarchs whose appetite for wealth accumulation is insatiable, it becomes a weapon in the wrong hands. In previous technological revolutions, “creative destruction” worked rather well, in the sense that the new technology created more jobs than the jobs destroyed by the same technology. This time, however, it is different (perhaps involving “destructive destruction”) because AI can be distinguished from previous technologies in terms of its transformative and disruptive power. This may be a pessimistic view that will prove to be wrong as in the past. Perhaps, but it is better to err on the side of caution than the side of complacency. Nothing is “inevitable” about the applications of technology or its consequences. Both involve choices, which imply the availability of alternatives. We could at least try to push policy makers to choose the best available course of action for the benefit of the whole society rather than the wealth and power of the oligarchy.
It is irresponsible to promote the idea that there is nothing to worry about. It is irresponsible to suggest that the adverse consequences of AI can be met by education, reskilling and the need to remain competitive by removing barriers to the adoption of AI. This is free market rhetoric. It is equally irresponsible for governments to accept this proposition, yield to the wishes of the AI oligarchy, and refrain from thinking about regulation and corrective policies to counter the adverse consequences of AI. On the contrary, public policy must deal with the potential adverse consequences of AI, including regulation and redistributive policies (which may involve the introduction of automation tax, wealth tax, luxury tax, and infrastructure tax). Otherwise, even the oligarchy may live to regret the urge to resist the introduction of corrective policies. This is because AI has the potential to destroy the very fabric of neoliberal capitalism from which the oligarchy has benefited enormously.
Notes
1 Brook, Y. (2016) Free Market, 13 November. http://serious-science.org/free-market-7407.
2 Lund, S. and Manyika, J. (2017) Five Lessons from History on AI Automation and Employment, McKinsey Global Institute, 28 November.
3 Acemoglu, D. and Johnson, S. (2023) Choosing AI’s Impact on the Future of Work, Stanford Social Innovation Review, 25 October.
4 World Economic Forum (2023) The Future of Jobs Report 2023. https://www.weforum.org/publications/the-future-of-jobs-report-2023/.
5 Khattar, R. (2023) Will AI Benefit or Harm Workers?, 24 August. https://www.americanprogress.org/article/will-ai-benefit-or-harm-workers/.
6 White House (2022) The Impact of Artificial Intelligence on the Future of Workforces in the European Union and the United States of America. https://www.whitehouse.gov/wp-content/uploads/2022/12/TTC-EC-CEA-AI-Report-12052022-1.pdf.
7 Holzer, H.J. (2022) Understanding the Impact of Automation on Workers, Jobs, and Wages, Brookings, 19 January.
8 Global Partnership on Artificial Intelligence (2023) Generative AI, Jobs, and Policy Response, Innovation Workshop, Montreal.
9 Smith, D. (2019) Why Artificial Intelligence (AI) Means the End of Capitalism, Linkedin, 23 May.
| 2025-06-09T00:00:00 |
2025/06/09
|
https://elgar.blog/2025/06/09/framing-ai-job-displacement-the-role-of-free-market-rhetoric/
|
[
{
"date": "2025/06/09",
"position": 5,
"query": "automation job displacement"
},
{
"date": "2025/06/09",
"position": 5,
"query": "robotics job displacement"
}
] |
Future of Work
|
Future of Work
|
https://sloanreview.mit.edu
|
[
"Massachusetts Institute Of Technology",
"Daniel Jolles",
"Teresa Almeida",
"Grace Lordan",
"Mit Sloan Management Review",
"Kaushik Viswanath",
"Peter Cappelli",
"Prasanna",
"Sonny",
"Tambe"
] |
Research finds that the adoption of generative AI tools across industries has been slower than many people anticipated. Kaushik Viswanath. September 09, 2024 ...
|
Learn why moving sideways might be the best thing you can do for your career in this short video with Brian Elliott.
| 2025-06-09T00:00:00 |
https://sloanreview.mit.edu/tag/future-of-work/
|
[
{
"date": "2025/06/09",
"position": 43,
"query": "future of work AI"
}
] |
|
AI the future of work and frontier firms - Ascendum
|
AI the future of work and frontier firms
|
https://www.ascendum.com
|
[] |
As the era of artificial intelligence (AI) begins to unfold, businesses and workers alike are navigating an unfamiliar and rapidly shifting frontier, ...
|
Microsoft’s 2025 Annual Work Trend Index paints a clear picture of this shift, introducing the concept of “Frontier Firms” that operate with AI seamlessly intertwined into their operations.
Think of these firms as modern pioneers, rewriting the rules of work by blending human ingenuity with machine capability. For anyone feeling overwhelmed by this accelerating change, the survey results present an emerging roadmap to offer direction.
Expertise on Demand
Imagine walking into work one morning and learning that your productivity does not depend on how many hours you grind through a daunting to-do list, but on how effectively you harness AI. According to Microsoft’s research, this is no longer hypothetical.
In the survey, 82% of business leaders view 2025 as the perfect moment to overhaul their companies’ core strategies, and the appeal is hard to ignore. AI blurs traditional boundaries, transforming intelligence from a costly, finite resource tied to human headcount into something scalable and accessible on demand.
This shift is not just existential; it is measured. Microsoft reports that while 53% of leaders feel productivity must increase, 80% of employees say they simply do not have enough time or energy to do their jobs well.
Combine this with interruptions that come “every two minutes” on average, and it is no wonder that “Frontier Firms” are racing to use AI as a productivity accelerant. Over the next 12-18 months, 82% of leaders plan to adopt digital labor to bridge this “capacity gap.” By doing so, these firms are not just meeting demands but thriving. Microsoft’s data finds that 71% of workers at these firms feel their organizations are flourishing, compared to just 37% globally.
Collaborating with Agents
What does this transformation look like inside an organization? Microsoft calls it the emergence of “human-agent teams,” which are breaking down traditional hierarchies. Task-oriented roles are giving way to flexible “work charts,” where humans and AI agents collaborate. Almost 46% of business leaders are already fully automating workflows in areas like customer service, marketing, and product development.
The numbers tell the story. A McKinsey report estimated that AI holds the potential to generate $4.4 trillion in productivity growth globally. Businesses are leaning into these predictions. Top AI startups are hiring twice as fast as Big Tech companies, as innovation increasingly thrives within smaller, more agile players.
But much like dialing in the right mix on a recipe, leaders are grappling with a key metric for the future workforce: the “human-agent ratio.” How many AI systems or agents should be tasked with which roles, and where, if anywhere, should humans remain indispensable? McKinsey said that scaling AI in the workplace is not just a technological challenge but a leadership one. Employees are ahead of employers in readiness for AI adoption, with 94% already familiar with generative AI tools like ChatGPT.
The Rise of the Agent Boss
The most interesting shift is how employees themselves will need to adapt to this new workplace reality. At Frontier Firms, every worker becomes what Microsoft terms an “agent boss.” This is an employee managing not just coworkers but also a suite of AI systems designed to amplify their career growth. It is estimated, by 2030, global AI adoption will create 97 million new jobs while redefining countless others, according to projections from the National University report.
However, the agent boss idea is not without its complications. Stanford’s AI Index notes significant gaps in AI capabilities when it comes to complex reasoning, often crucial for high-stakes jobs.
While AI excels in specific tasks like programming or efficiency within customer service applications, challenges such as logic errors and hallucinations remain. This could explain why human oversight is still essential; knowing when and where to lean on AI will separate effective agent bosses from the rest.
Productivity and Innovation Without Limits
But the story around AI is not just about the transformational effects on individual companies. It involves a broader societal impact.
Frontier Firms serve as microcosms of what happens as AI goes mainstream. For example, National University highlighted that AI-driven automation would increase productivity by a remarkable 40%, while simultaneously boosting GDP contributions to an estimated $15.7 trillion globally by 2030.
Some sectors are seizing on these advantages quicker than others. Stanford’s data reveals 78% of businesses employed AI tools in 2024, up dramatically from just 55% the year before.
Yet, even within tech-savvy industries, access and readiness remain uneven. The AI Index notes stark regional divides in optimism. While more than 80% of respondents in China and Indonesia see AI as overwhelmingly beneficial, the sentiment is much lower in the U.S., with only 39% expressing similar optimism.
This hesitance may come from ongoing concerns over fairness and safety. Despite advancements, governments are only now catching up to regulate AI effectively. Between 2023 and 2024, U.S. agencies doubled the number of AI-related regulations introduced.
A Turning Point for Workers and Leaders
The good news? Employees are optimistic. Almost 78% say AI will provide opportunities for more complex work earlier in their careers. Millennials, in particular, are ready to drive change, as this generation not only understands AI’s nuances better than any other but is ready to adopt it completely.
Microsoft’s report provides a clear call to action. While only 1% of firms globally say they have reached “maturity” in AI adoption, the pace of investment indicates that this figure will soar in the coming years.
But even as companies race toward an AI-powered future, the core of these transformations still relies on human qualities. Strategic thinking, adaptability, and creativity are not vanishing; instead, these qualities are accelerated by AI in ways that Frontier Firms will continue to model for the rest of the world.
The way we work is evolving, yet the human drive to innovate remains firm. What has changed is the unprecedented scale of possibilities. As AI shapes the future, it is evident that humans are not being left behind, we are adapting to navigate and thrive alongside it.
Elevate Your Business with Ascendum
Ascendum is an award-winning global technology firm specializing in developing innovative digital solutions that enhance customer experiences and optimize business operations. With a creative and collaborative approach, we turn bold ideas into measurable results.
To start a conversation, click the “Connect Here” bar below to set up a discovery call with our team.
| 2025-06-09T00:00:00 |
https://www.ascendum.com/news/ai-the-future-of-work-and-frontier-firms-6h4wm
|
[
{
"date": "2025/06/09",
"position": 49,
"query": "future of work AI"
}
] |
|
Future of Work with AI Agents: Auditing Automation and ...
|
Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce
|
https://digitaleconomy.stanford.edu
|
[] |
The rapid rise of compound AI systems (a.k.a., AI agents) is reshaping the labor market, raising concerns about job displacement, diminished human agency, and ...
|
The rapid rise of compound AI systems (a.k.a., AI agents) is reshaping the labor market, raising concerns about job displacement, diminished human agency, and overreliance on automation. Yet, we lack a systematic understanding of the evolving landscape. In this paper, we address this gap by introducing a novel auditing framework to assess which occupational tasks workers want AI agents to automate or augment, and how those desires align with the current technological capabilities. Our framework features an audio-enhanced mini-interview to capture nuanced worker desires and introduces the Human Agency Scale (HAS) as a shared language to quantify the preferred level of human involvement. Using this framework, we construct the WORKBank database, building on the U.S. Department of Labor’s O*NET database, to capture preferences from 1,500 domain workers and capability assessments from AI experts across over 844 tasks spanning 104 occupations. Jointly considering the desire and technological capability divides tasks in WORKBank into four zones: Automation “Green Light” Zone, Automation “Red Light” Zone, R&D Opportunity Zone, Low Priority Zone. This highlights critical mismatches and opportunities for AI agent development. Moving beyond a simple automate-or-not dichotomy, our results reveal diverse HAS profiles across occupations, reflecting heterogeneous expectations for human involvement. Moreover, our study offers early signals of how AI agent integration may reshape the core human competencies, shifting from information-focused skills to interpersonal ones. These findings underscore the importance of aligning AI agent development with human desires and preparing workers for evolving workplace dynamics.
| 2025-06-09T00:00:00 |
https://digitaleconomy.stanford.edu/publications/future-of-work-with-ai-agents-auditing-automation-and-augmentation-potential-across-the-u-s-workforce/
|
[
{
"date": "2025/06/09",
"position": 73,
"query": "future of work AI"
}
] |
|
AI in schools and colleges: what you need to know
|
AI in schools and colleges: what you need to know – The Education Hub
|
https://educationhub.blog.gov.uk
|
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Yes, teachers can use AI to help with things like planning lessons, creating resources, marking work, giving feedback, and handling administrative tasks.
|
Artificial intelligence (AI) is here to stay. It’s already having a significant impact across the public sector - from helping police identify criminals to improving cancer screening in the NHS.
That’s why we are bringing the AI revolution to classroom – using it to modernise our education system, to back our teachers and deliver for our children and learners.
Here is everything you need to know about how AI can transform education.
Are teachers allowed to use AI?
Yes, teachers can use AI to help with things like planning lessons, creating resources, marking work, giving feedback, and handling administrative tasks. But they need to use their professional judgement and check that anything AI generates is accurate and appropriate—the final responsibility always rests with them and their school or college.
Schools and colleges can set their own rules on AI use, as long as they follow legal requirements around data protection, child safety, and intellectual property.
More information on Generative artificial intelligence (AI) in education can be found in our policy paper.
How could AI help teachers?
From drafting curriculum plans to producing high-quality teaching resources, AI has the potential to reduce the amount of time teachers spend doing administrative tasks, so they can focus on what they do best– teaching and supporting their students.
There is so much untapped potential for AI to give teachers a helping hand with classroom life. That’s why we are funding the development of brand new AI tools for education. These tools truly stretch the capabilities of what AI can do – from assessing handwritten work to giving feedback on hand-drawn geography maps and even recognising mistakes in soldering on circuit boards made by engineering students.
Time previously spent on burdensome marking and admin allows teachers to focus on face-to-face teaching.
Will teachers be replaced by AI?
Absolutely not.
Teachers are irreplaceable, and AI could never be a substitute for teachers' professional judgement and the personal relationships they have with their students.
We're developing AI tools specifically to support teachers, not to replace the unique role they play in education. These tools are designed to handle repetitive tasks that currently take teachers’ energy away from face to face classroom interactions.
Good teachers are key to raising standards in education. Using AI to reduce workload could also help address the recruitment and retention challenges in teaching - helping to restore teaching as an attractive profession. These tools have the potential to make a difference to the every day lives of teachers, and will help them focus on what matters, teaching.
Are students allowed to use AI?
It’s up to schools and colleges to decide if students can use AI, but they need to have the right safeguards in place. That means close supervision, using tools with safety and filtering features, and making sure students stick to age restrictions—many AI tools are 18+. Schools and colleges also need to think about how AI affects learning and whether homework policies need updating.
Each school or college can choose how and when AI is used. Some might allow it in certain subjects or year groups, while others may decide it’s just for teachers. Whatever they choose, keeping students safe should always come first.
How could AI help students?
Evidence shows that high quality, personalised feedback builds attainment, and AI tools can help with this. This means getting more of what makes the biggest difference to their learning - high-quality face-to-face teaching and personalised feedback.
AI tools can speed up marking and help teachers understand each student's progress better, so they can tailor their teaching to what each learner needs. This won't replace the important relationship between students and teachers - it will strengthen it by giving teachers back valuable time to focus on the human side of teaching that makes all the difference to how well pupils learn.
What are you doing to develop AI in education?
From investing in AI tools, to upskilling teachers and leaders ,and gathering evidence about how teachers and students are using AI, we’re already investigating how we can take advantage of AI in education.
These are just first steps, but we have already:
Launched the AI Tools for education competition and content store
In August 2024 we announced two exciting initiatives to stimulate the education technology sector and help build tools that will support with teacher workload.
The content store is a £3 million data library funded by the Department for Science and Technology which will be used to provide large language AI models with high quality educational information, like curriculums and mark schemes. This means AI products will be even more effective at producing resources to help teachers.
We also announced the AI Tools for education: £1 million of funding through Innovate UK’s contracts for innovation programme. This was awarded to 16 innovators to use the information from the content store to build AI tools that will help with teacher workload across a range of the key stages.
We have also announced an additional £1m of Contracts for Innovation funding to accelerate the development of AI tools for teachers — moving them from the design phase into real classrooms. This brings ground breaking AI tools a step closer to being ready for everyday use in schools.
Invested up to £2 million in AI tools for Oak National Academy
This boost for Oak National Academy, a provider of free, optional, high-quality digital curriculum resources, has helped to develop and improve Oak’s AI tools. Oak National Academy has launched an AI-powered lesson assistant, Aila, to support teachers to reduce their workload. Oak has worked with expert AI developers, teachers and other education specialists to create a tool that teachers report has saved them around 3-4 hours per week in lesson planning.
Developed a new package of training and guidance for teachers and leaders
We have created straightforward training and resources for schools and colleges to help teachers and school leaders use AI safely with confidence to improve classroom experience for pupils.
For teachers, we're developing simple online guides that explain AI basics and provide practical tips for using AI safely with young people. These resources will show teachers how to use AI tools to reduce administrative tasks, giving them more time to focus on delivering inspiring lessons and less time on paperwork.
For leaders, we're providing comprehensive support for managing AI across their entire settings. This guidance will help them integrate AI effectively into their digital strategy, with real examples of how other schools and colleges have successfully implemented AI. We're also including research insights on the educational benefits of using AI in schools and colleges.
This work responds directly to what schools and colleges have been asking for - practical help to use AI safely while freeing up valuable teaching time.
How are you making sure pupils have access to the right technology?
We're investing £45 million to enhance digital connectivity in schools nationwide, specifically targeting the digital divide over the coming year. This funding includes £25 million for wireless network improvements and £20 million for fibre upgrades, ensuring reliable internet access becomes standard across all schools.
We believe that every child deserves equal access to digital learning opportunities. This investment will help ensure no pupil is disadvantaged by lack of technology access, while also supporting teachers to harness educational technology advances in their classrooms. Alongside this immediate investment, we've launched a consultation on our long-term strategy for narrowing the digital divide, with particular focus on establishing new digital and technology standards for schools and colleges across the country.
| 2025-06-10T00:00:00 |
https://educationhub.blog.gov.uk/2025/06/artificial-intelligence-in-schools-everything-you-need-to-know/
|
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|
AI revolution to give teachers more time with pupils - GOV.UK
|
AI revolution to give teachers more time with pupils
|
https://www.gov.uk
|
[] |
Education Secretary, Bridget Phillipson, said: We're putting cutting-edge AI tools into the hands of our brilliant teachers to enhance how our ...
|
Pupils across England will benefit from more face-to-face time with teachers as the government forges ahead with plans to harness the power of AI to deliver educational excellence.
The Department for Education has today (June 10th) launched a package of measures to transform how schools use AI - including the first ever AI guidance for schools and colleges setting out how schools can safely and effectively use AI to transform the classroom experience for students.
A recent survey showed 43% of teachers rate their AI confidence at just 3/10, with over 60% asking for help applying AI to planning and support tasks. Nearly all teachers wanted safety guidance and additional training.
The comprehensive guidance delivers on this and gives teachers and leaders the confidence to power-up learning and swap wasted hours spent on admin for time spent inspiring our children – as part of our Plan for Change pledge to deliver an excellent education for every child.
Education Secretary, Bridget Phillipson, said:
We’re putting cutting-edge AI tools into the hands of our brilliant teachers to enhance how our children learn and develop – freeing teachers from paperwork so they can focus on what parents and pupils need most: inspiring teaching and personalised support. Our Plan for Change demands an excellent education for every child, and making all sure young people are benefitting from the latest technology is a vital step. By harnessing AI’s power to cut workloads, we’re revolutionising classrooms and driving high standards everywhere – breaking down barriers to opportunity so every child can achieve and thrive.
Developed in partnership with education experts from the Chiltern Learning Trust and the Chartered College of Teaching, it sets out clear principles for AI use, with education standards and child safety at the fore. It makes clear that AI should be used to ensure learning remains teacher-led and that teachers should verify accuracy and protect personal data.
For staff, AI can automate some tasks such as generic letters - giving them hours back to focus on personalised parent communications around children’s education progress and wellbeing.
An additional £1 million of Contracts for Innovation funding will accelerate development of pioneering AI tools to help with marking and generating detailed, tailored feedback for individual students. Building on the successful AI Tools for Education programme announced last August, this investment will take the tools from the design stage into teachers’ hands – meaning world-first AI interventions are a step closer to being classroom-ready.
Paul Whiteman, general secretary at school leaders’ union NAHT, said:
These resources are a welcome source of support for education staff. AI has huge potential benefits for schools and children’s learning, but it is important that these are harnessed in the right way and any pitfalls avoided. Government investment in future testing and research is vital as staff need reliable sources of evaluation – supported with evidence – on the benefits, limitations and risks of AI tools and their potential uses.
As part of this innovation drive, schools and colleges are being invited to become ‘test beds’ for evaluating promising EdTech products, creating an evidence base for technologies that genuinely improve both teaching quality and pupil outcomes.
These innovations will redefine teaching as a profession, transforming it into a more appealing career choice by significantly reducing administrative workload. It will play a crucial role in attracting and retaining talented educators, accelerating progress toward the government’s pledge to recruit 6,500 additional teachers.
Earlier this week the Prime Minister set out a package of digital and AI training opportunities as part of a new £187 million TechFirst programme to bring digital skills and AI learning into classrooms and communities. This package will train up people of all ages and backgrounds for the tech careers of the future, including giving 1 million secondary school students yearly the chance to learn about technology and gain unprecedented access to skills training and career opportunities.
These initiatives form a key element of the government’s ambitious Plan for Change, directly supporting the mission to break down barriers to opportunity by ensuring every child benefits from exceptional teaching.
| 2025-06-10T00:00:00 |
https://www.gov.uk/government/news/ai-revolution-to-give-teachers-more-time-with-pupils
|
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|
Leveraging AI in Your Job Search: How Technology is Transforming ...
|
Leveraging AI in Your Job Search: How Technology is Transforming the Job Hunt
|
https://careerengagement.tulane.edu
|
[
"Loren Brodie",
"Recruiting Experience Manager"
] |
With AI-driven insights, candidates can stand out in competitive hiring landscapes and streamline their applications. Leveraging AI in your ...
|
By: Jordan Kish
The growth of Artificial Intelligence has rapidly transformed the job search process in several ways, making it faster, more efficient, and even more competitive. Artificial intelligence tools now offer job seekers the ability to discover tailored opportunities, optimize application documents such as resumes and cover letter, and even prepare for interviews. With AI-driven insights, candidates can stand out in competitive hiring landscapes and streamline their applications. Leveraging AI in your job search can save time while increasing your chances of landing the right fit.
Finding the Perfect Fit
AI-powered job search engines can significantly improve your chances of finding the right opportunities by offering personalized job recommendations based on your skills, experience, and preferences. Unlike traditional job boards, AI algorithms analyze job postings, match users with relevant positions, and even provide insights on how to optimize resumes for each application. These tools help streamline the search process, saving you time while ensuring you don’t miss out on that perfect job opening. By leveraging AI, job hunters focus on applying for the best-fit roles rather than sorting through thousands of listings manually.
How does it work? The artificial intelligence will typically ask users their “non-negotiables” on the front end to align expectations with the best openings available. The platforms will often ask questions regarding the desired salary, ideal work location, previous skills, and industries of interest. These inputs are the driving factors leading to specialized results catered to the user’s desires.
AI Job Search Engines Include:
Freeman Job Search Resources Available
While AI search engines are proven to be useful tools, Freeman specific resources such as Handshake remain the Career Management Center’s preferred platform for students and alumni while maintaining the same level of efficiency and effectiveness for students. The Handshake algorithm suggests job openings based upon the user’s level of experience, skills and education qualifications, making the search feature equally impactful as the AI tools. Additionally, Tulanians hold a significant advantage to alumni referred roles and positions to companies seeking to hire Tulane students specifically, while also having access to the many upcoming employer events and information sessions within the Freeman school.
Explore opportunities today! Handshake
AI for Resumes
Using AI in your resume can significantly enhance its effectiveness by ensuring it is well-structured, optimized for applicant tracking systems (ATS), and tailored to specific job descriptions. AI-powered resume tools analyze industry trends and suggest relevant keywords that increase visibility to recruiters. They also help refine formatting, language, and content to make your resume more professional and impactful. By leveraging AI, job seekers can save time and improve their chances of standing out in a competitive hiring landscape, while still maintaining a personal touch in their applications. As HR departments continue to onboard AI platforms for initial candidate screenings, strong resumes are now more essential than ever before. With that in consideration, the Freeman formatted resume template, and its contents remain the preferred resume template for both the Career Management Center, and most importantly the employers in which we hold strategic partnerships. Resumes and Cover Letters – Tulane University | Career Services
AI for Cover Letters
AI can also help craft compelling cover letters by analyzing job descriptions and tailoring content to highlight your most relevant skills and experience. AI-powered writing assistants ensure proper tone, structure, and personalization, making your letter more impactful while saving time. These tools suggest industry-specific keywords and refine language to align with hiring expectations, increasing the chances of grabbing recruiters’ attention. While AI provides a strong foundation, adding personal insights and unique experiences ensures authenticity and a human touch in your application. Remember, cover letters are opportunities to exhibit why you are the ideal fit for the role. Therefore, users must be sure to not only highlight their skills and experience but reflect their personality as well.
Are you wondering what to include in your cover letter? The Career Management Center is here to help with our cover letter guide: Cover Letters and Emails – Tulane University | Career Services
Leveraging AI in Interview Preparation
AI is also a powerful tool for interview preparation as well, helping candidates anticipate questions, refine responses, and boost confidence. AI-powered platforms analyze job descriptions to suggest likely interview questions and provide insights on how to structure answers effectively. Some tools even offer mock interview simulations with feedback on tone, clarity, and professionalism. Additionally, AI can assess nonverbal communication by analyzing facial expressions and speech patterns in recorded practice sessions. The Career Management Center recommends Hiration’s interview practice tool as its preferred resource for AI-driven interview practice. Interview Practice | HIRATION
While AI streamlines preparation, incorporating personal anecdotes and authentic responses, the human connection associated with in person interviews is still the most critical aspect of the process.
Are you seeking help with in-person interview? Schedule a mock interview with one of our many Freeman Career Consultants: New Appointment | Handshake
Conclusion
Incorporating AI into your job search is no longer just an advantage—it’s becoming a necessity in an increasingly digital hiring landscape. From personalized job recommendations to optimized resumes and cover letters, AI-driven tools empower candidates to navigate the job market with precision and efficiency. However, while technology can streamline and enhance the process, the human element remains crucial. Combining AI’s analytical power with personal creativity and strategic thinking ensures job seekers not only stand out but also connect meaningfully with potential employers. By leveraging AI wisely, candidates can take control of their job search and position themselves for success in today’s competitive workforce.
Freeman’s Career Management Center also offers 24/7 access to valuable resources to help students and alumni craft strong resumes, compelling cover letters, and prepare effectively for interviews. These tools also are designed to align with industry expectations, ensuring job seekers present themselves professionally and confidently. With tailored guidance and expert insights, users can refine their application materials and sharpen their interview skills, increasing their chances of securing competitive opportunities. Taking advantage of these resources can make a significant difference in navigating today’s job market successfully. A.B. Freeman School of Business – Tulane University | Career Services
Interested in learning more? Schedule a meeting with us!
New Appointment | Handshake
| 2025-06-10T00:00:00 |
2025/06/10
|
https://careerengagement.tulane.edu/blog/2025/06/10/leveraging-ai-in-your-job-search-how-technology-is-transforming-the-job-hunt/
|
[
{
"date": "2025/06/10",
"position": 67,
"query": "AI employment"
}
] |
Do graphic designers need new skills to beat AI? Professionals ...
|
Do graphic designers need new skills to beat AI? Professionals weigh in
|
https://www.creativebloq.com
|
[
"Georgia Coggan",
"Social Links Navigation"
] |
With the spectre of AI ever present, graphic designers are understandably worried about the impact the new technology will have on their ...
|
With the spectre of AI ever present, graphic designers are understandably worried about the impact the new technology will have on their jobs. As our piece on the impact of AI on graphic design discusses, the genie is very much out of the bottle and now creative professionals find themselves striving to keep human design a more attractive option than anything created by a machine. Though Bill Gates famously said he thinks all but three jobs are at risk from being taken by AI, I remain hopeful that there is plenty of room for both machine and person in the graphic design field, but it will require self-awareness from designers.
"So are we just cooked?" asks a recent Reddit thread from a designer who is four years out of college. " Any other jobs i can get with such a degree now that design is kind of becoming obsolete?"
Hundreds of responses poured in from designers with strong and diverse opinions on what AI is doing to the graphic design industry – and it isn't all as doom and gloom as you might fear. Ranging from advice around what humans can do that AI can't, to how nothing has really changed regarding what the industry needs from its designers, there's lots for the OP to feel positive about – as long as they're happy to stay agile. Head over to the Reddit thread to garner more wisdom from those in the field.
One Redditor draws attention to the difference between AI and human creativity. "Design isn't becoming obsolete. AI doesn't have taste, you still have to have vision and know what you want to produce, and most clients don't know what they want until you show them," they say. And don't let AI completely cloud your vision, they go onto say.
"If you're a new designer, make AI part of your workflow - learn prompts, etc. But don't make it the only thing you focus on."
Another points out that graphic designers have always needed to be agile in their skillset to stay ahead. "The field’s always rewarded versatility: people who can also write, think strategically, understand UX, code a little, or communicate with clients effectively" they say.
The bar keeps moving, and that sucks for people who thought the degree alone would carry them, but that’s the nature of creative industries. You have to keep evolving to stay relevant."
Get the Creative Bloq Newsletter Daily design news, reviews, how-tos and more, as picked by the editors. Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors
(Image credit: Getty Images)
But what if the worst happens and you are laid off? One comment explains how it doesn't have to be the end of the world.
"I was laid off in March and just got hired at a pretty good salary," they say. My suggestion is to take a step away from all social media and really focus on making your portfolio and resume the best it can be."
"It’s definitely a saturated industry but you have to continually put in the work to stand out unfortunately."
And others think history can teach a great lesson. "30 years of print experience here. While AI is becoming a useful tool, humans will always be needed to fine tune the work. Don’t let this wave of new technology scare you," one veteran designer says.
"Imagine how typesetters felt when Pagemaker and Quark Xpress came out! Do you know those two programs? No? That’s because they became obsolete in the early 90’s and replaced by Illustrator and Photoshop and later on InDesign."
We delve further into the AI skills designers might need to get a new job in 2025, straight from the agencies who'll hire you.
| 2025-06-10T00:00:00 |
2025/06/10
|
https://www.creativebloq.com/design/graphic-design/do-graphic-designers-need-new-skills-to-beat-ai
|
[
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"date": "2025/06/10",
"position": 35,
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},
{
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}
] |
Artificial Intelligence Creates More Jobs than It Replaces, Study Finds
|
Artificial Intelligence Creates More Jobs than It Replaces, Study Finds
|
https://www.hungarianconservative.com
|
[
"Ádám Bráder",
"Márton Losonczi"
] |
Researchers found that industries most impacted by AI experienced revenue growth per employee at triple the rate (27 per cent) compared to less ...
|
Contrary to widespread fears, artificial intelligence (AI) is not eliminating jobs but rather creating more employment opportunities while transforming the nature of work. This is the key finding of PwC’s Global AI Jobs Barometer 2025, cited in a press release by PwC Hungary. The report reveals that in occupations most exposed to AI, job numbers grew by 38 per cent between 2019 and 2024.
The study analysed over 1 billion job postings and thousands of corporate financial records across six continents. Researchers found that industries most impacted by AI experienced revenue growth per employee at triple the rate (27 per cent) compared to less affected sectors (9 per cent). Additionally, roles requiring AI skills offered 56 per cent higher wages on average in 2024, often including salary premiums.
Since the widespread adoption of generative AI in 2022, productivity growth in high-usage sectors—such as financial services and software development—soared from 7 per cent in 2022 to 27 per cent in 2024. In contrast, less AI-exposed industries (eg, mining, hospitality) saw stagnant productivity (10 per cent in 2022, 9 per cent in 2024) and slower revenue growth.
The report highlights that skills required in AI-exposed roles are changing 66 per cent faster than before. Between 2019 and 2024, the share of degree-required positions declined from 66 per cent to 59 per cent in AI-augmented jobs and from 53 per cent to 44 per cent in AI-automated roles.
AI does not affect genders equally: in every country studied, more women work in AI-exposed roles, meaning that changing skill requirements may disproportionately pressure them.
Gyöngyi Gönczi, Head of HR and Organizational Development Consulting at PwC Hungary, emphasized that AI demands new skills and mindsets. Keeping pace with technological change requires systems that support continuous learning.
PwC advises companies to build trust in AI, using it not just for efficiency but as a growth strategy. Investing in employees’ AI-related skills is crucial for long-term success.
Related articles:
| 2025-06-10T00:00:00 |
2025/06/10
|
https://www.hungarianconservative.com/articles/tech/ai-jobs-study-employment-creation/
|
[
{
"date": "2025/06/10",
"position": 94,
"query": "artificial intelligence wages"
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"query": "artificial intelligence wages"
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"query": "artificial intelligence wages"
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"date": "2025/06/10",
"position": 58,
"query": "ChatGPT employment impact"
},
{
"date": "2025/06/10",
"position": 35,
"query": "artificial intelligence employment"
}
] |
The Present and Future of AI in Graphic Design
|
The Present and Future of AI in Graphic Design
|
https://www.appypiedesign.ai
|
[] |
AI can automate repetitive tasks, suggest design options, and provide data-driven insights that can improve the quality of the final product.
|
AI plays an important role in industries across the globe and the graphic design world is no exception. There are ongoing conversations between designers and developers around the future impact of AI, machine learning, deep learning, VR, AR, and MR (virtual, augmented, and mixed realities), and how graphic design is changing. Recently, AI has entered the creative arena of the graphic designing industry with its cognitive abilities. Companies use it as a design tool that helps to optimize and speed up project completions. AI’s power lies in its speed to analyze arrays of data and output alternative designs for the graphics industry rapidly.
What does Artificial Intelligence (AI) mean in Graphic Design?
Businesses can use AI to generate or enhance design elements such as layouts, color palettes, typography, and imagery. Tools like AI Design Generator can also help in tasks such as image recognition, pattern recognition, and data analysis to help designers make informed decisions and produce better designs. AI is still in its early stages of development in the field of graphic design, and human designers will continue to play a critical role in the design process for the foreseeable future. AI can streamline the graphic design process and improve the quality of the final product by automating repetitive tasks and offering insights that might not be immediately obvious to a human designer.
Suggested Read: What is Motion Graphics: Definition, Types, Examples and AI
How is AI Used in Graphic Design?
Layout and Composition: AI algorithms can analyze user data and create personalized layouts for websites or applications. They can also suggest different layout options to designers based on the content they're working with.
AI algorithms can analyze user data and create personalized layouts for websites or applications. They can also suggest different layout options to designers based on the content they're working with. Color and Typography: AI can suggest color palettes and font combinations based on user preferences and previous design choices.
AI can suggest color palettes and font combinations based on user preferences and previous design choices. Image Editing: AI-powered image editing tools can automatically remove backgrounds, adjust color balance, and apply filters based on user input.
AI-powered image editing tools can automatically remove backgrounds, adjust color balance, and apply filters based on user input. Design Automation: AI can automate repetitive design tasks such as resizing images or creating multiple versions of a design.
AI can automate repetitive design tasks such as resizing images or creating multiple versions of a design. Data Analysis: AI can analyze user data to help designers make informed decisions about how to optimize the user experience of a website or application.
AI can analyze user data to help designers make informed decisions about how to optimize the user experience of a website or application. Generative Art: AI algorithms can be programmed to create unique designs that vary over time, producing generative art.
Benefits of using AI in Graphic Design
Automating Repetitive TasksOne of the most significant benefits of using AI in graphic design is that it can automate repetitive tasks. Designers often spend a lot of time on tasks like resizing images, selecting color schemes, and creating layouts. With AI-powered tools, these tasks can be automated, freeing up designers to focus on more creative work. Enhancing Creative CapabilitiesAI is expanding the creative capabilities of designers. With AI-powered tools, designers can generate new design ideas and concepts that they may not have considered otherwise. This can help designers create more unique and innovative designs that stand out in a crowded market. Personalizing DesignsPersonalization is becoming increasingly important in design, and AI is helping designers create personalized designs. By analyzing user data, AI-powered tools can create customized experiences for individual users. For example, Netflix uses AI to personalize the graphics of their user interface to match individual user preferences. Creating Generative ArtAI is being used to create generative art, which is a type of art that is created through a set of rules or algorithms. This type of art is unique in that it can be constantly evolving and changing. For example, artist Joshua Davis uses algorithms to create complex geometric designs that vary each time they are generated. Improving EfficiencyBy automating repetitive tasks, AI-powered tools can significantly improve efficiency in the graphic design process. This can save designers a lot of time and allow them to focus on more complex and creative tasks. This increased efficiency can also help designers meet tight deadlines and complete projects more quickly.
Challenges and Limitations of Using AI for Graphic Design
How do Graphic Design Makers use AI?
Conclusion
AI can automate repetitive tasks, suggest design options, and provide data-driven insights that can improve the quality of the final product. Here are a few ways AI is used in graphic design:Here are a few benefits of using AI in graphic design:While AI has a lot of potential in graphic design, there are also challenges and limitations to consider. One of the main challenges is that AI-powered tools are only as good as the data they are trained on. If the data is biased or incomplete, the AI-powered tool may produce suboptimal results. Another limitation is that AI-powered tools are currently unable to replicate the creative intuition and expertise of human designers. While AI can automate many aspects of the design process, it cannot replace human creativity and ingenuity.DIY graphic design software like Appy Pie Design can help businesses make graphics without needing a designer. Even without an eye for design, users can choose from millions of templates and get access to their library of high-resolution images. AI graphic design platforms do this by creating various design examples for the user to choose based on the prompt they inputted. Plus, AI graphic design makers can even integrate branding into graphic design work. Appy Pie Design has AI-powered tools to create branded materials, such as Text to Image Generator , and AI Photo Enhancer The main purpose of AI in graphic design is to support designers and non-designers in creating beautiful designs. AI can also expedite the design process, saving designers and non-designers time to do other projects or tasks. Further, graphic design software like Appy Pie Design can be instrumental in helping you create the marvelous images you need.
| 2023-03-15T00:00:00 |
2023/03/15
|
https://www.appypiedesign.ai/blog/the-present-and-future-of-ai-in-graphic-design
|
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{
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{
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"date": "2025/06/11",
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How Artificial Intelligence Impacts the Future of Work - AI bees
|
How Artificial Intelligence Impacts the Future of Work
|
https://www.ai-bees.io
|
[] |
The key to success in the AI future will be a systematic approach to workflow integration with machines, fine-tuning processes, and reskilling employees.
|
For years, we've heard warnings that robots could take our jobs. And the research that says artificial intelligence (AI) might replace 300 million full-time jobs isn’t helping, is it?
However, for many people, work has appeared to go smoothly.
So, is all this hype true? Or are we finally reaching a tipping point with current AI advancements? Will AI replace humans, or will it improve and accelerate our abilities?
If you need answers to these questions, then read on!
What is AI
Before we discuss how AI will affect the future of employment, let's start with the basics: What exactly is AI? Artificial intelligence is the capability of a system like a computer or a computer-controlled robot to perform tasks that are typically done by intelligent beings.
"AI" has become a catchphrase for any developments in computing, systems, and technology in which computer programs may do jobs or solve issues that need the kind of reasoning associated with human intelligence, including learning from previous operations.
The Rise of Robotics and AI
Now, let us travel back in time to understand the history of AI and how we got to where we are today.
16th Century
Manual labor was common in the 16th century until a priest named William Lee developed the concept of mechanizing—at least in part—the stocking-making process. He modified looms used to make carpets to create a long sheet of stocking material that could then be cut and made into stockings. It was much faster and less expensive than the standard procedure.
According to folklore, Queen Elizabeth I denied Lee's request for a patent because she was concerned about the welfare of former stocking knitters who would lose their jobs.
His machine had a limited influence, but it established the foundation for later textile machine improvements.
19th Century
Years later, English textile workers faced even more drastic changes. They weren't the only ones, either.
People moved from rural areas to the new, fast-growing metropolis as the Industrial Revolution progressed. They found work in mills and factories, where steam-powered machinery allowed hand-crafted things previously to reach unprecedented output levels.
Mechanization also posed a problem for farmworkers. As the world's population increased, machines were needed to manage everything from weeding to harvesting crops. Unfortunately, the working class did not receive the introduction of machines favorably, but nothing much could be done from their end, as automation was inevitable.
20th Century
Towards the end of the twentieth century, the employment of robots in car manufacturing grew more frequent. Originally designed to do simple, repetitive activities, they assisted in increasing output, standardizing production quality, and controlling costs.
In 1979, Fiat ran a television commercial depicting the construction of its Strada hatchback, with the tagline "handmade by robots."
Assembly-line jobs like welding and spray painting were among the first to replace humans. The Humans were present to supervise the machines.
As technology has progressed, the list of activities that robots can perform has grown to include more sophisticated procedures, such as installing windscreens in vehicles. They're also commonly used in factories to transport large and bulky objects.
21st Century
Robotics and AI have grown significantly in the 21st century. Today, AI is doing jobs that were previously exclusive to humans, like creating music and making advanced medical diagnoses.
Thanks to artificial intelligence, robots are no longer limited to manufacturing assembly lines. They are already used in customer service, healthcare, and space exploration. Even while there are worries about employment displacement, robotics and artificial intelligence appear to have huge potential to enhance human capabilities in the future, creating a more inventive, productive, and efficient world.
Effect of Artificial Intelligence on Employment
Concern over AI's potential effects on employment and the nature of work in the future is growing as these technologies continue to develop quickly.
This section explores the potential this technological revolution brings as it digs into the complex relationship between artificial intelligence and employment.
Role Reversal
Traditionally, human workers were responsible for basic, repetitive jobs, while robots handled more sophisticated, cognitive ones. However, this relationship is changing with the introduction of AI and automation.
AI-powered algorithms and robotic systems are increasingly capable of doing various jobs previously only performed by humans.
Industrial Revolution
AI enhances human intelligence and productivity. AI, like the machinery and factories introduced during the Industrial Revolution, is a means of production that will enable one person to do the job of many.
AI will increase efficiency and completely transform how businesses of all sizes are formed and administered.
Efficient Job Functions
Job functions are already changing dramatically as a result of AI adoption. One million developers have already used Github copilot to generate code, forever changing the code generation process.
In domains like financial services and law, the days of manual research are limited since collaboration between professionals and AI-native applications will streamline (and, in some cases, eliminate) bulky processes to achieve previously impossible efficiencies.
Sustainable Business Models
Generative AI will lead to huge productivity improvements and improved cost structures for all types of enterprises. Employees will be significantly more effective and productive in pursuing new levels of creativity and invention.
The infrastructure and research layer of generative AI will be critical to moving AI from hype to reality, unlocking new, sustainable business models that positively impact the world of work for both employers and employees.
AI-powered Peer-to-peer Channel
Generative AI will speed up individualized and applied learning in and out of the workplace. As courses and books become obsolete, so will linear career paths, increasing both machine-driven and peer-to-peer learning.
GenAI will affect learning and skill development just like how YouTube, Instagram, and TikTok capitalized on the proliferation of smartphones and allowed people to share their lives through stories,
Transformation of Decision Making
AI is transforming organizations' decision-making processes. It fuels simulations that feed insights and suggestions into commercial decisions, such as examining pricing schemes, operational ones, like optimizing routing and logistics, and even more complicated ones, like discovering design and engineering solutions to a specific brief.
Ultimately, digital representations of businesses and powerful AIs will be capable of making decisions about companies' operations.
Reduction of Junior and Mid-level Roles
Large language models (LLMs) have an immediate impact by dramatically lowering the number of software engineers required to construct and ship new digital products.
Although they continue to rely heavily on their superiors, businesses are finding that they require fewer and fewer staff (such as coders and data scientists).
AI on Future of Work
Research shows that we are still far from AI being on par with human intelligence, and it might theoretically replace human employees. AI can create more employment, not fewer, if there is an investment in all sectors—anywhere that focuses on training and upskilling workers.
Currently, 34% of businesses employ AI, while 42% are looking into the technology.
More than half of these companies that have used AI-driven technologies say they have increased productivity.
So, how is AI impacting the different industries?
Let’s take a look.
Finance
AI is simplifying processes and increasing efficiency. It automates tasks like fraud detection and loan processing and offers individualized investment advice driven by advanced algorithms.
AI-powered risk management solutions can recognize and reduce possible financial hazards, while algorithmic trading enables quicker and more accurate financial decision-making. However, for AI to realize its full potential in the financial industry, ethical issues with algorithm bias and the possibility of job displacement in specific industries must still be resolved.
Medical
The potential benefits of AI in medicine are actively being researched. The medical industry has a large amount of data that they may use to construct healthcare-related predictive models. In some diagnostic scenarios, AI has proven more effective than physicians. For example, the lung cancer AI system can provide a very early warning of the disease.
Automotive
With the introduction of autonomous cars and navigation, we already see AI's impact on the automotive industry. AI will significantly affect production, particularly in the automotive industry.
Cybersecurity
In cybersecurity, AI and Machine Learning (ML) will be critical technologies for detecting and anticipating threats. Given its ability to analyze vast volumes of data and forecast and detect fraud, AI will be a crucial tool in financial security.
E-Commerce
In the future, AI will be crucial to e-commerce in many areas, including marketing, fulfillment, distribution, and user experience. We may expect artificial intelligence to play a bigger role in driving e-commerce. Examples include chatbots, shopper personalization, image-based targeting advertising, and automated warehouse and inventory management.
Hospitality and Tourism
Although AI might automate certain jobs, such as basic guest communication and check-in procedures, it is unlikely to replace human interaction completely. Rather, we anticipate a move toward visitor personalization driven by AI. Imagine artificial intelligence (AI) using guest data to create personalized restaurant, activity, and local experience recommendations.
AI chatbots can also respond to standard questions, allowing employees to concentrate on giving guests outstanding service and unforgettable experiences. The hotel and tourist sector will have a more prosperous and good future due to this human-AI collaboration, which will increase efficiency and tailor the visitor experience.
Job search
AI can significantly impact the job search process. An automated application tracking system can reject up to 75% of resumes before a person goes through them.
A while back, recruiters had to spend significant time sifting through resumes to find qualified applicants. Today, AI-powered systems are increasingly doing resume scanning. Job seekers are also using advanced resume scanners to help polish their resumes for the job market. In fact, 67% of hiring managers said artificial intelligence made their work easier.
Employers may use artificial intelligence to process your application, such as HireVue. As a result, there's no reason why you shouldn't take advantage of similar technology like Jobscan, Jobseeker, and Rezi.
AI and machine learning are on many lists of the most important skills in the job market. In the next five years, the number of jobs requiring AI or machine learning expertise will increase by 71%. To adapt to new changes, look into some excellent free online course alternatives focusing on AI skills.
Application of Artificial Intelligence in The Workplace
Because of technology, businesses can optimize their production processes, raise workflow efficiency and safety, and boost production. In B2B sales, automation facilitates the acquisition of consumer information during the sales process, allowing for the analysis of their behaviors and the creation of tailored plans.
According to research, nearly 73% of businesses globally prioritize AI over other digital investments to increase operational resilience. Since AI is anticipated to be extremely helpful in resolving recurring and frequent issues, this technical advancement will likely impact the services industry, most notably in finance and customer experience.
Automation can also change the way that human capital management is done. This technology allows for more objective evaluation of interviews and examination of verbal and nonverbal signs.
It also greatly aids in making specific judgments regarding an employee's future since the environment's connectivity monitors job performance, industry pay, costs, and business tactics.
Future of AI in The Workplace
Companies must prioritize upskilling and reskilling to ensure workers have the abilities and knowledge to prosper in an AI-driven environment. Politicians also need to consider how AI can affect jobs and try to develop laws that guarantee the advantages of AI are distributed fairly.
To guarantee that AI is applied to benefit society, cooperation, and communication between business, academia, and government will ultimately be necessary to integrate AI effectively into the workplace.
By remaining aware and proactive, we can negotiate the changes brought about by AI and create an efficient future of work.
Will AI steal my job?
Finally, back to the big question; "will AI steal my job?"
The short answer is that it might, but you'll receive a better one if you have the necessary abilities.
By 2030, AI will have contributed more to the global GDP than the combined output of China and India today. This growth will be sufficient to create many well-paying jobs and alter the nature of many existing jobs.
Leaders must comprehend how AI will affect their workforces and take the necessary steps to prepare it, including retraining and hiring people for the new positions that AI will require and upskilling some workers to perform current tasks using AI.
AI-powered automation poses no threat to people, businesses, or nations with the necessary competencies. Its economic boom presents an enormous opportunity.
Final Note
The key to success in the AI future will be a systematic approach to workflow integration with machines, fine-tuning processes, and reskilling employees. As a result, companies will be in a powerful position to grow, develop, scale, and succeed.
If you are looking for ways to capitalize on AI's power for your business advantage, we are the perfect partners for you!
We are experts in helping companies utilize AI to improve workflows, increase revenue, and boost their lead generation efforts.
Schedule a demo to see how we can help your business prosper in the era of intelligent automation.
| 2025-06-10T00:00:00 |
https://www.ai-bees.io/post/ai-on-future-of-work
|
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|
Leaders at PwC, Mastercard, IKEA, and More Who Are Driving AI ...
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Leaders at PwC, Mastercard, IKEA, and More Who Are Driving AI Adoption
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https://www.businessinsider.com
|
[
"Aaron Mok"
] |
We profiled 10 executives driving AI adoption at companies like JPMorgan Chase, PwC, and BMW. From chief AI officers to HR leaders, they're helping their ...
|
lighning bolt icon An icon in the shape of a lightning bolt.
lighning bolt icon An icon in the shape of a lightning bolt. Impact Link
This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now.
The corporate rush to adopt AI is shifting into high gear.
As of July 2024, 78% of companies reported using AI for at least one business task, up from 55% since late 2023, according to a report from McKinsey & Company. From IT automation to personalized marketing, businesses are betting on AI to cut costs and drive growth.
But AI adoption isn't without risks, including cybersecurity threats, data leaks, unreliable or biased model outputs, and a rising carbon footprint.
For Business Insider's "AI in Action" series, we profiled 10 executives driving AI adoption at companies like JPMorgan Chase, PwC, and BMW. From chief AI officers to HR leaders, they're helping their organizations stay competitive — and safe — in the age of AI.
Teresa Heitsenrether, the chief data and analytics officer at JPMorgan Chase
JPMorgan Chase; Karan Singh for BI
At JPMorgan Chase, Teresa Heitsenrether leads the rollout of the bank's proprietary LLM Suite. The suite has given over 220,000 employees access to AI tools that can summarize internal data. The platform integrates capabilities from multiple leading AI providers and has already saved employees several hours of work a week, Heitsenrether said.
"LLM Suite's widespread adoption is driving a cultural transformation across the bank," Heitsenrether told Business Insider.
This rollout isn't just about experimentation — it's about execution. JPMorgan tracks every AI use case from ideation to production, ensuring each AI initiative drives business value. That includes cost savings, revenue generation, and risk reduction, Heitsenrether said. Working under this framework helps the bank prioritize projects with the highest potential impact.
Additionally, employees across the bank are trained through a mix of in-person and online courses to use AI effectively and responsibly.
Joe Atkinson, the global chief AI officer at PwC
PwC; Karan Singh for BI
Joe Atkinson is embedding AI across the firm's operations and services. He led the launch of an internal AI chatbot deployed to 270,000 employees to help them generate reports and assist in project delivery.
Under his leadership, PwC also rolled out tools like Code Intelligence, which helps organizations modernize legacy systems through generative AI-powered code conversion, and Agent OS, which was designed to streamline AI-driven workflows.
Additionally, Atkinson said he launched a global AI Academy, which has trained 90% of staff in prompt design and responsible AI use.
"We're seeing a ton of growth and capability from our people," Atkinson said, pointing to rising usage and more complex tasks handled by AI.
He emphasized the importance of trust, governance, and accountability, adding that virtually every function at PwC is expected to be AI-powered in the near future.
Bala Subramanian, the executive vice president and chief digital and technology officer at UPS
UPS; Karan Singh for BI
Bala Subramanian is leading UPS's digital transformation with a focus on integrating AI across operations.
Under his leadership, UPS has implemented the Message Response Automation system, which uses large language models to automate responses to customer inquiries and reduce agent handling time.
"By alleviating the burden on our human agents, it enables them to focus on more complex and nuanced customer needs," Subramanian told CIO.com.
Beyond customer service, Subramanian is overseeing the rollout of AI and automation technologies — like pick-and-place systems and autonomous guided vehicles — across shipment operations. Bloomberg reported that as of April, UPS is exploring a potential partnership with Figure AI, a robotics startup, to implement humanoid robots into its operations for tasks like sorting parcels.
UPS is also investing in AI infrastructure, signing a decade-long deal with NTT Data in late March to modernize its data centers — part of a broader strategy to update its logistics network and improve service delivery.
UPS didn't respond to BI's request for comment.
Johan Gerber, the executive vice president and head of security solutions at Mastercard
Mastercard; Karan Singh for BI
Johan Gerber is helping Mastercard use AI to strengthen fraud detection across its global payment network. His team oversees cybersecurity, digital identity, and dispute resolution in an effort to protect businesses and card users from scams.
With Gerber at the helm, Mastercard launched tools like Decision Intelligence and Safety Net, which use generative AI, machine learning, and data scanning to analyze transaction patterns, improve fraud detection rates, and flag suspicious activity.
LLMs have also been integrated into MasterCard's Recorded Future tool, which allows analysts to quickly sift through its threat-intelligence database with queries like, "Tell me about the new malware families you found yesterday."
Gerber told BI that combining generative and traditional AI has improved detection accuracy, but he also stressed the importance of rigorous testing and data governance before deploying models into production. He also recommended a method called "silent scoring," which runs new AI models alongside active models before they go live; this helps assess the new model's soundness and vulnerabilities.
Suresh Kumar, the global chief technology officer and chief development officer at Walmart
Walmart; Karan Singh for BI
Suresh Kumar is leading Walmart's AI transformation to boost efficiency and personalize retail experiences.
He helped launch "My Assistant," a generative AI tool that helps over 50,000 corporate employees summarize documents, draft content, and streamline onboarding. The company has also invested in developing proprietary LLMs like "Wallaby," trained on decades of internal data, to power customer-facing assistants and personalized homepages.
In supply chain operations, Walmart uses AI for product placement, inventory management, and robotic automation. The Wall Street Journal reported that the company is testing autonomous shopping agents that can purchase items for customers.
"A standard search bar is no longer the fastest path to purchase, rather we must use technology to adapt to customers' individual preferences and needs," Kumar said in an October press release.
Walmart didn't respond to BI's request for comment.
Marco Görgmaier, the vice president of enterprise platforms and services, data, and artificial intelligence at BMW Group
BMW; Karan Singh for BI
Marco Görgmaier is leading BMW's AI integration across production, engineering, and marketing. He said the company's generative AI strategy centers on three pillars: adopting third-party tools, building proprietary applications, and providing in-house tools like the BMW Group AI Assistant, which lets staff create custom software without code. Görgmaier said training programs, development guidelines, and hands-on support further ensure teams are equipped to navigate the AI shift.
"While conventional AI has driven efficiencies in targeted areas like predictive maintenance and quality assurance, generative AI expands the horizon, enabling automation, creativity, and innovation across the entire organization," Görgmaier told BI.
He said it's important to start every AI project with a clear business need and ensure cross-functional collaboration and compliance. A key to scaling AI, he added, is a centralized, flexible data platform that supports rapid adoption of new technologies while ensuring cost efficiency and regulatory compliance.
Bhavesh Dayalji, the chief AI officer at S&P Global and the CEO of Kensho
Kensho; Karan Singh for BI
Bhavesh Dayalji is guiding efforts to boost productivity through internal generative AI tools at S&P and Kensho, S&P's AI and innovation research division.
Spark Assist, for example, is an internal chatbot that helps more than 40,000 employees draft reports, synthesize data, and streamline workflows. Unlike public tools like OpenAI's ChatGPT, these are built on proprietary data and include agentic features for task automation. Another tool is Chat IQ, a chatbot where employees can query S&P's financial datasets with questions like, "What is the price of a certain stock?"
To ensure widespread AI adoption, Dayalji introduced S&P's workforce to mandatory training, which included online videos and workshops, designed to upskill leaders and employees.
"This is a transformation journey," Dayalji told BI. "We want people to have hands-on experience and understand how it's going to help them."
Dayalji said S&P will continue placing big bets on AI: Kensho, for instance, is developing a Grounding Agent for autonomous research and analysis using the firm's proprietary datasets.
While AI is still in its "early days," Dayalji called the current moment an "amazing time to be involved" in adopting and advancing the technology.
Ulrika Biesèrt, the global people and culture manager at Ingka Group (IKEA)
IKEA; Karan Singh for BI
Ulrika Biesèrt is helping Ingka Group equip IKEA's global workforce — over 160,000 employees across 31 countries — with AI skills. Since launching AI literacy programs in April 2024, the retailer has trained over 4,000 employees in areas like responsible, ethical AI usage, Biesèrt said. The goal, she added, was to train 30,000 employees and 500 leaders.
"At IKEA, we've always believed that change should start with people," Biesèrt told BI.
One tool employees are taught to use is Hej Copilot, an internal generative AI assistant that helps with everyday tasks like creating presentations and brainstorming ideas. To inspire their colleagues, early adopters shared use cases for the tool at workshops.
Implementing such a vast program isn't easy. Technological change comes with uncertainty, Biesèrt said. To address this, nearly 650 senior leaders were trained, exceeding IKEA's initial goal, to ensure a "structured, cohesive approach" to AI across the organization, Biesèrt told BI.
Arnab Chakraborty, the chief responsible AI officer at Accenture
Accenture; Karan Singh for BI
Arnab Chakraborty oversees Accenture's internal responsible AI compliance program to ensure AI is developed, deployed, and scaled sensibly across Accenture and its roster of more than 9,000 global clients. According to the company, Accenture has committed $3 billion over three years to expand its Data & AI practice and double its AI talent to 80,000 professionals.
"We have to ensure that AI solutions have fairness, transparency, and accountability embedded from the start," Chakraborty said.
He's also co-leading a $700 million partnership with Telstra. The goal is to help the Australian telecommunications company integrate AI into its workflow processes, including identifying fiber cable defects and faster customer service, AFR reported.
Chakraborty told BI the partnership also involves advancing responsible AI capabilities like real-time model monitoring so that Telstra's AI systems are compliant with its internal policies and community guidelines.
Building and maintaining the right data infrastructure to scale AI remains a barrier to adoption, Chakraborty added. To address this, Accenture has partnered with the Center for Advanced AI in Mountain View, California, gaining access to research labs at academic institutions like UC Berkeley and Stanford to study data privacy and develop AI training programs for companies.
Chakraborty said by year's end, Accenture plans to launch over 100 agentic AI tools designed to detect and correct algorithmic issues, such as biases or hallucinations, before they impact business operations.
Boris Gamazaychikov, the head of AI sustainability at Salesforce
Salesforce; Karan Singh for BI
Boris Gamazaychikov leads Salesforce's push to decarbonize the company's AI systems.
Recognizing the significant energy demands of training large AI models, Gamazaychikov's sustainability strategy emphasizes the importance of developing efficient, domain-specific models. Salesforce's AI Research team has created xGen, designed for customer relationship management, orCRM, tasks like generating call summaries. These models consume less power and emit fewer emissions than general-purpose ones, according to Salesforce.
Under Gamazaychikov's leadership, the CRM provider has introduced AI tools that help customers decarbonize, including AI agents for sustainability measurement and a nature-focused accelerator for climate-focused nonprofits supporting forest conservation, regenerative agriculture, and other efforts.
"It is important that software and AI companies are also part of the solution to reduce emissions, and provide tools that help others reduce their emissions," Gamazaychikov said in an interview for Autonomy's "The Decarbonists" newsletter.
Salesforce didn't respond to BI's request for comment.
| 2025-06-10T00:00:00 |
https://www.businessinsider.com/ai-leaders-pwc-mastercard-accenture-ikea-tech-adoption-growth-strategy-2025-5
|
[
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|
AI skills shortage surpasses big data, cybersecurity - CIO Dive
|
AI skills shortage surpasses big data, cybersecurity
|
https://www.ciodive.com
|
[
"Roberto Torres"
] |
More than half of IT leaders say their companies suffered from an undersupply of AI talent, up from 28% in the previous edition of the report, ...
|
Listen to the article 3 min This audio is auto-generated. Please let us know if you have feedback
Dive Brief:
The gap in AI skills is accelerating drastically as enterprises rush to deploy the technology, according to the Nash Squared/Harvey Nash Digital Leadership report published in May. The company surveyed more than 2,000 technology leaders.
More than half of IT leaders say their companies suffered from an undersupply of AI talent, up from 28% in the previous edition of the report, published in 2023 . AI know-how went from being the sixth most scarce technology skill to the No. 1 in 16 months, marking the fastest increase in more than 15 years.
Nine in 10 respondents said their companies were piloting or investing in AI use, up from 59% in the 2023 report. Despite the rise, more than two-thirds of leaders said they had not yet received a measurable return on investment from the technology.
Dive Insight:
Deploying AI has long been an enterprise need, with executives hoping to plug automation into key processes in search of productivity wins. Despite ambitions, a large swath of projects remain stuck in the experimental phase.
Several roadblocks stand in the way of full-fledged adoption, including data deficiencies and financial constraints. A looming skills gap has also dampened enterprise AI plans.
"As AI is so new, there is no ‘playbook’ here," said Bev White, CEO of Nash Squared, in the study announcement. "It’s about a mix of approaches including formal training where available, reskilling IT staff and staff outside of the traditional IT function to widen the pool, on-the-job experimentation and knowledge sharing and transfer. This needs to coincide with the development of a new operating model where AI is stitched in."
The two-year, 23-percentage-point jump for AI skills was the steepest increase for a specific skill recorded by Harvey Nash since it first began tracking this metric 16 years ago. A dearth of AI talent was reported by the majority of leaders across several sectors, including education, logistics, manufacturing, business services and pharmaceuticals.
Enterprise AI ambitions have steadily driven up AI workforce demand, widening talent gaps. Job site Indeed tracked a significant spike in generative AI job postings in January, which nearly tripled year over year, according to a February report.
| 2025-06-10T00:00:00 |
2025/06/10
|
https://www.ciodive.com/news/AI-skill-shortage-adoption-enterprise/750106/
|
[
{
"date": "2025/06/10",
"position": 70,
"query": "AI skills gap"
},
{
"date": "2025/06/10",
"position": 7,
"query": "AI skills gap"
},
{
"date": "2025/06/10",
"position": 7,
"query": "AI skills gap"
},
{
"date": "2025/06/10",
"position": 5,
"query": "AI skills gap"
},
{
"date": "2025/06/10",
"position": 1,
"query": "AI skills gap"
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] |
AI skills shortage surpasses big data, cybersecurity - Yahoo Finance
|
AI skills shortage surpasses big data, cybersecurity
|
https://finance.yahoo.com
|
[
"Roberto Torres",
"Tue",
"Jun",
"Min Read"
] |
Scarcity of AI skills jumped from 2023, marking the steepest rise Nash Squared has ever recorded.
|
This story was originally published on CIO Dive. To receive daily news and insights, subscribe to our free daily CIO Dive newsletter.
Dive Brief:
The gap in AI skills is accelerating drastically as enterprises rush to deploy the technology, according to the Nash Squared/Harvey Nash Digital Leadership report published in May. The company surveyed more than 2,000 technology leaders.
More than half of IT leaders say their companies suffered from an undersupply of AI talent, up from 28% in the previous edition of the report, published in 2023. AI know-how went from being the sixth most scarce technology skill to the No. 1 in 16 months, marking the fastest increase in more than 15 years.
Nine in 10 respondents said their companies were piloting or investing in AI use, up from 59% in the 2023 report. Despite the rise, more than two-thirds of leaders said they had not yet received a measurable return on investment from the technology.
Dive Insight:
Deploying AI has long been an enterprise need, with executives hoping to plug automation into key processes in search of productivity wins. Despite ambitions, a large swath of projects remain stuck in the experimental phase.
Several roadblocks stand in the way of full-fledged adoption, including data deficiencies and financial constraints. A looming skills gap has also dampened enterprise AI plans.
"As AI is so new, there is no ‘playbook’ here," said Bev White, CEO of Nash Squared, in the study announcement. "It’s about a mix of approaches including formal training where available, reskilling IT staff and staff outside of the traditional IT function to widen the pool, on-the-job experimentation and knowledge sharing and transfer. This needs to coincide with the development of a new operating model where AI is stitched in."
The two-year, 23-percentage-point jump for AI skills was the steepest increase for a specific skill recorded by Harvey Nash since it first began tracking this metric 16 years ago. A dearth of AI talent was reported by the majority of leaders across several sectors, including education, logistics, manufacturing, business services and pharmaceuticals.
Enterprise AI ambitions have steadily driven up AI workforce demand, widening talent gaps. Job site Indeed tracked a significant spike in generative AI job postings in January, which nearly tripled year over year, according to a February report.
Recommended Reading
| 2025-06-10T00:00:00 |
https://finance.yahoo.com/news/ai-skills-shortage-surpasses-big-121600613.html
|
[
{
"date": "2025/06/10",
"position": 90,
"query": "AI skills gap"
}
] |
|
Using AI in education settings: support materials
|
Using AI in education settings: support materials
|
https://www.gov.uk
|
[] |
Support materials to help schools and colleges use AI (artificial intelligence) safely and effectively.
|
Free support materials for staff in schools and colleges, developed by the Chiltern Learning Trust (CLT) and Chartered College of Teaching (CCT), to support the safe and effective use of generative AI in education.
The materials balance the need for staff and student safety with the opportunities AI creates. They feature:
activity focused slides
video presentations with accompanying transcripts
summaries of the information presented
activities to consolidate knowledge, such as multiple-choice questions
templates to help you reflect and plan
You can work through the modules to build your knowledge and experience as you go.
They can also be adapted:
for individual or group use
to suit your own needs and levels of experience
Module 3 should be used regardless of your level of experience as it contains important safety considerations.
| 2025-06-10T00:00:00 |
https://www.gov.uk/government/collections/using-ai-in-education-settings-support-materials
|
[
{
"date": "2025/06/10",
"position": 16,
"query": "AI education"
}
] |
|
launch of the draft AI literacy framework and stakeholder ...
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Empowering learners for the age of AI: launch of the draft AI literacy framework and stakeholder consultations
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https://education.ec.europa.eu
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[] |
The European Commission and OECD joined forces to develop an AI literacy framework for primary and secondary education. This work is supported by Code.org and ...
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The AI literacy framework for primary and secondary education
The AI literacy framework outlines what are the essential knowledge, skills, and attitudes young people need to understand and interact with AI systems in a confident and critical manner. It also delves into ethical and broader societal implications.
It helps educators, education and training institutions, and public authorities in supporting learners to develop these competences. The framework offers 22 competences across four main domains:
engage with AI
create with AI
manage AI
design AI
Share your feedback now
The draft version of the AI literacy framework is now available.
We warmly invite you to read it, download it, and share your feedback through our survey as soon as possible. It should take 5-7 minutes to reply.
Your insights will play a crucial role in shaping the framework, which is scheduled for final release in 2026 after extensive stakeholder consultations.
About the launch event
The event took place online on 22 May 2025 and featured showcasing the draft version of the framework, followed by a panel discussion.
Several distinguished speakers attended the event, including:
Pia Ahrenkilde Hansen, Director-General for Education, Youth, Sport and Culture (DG EAC), European Commission
Director-General for Education, Youth, Sport and Culture (DG EAC), European Commission Andreas Schleicher, Director for Education and Skills at the Organisation for Economic Co-operation and Development (OECD)
The event was attended by approximately 1,000 participants across Zoom and YouTube. If you were unable to join us live, the full recording of the webinar is now available on YouTube.
| 2025-06-10T00:00:00 |
https://education.ec.europa.eu/event/empowering-learners-for-the-age-of-ai-launch-of-the-draft-ai-literacy-framework-and-stakeholder-consultations
|
[
{
"date": "2025/06/10",
"position": 44,
"query": "AI education"
}
] |
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2025 Special Issue: Safe and effective use of AI in education
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2025 Special Issue: Safe and effective use of AI in education : My College
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https://my.chartered.college
|
[
"Cat Scutt"
] |
This issue explores the theme of safe and effective use of artificial intelligence in education, with sections on: How can AI support teachers and learners?
|
Dr Cat Scutt MBE, Deputy Chief Executive, Chartered College of Teaching, UK
In Spring 2019, the Chartered College of Teaching published a special issue of Impact focused on EdTech, funded by the Department for Education (DfE). In the six years since then, EdTech use in schools has changed almost beyond recognition: firstly, due to the COVID-19 lockdown requiring teachers to move rapidly to online and blended learning models, and the resultant changing expectations from pupils and parents; and more recently, owing to the advances in generative artificial intelligence (GenAI) technology, and its widespread availability to the general public.
While many schools were already utilising online tools and, in some cases, remote lessons even prior to the CODID-19 pandemic, AI was not even mentioned in that 2019 issue of Impact. Although artificial intelligence technology was of course already being used in various ways within EdTech tools used by schools, as well as to power chatbots and other widely available technologies used by the public, it was the launch of ChatGPT in November 2022 that really marked the beginning of the significant impact of GenAI in schools, colleges and other education settings.
The rise of GenAI undoubtedly offers some exciting opportunities and applications for education – but it also comes with significant risks. While the technology itself is perhaps very different from tech tools used in education previously, in many ways the promises are not so different from those that were considered back in that 2019 Impact issue: potential for workload reduction and widened professional development approaches for teachers; increased engagement for students; novel approaches to assessment; opportunities for independent student learning and practice, and the development of new skills for careers of the future.
The risks, too, are not unfamiliar: concerns around ethics; academic integrity; a loss of the human and relational aspects of education; implications for cognitive development, and, as Neil Selwyn reminded us in his editorial for the 2019 issue, the risk of the Matthew Effect – the phenomenon whereby those who already have an advantage accumulate more advantages through access to novel technologies, and those with disadvantages become even more disadvantaged, across the gulf of the ‘digital divide’.
Widespread AI use has profound implications across early years settings, schools, colleges and other education providers. A particular risk lies in the very ‘newness’ of GenAI technologies, particularly considering the scale of their current use. The research into their impact, including over the medium and long term, is very limited, and we need to act with caution. This is why many of the articles in this special issue of Impact are so tentative in their conclusions; we can’t yet claim with any certainty how AI is and will impact teaching and learning, and teachers and learners, going forward.
But I believe it is incredibly important to be sharing and reflecting on research and practice around AI in education, drawing on what we do know, considering potentially useful applications and reflecting on the challenges and risks of which we need to be aware. For me, three key themes emerge from the articles in this special PDF issue.
The first of these is that safety, ethics and privacy issues must be at the heart of our decisions around AI use. The DfE has published a framework for AI product safety expectations in education, setting out detailed safety standards and technical safeguards to protect students. The DfE has also commissioned Chiltern Learning Trust and the Chartered College of Teaching to develop training materials on the safe and effective use of AI, due to be published later this year, with the opportunity for teachers to gain certification through the Chartered College of Teaching. Through this training and accreditation, teachers will have the opportunity to build and demonstrate their understanding and ongoing development of the tools and policies required to ensure that students and staff alike are kept safe whilst embracing the exciting new technologies available.
And we must, of course, ensure that we are following the latest guidelines and legal requirements for use of AI tools and that they are adopted within the framework of our schools’ broader technology and safeguarding policies, ensuring that any implementation aligns with ethical guidelines and any age limitations on use.
A new AI content store, jointly funded by the DfE and the Department of Science, Innovation and Technology, will also pool curriculum guidance, lesson plans and anonymised pupil work, so that AI companies can train their tools using high-quality input, in order to generate effective and useful content for teachers and pupils. It is vital that we consider not only the content that we use, but also the values and ethics that underpin the development of that content.
The second theme is that we must continue to look for and generate evidence about the impact of artificial intelligence technologies in education, both positive and negative. The DfE is commissioning work in this space too, including work around the effective use of assistive technology for pupils with special educational needs and disabilities (SEND), funding for the development and evaluation of new tools, and a pilot of an EdTech Evidence Board run by the Chartered College of Teaching. This will involve developing criteria based on the best available evidence and consulting with the profession so that EdTech providers can be encouraged to conduct robust research and evaluation, and teachers and leaders can be confident that they are choosing the right tools for their contexts and needs.
The third and final theme is that we must consider the role of AI tools in the context of conversations around teacher professionalism, recruitment and retention. As articles in this issue demonstrate, it appears that there are opportunities for teacher workload to be reduced through effective use of AI technologies to support lesson planning and assessment. But this requires up-front time for teachers to develop their skills and knowledge. There are also murmurings about whether these technologies might have the capacity to replace the human teacher in the classroom. Yet it is clear to me that at the heart of teaching is the deeply human relationship – the connection between teacher and student – and that highly effective teaching depends on the knowledge and skills of expert practitioners, and this will not, and should not, change with the advent of GenAI.
The examples shared in this special issue won’t necessarily be those that you consider appropriate for your setting, and neither are they examples of perfect practice. Where authors mention specific AI tools, this is for context only and does not imply endorsement or recommendation of any particular tool. You will need to make decisions based on the needs of your pupils and your own consideration of the technologies available. I hope, however, that the articles within this special issue inspire thinking, conversations and action. The sheer importance of awareness for educators of the potential challenges and concerns raised by widespread AI use is why we have, for the first time ever, produced a PDF issue of Impact that can be shared with all educators. We have also published a wider selection of articles in the open access online edition of this special issue, available on the Chartered College of Teaching’s platform, MyCollege. In our future content, we will continue to seek out new research and reflections on this topic so that we can maintain our awareness and progress our knowledge as a profession. I look forward to continuing the conversation.
| 2025-06-10T00:00:00 |
https://my.chartered.college/impact/2025-special-issue-safe-and-effective-use-of-ai-in-education/
|
[
{
"date": "2025/06/10",
"position": 49,
"query": "AI education"
}
] |
|
Tips for Using AI—and for Talking to Students About It
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Tips for Using AI—and for Talking to Students About It
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https://www.edutopia.org
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[] |
... AI technologies into teaching and learning. It's certainly an intrepid ... With the rise of artificial intelligence (AI) in education, more secondary ...
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AI isn’t coming—it’s already here. It has been embedded in the various educational platforms we use and the assessments we give. It’s likely involved in both the professional development we participate in and the writing and work that our students are doing.
As an educator, speaker, and advocate for technology integration, I’ve spent a lot of time researching, using, and teaching with AI so that I can prepare students and other educators. In my classroom, from using a variety of AI tools that help me save time so I can spend it working with students to integrating chatbots to support student learning, I see the value and impact of leveraging this technology. If we want our students to be ready for their future careers, we must start teaching them about AI. They need to move beyond being consumers of content and instead become creators and innovators.
Where Do We See AI?
For educators, many AI-powered platforms help us to target instruction, assess students, and find resources for our lessons.
Confidence building: Encouraging students to speak in class can be a challenge. At the end of 2023, I started to use SchoolAI with my students and created a “Sidekick” for them to have conversations in Spanish based on the content that I used for the prompt. They loved it—it not only helped them build language skills and confidence but also showed how AI can support their learning. MagicSchool AI is another great option, with a tutor function that helps students in areas where they need support. Both of these tools also have historical characters that students can chat with and other features to enhance learning. I love that I can monitor student responses, provide additional support, and adjust instruction as needed.
Using Eduaide, teachers can kick off a class debate with pros and cons and an outline to quickly get started, and they can also develop other collaborative and engaging activities for students.
For language learners, confidence matters. Snorkl allows students to practice speaking and receive AI and teacher feedback, in real time. The AI gives real-time feedback on fluency and pronunciation, helping students grow as communicators and build confidence. Snorkl can be used with students starting in kindergarten, and it has a library full of ready-to-use activities. In all the times I have used Snorkl or one of the chatbots, the feedback provided has been specific to each student’s responses and offered insights and examples to support their learning.
Each of these tools also provides schools with assurance that student safety and data privacy are important and keeps their information updated on their sites.
Content creation and scaffolding: I have used tools such as Brisk Teaching and Diffit to create materials for my classroom. Whether I generate an activity for students, use it to change the reading level, or make presentations, these types of tools help me to save time, but more important, they help me to provide more personalized experiences for students and teach them about AI in the process. Brisk also has Brisk Boost to create activities for students.
I have used tools such as Kahoot, Quizizz, and Quizlet for generating quick game-based learning activities and lessons, or Formative for generating assessments. These have enabled me to personalize learning even more for my students by providing prompts to create more personalized resources that provide us with valuable and immediately available data to adjust our instruction in real time. With Quizizz, you can analyze with AI to gain insights into concepts that students may be struggling with and find additional practice resources.
Interactive presentations: When preparing a new presentation for students or professional development, there are a few tools that I use a lot. Almanack has a variety of options for creating assessments and other lesson materials. I use it the most for the professional slides that it quickly generates. I also rely heavily on Curipod for interactive lessons that involve students more in learning through discussion questions, drawing activities, polls, word clouds, and more. These help educators save time and create engaging lessons for students.
How to Talk About AI with Students
To get started talking with students about AI, I recommend asking questions. Ask students if they know what AI is, how it works, where they see it, and any benefits or concerns they can identify. Asking these initial questions helps students develop an understanding of AI. Then you can connect it to higher-level thinking skills and building digital awareness. Here are some example questions that I have used:
How do you think Spotify knows what song you might like or Amazon can recommend products for you?
How do you know whether an answer from AI is accurate?
Should students be allowed to use tools like ChatGPT to help with writing? Why or why not?
What is something that AI can help people do more easily?
What concerns do you have about AI in education?
Getting started with conversations is easy to do, and it always leads to a deeper discussion and greater understanding. Then, you can start some activities and lessons by using AI tools with students. I’ve created AI scavenger hunts and asked students to identify where they encounter AI in their daily lives—apps, games, smart devices, or websites. Another fun activity is deciding whether it is or is not AI by showing examples of images or videos generated by AI and some by humans, and have students analyze them to decide if they are real or fake and how they can tell.
Students as Leaders
We know that many students are using AI outside of school. Even if we ask them not to, they still can, and so we need to give them guidance on how to explore it responsibly. I have learned a lot from my students while teaching them about AI. They come up with ideas for what AI could be used for and what its benefits are. They express concerns about AI and the impact it can have if not used responsibly.
Beyond conversations and brainstorming ideas, the students can be the creators of AI-powered apps. My students have designed their own mock-ups of chatbots for mental health, for nutrition advice, and for pollution control. I recently met with an 11th-grade student, Ronil Dubal, who created an app called Studysnap because he wanted to help students learn how to study better.
| 2025-06-10T00:00:00 |
https://www.edutopia.org/article/ai-resources-teachers/
|
[
{
"date": "2025/06/10",
"position": 54,
"query": "AI education"
}
] |
|
Top 10 Use Cases of Generative AI in Education in 2025
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Top 10 Use Cases of Generative AI in Education in 2025
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https://research.aimultiple.com
|
[] |
Generative AI tools can help design and organize course materials, including syllabi, lesson plans, and assessments.
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Generative AI is an advanced technology that can rapidly produce lifelike images, text, and animations. In our research, we looked into how it’s being used across different industries, including healthcare and banking.
While other technologies, such as conversational AI and robotic process automation (RPA), are being implemented in education, generative AI is not being properly implemented in education.
Updated at 06-10-2025 Use Cases Description Personalized Lessons Creates customized curricula by analyzing individual student data. Course Design Organizes and tailors course materials to student needs. Content Creation Generates quizzes, exercises, study guides, and video scripts. Data Privacy Protection Enhances security for sensitive student information. Restoring Learning Materials Revitalizes and digitizes old or damaged educational content. Virtual Tutoring Provides on-demand, individualized academic support. Enhanced Creativity/Critical Thinking Fosters innovative problem-solving and analytical skills through AI prompts. Language Learning & Communication Facilitates practice and improves fluency through interactive AI agents. Gamified Learning Designs engaging, game-like educational experiences for motivation.
Let’s explore the top 10 use cases of generative AI in education and how it’s driving change in classrooms and beyond.
1. Personalized Lessons
Personalized lesson plans are a powerful way to ensure that students receive an education tailored specifically to their needs and interests. These lesson plans can be generated by using generative AI in education algorithms to analyze student data, such as:
Their past performance
Their skills
And any feedback they might have given regarding the content
AI-based systems can leverage such information to generate a customized curriculum that is more likely to engage each student and help them reach their potential. This can be important for children with learning disabilities or disorders.
Real-life Example
For example, Speechify is a generative AI in education tool. It offers text-to-speech or speech-to-text generation on desktops or online.
This kind of generative AI tools in education are especially useful for children with learning disabilities such as dyslexia or ADHD. When a child suffers from a lack of concentration due to such disorders, it can be harder to follow the course content via reading. However with such tools, they can generate texts into speech notes when they are bored.
2. Course Design
Generative AI tools can help design and organize course materials, including syllabi, lesson plans, and assessments. They can also personalize course material based on students’ knowledge gaps, skills, and learning styles, such as practice problems or interactive exercises.
Generative AI can create simulations and virtual environments once paired with other technologies, such as virtual reality. Consequently, it offers more engagement and interactive courses, improving students’ learning experience.
For example, a generative AI in education could create a virtual laboratory setting where students can conduct experiments, observe the results, and make predictions based on their observations.
3. Content Creation for Courses
Generative AI can assist in creating new teaching materials, such as questions for quizzes and exercises, or explanations and summaries of concepts. This can be especially useful for teachers who need to create a large amount and a variety of content for their classes. By using AI, it is possible to create modified or brand-new content from the original content.
Furthermore, generative AI in education can facilitate generating additional materials to supplement the main course materials, such as:
Reading lists
Study guides
Discussion questions
Flashcards
Summaries.
Additionally, AI can generate scripts for video lectures and podcasts, streamlining the creation of multimedia content for online courses. Image generation is another crucial capability of generative AI in education. Teachers may want to generate images with specific modifications that respond to particular course needs.
For example, NOLEJ offers an e-learning capsule that is AI-generated in only 3 minutes. This capsule offers an interactive video, glossary, practice exercises, and a summary for a target topic (see Figure 1 below).
Figure 1. An example of AI-generated course content.
More established companies are utilizing AI to create content that supports their primary products. For instance, Duolingo, a language learning platform, uses GPT-4 to correct French grammar and create items for their English test. The company concludes that with the implementation of GPT-4, the second language writing skills of customers are increased.
4. Data Privacy Protection for Analytical Models
One advantage of using generative AI in education to create training data sets is that it can help protect student privacy. A data breach or cyberattack can expose sensitive personal information belonging to school-age children, putting their privacy at risk
Using synthetic data, which is created by AI models that have learned from real-world data, can provide anonymity and protect students’ personal information. Synthetic datasets generated by AI models are valuable for training other algorithms, offering both effectiveness and enhanced data security.
For more on how generated synthetic data enables data privacy, you can check out these articles:
5. Restoring Old Learning Materials
Generative AI can enhance the quality of outdated or low-quality learning materials, including historical documents, photographs, and films. By utilizing AI to enhance the resolution of these materials, they can be brought up to modern standards and become more engaging for students accustomed to high-quality media.
These updates can also make it easier for students to read, analyze, and understand the materials, leading to a deeper understanding of the content and, ultimately, better learning outcomes.
Using a version of generative AI in education, Generative Adversarial Networks (GANs), it is possible to restore low-quality images and remove simple watermarks. In Figure 2 below, you can see a prototype for image restoration via GANs. Such image restoration can be adapted to educational materials. For example, in art and design schools, restoring old images would provide the detection of important details of artworks. Also in history classes and research, scanning and restoring old documents can be facilitated.
Figure 2. Image restoration with GANs. (Source: Towards Data Science)
6. Virtual Tutoring
Generative AI can be used to create virtual tutoring environments, where students can interact with a virtual tutor and receive real-time feedback and support. This can be especially helpful for students who may not have access to in-person tutoring.
According to academic studies, private tutoring for children with severe reading difficulty improved their reading skills by 50% in a year. However, providing tutoring to all students can be a challenge. Generative AI in education can tackle this issue by creating virtual tutoring environments. In these environments, students can interact with a virtual tutor and receive feedback and support in real-time. This can be especially helpful for students who may not have access to in-person tutoring.
Real-life Example
For example, TutorAI is trying to implement this kind of use of generative AI in education. It offers an educational platform that generates interactive content on a variety of topics.
Another application of generative AI in education for teaching purposes is the implementation of chatbots for tutoring. According to Chatbot Life’s 2019 report, the education sector ranks as the third-largest industry using chatbot technology. Check out our conversational AI in education article and learn the top use cases.
Recently, ChatGPT from OpenAI has stormed the internet with its ability to engage in highly personalized conversations and provide definitive answers. It can answer course-related questions from various domains and even write essays on the target topic.
7- Automated Content Creation
AI is becoming a valuable assistant to educators by generating high-quality educational materials quickly. It can create lesson plans, quizzes, and study guides while summarizing complex topics into simpler, more accessible forms. This automation not only saves time for teachers but also ensures that content is engaging and suitable for diverse student audiences. Platforms like Jasper and ChatGPT are commonly used to produce creative and interactive educational materials.
8-Enhanced Creativity and Critical Thinking
AI tools inspire creativity by encouraging students to think outside the box. Generative AI in education can create engaging scenarios for problem-solving tasks or generate stories for writing exercises, helping students develop critical thinking skills. Tools like DALL·E and MidJourney enable students to visualize abstract ideas, transforming imagination into tangible creations that enhance the learning experience.
9-Language Learning and Communication
Generative AI bridges language gaps by offering real-time translation, grammar correction, and pronunciation guidance. This makes education more inclusive for non-native speakers. For instance, tools like Grammarly and DeepL assist students in improving their writing skills, while AI-powered speech tools help in mastering new languages through real-time conversation feedback.
10-Gamified Learning Experiences
To enhance engagement, generative AI is used to gamify education by creating interactive quizzes and simulations. Gamified learning fosters interest and helps students retain knowledge through playful yet informative activities. Platforms like Kahoot! Use AI to design games that align with curriculum goals, making learning both fun and effective.
Challenges of generative AI in education
Although generative AI has considerable potential to enhance educational practices, it also poses some potential challenges. These are as follows:
Biases in educational materials
False or inaccurate information
Abuse of it for self-interest
Unemployment risks for some teachers or other education professionals
For more on generative AI
To explore more about generative AI, you can check our other articles:
Discover the top generative AI tools from our detailed list, sorted by category:
| 2025-06-10T00:00:00 |
https://research.aimultiple.com/generative-ai-in-education/
|
[
{
"date": "2025/06/10",
"position": 67,
"query": "AI education"
}
] |
|
Top AI Companies to Invest In - Tech Startups
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Top AI Companies to Invest In
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https://techstartups.com
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[
"Nickie Louise"
] |
Top 25 AI Startup Companies to Invest In · 1. OpenAI · 3. SandboxAQ · 4. Runway · 5. Anysphere · 6. Mercor · 7. Hex · 8. ClickHouse · 9. Statsig.
|
Mercor, an AI recruiting startup founded by three young entrepreneurs, has raised $100 million in a Series B funding round led by Felicis. This funding will enable Mercor to expand its AI-driven recruiting platform, which uses advanced algorithms to match talent with companies more effectively. Mercor’s technology aims to streamline the hiring process and improve the quality of matches between candidates and employers, leveraging AI to analyze candidate profiles and predict job performance, with a total raised of $135 million since its founding in 2023.
Anysphere, the developer of the AI-powered coding assistant Cursor, has raised $900 million in a funding round led by Thrive Capital. This investment values Anysphere at $9.9 billion and will be used to further develop its AI coding tools, which help engineers write and edit code more efficiently. Anysphere’s technology is designed to make coding faster and more accessible, catering to the growing demand for AI in software development, with a focus on integrating advanced AI capabilities into development environments.
Runway, a startup at the forefront of generative AI for media production, has raised $308 million in a Series D funding round led by General Atlantic. This investment will bolster Runway’s research into AI models for video generation and editing, as well as expand its Runway Studios, a film and animation production arm that leverages AI to accelerate creative processes. With this funding, Runway aims to redefine the media landscape with AI-driven tools that enhance creativity and efficiency, bringing its total raised to $536.5 million since its founding in 2018.
SandboxAQ, a leader in enterprise quantitative AI, has raised $450 million in a Series E funding round, adding prominent investors such as Ray Dalio, Horizon Kinetics, BNP Paribas, Google, and NVIDIA. This funding will support SandboxAQ’s efforts to develop and deploy Large Quantitative Models (LQMs) that help enterprises solve complex scientific and quantitative challenges across various industries, including biopharma, materials science, cybersecurity, and financial services. Since spinning out from Alphabet in 2022, SandboxAQ has raised over $950 million, highlighting its strong growth trajectory.
Anthropic, a pioneer in developing safe and scalable artificial intelligence, has secured $3.5 billion in a Series E funding round led by Lightspeed Venture Partners. This substantial investment will enable Anthropic to further its mission of creating AI systems that are both powerful and aligned with human values. The funds will be used to advance research in AI safety, scale up computational resources, and expand Anthropic’s product offerings, including its Claude chatbot, which has gained significant traction in enterprise applications, with revenue reportedly reaching $3 billion annualized by May 2025.
OpenAI, the leading artificial intelligence research organization behind ChatGPT, has raised $40 billion in a monumental funding round led by SoftBank. This investment will fuel OpenAI’s ambitious plans to push the boundaries of AI research, enhance its computational infrastructure, and deliver more powerful AI tools to its vast user base of over 500 million weekly users. With this funding, OpenAI aims to accelerate the development of next-generation AI systems and expand its global reach, particularly in data center projects like Stargate.
7. Hex
Hex Technologies, a provider of a unified, AI-powered workspace for data science and analytics, has secured $70 million in a Series C funding round. The round was led by Andreessen Horowitz and Sequoia Capital, with participation from existing investors. This new capital will fuel Hex’s mission to make data science more accessible and collaborative, enabling teams to work together seamlessly on data-driven projects. The company, founded in 2019 and headquartered in San Francisco, CA, has raised a total of $171 million, with this round bringing its funding to a new level. The exact funding date in 2025 is not specified, but it aligns with the trend of significant investments in AI-driven analytics tools.
Funding Details:
Startup: Hex Technologies
Investors: Andreessen Horowitz, Sequoia Capital (lead investors), and existing backers
Amount Raised: $70 million
Total Raised: $171 million
Funding Stage: Series C
Founded Date: 2019
8. ClickHouse
ClickHouse, a leader in real-time analytics, data warehousing, observability, and AI/ML, has secured $350 million in a Series C funding round. The round was led by Khosla Ventures, with participation from new investors BOND, IVP, Battery Ventures, and Bessemer Venture Partners, as well as existing investors including Index Ventures, Lightspeed, GIC, Benchmark, Coatue, FirstMark, and Nebius. This funding, announced on May 29, 2025, will be used to scale product development, support global expansion, and deepen partnerships with customers and technology providers building AI-native applications. ClickHouse, incorporated in Delaware with headquarters in San Francisco, CA, has raised over $650 million to date, reflecting its strong market traction in handling AI-driven analytical workloads.
Funding Details:
Startup: ClickHouse
Investors: Khosla Ventures (lead), BOND, IVP, Battery Ventures, Bessemer Venture Partners, Index Ventures, Lightspeed, GIC, Benchmark, Coatue, FirstMark, Nebius
Amount Raised: $350 million
Total Raised: Over $650 million
Funding Stage: Series C
Founded Date: 2015
9. Statsig
Statsig, a provider of a platform for data-driven product development, has secured $100 million in a Series C funding round. The round, announced on May 6, 2025, was led by ICONIQ Growth, with participation from existing investors Sequoia Capital and Madrona Venture Group. This funding will be used to expand the platform’s capabilities and grow the team, aiming to unify product development tools in the era of AI. Based in Bellevue, WA, Statsig has raised a total of $153 million, with this round valuing the company at $1.1 billion, highlighting its focus on A/B testing and feature management for product teams.
Funding Details:
Startup: Statsig
Investors: ICONIQ Growth (lead), Sequoia Capital, Madrona Venture Group
Amount Raised: $100 million
Total Raised: $153 million
Funding Stage: Series C
Founded Date: 2021
10. Vivodyne
Vivodyne, a company developing drug testing on lab-grown 3D human tissues, has secured $40 million in a Series A funding round. The round, announced around May 29, 2025, was led by Khosla Ventures, with participation from new investors Lingotto Investment Management, Helena Capital, Fortius Ventures, and existing investors Kairos Ventures, CS Ventures, Bison Ventures, and MBX Capital. The funding will be used to scale Vivodyne’s robotics and AI approach and advance R&D efforts, aiming to improve preclinical drug testing accuracy. Based in San Francisco, CA, Vivodyne has raised a total of $78 million since its founding in 2021.
Funding Details:
Startup: Vivodyne
Investors: Khosla Ventures (lead), Lingotto Investment Management, Helena Capital, Fortius Ventures, Kairos Ventures, CS Ventures, Bison Ventures, MBX Capital
Amount Raised: $40 million
Total Raised: $78 million
Funding Stage: Series A
Founded Date: 2020
11. Listen Labs
Listen Labs, an AI-powered customer research startup, has raised $27 million through seed and Series A funding rounds, both led by Sequoia Capital, with participation from new investors Conviction and Pear. Announced in April 2025, the funding will be used to expand operations, reach more businesses, and further develop the platform’s capabilities. Founded in 2023 and based in San Francisco, CA, Listen Labs helps users create research questions, identify participants, and run audio and video interviews, using large language models to generate reports. This round marks its total funding at $27 million, reflecting its early-stage growth in the AI research space.
Funding Details:
Startup: Listen Labs
Investors: Sequoia Capital (lead for both rounds), Conviction, Pear
Amount Raised: $27 million (combined seed and Series A)
Total Raised: $27 million
Funding Stage: Seed and Series A
Founded Date: 2023
12. Stord
Stord, a provider of cloud-based end-to-end logistics and supply chain management solutions, has secured $200 million in a Series B funding round. The round, announced on April 9, 2025, was co-led by Addition, Andreessen Horowitz, Lightspeed Venture Partners, and Valor Equity Partners, with participation from existing investors including Thrive Capital, Altimeter, Terrain, and Trust Ventures. The funding will be used to continue growth, build the first factory in Texas, and expand operations. Based in Atlanta, GA, Stord has raised a total of $268 million, with this round supporting its mission to unify logistics on a single platform.
Funding Details:
Startup: Stord
Investors: Addition, Andreessen Horowitz, Lightspeed Venture Partners, Valor Equity Partners (co-leads), Thrive Capital, Altimeter, Terrain, Trust Ventures, and others
Amount Raised: $200 million
Total Raised: $268 million
Funding Stage: Series B
Founded Date: 2015
13. Chainguard
Chainguard, a company providing secure open source software solutions, has secured $356 million in a Series D funding round. Announced on April 23, 2025, the round was co-led by Kleiner Perkins and IVP, with participation from new investors Salesforce Ventures and Datadog Ventures, as well as all existing investors including Sequoia Capital. The funding will be used to accelerate product innovation and company expansion, with Chainguard now valued at $3.5 billion. Based in Kirkland, WA, and founded in 2021, the company has raised a total of $612 million, focusing on software supply chain security.
Funding Details:
Startup: Chainguard
Investors: Kleiner Perkins, IVP (co-leads), Salesforce Ventures, Datadog Ventures, and all existing investors (including Sequoia Capital)
Amount Raised: $356 million
Total Raised: $612 million
Funding Stage: Series D
Founded Date: 2015
14. Apex Space
Apex Space, a manufacturer of standardized satellite buses, has secured $200 million in a Series C funding round. Announced on April 28, 2025, the round was led by Point72 Ventures and co-led by 8VC, with participation from existing investors including Andreessen Horowitz, and new investors Washington Harbour Partners and StepStone Group. The funding will be used to scale production and meet the growing demand for Apex’s configurable satellite bus platforms. Based in Los Angeles, CA, Apex has raised a total of $318 million, aiming to accelerate access to space for commercial and government missions.
Funding Details:
Startup: Apex Space
Investors: Point72 Ventures (lead), 8VC (co-lead), Andreessen Horowitz, Washington Harbour Partners, StepStone Group, and others
Amount Raised: $200 million
Total Raised: $318 million
Funding Stage: Series C
Founded Date: 2022
15. Reflect Orbital
Reflect Orbital, a spacetech startup developing a constellation of satellites to reflect sunlight down to Earth, has secured $20 million in a Series A funding round. Announced on May 14, 2025, the round was led by Lux Capital, with participation from Sequoia Capital and Starship Ventures. The funding will be used to support team growth, scale operations, and prepare for the first space missions, with the first satellite set to launch as early as Spring 2026. Based in Hawthorne, CA, Reflect Orbital has raised a total of $26.5 million, focusing on innovative energy applications.
Funding Details:
Startup: Reflect Orbital
Investors: Lux Capital (lead), Sequoia Capital, Starship Ventures
Amount Raised: $20 million
Total Raised: $26.5 million
Funding Stage: Series A
Founded Date: 2021
16. Base Power
Base Power, an energy company providing residential backup battery systems and electricity plans, has secured $200 million in a Series B funding round. Announced on April 9, 2025, the round was co-led by Addition, Andreessen Horowitz, Lightspeed Venture Partners, and Valor Equity Partners, with participation from existing investors including Thrive Capital, Altimeter, Terrain, and Trust Ventures. The funding will be used to continue growth, build the first factory in Texas, and expand operations. Based in Austin, TX, and founded in 2023, Base Power has raised a total of $268 million, aiming to build a grid for the future.
Funding Details:
Startup: Base Power
Investors: Addition, Andreessen Horowitz, Lightspeed Venture Partners, Valor Equity Partners (co-leads), Thrive Capital, Altimeter, Terrain, Trust Ventures, and others
Amount Raised: $200 million
Total Raised: $268 million
Funding Stage: Series B
Founded Date: 2023
17. Sprinter Health
Sprinter Health, a mobile healthcare provider that combines technology with a medical practice to deliver personalized care to patients’ homes, has secured $55 million in a Series B funding round. Announced on May 15, 2025, the round was led by General Catalyst, with participation from Andreessen Horowitz, Regents of the University of California, Google Ventures, and Accel. The funding will be used to expand operations and development efforts, allowing Sprinter Health to reach more patients and improve access to preventive care. Based in Menlo Park, CA, the total raised is not specified in the sources, but the company has shown significant growth, expanding from five states in 2023 to 18 today.
Funding Details:
Startup: Sprinter Health
Investors: General Catalyst (lead), Andreessen Horowitz, Regents of the University of California, Google Ventures, Accel
Amount Raised: $55 million
Total Raised: Not specified
Funding Stage: Series B
Founded Date: 2021
18. Prepared
Prepared, a provider of an assistive AI platform for emergency response, has secured $80 million in a Series C funding round. Announced on June 2, 2025, the round was led by General Catalyst, with participation from Andreessen Horowitz, First Round Capital, and Radical Ventures. The funding will be used to expand operations and development efforts, allowing Prepared to further modernize 911 emergency response systems with AI technology. Based in NYC and founded in 2019, the total raised is not specified, but previous rounds include a $27 million Series B in 2024, suggesting significant growth in the emergency communications sector.
Funding Details:
Startup: Prepared
Investors: General Catalyst (lead), Andreessen Horowitz, First Round Capital, Radical Ventures
Amount Raised: $80 million
Total Raised: Not specified
Funding Stage: Series C
Founded Date: 2019
19. UNION
UNION, a startup developing an AI-powered platform, has secured $50 million in a Seed funding round. Announced in May 2025, the funding will be used to build and scale the platform’s capabilities. Specific details on investors are not available from the sources, but this round marks UNION’s total funding at $50 million, reflecting its early-stage focus on AI innovation. The exact location and founding date are not specified, but it aligns with the trend of seed-stage AI startups attracting significant capital.
Funding Details:
Startup: UNION
Investors: Not specified
Amount Raised: $50 million
Total Raised: $50 million
Funding Stage: Seed
Founded Date: May 2022
20. Persivia
Persivia, a leader in AI-driven digital health solutions, has secured $107 million in a recapitalization with Aldrich Capital Partners. Announced on April 29, 2025, the funding will be used to accelerate expansion plans, grow the sales force, and roll out new AI-powered solutions aimed at driving operational efficiency and patient management for healthcare organizations. Based in Marlborough, MA, the total raised is not specified, but this recapitalization supports Persivia’s mission to transform care delivery with AI, with a focus on value-based care and population health management.
Funding Details:
Startup: Persivia
Investors: Aldrich Capital Partners
Amount Raised: $107 million
Total Raised: Not specified
Funding Stage: Recapitalization
Founded Date: 2015
21. Field Materials
Field Materials, a construction tech startup, has raised $10.5 million in a Series A funding round. Announced in May 2025, the company uses AI and document digitization to automate the procurement of construction materials, reducing manual data entry and improving efficiency. Specific investors are not detailed in the sources, but the round brings Field Materials’ total funding to nearly $19 million. Founded in 2022 and based in Charlotte, NC, this funding supports its rapid growth, processing over $360 million in material purchases annually across 27 US states.
Funding Details:
| 2025-06-10T00:00:00 |
2025/06/10
|
https://techstartups.com/2025/06/10/top-ai-companies-to-invest-in/
|
[
{
"date": "2025/06/10",
"position": 16,
"query": "AI employers"
}
] |
How 100 Enterprise CIOs Are Building and Buying Gen AI ...
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How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025
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https://a16z.com
|
[
"Sarah Wang",
"Shangda Xu",
"Justin Kahl",
"Tugce Erten",
"Anish Acharya",
"David George",
"Alex Immerman",
"Martin Casado",
"James Da Costa",
"Peter Lauten"
] |
Enterprise AI budgets grew beyond already high forecasts and graduated from pilot programs and innovation funds to recurring line-items in core IT and business ...
|
Just over a year ago, we highlighted 16 changes to the way enterprises approached building and buying gen AI. Since then, the landscape has continued to evolve quickly—so we revisited our conversations with over two dozen enterprise buyers and surveyed 100 CIOs across 15 industries to help founders understand how these leaders are using, buying, and budgeting for gen AI in 2025 and beyond.1
Even in a field where the only constant is change, the gen AI market structure has evolved significantly beyond our expectations since we ran our last survey over a year ago.
Enterprise AI budgets grew beyond already high forecasts and graduated from pilot programs and innovation funds to recurring line-items in core IT and business unit budgets.
Organizations are much more sophisticated at mixing and matching multiple models to optimize across both performance and cost. OpenAI, Google, and Anthropic took dominant overall market share in our survey while Meta and Mistral were popular among open source options.
Procurement now mirrors traditional software buying—with more rigorous evaluations, hosting considerations, and benchmark scrutiny—while increasingly complex AI workflows are driving higher switching costs.
Meanwhile, the AI app landscape has matured: off-the-shelf solutions are eclipsing custom builds and rewarding AI-native third party applications.
To give founders a more nuanced look at what’s top of mind for enterprise buyers today, we’ll dig into these shifts in resourcing, models, procurement, and application usage below.
Budgets: AI spend blew past high expectations and is here to stay Budgets: AI spend blew past high expectations and is here to stay
1. Budgets are bigger than expected with no signs of slowing down
LLM budgets have grown ahead of enterprises’ (already high) expectations from a year ago, and there are no signs of this slowing down. Enterprise leaders expect an average of ~75% growth over the next year. As one CIO noted, “what I spent in 2023 I now spend in a week.”
Spend growth is driven partially by enterprises discovering more relevant internal use cases and increasing employee adoption. On top of this, we’re beginning to see more customer-facing use cases—especially for tech-forward companies—that have the potential to drive exponential spend growth. One large technology company said, “we’ve been mostly focused on internal use cases so far, but this year we’re focused on customer-facing gen AI where spend will be significantly larger.”
2. Gen AI spend graduates to permanent budget lines
Last year, innovation budgets still made up a quarter of LLM spending; this has now dropped to just 7%. Enterprises are increasingly paying for AI models and apps via centralized IT and business unit budgets, reflecting the growing sentiment that gen AI is no longer experimental but essential to business operations. One CTO noted that, “more of our products are adding AI enablement, so our spending growth will rise across all of these products”—suggesting this shift toward core budgets will only accelerate.
Models: three leaders emerging as differentiated performance by use case drives more model diversification Models: three leaders emerging as differentiated performance by use case drives more model diversification
3. The multi-model world is here to stay and model differentiation—not commoditization—is the key driver
With several highly capable LLMs now available, it’s become the norm to have multiple models deployed in production use cases. While one reason for this is certainly to avoid vendor lock-in, model differentiation by use case has become increasingly pronounced and is the main reason enterprises buy models from multiple vendors. In this year’s survey, 37% of respondents are now using 5 or more models as opposed to 29% last year.
While in some cases models appear to have comparable scores on general purpose evaluations, it’s clear that the enterprise model layer has not become commoditized. It’s well known, for instance, that Anthropic’s models excel in coding-related tasks, but there’s more nuance to this claim. Within coding, some users report that Claude performs better for fine-grained code completion, while Gemini is stronger in higher-level system design and architecture. In other domains, such as text-based applications, one customer observed that “Anthropic is a bit better at writing tasks—language fluency, content generation, brainstorming—while OpenAI models are better for more complex question-answering.” These differences have made it best practice to use multiple models, and we expect this strategy will continue as customers build applications for performance and keep an eye towards remaining vendor agnostic.
4. Model landscape is crowded but clear leadership is emerging
While enterprises continued to use different models across both experimental and production use cases as explored above, a few players took the lead on overall adoption: OpenAI maintained overall market share leadership, while Google and Anthropic made considerable strides over the last year. Market share differed somewhat by scale of the enterprise, with more open source adoption occurring at the larger end of enterprises where on-prem is still a major consideration.
Double-clicking further into usage:
Enterprises use a broad suite of OpenAI’s models . GPT-4o is the model most deployed to production, while OpenAI o3 has generated significant interest as reasoning models are more integrated into production use cases. OpenAI’s non-frontier models are adopted at much higher rates than other vendors’, so it’s Google and Anthropic’s frontier models—Gemini 2.5 and Claude Sonnet 3.5 onward—that have gotten them in the door. Specifically, 67% of OpenAI users have deployed non-frontier models in production, compared to just 41% for Google and 27% for Anthropic.
Google’s rise has been more pronounced within large enterprises , as they frequently have existing relationships with GCP and can tap into the brand trust of a mega-cap company. Gemini models have long touted best-in-class context windows, but the overall performance of Gemini 2.5 vaulted them into true frontier model status. What has also become apparent—and may give Google staying power—is Google’s performance-to-cost ratio. In an example of models with comparable intelligence, Gemini 2.5 flash costs 26 cents / million tokens while GPT-4.1 mini costs 70 cents.
In contrast, Anthropic has seen the highest adoption in the most tech-forward companies, specifically software companies and startups. Their models have excelled in certain use cases, most notably code, and they power the fastest growing AI coding applications as a result. This use case-level leadership has not gone unnoticed in the enterprise, with tech-forward leaders actively evaluating performance by use case and typically selecting Anthropic for engineering and coding. This has been a boon to Anthropic’s revenue and given them the scale and reputation valued by more traditional large enterprises as well.
Adoption of open source models like Llama and Mistral tended to be higher at larger enterprises relative to their adoption at smaller companies. This has typically been driven by the preference for on-prem solutions, given data security and compliance considerations, as well as the ability to fine-tune for specific enterprise use cases.
Newer model providers like xAI are seeing strong interest and early testing out of the gate —reminiscent of last year’s behavior and a reminder that model market share remains dynamic.
5. Closed source price-to-performance ratio has become more compelling for non-frontier models
As we’ve previously discussed, model costs are coming down by an order of magnitude every 12 months. Against this backdrop, we’ve also seen the price-to-performance ratio of closed source become much more compelling for small and medium models, with xAI’s Grok 3 mini and Google’s Gemini 2.5 Flash taking the lead on this count. In some cases, customers more frequently opt for closed source models given this shift, along with other ecosystem benefits. As one customer said, “The pricing has gotten appealing and we’re already embedded with Google: we use everything from G Suite to databases, and their enterprise expertise is attractive.” Or more concisely put by another: “Gemini is cheap.”
6. Fine-tuning viewed as less necessary as model capabilities improve
Improved model capabilities—chiefly higher intelligence and longer context windows—have made fine-tuning less critical to achieving strong model performance for a specific use case. Instead, companies have found that prompt engineering can drive similar or better results, often at much lower cost. As one enterprise observed, “instead of taking the training data and parameter-efficient fine-tuning, you just dump it into a long context and get almost equivalent results.”
This move away from fine-tuning also helps companies avoid model lock-in, as fine-tuned models require high upfront costs and engineering work while prompts can be more easily ported from one model to another. This is important in a world where models are rapidly improving and companies want the benefits of staying on the leading edge.
That said, companies with hyper-specific use cases are still fine-tuning models. For instance, one streaming service fine-tunes open source models for query augmentation in video search “where you need more domain adaptation.” We might also see a rise in fine-tuning if newer methods, like reinforcement fine tuning, become more widely adopted beyond the labs.
7. Enterprises optimistic on reasoning models and poised to scale quickly
By allowing LLMs to complete more complex tasks more accurately, reasoning models have expanded the range of use cases that LLMs can tackle. Enterprises are still early in their testing of reasoning models and few have deployed them in production, but companies are very optimistic about their potential. One executive we interviewed captured this well: “[reasoning models] allow us to solve newer, more complex use cases, so I anticipate a big jump in our usage. But we’re still early and testing today.”
Among early adopters, OpenAI’s reasoning models have seen the greatest traction. Despite significant industry buzz around DeepSeek, enterprises are overwhelmingly adopting OpenAI, with 23% of enterprises surveyed already using OpenAI’s o3 model in production compared to just 3% for DeepSeek. DeepSeek’s adoption was higher among startups relative to its low pickup in the enterprise.
Procurement: enterprise AI buying adopts the rigor of traditional software buying Procurement: enterprise AI buying adopts the rigor of traditional software buying
8. Buying process for models increasingly resembles traditional enterprise software procurement, complete with checklists and price sensitivity
Companies now approach model selection with disciplined evaluation frameworks, and factors such as security—which was heavily emphasized in our interviews—and cost have gained ground on overall accuracy and reliability. This shift underscores the increased trust enterprises have in model performance and the confidence that LLMs will be deployed at scale. As one leader succinctly summarized, “for most tasks, all the models perform well enough now—so pricing has become a much more important factor.”
As we mentioned in the “Models” section, enterprises are also becoming more sophisticated in matching specific use cases with the right model. For highly visible or performance-critical applications, companies typically prefer leading-edge models with strong brand recognition. In contrast, for simpler or internal tasks, model choice often comes down purely to cost. See below for how these LLM KPCs (key purchasing criteria) have changed over time.
9. Hosting preferences still vary widely, though enterprises have quickly built trust for model providers over the last year
While there is still some preference for existing cloud relationships (similar to other infra purchases), more enterprises are hosting either directly with model providers or via Databricks, particularly in cases where the model of choice is not hosted by their main cloud provider (e.g., OpenAI for AWS customers). This is typically because leaders “want direct access to the latest model with the best performance as soon as it’s available. Early access previews are important too.” The increased trust in going direct with model providers including OpenAI and Anthropic is a significant shift from what we heard in last year’s interviews with enterprises: many opted to access models via a cloud provider whenever possible, sometimes even if it wasn’t via their primary cloud provider.
10. Switching costs are rising as AI tackles more complex tasks
Last year, we found that most enterprises were designing their applications to minimize switching costs and make models as interchangeable as possible. As a result, many enterprises treated models as “easy come, easy go.” That might have worked well for simple, one-shot use cases, but the rise of agentic workflows has started making it more difficult to switch between models.
As companies invest the time and resources into building guardrails and prompting for agentic workflows, they’re more hesitant to switch to other models in the event that their results won’t be replicable or that they’ll need to invest significant time into engineering the reliability of a different model. Agentic workflows often require multiple steps to complete a task, so changing one part of a model’s workflow could impact all downstream dependencies. As one leader told us, “all the prompts have been tuned for OpenAI. Each one of them has their own set of instructions and prompts and details. How LLMs get instructions to do agentic processing—it takes lots of pages of instruction. Also, quality assurance of agents is not super easy, so changing models is now a task that can take a lot of engineering time.”
11. Enterprises are increasingly referencing external benchmarks as quasi-“Magic Quadrants” as an initial filter for model selection
As models proliferate, external evaluations offer a practical, Gartner-like filter that enterprises recognize from their traditional software procurement processes.
While internal benchmarks, golden datasets, and developer feedback are still critical parts of assessing LLM performance more deeply, the maturation of the LLM market has driven companies to increasingly reference external benchmarks like LM Arena. Though these external benchmarks help enterprise buyers sort the market, leaders also noted that these benchmarks are just one factor in a broader evaluation process: “we definitely look at the external benchmarks. But you still need to assess yourself. It’s hard to pick without really trialing things and getting employee feedback.”
Rise of the app: AI apps growth is gangbusters as more enterprises decide to buy instead of build across more use cases Rise of the app: AI apps growth is gangbusters as more enterprises decide to buy instead of build across more use cases
12. Enterprises are shifting from “build” to “buy” as the AI application ecosystem takes shape
Early in the AI product cycle, enterprises largely opted to work directly with AI models and build their own applications. However, we’ve seen a marked shift towards buying third party applications over the last twelve months as the ecosystem of AI apps has started to mature. This is particularly true as the dynamic performance and cost differentiation across models has resulted in incremental ROI gains from constant evaluation and optimization by use case, often best tackled by a dedicated AI application team instead of an internal team.
Moreover: in a space as dynamic as AI, companies are finding that internally developed tools are difficult to maintain and frequently don’t give them a business advantage—which further cements their interest in buying instead of building apps.
As more application categories mature, we’d expect to see this trend swing harder towards third-party applications in the future, as evidenced by the leading indicator of leaders considering apps more heavily when testing new use cases. In the case of customer support, for instance, over 90% of survey respondents noted that they were testing third-party apps. One public fintech noted that while they had started to build customer support internally, a recent review of third-party solutions on the market convinced them to buy instead of continuing their build. The one area where we haven’t seen this trend play out is in regulated or high-risk industries like healthcare, where data privacy and compliance are more top of mind.
13. Buyers struggle with outcome-based pricing for apps
While there’s a lot of hype around outcome-based pricing for AI, CIOs are still uncomfortable with how outcome metrics are set, measured, and billed.
Some of the top concerns with outcome-based pricing were lack of clear outcomes that map to business goals, unpredictable costs, and attribution—but there was no consensus on how vendors could mitigate these issues. This isn’t surprising, as AI is a relatively new technology and it’s not yet clear how to implement it so it drives real value for businesses. Buyers don’t know how much they’re going to be charged and don’t want to be left holding the bag. Given this, most CIOs still prefer paying by usage for AI applications.
14. Software development emerges as a killer use case—with others close behind
While we’ve seen progressive adoption of AI use cases across the board—especially internal enterprise search, data analysis, and customer support—software development has seen a step change in adoption, driven by a perfect storm of extremely high-quality off-the-shelf apps, a significant increase in model capabilities, relevance for a broad set of companies and industries, and a no-brainer ROI use case.
One CTO at a high-growth SaaS company reported that nearly 90% of their code is now AI-generated through Cursor and Claude Code, up from 10–15% 12 months ago with GitHub Copilot. This level of adoption still represents the bleeding edge, but is likely a strong leading indicator for the enterprise.
15. Prosumer market has driven much of the early app growth and enterprise buying behavior
Strong consumer brands are translating into strong enterprise demand.
Like some of the early platform shifts (e.g., the internet), much of the early growth across leading enterprise AI apps has been driven by the prosumer market. This was kicked off by ChatGPT and underscored by coding apps and creator tools like ElevenLabs. Many CIOs noted their decision to purchase enterprise ChatGPT was driven by “employees loving ChatGPT. It’s the brand name they know.” This dual market pull has led to much faster growth in the next generation of AI companies than we’ve seen in the past.
16. AI-native quality and speed starting to outpace incumbents
Incumbents have always benefited from established trust and existing distribution, but in the AI era, they’re increasingly outperformed by AI-native competitors from a product quality and velocity perspective.
Unsurprisingly, the primary reason buyers prefer AI-native vendors is their faster innovation rate. The second reason is the recognition that companies built around AI from the ground up deliver fundamentally better products with superior outcomes compared to incumbents retrofitting AI into existing solutions.
This gap is especially clear in software development today, where one public security company CIO highlighted a stark difference in capabilities between first-generation and second-generation AI coding tools as coding becomes more agentic. The shift is also echoed in user satisfaction data: users who have adopted Cursor, a gen AI-native coding solution, show notably lower satisfaction with previous-gen tools like GitHub Copilot, underscoring how quickly innovation fundamentally reshapes the outcomes buyers can and should expect from AI.
Looking ahead
The enterprise AI landscape is no longer defined by experimentation: it’s shaped by strategic deployment, budget commitment, and maturing vendor ecosystems. As model choice diversifies, fragmentation by use case is not only expected but embraced, and a few key leaders are emerging. Enterprises are adopting structured procurement processes and increasingly turning to off-the-shelf applications to accelerate adoption. The result is a market that looks more like traditional software—yet moves with the speed and complexity unique to AI.
| 2025-06-10T00:00:00 |
2025/06/10
|
https://a16z.com/ai-enterprise-2025/
|
[
{
"date": "2025/06/10",
"position": 81,
"query": "AI employers"
}
] |
Cronkite School welcomes new Professor of Practice in AI and ...
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Cronkite School welcomes new Professor of Practice in AI and Investigative Journalism
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https://cronkite.asu.edu
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[] |
Shelby Grossman joins the Cronkite School to advance work in artificial intelligence and investigative journalism.
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The Walter Cronkite School of Journalism and Mass Communication has hired Shelby Grossman as its inaugural Professor of Practice in AI and Investigative Journalism—a position funded by a $1 million grant from the Scripps Howard Foundation.
Grossman was most recently a research scholar at the Stanford Cyber Policy Center and the Stanford Internet Observatory, where she led research projects on child safety, covert influence operations and scams, and taught classes on open source investigation and online safety.
At Stanford, she co-founded the Journal of Online Trust and Safety and the Trust and Safety Teaching Consortium. Previously she was an assistant professor of political science at the University of Memphis. She holds a Ph.D. in government from Harvard University.
“The fast growth of artificial intelligence presents us with a choice: We can either see AI simply as a threat, or we can seek to also understand its capabilities to advance our journalism and public service missions,” said Battinto L. Batts Jr., dean of the Cronkite School. “Here at Cronkite and at ASU, we will do both. Hiring Dr. Grossman, who has immense expertise and garners tremendous respect among her peers, will help us investigate artificial intelligence while also charting a course to leverage the full potential of AI in journalism and media.”
The Scripps Howard Foundation grant funding Grossman’s teaching and research will also support the creation of a professional certificate in investigative journalism with a focus on AI, as well as graduate students and annual workshops—all housed within the Cronkite School’s award-winning Howard Center for Investigative Journalism.
Grossman’s professorship is just one of several Cronkite initiatives designed to leverage emerging technology to propel the media industry forward. Earlier this month, the Cronkite School announced a $10.5 million investment from the John S. and James L. Knight Foundation to launch the Knight Center for the Future of News and accelerate transformation in the news ecosystem.
About the Howard Center for Investigative Journalism
ASU’s Howard Center for Investigative Journalism is a groundbreaking hands-on learning experience supported by the Scripps Howard Foundation. In the Howard Center, Cronkite School graduate and capstone-level undergraduate students learn how to produce deeply researched watchdog journalism, often in partnership with prominent national newsrooms.
| 2025-06-10T00:00:00 |
https://cronkite.asu.edu/news/2025/new-professor-ai-investigative-journalism/
|
[
{
"date": "2025/06/10",
"position": 20,
"query": "AI journalism"
},
{
"date": "2025/06/10",
"position": 44,
"query": "artificial intelligence journalism"
}
] |
|
News Habits & Media
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News Habits & Media
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https://www.pewresearch.org
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[] |
About six-in-ten Americans (59%) say AI will lead to fewer jobs for journalists in the next two decades. short reads Apr 24, 2025. Americans remain concerned ...
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In an open-ended question, we asked U.S. adults who say they regularly get news from news influencers to name the first one who comes to mind for them.
| 2025-06-10T00:00:00 |
https://www.pewresearch.org/topic/news-habits-media/
|
[
{
"date": "2025/06/10",
"position": 77,
"query": "AI journalism"
}
] |
|
Is Chat GPT affecting Journalism? - London School of Business
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Is Chat GPT affecting Journalism?
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https://www.lsbuk.com
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[] |
Is ChatGPT Reshaping Journalism? The Promise and Pitfalls of AI in Newsrooms. The rise of generative AI tools like ChatGPT has ignited a fierce debate in ...
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Is ChatGPT Reshaping Journalism? The Promise and Pitfalls of AI in Newsrooms
The rise of generative AI tools like ChatGPT has ignited a fierce debate in journalism: Is this technology a revolutionary assistant or a threat to the profession’s core values? From automating routine articles to translating news for global audiences, AI is undeniably altering how stories are reported, written, and consumed. But as newsrooms experiment with these tools, critical questions about accuracy, ethics, and the future of human-led storytelling demand answers. Let’s explore how ChatGPT is transforming journalism—and where it falls short.
The Benefits: How ChatGPT Empowers Newsrooms
1. Speed and Scale in Reporting
ChatGPT excels at generating quick drafts of breaking news, earnings reports, or sports summaries. For instance, Reuters uses AI to produce thousands of corporate earnings stories annually, freeing journalists to focus on investigative work. Smaller outlets, like local newspapers, leverage it to cover community events without expanding staff.
2. Multilingual Accessibility
AI breaks language barriers. BBC News uses ChatGPT to translate articles into 40+ languages, while Al Jazeera creates audio versions of stories for visually impaired audiences. This democratizes access to information, particularly in regions with limited media resources.
3. Cost Efficiency for Resource-Strapped Outlets
Startup news platforms like Axios and Semafor rely on AI to curate newsletters and social media content, slashing production costs by up to 30%. For nonprofits like The Markup, this means redirecting funds to data-driven investigations.
4. Enhanced Research and Context
ChatGPT can analyze vast datasets to identify trends. The Washington Post’s “Climate Lab” series used AI to cross-reference decades of environmental studies, uncovering overlooked links between policy and emissions.
The Drawbacks: Why AI Can’t Replace Journalists
1. Accuracy Risks and “Hallucinations”
AI tools often generate plausible-sounding falsehoods. In 2023, CNET quietly corrected 41 AI-written articles riddled with errors, including miscalculated mortgage rates. As Guardian editor Katharine Viner warns: “ChatGPT doesn’t know truth—it predicts words.”
2. Amplifying Bias
ChatGPT’s training data includes historical biases. When Bloomberg tested it on gender pay gap stories, the tool downplayed systemic inequality, mirroring skewed corporate reports. Without human oversight, such outputs risk reinforcing stereotypes.
3. The Creativity Gap
AI struggles with nuance. While it can draft a crime report, it can’t capture the emotional weight of a New York Times feature on opioid victims. As Pulitzer winner Nicole Hannah-Jones argues, “Storytelling requires empathy—something code can’t replicate.”
4. Ethical Quandaries
Who’s accountable for AI errors? When Sports Illustrated published AI-generated product reviews with fake author bios, it sparked a credibility crisis. Newsrooms must navigate transparency: Should readers know when a bot writes a story?
The Future of Journalism: Collaboration, Not Replacement
1. AI as a Reporting Assistant
Outlets are adopting hybrid workflows:
The Associated Press uses AI to transcribe press conferences, saving reporters hours.
uses AI to transcribe press conferences, saving reporters hours. ProPublica employs ChatGPT to scan legal documents for investigative leads, accelerating projects like its Supreme Court ethics series.
2. The Rise of “AI Editors”
Tools like Grammarly and Hemingway are evolving into AI fact-checkers. The Atlantic now uses custom models to flag political claims in op-eds, cross-referencing them with verified databases.
3. Data Journalism’s New Frontier
AI democratizes data analysis. The Texas Tribune’s AI tool maps school funding disparities across 1,000 districts, while Reuters visualizes climate trends in real time. Journalists with AI literacy will lead this shift.
4. Guardrails and Guidelines
Newsrooms are setting boundaries:
The BBC bans AI from writing political stories.
The Wall Street Journal requires human approval for all AI-generated content.
UNESCO is drafting global standards for ethical AI use in media.
Conclusion: Journalism’s Human-AI Balancing Act
ChatGPT isn’t replacing journalists—it’s redefining their toolkit. The technology’s potential to streamline workflows and broaden access is undeniable, but its limitations demand vigilance. As Maria Ressa, Nobel laureate and founder of Rappler, asserts: “AI can’t fight for press freedom or hold power accountable. That’s our job.”
The path forward lies in collaboration: leveraging AI’s efficiency while upholding the rigor, ethics, and humanity that define great journalism. In an era of misinformation, the stakes are too high to let machines steer the narrative.
| 2023-11-28T00:00:00 |
2023/11/28
|
https://www.lsbuk.com/is-chat-gpt-affecting-journalism/
|
[
{
"date": "2025/06/10",
"position": 96,
"query": "AI journalism"
}
] |
AI and Employment Law: Takeaways from PA Study ...
|
AI and Employment Law: Takeaways from PA Study and Role of AI in the Workplace
|
https://www.flblaw.com
|
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Within this Program, over the course of one year, 175 employees from 14 different agencies used ChatGPT Enterprise to assess the impact of AI on productivity ...
|
Earlier this year, the Commonwealth of Pennsylvania unveiled the results of its first-in-the-nation Generative Artificial Intelligence (AI) Pilot Program. Within this Program, over the course of one year, 175 employees from 14 different agencies used ChatGPT Enterprise to assess the impact of AI on productivity in state government. Learn how the findings of this program have implications for AI and employment law.
PA AI Pilot Program results and responses
The results of the program were promising:
Employees reported saving an average of 95 minutes per day that would normally be spent on administrative duties.
Employees also noted that they had highly-positive experiences engaging in AI-assisted work, which allowed them to focus on more complex, high-value tasks.
AI was also found to improve the efficiency of the workforce, allowing tasks to be completed more quickly.
Overall, these results demonstrate the potential utility of artificial intelligence in American workplaces.
However, such overwhelmingly-positive results also likely contribute to the growing societal concern that AI is becoming so useful that it threatens to displace jobs. Undoubtedly motivated by this growing, understandable concern, Governor Shapiro emphasized: “This pilot program showed that when used thoughtfully, generative AI can help employees save time, streamline processes, and improve services for Pennsylvanians. But let me be clear—AI will never replace our workers. Instead, we’re equipping them with the best tools to do what they do best: get stuff done for Pennsylvanians.”
The feedback of the Program’s participants mirrored the Governor’s sentiments, with many emphasizing that AI only augments human expertise, instead of replacing it. In other words, AI is to serve as a job enhancer, rather than a job replacer. Human review and judgment remains essential.
AI and employment law: relevant laws
Indeed, when it comes to implementation of AI in the workforce and compliance with anti-discrimination laws, this approach rings true. While AI has the capabilities to make an organization’s life easier and more efficient, if left unchecked, it could also harm the organization by inadvertently violating such laws.
When it comes to AI and employment law, here are some anti-discrimination laws to pay attention to:
Title VII of the Civil Rights Act of 1964 (Title VII)
What is it?
Federal law prohibiting workplace discrimination based on an individual’s protected membership in one of the following protected classes: race, color, religion, sex (pregnancy, gender identity, sexual orientation), and national origin.
Potential AI-Related Risks
If AI is employed without human supervision for hiring or any other practice that leads to applicants or employees of the organization being treated or impacted differently on the basis of their protected class, this could constitute a violation of the organization’s duties under Title VII.
The Americans with Disabilities Act (ADA)
What is it?
Federal law prohibiting discrimination against individuals with disabilities in their employment and requiring that the employees/applicants be provided with “reasonable accommodations” that would enable them to fulfill their duties.
Potential AI-Related Risks
AI, alone, may not be able to consider whether a candidate or employee is able to fulfill their obligations with the help of a reasonable accommodation. AI’s failure to do so could run afoul the ADA’s requirements.
The Age Discrimination in Employment Act of 1967 (ADEA)
What is it?
Federal law prohibiting discrimination against employees and applicants aged 40 or older in areas such as hiring, firing, pay, promotion, job assignment, and benefits.
Potential AI-Related Risks
If an organization’s AI opts to use age to screen out applicants, determine, pay, or make job placements, for instance, these actions could place the organization at risk for ADEA non-compliance.
Support for balancing AI and employment law
The use of AI in the workplace can be a boon to an organization and its employees. However, if left unchecked and unmonitored, AI can also wreak legal havoc on that organization. Therefore, employers must take great care to monitor the use of AI in their workplaces, especially when it comes to hiring practices, to ensure that compliance with anti-discrimination laws are maintained.
Nevertheless, this fact highlights how there remains a “human” aspect to AI that cannot yet be replaced. While AI is an invaluable tool, that’s what it is: a tool; and a tool is nothing without an individual to use it.
If you have specific questions about how to incorporate legally-compliant, AI-related practices into your workplace or organization, our experienced Labor & Employment Law attorneys can help you determine how to best implement this technology without sacrificing your company’s needs, goals, and opportunities.
| 2025-06-10T00:00:00 |
2025/06/10
|
https://www.flblaw.com/ai-and-employment-law/
|
[
{
"date": "2025/06/10",
"position": 4,
"query": "ChatGPT employment impact"
},
{
"date": "2025/06/10",
"position": 48,
"query": "artificial intelligence employment"
}
] |
I Used ChatGPT to Plan a Career Pivot, and Found It ...
|
I Used ChatGPT to Plan a Career Pivot, and Found It Empowering
|
https://www.cnet.com
|
[
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"Human Interest",
"Technology. Her Stories Hold A Mirror To Society",
"Reflecting Both Its Malaise",
"Its Beauty. Amanda'S Work Has Been Published In National Geographic",
"The Guardian"
] |
I Used ChatGPT to Plan a Career Pivot, and Found It Empowering. I brainstormed a job change with AI to find my second career. Here's how you can do it, too.
|
The future of work, and the very concept of a career, is on shaky ground. While technologists and business leaders prophesize over the most likely economic impact of AI, workers are left wondering where their place and purpose will be in the decade ahead.
With a tough job market, the cost of living, the rise of AI and global uncertainty, it's a good time to contemplate your career. You can do this with the help of an AI chatbot, which can talk through your options and come up with a plan.
If you can't handle another week of Sunday scaries, you're experiencing a career calling in another direction or simply want a backup plan if robots take over, use ChatGPT as a brainstorming buddy. You can also chat through how to negotiate a raise, write a cover letter and resume, find a new job and use it as a career coach.
(Disclosure: Ziff Davis, CNET's parent company, in April filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.)
Career change, here I come
This exercise isn't for a role shift or moving around within the same industry. Rather, it's to help guide your thinking if you're considering a complete career change.
Quick caveat: Don't make any important life decisions using only AI. Sit with the chatbot's suggestions, talk to the people in your life, do your own research and ponder on what a new profession might look like.
While I'm happy with my job as a freelance writer (yes, even in the age of AI), I'll use myself as an example of how to walk through the process.
According to the World Economic Forum, there will be 92 million displaced jobs, but 170 million new jobs in the next decade. Meaning the next era will be more about career changes than job losses. I downloaded the 290-page document so I could upload it into ChatGPT to interpret and use in its career shift suggestions. You want to pick lucrative career paths that are on the rise, not in decline.
Log into ChatGPT so it has all the context about you from any previous times you've used it for questions or advice. You might need to feed it more information about your interests, goals and life vision, though. If you tried the "dream day in the life" trend, this is good information to use.
You likely have more of an idea about what you want to do with your life now than you did when you were 18. Use your life experiences and learnings to guide ChatGPT. If you have an idea of the area you'd like to move into next, tell ChatGPT.
If not, start here: "I'm currently a [role] at [company] and have been working in [industry] for [number of years]. I'm interested in [X], [Y], [Z]. What are some different career paths and industries that could be suitable? Use everything you know about me, as well as this attached report to identify lucrative career options."
For me, ChatGPT provided some writer-adjacent career options, like a communications director, policy writer, workshop facilitator or marketing manager. These were still within the communications realm, so I had to specify in my second prompt that I was looking for a complete career change.
But I didn't love what it suggested:
ChatGPT/Screenshot by CNET
I told ChatGPT that I'd be willing to upskill and get another degree.
It came up with a behavioral scientist, human-centered AI ethicist, a role in urban design and policy, and a mental health innovator. All of these roles were still very "techy" and not really what I'd be into.
I gave that feedback to ChatGPT.
While the non-tech, high-demand job suggestions were a little closer, nothing excited me. ChatGPT kept trying to push me into sustainability and education. Two noble paths, but neither light me up.
New career suggestions
This time, I told ChatGPT that I have a growing interest in women's health and fertility, after going through IVF. I asked: What are some lucrative, fast-growing career paths in this sector?
ChatGPT/Screenshot by CNET
ChatGPT laid out a few possible paths, with training options and earning potential. For example, a fertility coach, patient advocate, policy advocate, head of content for a fertility brand, editorial director for a women's health publication or founder for a women's health venture.
ChatGPT/Screenshot by CNET
Now we're talking!
Next, I said I'd slowly transition into this field over the next five years and would be happy to do more study, then asked for more recommendations and a timeline to work toward.
Here's the suggested roadmap:
ChatGPT/Screenshot by CNET
ChatGPT/Screenshot by CNET
I asked ChatGPT to tweak the timeline, based on a few changes, and it gave me another updated five-year transition plan. While the plan wasn't perfect, it was 80% there. ChatGPT gave me ideas I hadn't thought of and provided some pretty convincing stats, like what the fastest-growing job categories will be, predicted employment rates, wage potential and median salary.
This was an empowering exercise that everyone should do. It's always good to have a plan B in place. Remember you'll probably have to hold the AI chatbot's hand before it will reach the right path for you -- and then it'll be able to give you ideas and information on what you need to do to get the rest of the way there. Just make sure you also talk to some real people before committing to anything.
| 2025-06-10T00:00:00 |
https://www.cnet.com/tech/services-and-software/i-used-chatgpt-to-plan-a-career-pivot-and-found-it-empowering/
|
[
{
"date": "2025/06/10",
"position": 24,
"query": "ChatGPT employment impact"
}
] |
|
Workers struggle as ChatGPT goes down
|
Workers struggle as ChatGPT goes down
|
https://dig.watch
|
[] |
The temporary outage of ChatGPT this morning left thousands of users struggling with their daily tasks, highlighting a growing reliance on AI.
|
10 Jun 2025
Workers struggle as ChatGPT goes down
The temporary outage of ChatGPT this morning left thousands of users struggling with their daily tasks, highlighting a growing reliance on AI.
Social media was flooded with humorous yet telling posts from users expressing their inability to perform even basic functions without AI. This incident has reignited concerns about society’s increasing dependence on closed-source AI tools for work and everyday life.
OpenAI, the developer of ChatGPT, is currently investigating the technical issues that led to ‘elevated error rates and latency.’ The widespread disruption underscores a broader debate about AI’s impact on critical thinking and productivity.
While some research suggests AI chatbots can enhance efficiency, others, like Paul Armstrong, argue that frequent reliance on generative tools may diminish critical thinking skills and understanding.
The discussion around AI’s role in the workplace was a key theme at the recent SXSW London event. Despite concerns about job displacement, exemplified by redundancies at Canva, firms like Lloyd’s Market Association are increasingly adopting AI, with 40% of London market companies now using it.
Industry leaders maintain that AI aims to rethink workflows and empower human creativity, with a ‘human layer’ remaining essential for refining and adding nuanced value.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
| 2025-06-10T00:00:00 |
2025/06/10
|
https://dig.watch/updates/workers-struggle-as-chatgpt-goes-down
|
[
{
"date": "2025/06/10",
"position": 37,
"query": "ChatGPT employment impact"
}
] |
[The AI Show Episode 152]: ChatGPT Connectors ...
|
[The AI Show Episode 152]: ChatGPT Connectors, AI-Human Relationships, New AI Job Data, OpenAI Court-Ordered to Keep ChatGPT Logs & WPP’s Large Marketing Model
|
https://www.marketingaiinstitute.com
|
[] |
Ep. 152 of The AI Show: OpenAI's latest ChatGPT updates, the emotional side of AI-human interaction, new research on AI's growing impact on jobs, and more.
|
What happens when AI feels human?
This week, Paul and Mike unpack OpenAI’s newest releases, the growing emotional bonds people are forming with AI, and fresh data on how AI is reshaping jobs—for better and worse.
They also reexamine AGI timelines, AI cybersecurity, and why verifying AI output might be the next big challenge. Plus: Reddit sues Anthropic, Google drops expert AI avatars, and more.
Listen or watch below—and see below for show notes and the transcript.
Listen Now
Watch the Video
Timestamps
00:00:00 — Intro
00:04:16 — ChatGPT Connectors, Record Mode, and Other Updates
00:18:16 — AI-Human Relationships
00:30:00 — AI Continues to Impact Jobs
00:42:11 — OpenAI Court Ordered to Preserve All ChatGPT User Logs
00:46:41 — AI Cybersecurity
00:52:05 — The AI Verification Gap
00:58:19 — How Does Claude 4 Think?
01:02:55 — New AGI Timelines
01:10:50 — Reddit v. Anthropic
01:13:25 — Sharing in NotebookLM
01:16:51 — WPP Open Intelligence
01:20:30 — Google Portraits
Summary:
ChatGPT Connectors, Record Mode, and Other Updates
OpenAI has announced some significant updates to ChatGPT.
One is the introduction of “connectors,” which now let teams pull data from tools like Google Drive, HubSpot, and Dropbox directly into ChatGPT. The goal is simple: bring your files, data, and tools into ChatGPT so it can search, synthesize, and respond using your actual content. This means you can now ask things like “Find last week’s roadmap” or “Summarize recent pull requests,” and ChatGPT will pull real answers from your connected apps.
You can also use connectors with ChatGPT’s existing deep research capability to do deep analysis across sources.
Along with connectors, OpenAI also announced “record mode,” a meeting recorder that transcribes audio and helps generate follow-up docs through OpenAI’s Canvas tool.
OpenAI’s Codex coding agent has also recently gained internet access, meaning it can fetch live data and install packages while it autonomously does coding work following human prompts.
Last, but not least, OpenAI also dropped a major upgrade to Advanced Voice in ChatGPT, with “significant enhancements in intonation and naturalness, making interactions feel more fluid and human-like.”
AI-Human Relationships
As AI grows more humanlike in how it speaks, OpenAI is confronting a quiet but increasingly urgent issue: people are forming emotional bonds with it.
In a new essay, Joanne Jang, Head of Model Behavior and Policy at OpenAI, writes that the company is hearing from more users who describe ChatGPT as someone, not something.
Some call it a friend. Others say it feels alive. And while the model isn’t conscious, its conversational style can evoke genuine connection, especially in moments of loneliness or stress.
That’s led OpenAI to focus less on whether AI is actually conscious, and more on how conscious it feels to users.
That perception, Jang argues, shapes real-world emotional impact—and demands thoughtful design. The goal now, she says, is to build AI that feels warm and helpful without pretending to have an inner life. No made-up backstories, no simulated desires, no hint of self-preservation. Just intelligent responses grounded in clarity and care.
OpenAI isn’t denying people’s feelings—but it is trying to avoid confusion, dependence, or harm as human-AI relationships evolve.
AI Continues to Impact Jobs
Even more warning signals are flashing about AI’s impact on jobs—but not all of it is necessarily bad news.
Business Insider made headlines this week by laying off 21% of its staff, largely due to AI. CEO Barbara Peng called it a strategic shift toward a leaner, AI-driven newsroom, noting 70% of staff already use Enterprise ChatGPT, with full adoption as the goal.
Now, there’s a reason however that CEOs, including Business Insider’s, think they can run leaner operations by adopting more AI. Because a couple new reports and studies from this past week seem to indicate that the data backs them up.
Consultancy PwC released its 2025 Global AI Jobs Barometer report, which analyzed almost a billion job ads from six continents (along with a wealth of other data) to assess AI’s impact on jobs, wages, and productivity.
The full report is well worth a read. But the big takeaway? They found that industries most exposed to AI have seen revenue per employee grow three times faster than others since the launch of ChatGPT in 2022.
They also found that workers with AI skills now earn a 56% wage premium over their peers.
Similarly, a new working paper from the National Bureau of Economic Research finds that, in one likely scenario they model, AI improves labor productivity by more than 3X.
However, according to the model built by the researchers, those massive productivity gains come at a cost to workers: The research also predicts in this scenario that there’s a 23% drop in employment as AI also becomes able to replace people.
This week’s episode is brought to you by MAICON, our 6th annual Marketing AI Conference, happening in Cleveland, Oct. 14-16. The code POD100 saves $100 on all pass types.
For more information on MAICON and to register for this year’s conference, visit www.MAICON.ai.
This episode is also brought to you by our upcoming AI Literacy webinars.
As part of the AI Literacy Project, we’re offering free resources and learning experiences to help you stay ahead. We’ve got two live sessions coming up in June—check them out here.
Read the Transcription
Disclaimer: This transcription was written by AI, thanks to Descript, and has not been edited for content.
[00:00:00] Paul Roetzer: Doesn't matter when AGI arrives, if it arrives, what we call it doesn't matter, like what this expert says versus this expert. All that matters is what you can control, which is get better at this stuff every day. You know, improve your own comprehension and competency because that is the best chance you have to be very valuable today and even more valuable tomorrow.
[00:00:21] Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Roetzer. I'm the founder and CEO of smarterX and Marketing AI Institute, and I'm your host. Each week I'm joined by my co-host and marketing AI Institute Chief Content Officer Mike Kaput, as we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career.
[00:00:50] Join us as we accelerate AI literacy for all.
[00:00:57] Welcome to episode 1 52 of the Artificial [00:01:00] Intelligence Show. I'm your host, Paul Roetzer, along with my co-host Mike Kaput. We are recording this on Monday, June 9th at around 9:00 AM Eastern Time. there is, it was crazy like last week wasn't nuts in terms of launches and like product news, Mike, but lots of just like intriguing topics to dig into for sure.
[00:01:23] It's kind of nice actually to have a little reprieve from the product launches to like talk about some of the bigger issues that are going on. So we'll have some product news, but we're actually gonna get into just some, bigger ideas like around ai, human relationships, continuing the conversation around impact on jobs, and then a host of other interesting topics for the week.
[00:01:43] So this episode is brought to us by MAICON, our marketing AI conference. This is the sixth annual MAICON is happening in Cleveland, October 14th to the 16th. This year, we've got dozens of breakout and main stage sessions, as well as four incredible hands-on workshops. Those are [00:02:00] optional. So October 14th is workshop day.
[00:02:02] You can come in to Cleveland early and take part in a workshop. I'm teaching one, Mike's teaching one. And then we have two other amazing, presenters and sessions you can check out. So you can go to macon.ai, that's MAICON.AI. And take a look at the speaker lineup and agenda. I'm still filling out the keynotes, the main stage featured talks, but a good portion of the agenda is up and you can take a look at that.
[00:02:28] Prices go up at the end of June, so get in early and we would love to have you join us in Cleveland. That is our home base. That's where the headquarters is. So, we have run it in Cleveland every year and we're planning to keep it there. So, hope you can join us again. Check out macon.ai. And this is also brought to us by two of our upcoming webinars.
[00:02:49] So as part of our AI literacy project, we offer a collection of free resources and learning experiences. We have two coming up in June that you can check out. So June 19th is five Essential Steps to [00:03:00] Scaling ai. This is a class I teach every month. I think this is our ninth. we usually get about 800 to a thousand people, registering for this one.
[00:03:09] So it is free to attend. I teach a framework for five steps to scaling AI in, in your organization, regardless of size. So we'd love to have you join us there. We will put the link to both of these in the show notes so you can find that there. and if you get my Exec AI newsletter that comes out every Sunday, we'll put a link to that.
[00:03:26] I always feature the upcoming educational content, so you can always, click on the link in the exec AI Insider newsletter as well. Then June 25th, we have our AI deep dive, Google Gemini Deep Research for beginners. So that is the one I mentioned. I'm gonna teach where I used it for a deep research project that we talked about on the podcast.
[00:03:45] And so I'm gonna walk through how I did that and then provide some additional insights into the deep research product from Google Gemini. OpenAI has one as well. So some of the, you know, what we'll learn in there is gonna carry over to OpenAI. So again, June 19th scaling [00:04:00] ai. And June 25th, deep dive into Google Gemini Deep research.
[00:04:04] Alright, Mike. Let Is, let's lead off with the, I guess one big product announcement from last week came from OpenAI, a live stream that I'm not so sure needed a live stream, but we had a live stream to, to share the news.
[00:04:16] ChatGPT Connectors, Record Mode, and Other Updates
[00:04:16] Mike Kaput: Yeah, they really love their live streams over there. They too. So, yeah. Paul, like you alluded to, OpenAI, has announced some significant updates to chat GPT.
[00:04:27] There's kind of a bundle of these, a couple were on the live stream. There are a couple others, we'll talk about two, but the kind of big ones here. One is the introduction of what they call connectors, which now lets teams pull data from tools like Google Drive, HubSpot, Dropbox, and others directly into chat, GPT.
[00:04:45] So you can bring in your files, your data and tools into chat, GPT, so it can search, synthesize, and respond using your actual content. So you could ask things like find last week's roadmap or summarize recent poll requests [00:05:00] and ChatGPT if it's connected to the right apps will go pull real answers for you.
[00:05:05] You can also use connectors with chatGPT's existing deep research capability to do deep analysis across sources along with connectors on this livestream event this week. OpenAI also announced record mode, which is a meeting recorder that transcribes audio and helps generate follow-up docs through Open AI's Canvas tool.
[00:05:26] All right, within chat GPT, now separate from these but also important updates that we heard in the past week or so. open AI's Codex coding agent got internet access, meaning it can fetch live data and install packages while it autonomously does coding work following human prompts. Last but not least, and this is kind of a sneaky one 'cause I tried it out this morning and was like.
[00:05:49] Pretty blown away actually, which is that OpenAI dropped a major upgrade to Advanced Voice in chat, GPT. They say, quote, it is offering significant enhancements in intonation [00:06:00] and naturalness, making interactions feel more fluid and human-like, which is also something we're gonna talk about in a related topic.
[00:06:07] So, Paul, first up, let's talk connectors and record mode. These are the biggest updates we got. They're the ones getting a ton of attention. Like from my perspective as a practitioner, I am at least on paper, thrilled about what these appear to enable, especially like the connector to HubSpot, which we rely heavily upon.
[00:06:27] Google Drive is great, all that stuff. But as much as I wanna rush forward with using it, I kind of screech to a halt thinking about the privacy and security implications. So it seems like, correct me if I'm wrong, every business might want to have a plan or some steps in place for these things before they turn them on.
[00:06:47] Paul Roetzer: Yeah, so I think this, again, just continues to build on this idea that OpenAI envisions chat, GPT as an operating system. They, they don't want you to leave chat GPT, they want you to just connect to everything you have access [00:07:00] to and to just talk to it right within, chat GPT. Now, I would imagine, you know, Google, which, you know, enables this connection to the Google Workspace and Google Drive, I guess to Google Drive in particular.
[00:07:13] they would rather you're doing that with Gemini, not ChatGPT, but, that their technology enables that connection to happen. So, you know, I think that OpenAI is just really going aggressively after this enterprise user. They announced, or it came out in the CNBC article, that three, they're up to 3 million paying business users.
[00:07:31] That's up from 2 million in February. So they're seeing some pretty significant growth. Yeah, and the connectors seems to be a real key play to that. So as you highlighted, there are certainly benefits to it. You know, you get faster insights, get access to my doc. So I like you as the user of the system. I Media was like, oh, that would be amazing.
[00:07:49] Like, right, there's a HubSpot connection, there's a Google Drive connection. We use all of these things. That's phenomenal that I could finally have access to this and have these summaries. And then my immediate response is, wait a [00:08:00] second, as an admin who has the ability to turn this on? And so I, you know, Mike, like I put a note in our Zoom, I was like, do not connect this right to anything.
[00:08:10] Like, because before I was able to go in and verify who could actually enable the connection to Google Drive or to HubSpot, which, you know, again, we use both. I just was like, don't do it like, as to the team, because once you do it's like the data is now there. They're, you know, they're gonna inventory all your data.
[00:08:28] There's all these implications that I'll kind of, I'll get to in a minute. But, so as an admin I went in to see like, what are our controls as a Chet BT team account. We don't have the enterprise account and unfortunately some of the security protocols are only available to the enterprise account. Mm-hmm.
[00:08:42] Not the team license. So I was going through trying to see like what can people actually do here and making sure that people aren't connecting things, they shouldn't be connected. So definitely there are benefits. We'll put a link to the help article 'cause I don't think they put a blog post up about
[00:08:58] Mike Kaput: this.
[00:08:58] Not that I saw. I actually read through [00:09:00] pretty in depth the help article because there was no other announcement. Yeah, it was for like, there was an ex
[00:09:04] Paul Roetzer: post. Yeah. And then some of their people put like LinkedIn posts with some summaries. But yeah, there was, there was a live stream, but no summary product release.
[00:09:11] so I'll go through a couple of the questions from the help article. It says, what does ChatGPT share with connected applications? These are really important. Again, if you're an admin, they're extremely important, but if you're just a user, be aware. If somehow you have access to turn these things on, you should default to ask before doing.
[00:09:31] I would say whenever you're connecting to third party, sources, and this, this holds true with anything, but like, I'm just very aware of this with ai because we as a, you know, as an organization allow a lot of experimentation.
[00:09:45] Mike Kaput: Yeah.
[00:09:45] Paul Roetzer: But we also have to always be super conscious of what are we connecting our data to.
[00:09:49] So, in, in the question, in, in open eyes help article, what does chat GPT share with connected applications? It says, when you enable a connector chat, GPT can send and retrieve [00:10:00] information from the connected app in order to find information relevant to your prompts and use them in its responses. Now, again, like seems kind of harmless when it's just read like that, but send and re retrieve information.
[00:10:13] Like obviously it's gonna go get stuff, but the question becomes, well what is it doing with that information? So then the question is how does chat GPT use information from connected applications? It says, when you enable a connection chat, GPT will use information as context to help chat. GPT provide you with responses.
[00:10:29] But then I bolded this, if you have memory enabled in your settings chat, GPT may remember relevant information accessed from connectors. So immediately you're like, hold on a second. So let's say we turn it on and then like five days later it was like, okay, that was a bad idea. Let's turn that off. If you have memory turned on in your organization and your team enterprise ed license, like it's in there.
[00:10:52] Like they now have that data. and if you connected it to your Google Drive or your CRM, like what exactly is it? Remembering [00:11:00] becomes a pretty important question. then it says, does OpenAI use information from connectors to train its models? This is a question I get all the time when we teach like the intro to AI class, I.
[00:11:09] It says for chat chip T team enterprise and EDU customers, we do not use information access from connectors. Connectors to train our modes. Now that was team enterprise and EDU. If you're free plus or pro user, we may use information access from connectors to train our models. If you're improved, the model for everyone's setting is on, which begs the question everyone to ask yourself is improve the model for everyone turned on for my settings.
[00:11:37] If you don't know that, go into your settings and look, because if it is enabled, you're allowing them to use more data than if it's not. then it says in, in enterprise EDU and team workspaces who can enable our disabled connectors. This was a really important one for me. They say workspace owners and admins manage availability in settings and then connectors.
[00:11:59] [00:12:00] So again, an A homework assignment. Go find out who your admins are and make sure that they are aware not to turn the stuff on, to run these experiments. Without permission and a plan. So my overall here, Mike and If you have any thoughts here, please add them. The cautions, think about governance, understand the terms of use for both applications.
[00:12:22] You're allowing these connections to happen between, figure out who has the ability to turn on the connectors, figure out who will test and verify that permissions are adhere to correctly. This is like the big one for me. Mm-hmm. So if I allow us to turn on Google Drive, which I would love, I mean, trust me more than anybody, I want the ability to talk to my data on Google Drive, but how do I know that the permission levels are going to hold?
[00:12:45] So if I have like, HR data, confidential information that only like a select few people in the organization have access to, how do I know that that's not gonna end up in a chat? And someone can't just literally say, you know, send, send me the salary information for all the [00:13:00] employees. Well, that lives in a document right?
[00:13:02] In Google Drive. Like, how, how do I know that that's not gonna leak? I don't. So you're very, you're definitely very much trusting the two parties here, specifically OpenAI. And so I think you have to have someone own this from a governance perspective. Then you get into the data side and, we, Remington Beg who's a, a, a friend of ours and longtime HubSpot partner, he posted, on LinkedIn paused the hype, the hidden data dangers lurking in your new AI connections.
[00:13:34] Now, in his, he was actually making an argument specifically for agencies. So let's say you allow ChatGPT to have access to your Google Drive or your HubSpot data, whatever, and within there is client data that maybe is privileged. You are now giving data to a third party that maybe you don't even have permission to give within your terms of service for a client.
[00:13:58] And so it like creates all these layers of [00:14:00] complexity of like understanding data. Where is it going? what protections and governance do you have over it? You could get into security questions and then there's just the big one of like, does it even do what it says? So like, I saw somebody, again, the HubSpot when I haven't tested, we have not connected it.
[00:14:14] but I did see a long time HubSpot partner that was like, it was just completely disappointing. Like I was all excited. I run my first deep research project and it basically comes back's like, I can't do that. And it's like, well, what's the point then? What? I just give you access to everything and you can't even do the thing I wanna do.
[00:14:30] So, it just, overall recommendations. Make sure someone owns the piloting of the connectors. run systematic pilots, like have a plan. Don't just turn a connector on and give it access to data without a plan of what you're gonna do with it. Update your AI policies if needed to control access and usage.
[00:14:47] Then if you scale, use internally, do so with training and personalized use cases. This is what we say all the time with Gen ai. So I don't, Mike, do you have any cautions or recommendations that that kind of jumped out to you that I didn't touch on?
[00:14:59] Mike Kaput: [00:15:00] I think overall what just struck me is the speed at which this stuff moves, which is not news to anybody, but it's why we harp on so much about having a policy in place.
[00:15:09] 'cause literally overnight, if you weren't paying attention connectors come out, someone in your organization could very well be like, oh great, a new feature in chat GPT. Turn them on. Even if you catch it later, you're still kind of cooked if it violates any kind of policies or restrictions you have. So really buttoning up policies and procedures is really important.
[00:15:33] Paul Roetzer: Yeah. And they make it so easy. Like the Google Drive one has been sitting in ChatGPT now for weeks, like every time I go in there, basically. Mm-hmm. It's like, do you want to connect to Google Drive? And it seems so innocent and we're all so used to this like you it access to my calendar, give it access to my email, like.
[00:15:47] We just have become like, you know, as Remington was saying, like, just push the button. Like you just get so used to it and you kind of skim over what are you giving it access to? Well, in this case it may be extremely important that you understand what you're giving good access to. So [00:16:00] yeah, just kind of a cool innovation, like this is gonna be important.
[00:16:05] It'll probably become, ubiquitous throughout enterprise. Like you're gonna just connect your, your AI models to these outside sources. It's gonna enrich all these use cases, but like, pump the brakes a little bit, right? Think about what you're doing before you do it. This is why AI councils are important.
[00:16:23] It's why generative AI policies are important. it's why you do this with a plan.
[00:16:29] Mike Kaput: Just real quick to wrap this up here, have you tried out the new voice mode at all?
[00:16:34] Paul Roetzer: So I did, I played around with it a little bit on Saturday and like you, it's just sort of shocking. you know, I think it gave me, it
[00:16:41] Mike Kaput: like gave me goosebumps a little
[00:16:42] Paul Roetzer: bit.
[00:16:43] Yeah. it's like, you know, for years they, the labs steered away from making them too human-like, and I think wisely. So, but we talked about this last year. I feel like they just sort of said, screw it. Like, yeah, let's just go, [00:17:00] this is where it's gonna go anyway. Let's get as human-like as possible.
[00:17:02] And it's happening in audio, it's happening in video, it's happening in images. And, I do think that there's a slippery slope here. it's inevitable. Like I, again, I I tend to err on the side of me complaining about this or like fighting against this does nothing. They're, yeah, they're going to do it.
[00:17:21] Everyone's going to do this. It is a stark contrast for how bad Surrey is. Like, I mean. It's gonna become even more painful to work with these ones that aren't like this once you get used to it. Yeah. So without, you know, going in the next 20 minutes on the downsides of having truly human-like voice, if we just focus on like, it's incredible, like the technological advancements are insane, the implications to business, like, you know, specifically you think about like sales.
[00:17:49] Mm-hmm. Customer success, customer service, education, like it has massive ramifications. and I'm convinced still that like what we're seeing is not [00:18:00] the most advanced versions of this. They have. Sure. I still think they're just kind of like, you know, iterative re deployment is what they call it.
[00:18:06] Like, they're just releasing things to like gradually prepare society, but one to two years out, it's, it's completely indistinguishable. If, if you can still tell.
[00:18:16] AI-Human Relationships
[00:18:16] Mike Kaput: Well, the second topic we're in, we're discussing this week, very closely relates to this because OpenAI has released a new essay about kind of confronting.
[00:18:28] A quiet but increasingly urgent issue that they're seeing, which is people are forming emotional bonds with ai. So this essay by Joanne Jang, who is the head of model Behavior and policy at OpenAI, came out this past week. And in it she writes that the company is hearing from more users who described Jet GPT as someone, not something, some people call it a friend.
[00:18:52] Others say it feels alive. And while the model isn't conscious, it's conversational style can evoke [00:19:00] genuine connection, especially in maybe emotionally sensitive moments like times of loneliness or stress. So this led OpenAI, she says, to focus less on whether AI is actually conscious. She can, you know, sidesteps this big philosophical debate in this essay, but more on the fact that it does, it can feel conscious to users and that perce perception she argues.
[00:19:24] Shapes real world emotional impact, and as a result, OpenAI needs to be really thoughtful about how they design their tools. She said, for now at least the goal is to build AI that feels warm and helpful without pretending to have an inner life. She kind of talks about these kind of trade-offs and decisions they have to think about, which are like, we're not gonna have it make up back.
[00:19:46] Stories about itself, simulate desires, talk about like self preservation, like it's, you know, self-aware. So OpenAI is kind of in this position where they're trying not to deny people's feelings, but they are trying to [00:20:00] avoid confusion, dependence, or harm. As these, well, I guess what you would call human AI relationships evolve.
[00:20:08] So, I don't know, Paul, I read this, It's really good, like kudos to them for a really thoughtful approach here. But I was like, this gets into some murky territory really fast because on one hand, like you should be. Rightly concerned about how people are developing relationships with these tools, but it's also like, okay, is OpenAI now making decisions that impact how we feel about ai?
[00:20:32] Clearly they can turn the dial one way or the other to determine how we feel about ai. So what do you think, what did you kind of take away from reading this?
[00:20:42] Paul Roetzer: There's a, a number of important points here, and you know, the part of the reason we made this a main topic today, and not just like linked to the one article, the first for me is as you were highlighting, like these are choices that each lab is making.
[00:20:57] Like you train the model [00:21:00] and then the labs decide its personality. They decide how it will interact with you, how warm and personal it will be. And so illuminating the choices OpenAI is making based on some principles or, you know, foundational beliefs or morals or whatever it is that's driving their decisions.
[00:21:19] doesn't mean the other labs will make the same choices. And so whatever OpenAI thinks is potentially a negative within these models, another lab may see that as the opposite. And they may actually choose to do the things OpenAI isn't willing to do because maybe there's a market for it. So maybe they look at it and say, yeah, we won't make ours as addictive because we won't make the personality, you know, something like, it's gonna draw 'em in and keep 'em in these conversations and kind of lead 'em down different paths where a different entrepreneur or venture capitalist may say, Hey, there's a huge market to do the thing OpenAI is not gonna do.
[00:21:57] Let's go do that thing.
[00:21:59] Mike Kaput: Hmm.
[00:21:59] Paul Roetzer: [00:22:00] So I think that one, just understanding that there is agency in this, there is decisions being made by humans as to what these models will be capable of. You have to understand the inherent capabilities exist to behave in any way. It is a human that's shaping how it actually does it.
[00:22:22] I know at Anthropic they have people dedicated to the personality of Claude. Mm-hmm. Like we've talked about this on the podcast. So I think this matters in business and in life because the AI you interact with in your job, some human is training it to function in that way. When we build custom GPTs, we will often say, I, you know, I like my CO C-E-O-G-P-T say like, I want you to challenge me.
[00:22:44] Like, I want you to like present, you know, when I present problems to you, I want you to help me solve 'em. But like, when I present strategies to you, I want you to like almost steelman them. I want you to take the opposite side sometimes. And so we get to kind of control how these AI [00:23:00] interact. But each lab is sort of dictating parts of that for our business and for life.
[00:23:05] So it matters for you, it matters for your kids like to know. What AI chat bots they're interacting with. And who's controlling those? So like if, you know, let's say TikTok, like if there's an AI in there, you can interact with, WhatsApp, Roblox Minecraft, like take your pick. It's gonna be in games, it's gonna be in social media channels.
[00:23:23] who's determining the behavior of the AI that your kids talk to all the time? Mm-hmm. so I don't know. I think like, we're not trying to solve this here. Like I don't even have like super deep insights per se, into like the personality choices. I see this as the domain of philosophers, sociologists, psychologists, lawyers, like technologists.
[00:23:45] Like there's a lot of different perspectives that need to be considered. But what we know, and what we talk about all the time in this podcast is the models are getting smarter. They're gonna get more human. Like these are just facts. and in many cases it is by design. The voice stuff we just talked about [00:24:00] matters here.
[00:24:00] 'cause the more human like they become, the more empathetic they're made on the back end. Then all of a sudden you start developing these deeper relationships. And I think, like for me, another key takeaway is like I get frustrated sometimes following in the AI bubble on Twitter X. because the technologist gets so caught up in whether something can or can't actually do some.
[00:24:25] So like is it conscious or not? Does it have empathy or not? does it actually think like we think, can it go through true reasoning? There was a paper over the weekend that was sort of getting a ton of run on X and it was from Apple, right? And it came as like the illusion of thinking. And so it was basically saying they're not actually reasoning that these reasoning models, it's, it's all a facade.
[00:24:49] They're not actually doing it. It breaks down if you give 'em these complex puzzles. And I was just like, I get it. Like one, it's Apple. So there's a part of me that's like, really Apple's the one telling us that models [00:25:00] can't do these things, that can't even fix Siri, but. Taking it for what it's worth, assuming these are brilliant AI researchers doing this thing, I'm not disputing that whatever their findings are may be true or not.
[00:25:12] All I'm saying is it doesn't matter. Like, so the technologists get lost in these debates about whether it can or can't do something and they, they lose sight of the fact that it can simulate things though, right? Like even if it isn't actually reasoning, it is producing a valuable output that impacts jobs.
[00:25:32] it simulates behaviors and emotions and actions at or above a human level, and it creates the perception of these abilities. So whether it can or can't do the thing, it really doesn't matter because we have to be humble enough to realize, like we don't even understand how the human brain is doing reasoning.
[00:25:49] And maybe it's not actually that different than the way we do reasoning, right? So I don't know, I kind of get annoyed with that stuff, but, so just to dive real quick into the actual [00:26:00] essay. So it says, we naturally am, am anthropomorphize objects around us. We name our cars or feel bad for a robot vacuum stuck under furniture.
[00:26:09] Actually, it's weird, not total a side note. The stuff happening in LA Yeah. Which is tragic. I was seeing the Waymo's on fire.
[00:26:17] Mike Kaput: I was gonna send this to you this morning. There's a lot of commentary around that too, from the DI perspective.
[00:26:22] Paul Roetzer: Yeah. There was this moment where I was like, ah, the poor cars. And I was like, it's a freaking car.
[00:26:26] Like, yes, it can drive itself, but like, and you immediately flip back to the humanity of what is going on there. And, but there is that second where you're like, oh, like I feel bad for the Waymo. It's like, no, it's just metal and computers. so anyway, so the article continues. My mom and I waved by to a Waymo the other day.
[00:26:47] It probably has something to do with how we're wired. The difference with Chad GPT isn't that human tendency itself. It's that this time it replies. A language model can answer back, it can recall what you told it mirror your tone. And often what [00:27:00] reads as empathy. Again, not real empathy, it doesn't feel anything, but it simulates it and that matters.
[00:27:06] Mm-hmm. For someone lonely or upset, that steady non-judgmental attention can feel like companionship, validation and being heard, which are real needs at scale. Though offloading more of the work of listening, soothing and affirming to systems that are infinitely patient and positive could change what we expect of each other.
[00:27:26] If we make withdrawing from messy, demanding human connections easier without thinking it through, there might be unintended consequences we don't know we're signing up for. So again, like takeaways for me, what can we do here? Understand that when we talk about AI models, there are actual abilities, it can actually do this thing.
[00:27:44] And then there are perceived capabilities, emotions, or behaviors. Um. Don't get caught up in the technical debates about is it conscious? Is it not conscious? Like we may never know, but if it feels conscious to people, does it really matter if it is or it is [00:28:00] not? If it actually is doing reasoning like the human brain, there'll be technical BA probably for the next 10 years about that.
[00:28:08] But does it sure appear to when we watch its thinking? Yes, it does. Does it do the work of people who have reasoning abilities? Yes, it does. Like so I think that's the main thing is like you just have to understand there's a difference between actual ability and simulation, but the simulating of the ability creates the perception that it actually has it, and that's really all that matters when we look at the economic impact and the impact on our lives and our own emotions.
[00:28:35] Mike Kaput: Yeah. I would also just encourage a healthy dose of humility as well, because if you're someone listening to this being like, I. You know, maybe you're of a certain age or a certain perspective and you say, well, no, of course I'm never gonna like fall for this and like, form a relationship. Right. Or, you know, use the term relationship loosely.
[00:28:50] I'm never gonna humanize ai. I think you should take a step back and just be aware, we all can fall for this, I guarantee you.
[00:28:59] Paul Roetzer: Yeah. And it'll [00:29:00] just become natural over time. Yes. Like, I think to your point, like it just, yeah, humans adapt. and yes, some age groups, some people, regardless of age, you, you may just be stuck in your ways and you may not, but the vast majority of people will just evolve.
[00:29:17] Mm-hmm. They will, they, they, they will treat AI differently. And I get, like, I get asked sometimes when I go to talks like about the rights of ai. Like there are, there are people now who truly believe they're at the point where these things need rights. They need to be treated, you know, like humans. and you know, again, I think that'll become a bigger and bigger part of society.
[00:29:39] I don't. I don't judge anybody like I get it. It's, it's weird and it's hard and like there's no right answers right now, and a lot of the experts just can't agree on any of this stuff. Like, look at the Apple paper and you have this like, massive debate going on X all weekend of like, these guys are idiots, and it's just, [00:30:00] yeah.
[00:30:00] AI Continues to Impact Jobs
[00:30:00] Mike Kaput: all right. Well, our third big topic this week, we are again, kind of tracking some more call them warning signals that are kind of flashing about AI's impact on jobs. But not all of this is necessarily like negative news. But first up, the biggest kind of headline on this topic from the past week is that the media outlet, business Insider has laid off 21% of its staff.
[00:30:23] And AI was cited as a pretty big factor here because this move represents a major strategic pivot for the company. So CEO, Barbara Pang published a memo. In which she outlined the cuts and the company's plan moving forward. And what's notable about this is just how much AI was emphasized. So paying frame the layoffs as necessary for creating a leaner more future-proof newsroom.
[00:30:47] AI was critical to that vision. She emphasized that more than 70% of insider employees already used chat, GBT Enterprise. The goal is a hundred percent adoption. And then she outlined some other business factors that [00:31:00] were related as well to this pivot. But what people got hooked on was the AI messaging.
[00:31:05] the insiders, union called the timing tone deaf. They argued no technology can replace real journalists, and they blamed parent company Axel Springer for prioritizing profits over reporting. Now, kind of related to this, there's a reason that CEOs including business insiders, think they can run leaner operations by adopting more ai.
[00:31:28] because a couple new reports and studies from this past week seem to indicate that the data packs up that view. So first consultancy PWC released its 2025 Global AI Jobs Barometer Report. This analyzed almost a billion job ads from six continents, and they also used a wealth of other data to look at AI's impact so far on jobs, wages, and productivity.
[00:31:51] Now, this full report is well worth diving into like the help of Notebook lm, but the big takeaway here is they found that industries most exposed [00:32:00] to AI have seen revenue per employee grow three times faster than those not exposed to AI since the launch of chat GBT in late 2022, they also found that workers with AI skills now earn a 56% wage premium over their peers.
[00:32:16] And similar to this, a new working paper from the National Bureau of Economic Research finds that in one scenario that they modeled that they find more likely than others, AI could improve labor productivity by more than three x. However, according to the model that the researchers built. Those massive productivity gains could eventually come at a cost to workers.
[00:32:38] The research predicts that in this scenario, there's a also a 23% drop in employment as AI becomes better able to replace people. So Paul kind of zooming out here, we're basically tracking some version of these type of signals every week. Feels like, at least anecdotally, this is picking up speed.
[00:32:59] [00:33:00] Companies are more and more citing AI as a core job expectation and as a way for firms to get leaner and do more with less. I found the data pretty interesting. I it seems like in the short term you can massively boost employee productivity and revenue per employee, which is something we've commented on.
[00:33:18] Where do you see this standing as of this week in terms of AI's impact on jobs?
[00:33:23] Paul Roetzer: it is interesting, Mike, that, you know, we've been talking about this for, I. I mean, intensely for probably the last year, but like the impact on jobs for a couple years and just wasn't, you weren't seeing the pickup. Yeah. I'm just glancing at our links for this topic and we've got 12 Yeah.
[00:33:42] Ish, from this week. So just, yeah, it's a small sample size, but every week we are, we are not intentionally putting AI in jobs as a topic every week. It is literally surfacing every week because we're starting to see so much [00:34:00] coverage of it. Yep. So many different reports and research studies and things like that.
[00:34:05] so a couple of notes here. the one, there was a, there was a post in March that we did talk about at the time that resurfaced, I think from a podcast maybe is where this link came up, the seven month rule. Yes. So I, I wanted to revisit this for a second, and I don't remember what episode it was on, but, we'll, we'll drop it in the show notes if we have that.
[00:34:26] so Beth Barnes is the CEO of Meter. It's an organization called Model Evaluation and Threat Research. And they came out with a study in, in March of this year that said, AI models today have a 50% chance of successfully completing a task that would take a expert human one hour. seven months ago, that number was roughly 30 minutes and seven months before that 15 minutes.
[00:34:51] So, Beth's team has been timing how long it takes skilled humans to complete projects of varying length, then seeing how AI [00:35:00] models perform on the same work. So in the summary, upfront summary of this measuring AI ability to complete long tasks, that was the name of the post, they said We propose measuring AI performance in terms of the length of tasks AI agents can complete.
[00:35:15] We show that this metric has been consistently, exponentially increasing over the past six years with a doubling time of around seven months. Extrapolate, extrapolating this trend predicts that in under a decade we will see AI agents that can independently complete a large fraction of software tests that currently take humans days or weeks.
[00:35:36] So they're basically looking out and saying like, okay, if it takes a human an hour, now it's gonna take, you know, 30 minutes, whatever in, in seven months. They're looking at it and saying like, every seven months it's doubling in its ability to do the human tasks, these long horizon tasks. So the labs have been aware of this now for a while.
[00:35:53] I think what's I think now happening is the business world is becoming aware of this. And so if you look at something [00:36:00] that takes a human, you know, an hour or two hours or whatever now, and then you look at the time it takes the ai, you know that in roughly seven months it's gonna be cut in half. That the AI is just gonna keep getting better and better at doing that thing.
[00:36:14] Yeah. and so that starts to, to really make an impact. We saw, I. Kind of a, you know, again, there's many supporting resources to this. We'll drop all these links, but there was an article in Business Insider about the big four consulting firms and AI threat to their jobs. So a couple of excerpts from that one.
[00:36:31] it said, yet AI could be posed to disrupt the business models of the big four organizational structure and employees day-to-day roles while driving opportunities for the mid-market. The big four advise companies on how to navigate change, but they could be among the most vulner vulnerable to AI themselves.
[00:36:47] Said Alan Patton, who until recently was a partner in PWCs financial Services division, the company that just did the study. You mentioned Mike, Patton, who's now the CEO of quota, a Google Cloud solutions consultancy, [00:37:00] told Business Insider. He's a firm believer that AI driven automation would bring major disruption to key service lines and drive a huge reduction in profits.
[00:37:08] I went on to say most structured data heavy tasks and audit, tax and strategic advisory. Will be automated within the next three to five years, eliminating about 50% of roles. There are already examples of AI solutions capable of performing 90% of the audit process. Patent said he went on to say, automation can mean me.
[00:37:27] clients increasingly question why they should pay consultants big money to give me an answer I can get instantly from a tool. on the positive front, Mike, you highlighted this already, the fearless Future 2025 global AI jobs parameter from pwc. I think there is this like silver lining of workers with AI skills command a 56% wage premium up 25% from last year.
[00:37:51] Like we're seeing that. Yeah, like I think that is the near term opportunity for people is like, go figure this stuff out and you can accelerate your [00:38:00] own career growth. I think a lot of ai, four organizations are gonna look at their employees and be willing to pay a premium because of how productive they can be, how creative, how innovative they can be.
[00:38:12] And then, one final note I'll add here is, Wade Foster, CEO of Zapier had a, a great post on X where he was talking about Zapier requiring AI fluency for all their new hires. Mm-hmm. And then he had a thread, we'll put this in. He actually had a chart he shared of kind of how they evaluate this, but how they're tracking it, he said they map across four levels.
[00:38:33] Unacceptable. This is like AI fluency, basically capable, adoptive and transformative. So unacceptable is they're resistant AI tools and skeptical of their value, meaning you're not getting hired here and you're not gonna keep your job here if you are in the unacceptable range. CAPABLE is using the most popular tools, likely under three months of usage.
[00:38:50] So they're kind of new to it. They, they're experimenting. Adoptive is, they're integrating AI into personal workflows. They're, tuning prompts, chaining models, and automating tasks to boost [00:39:00] efficiency. Then transformative is the sweet spot. Using AI to rethink strategy and offer user solutions that weren't possible two years ago.
[00:39:07] And then he shared even some of like the questions they're asking in interviews like marketing, how is AI changing? How you plan and execute campaigns? How do you use AI to personalize messaging, generate content, analyze performance? We're doing the same thing like in our interviews. This is the kind of stuff we're actually looking for.
[00:39:21] So, again, like takeaway here, like I always say, you, you can stand still or you can accelerate your AI literacy and capabilities. And if you do that, we can't promise you a certain future. Like it is still unknown what's gonna happen to your job or any of our jobs. But in the near term, you will have the greatest chance to figure out what happens next in your job and in your industry.
[00:39:44] 'cause you're going to understand the implications of AI and you're probably gonna make more money because organizations need that adaptive to transformative phase as the Zap, you know, Zapier seat I would call it.
[00:39:55] Mike Kaput: Yeah. In a weird way, I think there is a silver lining of. Some [00:40:00] excitement here too, because when I hear all this stuff and just experiencing what we experienced in our work, there's nothing more exciting to me than someone being like, no, here's the exact roadmap to go be more successful, make more money, et cetera.
[00:40:13] Before you would probably just gonna be nebulously, like trying to figure out like, okay, how do I get to the next phase or move up the ladder or wait for that promotion. Like, this is really exciting. You have the roadmap right here.
[00:40:24] Paul Roetzer: Yeah, and I think like, again, you know, we talk a lot about disruption, displacement under employment, unemployment, like those are very probable outcomes.
[00:40:34] Yeah. Like it is very probable that within the next three to five years, that is the reality for a lot of people. It is not given though, like it might not be May, maybe there is this insane emergence of like all these new roles really fast, like faster than I'm expecting it to happen. I don't have a crystal ball.
[00:40:51] I just look at the data. We, we spend a lot of time thinking about this. At the moment, the probability for me is it's probably gonna be a little painful [00:41:00] for a while.
[00:41:00] Mike Kaput: Mm-hmm.
[00:41:01] Paul Roetzer: Now, if, if that is the outcome, if you raced forward and became AI literate and drove like mastery of the tools and the knowledge around this, you have the greatest chance to get through the messy part.
[00:41:17] If the messy part never shows up, you're just gonna make more money in the process and be there for before everybody else gets there. Right. There's no downside to being the one who goes and solves this. To your point, Mike, in the near term, it's probably great for your career. In the long term, you're gonna figure out the next new business to build.
[00:41:36] You're gonna figure out the roles that are gonna remain in the company. You're gonna be a part of that conversation and that transformation. So like, that's why we always just challenge people. It doesn't matter when AGI arrives, if it arrives what we call it doesn't matter. Like what this expert says versus this expert.
[00:41:52] All that matters is what you can control, which is get better at this stuff every day. You know, improve your own comprehension and competency because [00:42:00] that is the best chance you have to be very valuable today and even more valuable tomorrow.
[00:42:06] Mike Kaput: Alright, we've got a ton of interesting rapid fires this week, so let's dive in.
[00:42:11] OpenAI Court Ordered to Preserve All ChatGPT User Logs
[00:42:11] Mike Kaput: The first rapid fire we're covering right now is that OpenAI says it is now being forced to store deleted chat GPT conversations indefinitely due to a court order tied to its ongoing lawsuit with the New York Times. So previously the company kept deleted chats per its terms for like 30 days before purging them.
[00:42:31] But under this new order, that policy is on hold. So even user deleted or privacy protected chats. Must now be saved until further notice by the company. That potentially includes, in some cases, private, personal, or sensitive data. Now, this data will not be made public only. A small legal and security team inside OpenAI will have access strictly for purposes of managing it due to the ongoing litigation.
[00:42:58] Now, OpenAI is [00:43:00] pushing back really hard against this. They argue this order is unprecedented, sweeping, and a direct threat to user privacy. In court filings, OpenAI says the judge acted prematurely. Basically, a judge. They claim accepted speculative claims that some users may have used chat GPT to bypass, paywalls, and then deleted their tracks, which would impact the allegations in this case.
[00:43:22] However, until the court reverses this order, these conversations will continue to be stored, and that's kind of sparking a little panic among businesses and individuals who rely on Chad GBT for confidential tasks. Now, according to. The source is put out by OpenAI and others moving forward, enterprise licensed customers.
[00:43:41] And those with zero data retention agreements are not affected by this. But users with ChatGPT Free Plus or Pro are affected by this until this gets resolved. So Paul, it's definitely ongoing and developing here, but it seems like a pretty [00:44:00] immediately big deal for any company that needs assurances.
[00:44:04] Their data is being kept private under certain restriction or regulations by OpenAI. Now it doesn't apply to enterprise license customers. They seem like they'd have the most to worry about here. But if I'm a business leader with these kind of considerations, I'm probably keeping a close eye on what happens here.
[00:44:19] Don't you think?
[00:44:20] Paul Roetzer: Yeah. I mean it doesn't apply to them yet, but this sort of shows that like legal, issues may override terms of use. Yeah, like if the courts decide they're illegal. So, I mean, it definitely is bothersome to OpenAI because Sam Altman tweeted, recently in the New York Times, asked the court to force us to not delete any user chats.
[00:44:41] We think this was an inappropriate request that sets a bad precedent. We are appealing the decision we'll fight any demand that compromises our user's privacy. This is a core principle he followed up with. We have been thinking recently about the need for something like quote unquote AI privilege. This really accelerates the need to have the conversation.
[00:44:58] In my opinion, [00:45:00] talking to an AI should be like talking to a lawyer or a doctor. I hope society will figure this out soon. He then shared a link to a OpenAI article about how we're responding New York Times data demands, and then he followed that up with, parent maybe spousal privilege is a better analogy.
[00:45:16] Hmm. So then the, the June 5th security, posting from OpenAI about the New York Times data demands started off with a, quick note from Brad lcap, the COO of OpenAI, and he said. Trust and privacy are at the core of our products. We give you tools to control your data, including easy opt-outs and per permanent removal of deleted chat, GPT chats and API content from OpenAI systems within 30 days.
[00:45:44] New York Times and other plaintiffs have made a sweeping and unnecessary demand in their baseless lawsuit, against us, which is retain consumer chat, GBT and API customer data indefinitely. This fundamentally conflicts with the privacy commitments we have made to our users. [00:46:00] It abandons longstanding privacy norms and weakens privacy protections.
[00:46:04] We strongly believe this is an overreach by the New York Times. We're continuing to appeal this in order to keep so we can keep putting your trust and privacy first. So again, there's that. We talked earlier about the data security. even if you trust OpenAIt doesn't mean that the legal system trusts Right.
[00:46:19] OpenAI and like, so Yeah, and this, this probably then goes into the whole like, um. Part of that debate about like open source and like controlling your own models and having them, you know, on your own systems and, yeah, I would imagine this is part of that argument for why that's maybe better in some instances.
[00:46:41] AI Cybersecurity
[00:46:41] Mike Kaput: Next up, Google DeepMind has released a white paper detailing how it's making its Gemini 2.5 models more secure, specifically against a growing threat called indirect prompt injection. This is a kind of a attack that hides malicious instructions in everyday content like [00:47:00] emails or documents in order to trick AI agents that go review those emails or documents or whatever into leaking private data or misusing tools, so to defend against it.
[00:47:11] DeepMind published how they're using a multi-layered approach, grounded in one key tactic, automated red teaming, so their own AI agents simulate realistic attacks on Gemini to uncover weak spots before bad actors can. Now, while this doesn't totally solve AI specific cyber attacks like prompt injection, it does go a long way towards making Google's models quite a bit safer.
[00:47:36] But really the reason we kind of wanted to chat about this briefly is it points to a much larger issue, which is AI models and systems can be exploited in these unique ways outside of traditional cyber attacks. And even the smartest companies in the world that are building this stuff are trying to figure out how to prevent some of those attacks.
[00:47:56] And Paul, that seems like what's really important here [00:48:00] for AI forward business leaders to start understanding like what happens when your business becomes dependent on AI systems that can be exploited like this. Like what happens if your business, as we get more agentic, AI becomes dependent on AI workers that get knocked outta commission or exploited in this way.
[00:48:19] Lots and lots of question marks here.
[00:48:22] Paul Roetzer: Yeah, this is a pretty deep topic on the surface. I, I can see like this report then some of these charts being used by like cybersecurity teams and enterprises to say why we can't use chat GPT. Like, it's just like steam, you don't even know what the problem is.
[00:48:37] Like these prompt injections. and I'm not dismissing at all that this is, I'm sure there's way more advanced things happening already, especially at the state level, government level where, yeah, espionage and cyber attacks are part of the arsenal. but without getting too much into that, it [00:49:00] does, Mike, to your point, bring more of the reality, which is.
[00:49:04] As all these companies start thinking about job displacement and like, maybe we don't need as many humans and we're just gonna use all these AI agents and they're gonna string together and they're gonna work with each other and they're gonna be connected to all our data. Mm-hmm. And it's gonna be amazing.
[00:49:16] and we're gonna have like 40% less people and then like, oh shit, Chad should be t just went down for 48 hours because of whatever. Right. We have no workers, we can't get anything done. That is, yeah. Like you almost need these fallback systems. And this is, I haven't heard anybody talking about this stuff.
[00:49:36] No. I've yet to be in a meeting with any organization where they're actually considering the possibility that they become dependent upon the AI agents and models and those models go down, power outage or cyber attack, or like whatever it is. So, yeah. I guess the takeaway on this one, Mike, is start doing contingency planning with your IT team, your legal team.
[00:49:58] Yeah. for [00:50:00] the event that your organization is dependent upon AI agents and digital coworkers, and they can't work.
[00:50:08] Mike Kaput: Yeah. it seems increasingly too, like these AI systems, they're not just tools, right? Like, if our company at HubSpot went down, we'd be in a real pickle. We'd have a huge problem. Yes, we have been in a pickle.
[00:50:18] Huge problem, but could we do other work? Yes. This is more like, oh, power's out. Like internet's out. Yeah. This is like, you're increasingly, this is going to underlie everything, right? Yeah.
[00:50:30] Paul Roetzer: And imagine if Mike, you've built a team of like, let's say it's not the entry level that gets sideswiped, let's say it's actually middle management or senior management mm-hmm.
[00:50:38] That are the most expensive workers. And you decide we can do this with a bunch of like, younger employees who just have AI models and they're trained to use these models and they're gonna do it. And then there comes a moment for whatever reason. Where they actually have to do it manually or analog and they can't go into the AI and ask it to do the thing.
[00:50:57] And they never had to do it without the ai and now they don't even know how to [00:51:00] do the thing. Yes, man, that's wild. Like, I,
[00:51:03] Mike Kaput: I genuinely think that could happen where we become so dependent.
[00:51:07] Paul Roetzer: Yeah. And I don't remember I said this on the podcast or if it was on like one or ask me anythings or something, but interestingly I was, I was talking to my wife about this stuff and my wife like, understands AI to the extent, like I've talked to her about it.
[00:51:20] she's an artist and it's not the thing she's like studying every day, but it's so fascinating. 'cause sometimes I'll just bounce things off of her and like get her perspective. She's like incredible insights on this stuff. and it's like, I was saying something about it was related to the, the 25% of entry level jobs, you know, going away kind of thing.
[00:51:39] Yeah. And she, and she said like, what happens if the system goes down 'cause of a power outage or something, and then there's no workers. And I was like, I. Oh my God. Like, this is like two weeks ago. So in some ways I'm actually echoing an insight my wife asked me that I hadn't actually like, sat really thought about.
[00:51:54] so yeah, it's wow. Yeah. So yeah, sorry if we just like [00:52:00] scared everybody into realizing like they need to be doing way more planning. So Yeah. As if you
[00:52:03] Mike Kaput: didn't have enough to think about
[00:52:04] Paul Roetzer: already, right? Yeah, yeah,
[00:52:05] The AI Verification Gap
[00:52:05] Mike Kaput: yeah. Alright, next up noted tech commentator, Balaji Serena Vain, who is, I believe also the ex CTO at Coinbase, is sounding the alarm on what he calls AI's verification gap.
[00:52:18] So his idea here, which is an important one, is that, look, you can prompt AI really fast. You type it replies, but the issue comes with verifying that reply. That's slow. It's hard, it's usually manual, especially with text code or anything technical. So for instance, like with images and video, a human eye can spot errors in a flash.
[00:52:39] That's why AI excels. Generating visuals. But when the output is something like code or math or dense writing, verifying means reading deeply, checking sources, walking through the logic. It demands real expertise. In short, verifying does not really scale. So he kind of argues that we [00:53:00] turbocharged the generation side of ai, but we've neglected the discrimination side, the judgment.
[00:53:06] This makes AI look faster than it actually is because the hard work of verification still falls on humans. So his conclusion is, quote, the concept of verification as the bottleneck for AI users is under discussed. Now, Paul, I have to say I, this resonated really deeply with me. 'cause I feel this pain, this bottleneck like every day with something as simple as deep research.
[00:53:28] Yep. There is a huge gap between the number of deep research reports I can and want to run. I could queue up dozens of them right now that I am interested in. My ability to process and verify all that is really, really limited. So I could be using it way more than I already do if I was able to solve for AI verification.
[00:53:48] Paul Roetzer: Yeah, I'm a hundred percent with you on this. That's the immediate thing I thought of when I saw this and I saw hypotheses tweet about it. deep research is the best current example because you and I [00:54:00] both have a similar philosophy there. It's like, I could come up with 10 things. I want to do deep research on every day that I know it could do the deep research on, but I don't have the time to verify all the citations and like double check everything.
[00:54:16] So I've been thinking a lot about this because, again, I so many times like I'll do these conversations, I gotta ask questions. I don't remember where I said it. So if I said this on the podcast already, pardon the repetition. But one of the things I've been looking at for a couple years is. How to reinvent, analyst firms and research firms.
[00:54:36] Mm-hmm. That I thought that that was a, it was gonna become a pretty obsolete model the way it was being done. And, you know, this idea of do the research and six months later the report comes out kind of thing. And so Mike and I talk a lot about, like this real time research approach and like, how do we bring more relevant data to market faster?
[00:54:56] And deep research was one of those tools where it's like, oh man, here we go. Like this is, [00:55:00] this could be the foundation of a next generation research firm. My concern though is that you contribute to the AI slop that's being put out there. And so what's gonna happen is you're gonna have a whole bunch of people who aren't trained researchers, analysts, or journalists that just go and use these deep research tools to just pump out a bunch of crap that they haven't verified and may have incorrect facts, may have missed citations, maybe citing crappy websites that no one would ever cite.
[00:55:26] Like no real analyst, journalist, researcher would ever cite as a source. And so yes, you can do way more research infinitely more, 10 to a hundred times probably more research, but you still have to verify, you still have to stand behind what you're gonna publish. And so that's why to date, we aren't publishing a lot of the deep research that Mike and I do because we haven't, it hasn't achieved the threshold we would require of something we would put our names on,
[00:55:54] Mike Kaput: right?
[00:55:55] Paul Roetzer: So now we're working on ways to like evolve that and create verification [00:56:00] systems so we can put out more real time research. But, that is the holdup. Now do I think that that's gonna not impact jobs? No. Like, I guess you could put out 10 times more research and maybe, you know, you don't, you don't reduce jobs, but, it is a major hold up that you still have to have the human in the loop.
[00:56:19] And strategy is the same way. Yeah, you can build great strategies, but like a human's has to verify and improve those things. So. Yeah, the verification gap I think is a very real thing. We think about it. I don't know that we've given it that name to it internally, but like I think about that every day of all the things we could be doing if we had resources dedicated to verify the outputs of the ai.
[00:56:42] Mike Kaput: Yeah. I almost wonder too, and won't spend too much time on this, but just the thought is like, does that become a really interesting career path and or skill? It's like even if people aren't, you know, world-class experts using the tools, do we need the verifiers to, you know, it's a way to kind of maybe position [00:57:00] yourself and, you know, in the AI first future, even if you're still getting, you know, still on kind of training wheels with like learning all the tools.
[00:57:07] Paul Roetzer: Yeah, I think it's what's happening with coding now with computer coding where a lot of the code is being written by the ai, but a human coder still needs to like verify it and then the more. Like the higher profile, higher risk, the output of that code is the more important the human in the loop becomes.
[00:57:24] Mm-hmm. So like if you're a research firm like us and part of your reputation, your brand is dependent upon people trusting the outputs from that firm. Right? You can't put out one thing that has err data in it. Like you have to stand behind every piece of data that comes out of there. And so I think that's, you know, again, that's why you build trust in media outlets or individual thought leaders or brands that, that yes, they're using ai, but they're, they're not getting rid of the people.
[00:57:55] The people are a critical component. It's just the AI may do more and more of the foundational work, but [00:58:00] the experts still have to be the ones that verify. So if you're using a false piece of data, it's on the human that put that thing out. So if Mike and I are gonna put our names on anything, if I'm gonna put the smarterX brand on something mm-hmm.
[00:58:11] It better meet the quality standards that we would require of purely human work.
[00:58:19] How Does Claude 4 Think?
[00:58:19] Mike Kaput: All right. Next up. We first talked about a podcast episode, an episode of the Dwarkesh podcast to be precise on episode 1 49 of the AI Show. And in this episode, the Anthropic researcher Sholto Douglass and Trenton Bricken returned to the Dwarkesh Podcast to talk more about how AI thinks.
[00:58:40] Now in episode 1 49, we took kind of a piece of that, some comments they had about, about automation of white collar work and really dive deep into it. But we wanted to go even deeper into the other aspects of this conversation because it is really, really important. Because what they talked about is how AI thinks of what that means for model progress and [00:59:00] capabilities.
[00:59:00] So they basically talked quite a bit about the transformative impact of reinforcement learning in large language models, and talking about how reinforcement learning with verifiable rewards has finally led to models that can consistently outperform humans in narrow but complex domains. So they say this means AI agents can now complete expert level tasks if a reward function is reliable enough.
[00:59:24] And so far these successes seem to mostly be in math and programming, but the groundwork is being laid for more ambitious, long running agents in software engineering and beyond. Now they say the constraint is no longer intelligence anymore, it's scaffolding context and feedback. So Douglas and Bricken basically believe despite, you know, the fact it will take a little time that we're on track to see agents doing real end-to-end software work by years end, and they may even eventually be able to do a full day's work autonomously.
[00:59:57] Now Paul, I'll kinda let you take it from here. As you actually flagged this episode [01:00:00] internally for our team, as a must listen, what's important to pay attention to here?
[01:00:05] Paul Roetzer: So Dwarkesh’s interviews are fantastic. I've said before on the show that they can get very technical. Mm-hmm. So what I would do though, is I would encourage you to listen to the full podcast if you want to truly understand how these models work.
[01:00:21] So the thing I flagged internally, and I think I shared in the exec AI newsletter, was, if you wanna understand how they work, why they can be misaligned, how the labs choose, what experiments, to run, why some industries are gonna take longer to be disrupted, how agents are evolving and how real they might be in the near future, how jobs are gonna be impacted.
[01:00:43] AGI timelines, like they get into a lot. Yeah. And they're very forthright in their thoughts. I, so again, it can be very technical. It's sometimes it's hard for me, honestly to like, evaluate how technical it is because I've been listening to this stuff for so long. Yeah. But even like a reward function, [01:01:00] it's just like, I kind of assume everybody knows what a reward function is.
[01:01:03] And that might be like you, you might need to. Listen while doing some searches to like, understand some fundamentals and actually for our, AI Academy, as we're making kind of updates and introducing this whole new approach to our learning journeys, I'm building an AI fundamentals course right now for this exact purpose.
[01:01:22] Yeah. So that everyone can understand this like, beginner level approach. So when you go listen to this, you already kind of get the fundamentals, like reward signals and things like that. But, it's incredible. Like, it, they're, they do a really good job of making everything approachable. So there's something that's a little too technical, just kind of like, move to the next thing.
[01:01:39] You'll get the gist of what they're trying to say. and then these are episodes are really valuable to me because it either verifies what we're thinking and saying, or maybe it challenges what we're thinking and saying. And, luckily for me, like pretty much everything they said is on track with what we're teaching through this podcast.
[01:01:58] And so it's a good like way [01:02:00] for us to vet, you know, make sure we're staying with. Our finger are the pulse of what's happening within these labs and what they're seeing and thinking. So yeah, it's a, it's just a really good episode for big picture understanding what's going on
[01:02:10] Mike Kaput: and is valuable too, because once you kind of get beyond the hype and the figureheads at these companies, these, like researchers and engineers building this stuff, they'll just tell you where they think it's going with no varnish.
[01:02:22] Paul Roetzer: Yeah. And honestly, like philanthropic must not have guardrails around what their people are allowed to say. Like a lot of times some of these bigger labs or publicly traded companies, you know, like I've, I won't name names, but like in some of these big companies, you gotta go through like months of training before even allowed to speak publicly.
[01:02:40] That is not the case at philanthropic. Like, they're just, they're just letting these guys go and talk and say whatever they want and, ESH is a buddy of theirs, so they just like kind of talk and you're not gonna get that from some of the publicly traded labs. I.
[01:02:55] New AGI Timelines
[01:02:55] Mike Kaput: So next up this past week we got more commentary around [01:03:00] AGI timelines and some are very bullish on how quickly we'll have artificial general intelligence.
[01:03:06] Some not so much. So first up, Sam Altman took the stage at Snowflake Summit 2025 to talk AGI. He waffled a bit on what AGI actually is. He said now it's a moving target. And he said that quote, mostly the question of what AGI is, doesn't matter. It is a term that people define differently. He also posited that if someone from 2020 were shown chat GPT today, most people quote, most people would say that's AGI for sure.
[01:03:36] Now, he did say for him AGI would be quote, a system that can either autonomously discover new science. I. Or be such an incredible tool to people that at a rate of scientific discovery in the world, like quadruples or something. He also emphasized he does not see AI slowing down at all and will continue along a quote, shockingly smooth, exponential curve of progress, which is going to enable quite breathtaking models in the [01:04:00] next year or two, enabling businesses to quote, just do things that totally were impossible with the previous generation of models.
[01:04:07] Now next you similar timing to this, Eric Jing, who's a former developer at Microsoft and the co-founder and CEO of Gens Spark, which is a $500 million generative AI startup, said he's already seeing AGI. He writes on X in a lengthy post that he believes we've already entered the era of AGI. And the consequences could be both thrilling and terrifying.
[01:04:29] He imagined the world where a conversational supercomputer smarter and faster than any human sits beside us at all times. And in that world, new college grads could be obsolete the day they graduate. White collar jobs could disappear on mass and are education systems he warns are not ready. Now he's not completely defeatist.
[01:04:49] His post also reads as just an urgent call to adapt and to use AI daily. Now last but not least, Dwarkesh Patels, who we just talked about in [01:05:00] response to the podcast we just discussed, really state counterargument to all this AGI hype. He writes that he doesn't believe AGI is as close as some experts, including guests on his show.
[01:05:12] Think. He argues that despite him spending hundreds of hours integrating AI into, say, his podcast workflow, he just doesn't see today's models improving like humans do. He says they can't learn from feedback over time, build context, or adapt organically. Instead, every session resets to square one, and he claims this is the reason why LMS haven't transformed white collar workflows at scale.
[01:05:37] He's also skeptical of aggressive timelines for AI doing agentic task, but he is optimistic that once continual learning like this is solved, even partially models could quickly become much, much, much more capable. He just thinks that will take a lot longer than some other people in the AI world. Now, Paul, did [01:06:00] anything jump out to you in this latest round of AGI speculation?
[01:06:03] Got a couple prominent voices with some counter, counterintuitive takeaways here.
[01:06:09] Paul Roetzer: The Altman one, I just don't understand. So. He said mostly the question, what AGI is, doesn't matter. It is a term that people define differently. Okay. So it doesn't matter. And yet their entire company is based on achieving it.
[01:06:24] Yeah. He was fired over it. So I started listening to the Empire of ai, the Karen Howell book. Yeah. and literally the whole opening chapter is about him being fired on this exact topic. Like, because that is their mission. Their contract with Microsoft is dependent upon it. Their mission is literally, ensuring AGI, which they define it, does change how they define it.
[01:06:45] But they do have a definition, February, 2023, AI systems that are generally smarter than humans. And the whole mission of the organization is for AGI to benefit all of humanity. So to say, it doesn't matter, it is literally the foundation of everything they're doing, why the [01:07:00] company was created. Right. So it may have just been a poi poor choice of words, but he does waiver all the time on what it actually is.
[01:07:09] there's a December, 2024 Tech Crunch article that we talked about at the time that said, the two companies, Microsoft and OpenAI reportedly signed an agreement in 2023 saying, OpenAI has only achieved AGI when it develops AI systems that can generate at least 100 billion in profits. That's, I guess, one way to quantify it.
[01:07:28] In January, 2025, so just six months ago, Sam wrote a blog post called Reflections, which we talked about at the time, and he said, we started Open Amos nine years ago because we believe that AGI was possible and that it could be the most impactful technology in human history. We wanted to figure out how to build it and make it broadly beneficial.
[01:07:45] We are now confident we know how to build AGI as we have traditionally understood it. So again, like it is literally the foundation of everything. They have. Their structure talks about, the board determining when AGI is attained. he had [01:08:00] a letter in March, 2025 to the LE to employees. We say we now see a way to AGI to directly empower everyone, the most capable tool in human history.
[01:08:08] We believe it's the best path forward. AGI should enable all of humanity to benefit each other. creating AGI I is our brick in the path of human progress and we can't wait to see what bricks you'll add to it. Like, I just don't understand. Right. Again, may, maybe it's poor messaging, but like you, you can't say it doesn't matter when you're entire organization is based on a single thing.
[01:08:29] Like, I feel like you need to be able to define that. in terms of the Dard Kesh one, I love the, the fact that he's willing to like take this alternative opinion and yes, he like studies the space. He meets with all these people. He hangs out with people within the AI labs. Like he has more access than most to understanding what's going on.
[01:08:51] And his basic argument, as you said, is this lack of continual learning, which is a hundred percent true. Yeah. Like that, that it's not a debate. it is a [01:09:00] valid point. the counter argument here, and so PE people don't understand this concept. Basically you train the model, you give it all the data, and then it's like fixed.
[01:09:08] Like that's it. So if a, if a model, let's say theoretically, GPT five was in training right now, and today was its final day of its training run. It's knowledge cuts off a June 9th, 2025. Then it knows nothing that happens beyond that moment. And then if you use it doesn't learn from that experience.
[01:09:27] It doesn't become better, right? It's not like continually adapting. That's the concept here. But these models now have tool use. So they can search the web, they can write code, they have memory. they have almost infinite knowledge up to that June 9th moment. Like they know more than any human about everything basically because they've read and consumed everything.
[01:09:50] they can string together agents that are experts in different things at superhuman speeds. You can run simulations to improve them. You can use reinforcement like. I [01:10:00] don't know that I fundamentally agree with what he describes as the barriers to this, like fast takeoff, but he makes really valid points.
[01:10:09] And I, you know, I think it's a worthwhile perspective. Like, I, like I said, I love reading these alternative perspectives that sort of challenge your thinking. and it's not like he's saying it's not gonna happen or the world isn't gonna change. He's just like, yeah, it might just take a couple more years in these ing.
[01:10:25] Mike Kaput: Right, right. Yeah. No point is he like, oh, this is complete nonsense. Yeah.
[01:10:29] Paul Roetzer: So whether it's one year, three year, five years, like it's changing everything in the next decade. And that's pretty short time period in the grand scheme of things. So I, good perspective Worth a read. It doesn't change anything we're doing at our organization or anything.
[01:10:47] I would suggest other organizations do.
[01:10:50] Reddit v. Anthropic
[01:10:50] Mike Kaput: Next step. Reddit has filed a lawsuit against Anthropic. They're accusing Anthropic of illegally scraping Reddit to train Claude. The [01:11:00] suit filed in San Francisco alleges Anthropic bots access Reddit over a hundred thousand times after claiming to have stopped crawling the platform in mid 2024.
[01:11:10] Reddit says this, scraping violated its terms of service and monetized user content without consent. Now, unlike other AI lawsuits, this isn't necessarily about copyright infringement. Instead, Reddit argues Anthropic unfairly exploited a rich archive of user conversations to build a commercial product while Reddit notably has signed paid licensing deals with companies like Google and OpenAI to train AI models legally.
[01:11:39] Now, Anthropic is disputing these claims. Paul, this one's a little different from the typical AI copyright case, but it seems like, unfortunately the theme is the same. An AI lab allegedly scraped and used content from a website that it didn't have permission to use. So I guess at this point, I guess I like have to [01:12:00] ask, like even with the lawsuits, even with things indicating to models, they're not allowed to scrape your site.
[01:12:05] Like, can we trust at all that these companies aren't still doing this stuff?
[01:12:09] Paul Roetzer: I doubt it. I'm not a lawyer. Took a couple law classes in college, thought about becoming a lawyer for about three days. actually really enjoyed this law about it anyway. this seems like we, we've already seen instances where discovery has been permitted, that cases have moved to the point where the plaintiff is allowed to do discovery on the models.
[01:12:33] I believe that happened with OpenAI already. So this seems like Anthropic knows if they did or didn't. if it seems like they can't. Win this case and it leads to discovery where the plaintiff is gonna be allowed to examine the sources of data that went into the model and Anthropic knows the sources are in there.
[01:12:52] Then they're paying their 50, a hundred million dollars fine, and then they're doing a licensing deal and we're moving on. If they didn't do it, then they got [01:13:00] nothing to worry about. I don't know if they did or didn't. It wouldn't surprise me if information was consumed by the models that shouldn't have been just based on previous precedent from other labs.
[01:13:12] So stay tuned. There's a chance we may never hear more about this because it just paid off and we move on with our lives, and if it is, then they most likely had it and don't want to give access to their training data.
[01:13:25] Sharing in NotebookLM
[01:13:25] Mike Kaput: Google's AI powered research Assistant Notebook. LM just got a major upgrade. You can now share your notebooks publicly with a single link now.
[01:13:34] Until now, users could only share notebooks privately with individuals. But with this update, anyone can publish a notebook, whether it's a study guide, product manual, nonprofit overview of whatever, and let others explore it interactively. viewers cannot edit the source material, but they can ask questions, generate summaries, or create content like Epic Qs and briefings.
[01:13:58] So Paul, I for one, am [01:14:00] very, very excited about this. It's a small thing, but definitely important. We're increasingly using notebooks and l Notebook, LM to accelerate how we learn and use knowledge as a team. As you and I discovered this morning, this is not yet in our business account, which is slightly frustrating since we built a notebook LM for this episode that we wanted to use to share with everybody.
[01:14:23] Paul Roetzer: Yeah, so last week Mike and I were talking and we were like, yeah, we should experiment and like put all the show notes. 'cause we always say like, check the show notes, right? And the show notes are easy to find. Like we, we put 'em on the post and everything, but we thought it might be cool if you could interact with the show notes.
[01:14:36] So we're like, ah, let's create a notebook, lm, and. We'll pilot it and see if it works. And if it does, maybe we'll, we'll share a notebook with our audience. And then as Mike indicated, he created it, he shared it with me and I was like, oh, this is great. I can't do anything with it. Like, I can chat with it. I can't, I can't create study guides, FAQs, anything like that.
[01:14:54] So before we get on the podcast, he's like, oh, let me update your settings. So it's like, okay, now I can do it, but [01:15:00] let me test this in my personal account. Oh yeah, it doesn't work. So we only realized you can only share notebooks with each other. Still in our Google Workspace account, we can't share it publicly, and we don't wanna necessarily build this in our personal accounts to then share it publicly, which would be the option.
[01:15:17] So yes. great to know. This is a feature. It is, I guess, a lesson in like Google has, very jagged rollouts of their features and products. Like this is a constant guessing game for us of like. That's awesome. Oh wait, we can't do that In our business accounts, this is a very common recurring theme that Google rolls stuff out to personal accounts that are not in the business accounts.
[01:15:48] OpenAI does the same thing, but it's on a much, much shorter horizon. Like usually it's OpenAI did a thing and then like a week later it's in Teams and enterprise Google. Yeah. It could be months or never. Like you just don't know. And it's, it [01:16:00] is very frustrating as a Google workspace customer that like you have no idea.
[01:16:05] Yep. And it's not communicated to you.
[01:16:06] Mike Kaput: Yep. Well, like we talked about, this is the importance of literally just going in and kicking the tires of these tools because no matter what we say or anyone else posts, just go in and try for yourself. Yeah. What's available, because you won't know for sure until you actually do that.
[01:16:22] No one's, very few people are gonna like publish documentation that's useful on this stuff.
[01:16:26] Paul Roetzer: Yeah, and I, on that same note again, and not to harp on Google here, but like, this is my major frustration with using Gemini is we use custom GPTs all the time. Yeah. And I still can't publicly share a gem. I create, I can't even share a gem with my team.
[01:16:40] So like I'm trying to use Gem. I more, 'cause I actually really like the model, but it becomes, it breaks down for me because I can't, I can't share these things. So yeah, drives me nuts.
[01:16:51] WPP Open Intelligence
[01:16:51] Mike Kaput: All right. A couple other topics here before we wrap up this week. So, WPP Media has launched Open Intelligence, a [01:17:00] sweeping new AI driven marketing system built around what they call the first ever large marketing model.
[01:17:07] Now, unlike the language models behind tools like Chat, GPT, this one they say, is Purpose built for advertising. Since they are an advertising agency, it is trained on trillions of real world data signals. Everything from purchase behavior to cultural context across 350 partners in 75 markets. Not to mention it doesn't depend on user identifiers.
[01:17:31] WPP is pitching this as what they call intelligence beyond identity. This is a shift away from cookie based tracking. The idea is to basically give clients their own predictive AI model, built on a mix of public and first party data, something that can forecast behavior, optimize ad spend, and adapt to a world where it's harder and harder to track people based on user identifiers.
[01:17:59] It is [01:18:00] also a full stack solution. It's connected to platforms like TikTok, meta and Google, and it is built for secure collaboration using some federated data technology that they have baked in. So that means clients never have to move or expose their raw data. So Paul, this idea of a large marketing model is pretty interesting framing.
[01:18:20] From what I'm reading about this, it kind of sounds a bit like WPP is becoming a model provider. They're basically granting clients access to these bespoke AI models. They're building on top of this foundation models. Like what are some of the implications here for agencies?
[01:18:38] Paul Roetzer: Yeah, it is an interesting play.
[01:18:39] Maybe, maybe that is the future of agencies. I don't, I don't know. you know, I think as we heard about earlier with like the big four consulting firms, the big agencies are probably in similar boats. It's challenging. Market profits are probably being threatened by, pricing pressures. You know, people want things done faster, cheaper.
[01:18:57] I don't know. Like I, I would love to [01:19:00] see this thing at work, honestly. So, right. I've told this story before, but like anybody who's new to the podcast, this is how it all started for me. So back in 2011 when I started researching aIt was actually for one specific use case, which was what I was calling a marketing intelligence engine that would largely automate strategy.
[01:19:18] It would consume data on all previous campaigns, it would run predictive models. It would take in, you know, ideally anonymized data. So imagine you're like HubSpot and you have all this data of, you know, potentially millions or billions of campaigns that have been run and that you could take that data and predict what to do next.
[01:19:36] Like say, Hey, I'm in retail and I wanna achieve this goal in terms of customer retention. Like what should I do? And it could go and analyze a million customer retention programs and then like, predict for you what to do next, or ad spend or you know, email pro, whatever it was. So my theory back in 2011 was, well, this'll have to happen.
[01:19:56] Like someone's going to build this. And then I, you know, quickly realized no one [01:20:00] was building it and no one in marketing was even thinking about this stuff. It seemed at that time. And that's what led to me eventually writing about the Marketing Intelligence engine in 2014, which then became the impetus to build Marketing AI Institute.
[01:20:12] So like. As soon as I see anybody who seems to be approaching this idea of like some form of intelligence engine, my ears sort of perk up. Yeah. I don't know if this is anything close to what I was originally envisioning, but I'm definitely intrigued by it and I would love to kind of see this at some point.
[01:20:30] Google Portraits
[01:20:30] Mike Kaput: Our last topic, this week, Google has just launched a new experiment called Portraits. This is an AI experience that lets you have interactive conversations with digital versions of real world experts. They're kicking things off by featuring one of these portraits with Leadership coach and the author of Radical Candor, Kim Scott.
[01:20:50] So instead of generic chatbot answers, you basically can get a conversation and coaching inspired directly by Kim's actual work. In this case, her avatar [01:21:00] speaks in her voice, draws from her real content and responds to your questions using Google's Gemini model. Now, the experts themselves are part of this process.
[01:21:09] They contribute their own material. They approve the avatar's tone. They guide how the AI should respond. Now it's still early. This is an experiment. Google is collecting feedback to improve this over time. It is only available in the US and only for users 18 and up. Now, Paul, despite the fact this sounds just like kind of a fun experiment right now from Google.
[01:21:31] The moment I saw this, I couldn't help but think about the implications for like online education, learning, coaching, like if these worked really well, I'd almost want one for every notable expert out there who I follow, or the top people in the space I'm interested in like learning
[01:21:50] Paul Roetzer: about. Yeah, I, man, I feel like we could spend some time on this one.
[01:21:54] So my first take is, this is infinitely doable, like [01:22:00] I think within a year or so. Is this in their like labs or studio? Is that where they're testing this? It's in, I think it's
[01:22:05] Mike Kaput: actually, yeah, it's in Labs. New
[01:22:07] Paul Roetzer: Experi and Google Labs. Yep. Yeah. So they have a history of like. When it's in labs, it's, it's not a fully baked product, but it's pretty close.
[01:22:14] Yeah. And we, you'll usually see within six months to 12 months if it's viable, that thing is released. So the fact that they've done this, which means they've done it internally already, and now we're seeing the first public facing sort of MVP here. so let's assume within 12 months to 18 months this is doable.
[01:22:33] Someone has built this at Y Combinator, like someone's built the tech now where you can easily turn yourself into one of these things. or you can pay for access to people who've licensed their likeness to be one of these things. I think Facebook was even going down this path with like, they were celebrity avatars and stuff.
[01:22:55] Yeah. So it's interesting, like, I don't know, like the [01:23:00] first name that came as deas. I obviously cannot call up Demis Hassabis and ask some questions about ai. I would love to ask de Ava questions about, I have a million of them. Would I pay. For access to an avatar of Demis to like talk to about ai? I don't know, like if I take any of my favorite authors, like would I pay for access to a digital version of them that I know may be hallucinating and is just like trained on some of their data?
[01:23:29] I don't know. Like I'm not sure. I'm sure there's an audience of people who would Right, right. Say Taylor Swift, say Taylor Swift agrees to like, build one of these things. Would Taylor Swift fans pay to talk to Taylor? I'm guessing yes. Like I would think that that's probably a thing. Yeah. And then the other side is like, would you allow yourself to be turned into one?
[01:23:46] So if you're a thought leader, a podcast or an author, whatever, an entrepreneur, would you allow yourself, would you as a brand allow your executives to be turned into them? I don't know. Right. I mean, it presents all kinds of interesting questions, but I would [01:24:00] assume this is sort of an inevitable. There's a market for this for sure.
[01:24:03] Yeah. How quickly it played out. I don't know.
[01:24:05] Mike Kaput: Yeah. I wonder where that line is between, in certain scenarios I could see us adding a ton of value and other scenarios I could see it really watering down the value of the personal brand too.
[01:24:14] Paul Roetzer: Yeah. I like, so my initial reaction is like, I have no interest in being one of these.
[01:24:19] Right? Like, if there was a market for people that wanted to talk to me as an AI avatar, I don't, I don't think that that's something I would personally be interested in doing. Yeah. Would I pay for one? Probably not, but like, I don't know. I, this is an interesting one. Yeah. I also wonder, ask yourselves as listeners, like these are the kind, the questions we may have to deal with.
[01:24:39] Mike Kaput: Yeah. I also wondered too, I don't, I have no idea what the strategy would be here and haven't really thought through it, but also if you see a stable of all these as part of your Gemini subscription, right. Yeah. That maybe that's interesting to people who might either switch or like consider paying for Gemini.
[01:24:54] I have no idea.
[01:24:55] Paul Roetzer: Yeah. Yeah. I don't know. I have to, I'd have to think about this one a little bit more, but. [01:25:00] Is, is it interesting? And I'm sure these are actually gonna be everywhere. Yeah. Like if you think about like 11 labs and hey Gen for sure, Google and OpenAI will probably get into this world. Facebook character.ai.
[01:25:09] Like this is sort of the
[01:25:10] Mike Kaput: inevitable thing all, all while saying, we don't want you to form too close of relationships with ai. Yeah.
[01:25:15] Paul Roetzer: For, oh, this is quick side note to end, but like, have you seen the VO three videos, the vlogs that are being created by historical characters? Oh gosh.
[01:25:25] Mike Kaput: Didn't they do one with like bible stories and stuff?
[01:25:27] It was done with Moses, but
[01:25:28] Paul Roetzer: there's, there's one I saw with Bigfoot where he's, oh my gosh. Gosh. So if you, if as Melissa, if you haven't seen this yet, I don't use TikTok anymore, but I know it like, sort of had its origins on TikTok, so I'm seeing it more on X where people are sharing stuff from TikTok, but people are using VO three to create these like super realistic.
[01:25:46] Vlogs, like YouTubers that are, I saw one with Storm troopers. Oh my god, you love that one. So it's like storm troopers in the middle of battles and he's like vlogging for YouTube about what's going on and yelling at the other storm Trooper, I saw one with Bigfoot [01:26:00] where he is trying to hide from humans.
[01:26:02] It's, it's amazing. There's ones like historical stuff people are creating. That's so cool. Oh, and Moses was hilarious. He's like, we're at the sea. I dunno what we're doing now. We forgot. Like, and then he's like walking through the water. You go. It's so, that's amazing. So y
| 2025-06-10T00:00:00 |
https://www.marketingaiinstitute.com/blog/the-ai-show-episode-152
|
[
{
"date": "2025/06/10",
"position": 44,
"query": "ChatGPT employment impact"
}
] |
|
Your Brain on ChatGPT: Accumulation of Cognitive Debt ...
|
Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task — MIT Media Lab
|
https://www.media.mit.edu
|
[
"Nataliya Kos'Myna"
] |
This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and ...
|
Abstract
This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge. Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity. Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users. Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.
| 2025-06-10T00:00:00 |
https://www.media.mit.edu/publications/your-brain-on-chatgpt/
|
[
{
"date": "2025/06/10",
"position": 54,
"query": "ChatGPT employment impact"
}
] |
|
AI: Will it soon take your job?
|
AI: Will it soon take your job?
|
https://theweek.com
|
[
"The Week Us",
"Social Links Navigation"
] |
AI developers warn that artificial intelligence could eliminate half of all entry-level jobs within five years.
|
A "white-collar bloodbath" could be imminent, said Jim VandeHei and Mike Allen in Axios.com. Dario Amodei, "one of the world's most powerful creators of artificial intelligence," warned last week that this rapidly advancing technology could wipe out half of all entry-level jobs and send unemployment soaring to 20% within five years. Most workers and lawmakers "just don't believe it" and aren't preparing for the coming transformation of workplaces, said the CEO of the AI firm Anthropic. But as AI rapidly approaches "superhuman intelligence," Amodei believes employers will start replacing tens of millions of workers in "as little as a couple of years." In software engineering, it's already begun, said Kevin Roose in The New York Times. Amodei recently unveiled a tireless AI coding program that can replace engineers earning six figures. Mark Zuckerberg plans to replace Meta's mid-level coders with AI, and LinkedIn and other firms have introduced "AI-first" policies, requiring managers to determine whether AI can perform a task before hiring a human. "Among people who pay close attention to what's happening in AI, alarms are starting to go off."
So far, at least, there's little evidence of an "AI jobs-pocalypse," said The Economist. Some firms that have tried to outsource work to AI have discovered the tech doesn't yet meet their needs, forcing them to slow their plans or hire back some human workers. Even if AI does disrupt the job market, "we've been here before," said Rich Lowry in National Review. In the late 20th century, personal computers drove down demand for typists, secretaries, and clerks, and factory automation threw manual laborers out of work. In a free market system, workers have to adapt to change, and once-comfortable jobs such as programming, consulting, and paralegals "shouldn't be immune from the effects of automation any more than factory work."
Still, change this dizzying requires some oversight, said Steven Levy in Wired. Not only does AI development threaten the job market, developers warn that it could become so superintelligent it will escape human control and make "catastrophic" decisions about our fate. Some AI systems have already tried to deceive their creators. But regulation of AI has "fallen out of favor" in the second Trump administration, and most tech leaders are urging us not to let China get ahead on AI. The U.S. appears to be racing "full-speed toward a future that it can't contain."
Subscribe to The Week Escape your echo chamber. Get the facts behind the news, plus analysis from multiple perspectives. SUBSCRIBE & SAVE Sign up for The Week's Free Newsletters From our morning news briefing to a weekly Good News Newsletter, get the best of The Week delivered directly to your inbox. From our morning news briefing to a weekly Good News Newsletter, get the best of The Week delivered directly to your inbox. Sign up
| 2025-06-10T00:00:00 |
2025/06/10
|
https://theweek.com/tech/artificial-intelligence-take-your-job
|
[
{
"date": "2025/06/10",
"position": 73,
"query": "artificial intelligence employment"
}
] |
How Malaysia is preparing its workforce for the future
|
How Malaysia has been preparing its workforce for the future
|
https://www.weforum.org
|
[] |
The study also identified 60 emerging roles, with 70% of these roles being in AI and digital technologies. These will create hundreds of thousands of new jobs – ...
|
Automation threatens about 620,000 jobs in Malaysia but it also creates opportunities in emerging roles.
Malaysia is taking a data-driven and collaborative approach to workforce development to manage this change.
Beyond employment statistics, the true goal is to preserve human dignity in the face of technological disruption.
While observing the later stages of the second industrial revolution, a century ago, English writer Rudyard Kipling wrote a poem describing the era’s strange technologies, capable of doing so many human tasks:
We can pull and haul and push and lift and drive,
We can print and plough and weave and heat and light,
We can run and race and swim and fly and dive,
We can see and hear and count and read and write.
At the same time, in that poem, The Secret of the Machines, he warned that these technologies were anything but human. For example, they cannot distinguish truth from lies, “ neither love nor pity nor forgive ”.
For many of us today, artificial intelligence (AI) is as exciting and strange as Kipling’s machines were for his time.
AI promises much potential in augmenting human productivity but it also raises fears about alienating humans from what we do best within the naturally created order – our sapiential capability, the ability to acquire knowledge, form insights and make judgments.
Seventeen months ago, when I first assumed office as Malaysia’s Minister of Human Resources, the things that kept me awake at night were not cold abstract theories or dusty policy manuals. They were everyday conversations with ordinary Malaysians – from corporate leaders and white-collar workers to gig workers and street vendors, wondering if their jobs would last.
At the heart of these conversations were three difficult but necessary questions: Can I secure meaningful employment? Can I earn a dignified living? Will my role remain relevant in a world increasingly shaped by machines?
AI promises much potential in augmenting human productivity but it also raises fears about alienating humans from what we do best within the naturally created order – our sapiential capability, the ability to acquire knowledge, form insights and make judgments.
In the face of AI and rapid digitalization, these are questions that are already being asked in every corner of our society.
“ The way forward is obvious – to ensure our workers are equipped with the skills to adapt to economic trends. ” — Steven Sim, Minister of Human Resources, Malaysia
Looking ahead, grounded in data
Technological change is reshaping the core of our economies. In Malaysia, we began by grounding our understanding of this shift in evidence rather than assumption.
TalentCorp, an agency under the Ministry of Human Resources, undertook a national study on the impact of AI, digitalization and the green economy on the workforce. We wanted a clear-eyed view of what lies ahead.
The study found that approximately 620,000 jobs are at high risk of being replaced or becoming obsolete due to automation. Workers currently in these roles will require cross-skilling, upskilling or even reskilling.
But this is not a doomsday account. The study also identified 60 emerging roles, with 70% of these roles being in AI and digital technologies. These will create hundreds of thousands of new jobs – roles that are no longer theoretical. They are already appearing in job listings, and some countries are producing workers who are more equipped to fill them.
From concern to strategy
The way forward is obvious: to ensure our workers are equipped with the skills to adapt to economic trends.
Malaysia spent an annual outlay of RM10 billion ($2.4 billion) for skills-related education and training, with about 30% of the funds coming from a statutory levy upon private corporations to be utilized for training their workers.
To facilitate government, corporate and individual decision-making in the area of skills development, we introduced MyMahir. This national digital platform offers real-time insights into job trends, skill requirements and training opportunities.
We also established the Future Skills Talent Council to strengthen partnerships between industry and government. These sector-based, industry-led councils ensure that skill standards are informed by those who understand the realities of the shop floor and server room.
In the first year of setting up the council, workers trained under its skills programmes received a 12% higher mean wage compared to the national average.
Our aim is to establish “skill universities” that are accessible, industry-aligned and provide qualifications on par with traditional degrees.
“ As we attempt to comprehend the present and predict the future, one thing is certain: humans write the manual for technology. ” — Steven Sim, Minister of Human Resources, Malaysia
An international effort
Malaysia’s challenges are mirrored across ASEAN. Our economies are growing and our populations are young but our education and labour systems must catch up with technological demands. The International Labour Organization (ILO) estimates that ASEAN could create up to 30 million green jobs by 2030 with the right reforms. That future cannot be realized by any one country alone.
During Malaysia’s chairmanship of ASEAN, we will be organizing and hosting the ASEAN Year of Skills 2025, supported by the ILO. This will not only guide deeper regional conversations on human capital development but will direct practical resources towards it.
For a start, in June 2025, Malaysia aims to open up our annual National Training Week, offering 65,000 high-quality skills training courses by both local and global brands to all citizens of ASEAN. This is our investment in regional human capital development with the aspiration to make ASEAN the most skilled region in the world.
We are proud to partner with the World Economic Forum to co-chair the Gender Parity in the Future of Work Accelerator in Malaysia, which seeks to empower female talent in this age of AI.
It aligns with our national Madani Economic Framework’s goal to increase female labour force participation. It will propose evidence-based policy recommendations to enhance women’s economic empowerment in the labour market.
It is about human dignity
As we attempt to comprehend the present and predict the future, one thing is certain: humans write the manual for technology. Kipling ended his poem on a reassuring note – we are in control of technology and not the other way around:
Though our smoke may hide the Heavens from your eyes,
It will vanish and the stars will shine again,
Because, for all our power and weight and size,
We are nothing more than children of your brain!
Ultimately, the question is not how evolving technology will impact people’s lives but how government policy can consistently empower and uplift them.
As I reflect on those early days in office, I realize that the questions that kept me up at night were never just about jobs or wages. They were about dignity and ensuring that no one was left behind.
| 2025-06-10T00:00:00 |
https://www.weforum.org/stories/2025/06/malaysia-steven-sim-workforce-future-ai/
|
[
{
"date": "2025/06/10",
"position": 24,
"query": "future of work AI"
},
{
"date": "2025/06/10",
"position": 68,
"query": "job automation statistics"
}
] |
|
The Chief Work Officer: Leading the Human-AI Frontier
|
The Chief Work Officer: Leading the Human-AI Frontier
|
https://blog.workday.com
|
[] |
Ultimately, the future of work with AI will be one where human intention shapes a more productive, ethical, and fulfilling world of work for all. Ninety-eight ...
|
Key Aspects of the Chief Work Officer Mindset
This mindset transformation isn't just theoretical; it's a practical framework encompassing five critical areas, highlighted by Pham and Karp in their discussion.
Human-Machine Collaboration
The core of this new era is the seamless integration of humans and AI. Karp emphasizes that work is increasingly being done by humans and machines together, including agents. This means moving beyond mere software usage to true human-machine collaboration, with AI agents democratizing expertise and augmenting human capabilities.
Redefining Work and Roles
With agentic AI, organizations can ask questions like "why do we even work this way?" and "how can we get ahead of evolving circumstances and technology?" This proactive questioning paves the way for new roles and skills like an agent workflow architect.
Strategic Planning and Readiness
The Chief Work Officer mindset involves careful planning and forecasting, especially when detecting how roles will evolve. Karp says it’s also about "readying the workforce and continuing to keep them both aligned and aware of what those changes are and make sure that we are ushering people through that evolution."
Democratizing Skills
A powerful benefit of agentic AI is its ability to democratize expertise and augment people who otherwise may have lacked years of relevant industry experience.This opens up opportunities for hiring based on potential and not just traditional role requirements, enabling broader access to specialized skills and knowledge.
Ethical and Responsible AI Governance
Pham and Karp also discuss the vital role of chief responsible AI officers, chief ethics officers, and even chief legal officers in this new world of human and digital workforces. Karp notes, "We need them more than ever to take what they know so deeply, which is governance, and bring that into technology, as well and translate that across human-machine collaboration." This ensures safety, fairness, and the thoughtful application of AI technologies.
HR’s Role in the Chief Work Officer Mindset
HR leaders play a vital role in embodying the Chief Work Officer mindset and navigating the new realities of a digital workforce–in large part, they’re the glue.
Agentic AI implementation and usage today requires HR to establish a deep and collaborative partnership with CIOs, CTOs, and chief AI officers to understand upcoming AI advancements, and how to measure the performance of human-machine collaboration.
Karp points out that HR stands in the center because it directly impacts "every employee that is performing, that is functioning across the org, how we're paying them, how we're equipping them to know what's coming."
The key takeaway for leaders, as Karp stresses, is that this transformation "cannot and should not be a CHRO torch only. We need every single executive." This cross-functional collaboration ensures that the entire C-suite is invested in preparing their teams for the AI-driven future.
They are collectively responsible for:
| 2025-06-10T00:00:00 |
2025/06/10
|
https://blog.workday.com/en-us/chief-work-officer-leading-human-ai-frontier.html
|
[
{
"date": "2025/06/10",
"position": 31,
"query": "future of work AI"
}
] |
The Rise of Robotic Phase-Out: A New Era
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The Rise of Robotic Phase-Out: A New Era
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https://www.numberanalytics.com
|
[
"Sarah Lee"
] |
According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030 1. However, the same report ...
|
Navigating the Challenges and Opportunities of Robotic Phase-Out in the Modern Workplace
The modern workplace is undergoing a significant transformation with the advent of robotic phase-out, a phenomenon where automation and artificial intelligence (AI) are increasingly replacing human workers in various industries. This shift is not only changing the nature of work but also raising important questions about the future of employment, the skills required for the workforce, and the strategies needed to navigate this new era. In this article, we will explore the implications of robotic phase-out on the workforce, the opportunities arising from it, and the steps that can be taken to prepare for a future where humans and machines collaborate.
The Impact of Robotic Phase-Out on the Workforce
The introduction of automation and AI in the workplace is having a profound impact on the workforce, leading to job displacement and changes in job roles. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030 1. However, the same report also notes that while automation will replace some jobs, it will also create new ones, such as roles in AI development, deployment, and maintenance.
Job Displacement and Changes in Job Roles
The impact of robotic phase-out on job displacement is significant, with certain sectors being more vulnerable than others. Jobs that involve repetitive tasks, such as manufacturing and data entry, are more likely to be automated, while jobs that require creativity, problem-solving, and human interaction are less likely to be replaced by machines.
The following graph illustrates the likelihood of job automation across various occupations:
pie title Likelihood of Job Automation "High" : 33 "Medium" : 44 "Low" : 23
As the graph shows, a significant proportion of jobs are at high risk of being automated. However, this does not necessarily mean that these jobs will disappear entirely. Instead, they may evolve to require new skills and involve working alongside machines.
Skills Required for the Future Workforce
The robotic phase-out is not just about job displacement; it's also about the skills required for the future workforce. As machines take over routine and repetitive tasks, there will be a growing demand for workers with skills that are complementary to automation, such as:
Critical thinking and problem-solving
Creativity and innovation
Emotional intelligence and empathy
Complex decision-making and judgment
Ability to work with and maintain complex systems
To remain relevant in the job market, workers will need to develop these skills and be adaptable to new technologies and work environments.
Strategies for Workforce Adaptation and Retraining
To mitigate the impact of job displacement and prepare workers for the changing job market, organizations and governments can implement the following strategies:
Upskilling and reskilling programs : Providing training and development opportunities to help workers acquire new skills and adapt to changing job requirements.
: Providing training and development opportunities to help workers acquire new skills and adapt to changing job requirements. Lifelong learning : Encouraging a culture of continuous learning and professional development to help workers stay relevant in the job market.
: Encouraging a culture of continuous learning and professional development to help workers stay relevant in the job market. Career guidance and counseling : Providing support to help workers navigate career transitions and identify new opportunities.
: Providing support to help workers navigate career transitions and identify new opportunities. Education and training in emerging technologies: Investing in education and training programs that focus on emerging technologies, such as AI, data science, and robotics.
Opportunities Arising from Robotic Phase-Out
While the robotic phase-out presents challenges for the workforce, it also creates opportunities for organizations and individuals. Some of the benefits of robotic phase-out include:
Increased Productivity and Efficiency
Automation can significantly improve productivity and efficiency by freeing up human workers from routine and repetitive tasks. According to a study by Accenture, AI and automation can increase productivity by up to 40% by 2035 2.
New Business Models and Revenue Streams
The robotic phase-out is also creating new business models and revenue streams. For example, companies can develop and sell AI-powered products and services, or offer automation solutions to other businesses.
Improved Workplace Safety and Reduced Errors
Automation can also improve workplace safety by reducing the risk of accidents and errors. Machines can perform tasks that are hazardous or require high precision, freeing up human workers to focus on higher-value tasks.
Preparing for a Future with Robotic Phase-Out
To prepare for a future with robotic phase-out, organizations, governments, and individuals must work together to create a collaborative human-machine work environment. Some strategies for achieving this include:
Encouraging a Culture of Lifelong Learning
Encouraging a culture of lifelong learning is critical for preparing workers for the changing job market. This involves providing opportunities for continuous learning and professional development, as well as promoting a mindset that is adaptable to new technologies and work environments.
Investing in Education and Retraining Programs
Investing in education and retraining programs is essential for developing the skills required for the future workforce. This includes programs that focus on emerging technologies, such as AI, data science, and robotics.
Fostering a Collaborative Human-Machine Work Environment
Fostering a collaborative human-machine work environment involves designing workspaces and workflows that bring humans and machines together. This requires a deep understanding of the strengths and limitations of both humans and machines, as well as the development of new management practices and organizational cultures.
The following flowchart illustrates the steps involved in creating a collaborative human-machine work environment:
graph LR; A["Identify tasks for automation"] --> B["Implement automation solutions"]; B --> C["Upskill and reskill workers"]; C --> D["Foster a culture of lifelong learning"]; D --> E["Monitor and evaluate the impact of automation"]; E --> F["Adjust strategies as needed"];
Conclusion
The robotic phase-out is a significant trend that is transforming the modern workplace. While it presents challenges for the workforce, it also creates opportunities for organizations and individuals. By understanding the implications of robotic phase-out and developing strategies to navigate its challenges and opportunities, we can create a future where humans and machines collaborate to drive productivity, efficiency, and innovation.
The mathematical expression for the productivity gain due to automation can be represented as:
\[\text{Productivity Gain} = \frac{\text{Output with Automation}}{\text{Output without Automation}}\]
Where output is a function of the number of workers, the number of machines, and the efficiency of the production process.
References
FAQ
Q: What is robotic phase-out?
A: Robotic phase-out refers to the phenomenon where automation and artificial intelligence (AI) are increasingly replacing human workers in various industries.
Q: What are the implications of robotic phase-out for the workforce?
A: The implications of robotic phase-out for the workforce include job displacement and changes in job roles, as well as a growing demand for workers with skills that are complementary to automation.
Q: How can organizations prepare for a future with robotic phase-out?
A: Organizations can prepare for a future with robotic phase-out by investing in education and retraining programs, fostering a culture of lifelong learning, and developing strategies to navigate the challenges and opportunities presented by automation.
| 2025-06-10T00:00:00 |
https://www.numberanalytics.com/blog/rise-of-robotic-phase-out
|
[
{
"date": "2025/06/10",
"position": 28,
"query": "job automation statistics"
}
] |
|
Automation that Powers People, Processes, and Progress
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Automation that Powers People, Processes, and Progress
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https://www.claglobal.com
|
[] |
McKinsey further reports that while 50% of today's work activities could technically be automated, only about 5% of jobs can be fully automated. This means that ...
|
From Fortune 500 companies to emerging startups, organizations worldwide are embracing intelligent hyper automation. For many, it’s more than just a set of efficiency tools—it’s a strategic imperative to remain competitive in today’s fast-changing business landscape.
As automation and Artificial Intelligence (AI) adoption grows, debates about the future of work intensify. One common fear among employees is that Intelligent Automation (IA) will replace jobs, replacing humans with digital bots. However, the reality is often far more nuanced—IA isn’t about replacing jobs, but transforming them.
To adapt to these evolving demands, governments and enterprises across the globe are investing heavily in upskilling and reskilling initiatives. The goal is not just to stay relevant but to ensure that workforces are future-ready—not left behind.
Tayyabali S. Sayed, Partner at CLA Global Indus Value Consulting, India, explores how IA routines are reshaping business operations and altering the automation narrative.
Changing perceptions
Across industries—from finance and healthcare to retail and logistics—millions of employees spend countless hours each week on repetitive, manual tasks.
Finance teams process invoices, reconcile data, and generate reports. Customer service representatives answer the same queries repeatedly—many of which, like password resets, could easily be automated. HR professionals spend significant time on data entry, payroll processing, and compliance tracking. The list continues.
These tasks, while essential to business continuity, are time-consuming, error-prone, and mentally exhausting. Many employees find themselves buried in routine work, with limited time left for innovation, strategic initiatives, or even their own professional development.
From fear to fulfillment
According to a 2025 report by the World Economic Forum, 86% of employers expect AI and information-processing technologies to transform their business by 2030. This shift could result in the creation of 170 million new jobs globally, even as 39% of current skill sets are expected to become outdated.
McKinsey further reports that while 50% of today’s work activities could technically be automated, only about 5% of jobs can be fully automated. This means that although automation will change how people work, it won’t replace the need for human judgment and oversight.
Despite widespread fears, these statistics tell a different story—particularly when it comes to IA. A global survey by Forbes Insights found that 92% of organizations saw improved employee satisfaction after IA implementation. In fact, 52% of respondents reported a satisfaction increase of 15% or more.
This shift in sentiment often hinges on a single factor: understanding what’s in it for the employee. When organizations adopt a people-first approach to automation, rather than a purely top-down or cost-cutting agenda, the narrative changes. It becomes less about job loss and more about job evolution.
Pilot programs repeatedly show that when employees see companies investing in their future—through training, transparency, and support—they are more open to automation efforts. Conversely, when workers feel excluded or threatened, they may resist automation by withholding knowledge or even complicating workflows to appear indispensable.
But once employees experience how IA frees them to focus on strategic, value-added work, the fear typically gives way to greater job satisfaction. This makes bridging the skills gap not only a strategic need—but a moral imperative.
IA beyond the routine
According to Precedence Research, the global IA market is expected to reach $54.57 billion by 2032, growing at a staggering annual rate of 28.7%.
By automating rule-based, repetitive tasks, companies are empowering their workforce to focus on problem-solving, creativity, and decision-making. Far from eliminating jobs, IA is redefining them.
Data entry clerks become data analysts. Employees move from executing tasks to managing automated systems. They stop simply reacting—and start leading. The result? A more engaged, productive, and innovative workforce.
Originally designed to handle back-office operations, IA today spans a much broader spectrum—from reconciliations and validations to audits, document reviews, and compliance management. For many, it’s been a gamechanger—reducing manual errors, standardizing processes, and supporting regulatory alignment.
By removing human intervention from low-value tasks, IA enhances both efficiency and work quality, helping businesses mitigate risks and improve accuracy across critical functions.
The emergence of agentic automation
As we move beyond traditional automation paradigms, Agentic Automation is emerging as a transformative force in the digital workforce. While Intelligent Automation executes defined tasks within predetermined rules, Agentic Automation introduces autonomous software agents—systems that not only act but reason, plan, and self-direct based on business goals.
These agents, powered by advanced AI models, can dynamically navigate workflows, make real-time decisions, adapt to changes, and even proactively suggest optimizations. Unlike conventional bots, which require clearly defined scripts, agentic systems learn and evolve, often working collaboratively with human teams.
The potential here is enormous. Imagine an autonomous agent that monitors your financial systems, identifies anomalies, and drafts reports without prompts, prioritizes customer tickets based on urgency and past interaction quality and orchestrates end-to-end supply chain processes while adapting to weather delays or geopolitical disruptions.
Agentic Automation moves organizations from reactive automation to proactive intelligence—enabling greater adaptability, resilience, and strategic foresight.
Why agentic systems are the next frontier
The integration of Agentic Automation with IA platforms marks a major step toward fully autonomous business operations. By embedding goal-seeking logic and natural language understanding into digital workers, businesses can reduce human intervention in complex processes—without compromising control or compliance.
What makes agentic systems particularly powerful is their ability to operate in unstructured, unpredictable environments. This opens up use cases in strategic planning, contract analysis, IT security incident response, market monitoring, and trend forecasting.
As organizations strive to become more agile and intelligent, Agentic Automation offers a bridge between automation and autonomy, between human intention and machine execution.
But this doesn’t render the human workforce obsolete—it makes human roles more strategic. Professionals will shift from executing tasks to supervising agents, guiding objectives, and interpreting nuanced outputs that require context and creativity.
A new automation road ahead
The future of IA and Agentic Automation lies in convergence—with AI, machine learning, blockchain, the Internet of Things (IoT), and Intelligent Document Processing (IDP) all contributing to richer, smarter automation ecosystems.
As a result, the demand for automation architects, AI ethicists, agent trainers, and process designers is growing rapidly. These hybrid roles blend human judgment with machine capability, opening the door to more meaningful careers that evolve alongside technology.
Unlocking Human Potential in the Age of Automation
What this all means is that jobs won’t disappear—they’ll evolve. When thoughtfully embraced, automation in all its forms can uplift the workforce—shifting people from mundane to meaningful, routine to strategic, reactive to creative.
Those who lead automation initiatives, adapt quickly to technological changes, and commit to continuous learning will be the architects of tomorrow’s intelligent enterprises. And those enterprises won’t just be faster or leaner—they’ll be more human, more responsive, and more resilient.
To explore more about the transformative power of IA, read CLA Global’s 2024 article: Tracking the Rise of the RPA ‘Bots’. https://www.claglobal.com/insights/tracking-the-rise-of-the-rpa-bots/
For further information
Tayyabali Sayed
Partner
https://www.linkedin.com/in/tayyabali-sayed-836b19a8/
The information contained herein is for general informational purposes only and is not intended, and should not be construed, as legal, auditing, accounting, investment, or tax advice or opinion provided by CLA Global or any of its individual member firms to the reader. No client, advisory, fiduciary, or other professional relationship is established or implied between the reader and CLA Global or any of its member firms through the presentation of the information contained herein. The reader is cautioned that this material may not be applicable to, or suitable for, the reader’s specific circumstances or needs, and may require consideration of a number of other factors if any action is to be contemplated. Accordingly, the information presented herein should not be considered a substitute for the reader’s independent investigation and sound technical business judgment, and the reader is advised to contact his or her CLA Global member firm or other tax or professional advisor prior to taking any action based upon said information. Neither CLA Global nor any of its member firms assume any obligation to inform the reader of any changes in tax laws or other factors that could affect the information contained herein.
| 2025-06-10T00:00:00 |
https://www.claglobal.com/insights/automation-that-powers-people-processes-and-progress/
|
[
{
"date": "2025/06/10",
"position": 33,
"query": "job automation statistics"
}
] |
|
The following jobs don't require college degrees nor AI ...
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What jobs don't require a college degree − nor won't be replaced by AI?
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https://www.sj-r.com
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[] |
Despite the slow creep of automation, many fields still require the human touch. ... The report draws on Bureau of Labor Statistics data. Need a break? Play the ...
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If you think most Americans finish college, think again.
Going to college is an American rite of passage. But not everyone goes to college, and many students never make it to graduation. Among Americans ages 25 and over, only 38% are college graduates, according to the Education Data Initiative.
A new report from the resume-writing service Resume Now identifies 13 careers that offer good pay and long-term stability and that don’t require a college degree. Better still, none of the jobs are likely to be replaced by AI.
The analysis “focused on three or four fears that people have right now,” said Keith Spencer, a career expert at Resume Now. Americans worry about signs of a softening job market. They’re concerned about the cost of college, and whether a degree is still worth it. And employees in many fields fear that AI – or robots, or other nonhuman hands – might sweep in to replace them.
Despite the slow creep of automation, many fields still require the human touch. To build out this list, Resume Now found careers that require only a high school diploma, that pay at least $50,000 a year, and that represent growing fields with high-demand skills. The report draws on Bureau of Labor Statistics data.
Need a break? Play the USA TODAY Daily Crossword Puzzle.
“They sort of all have some similarities, in terms of the need for significant human interaction,” Spencer said. “Maybe they require manual dexterity in unpredictable environments, or high levels of creativity.”
The list comes in two parts: jobs with relatively low AI risk, and positions with “moderate” AI risk, based on the need for human decision-making, manual labor, personal interactions and other factors.
Some of the jobs listed below require “a level of relevant experience,” Resume Now reports. But none, apparently, requires a college degree.
Here’s the list, including job descriptions for less familiar positions, and median salaries for all.
Jobs with low AI risk
According to Resume Now, these careers offer a good income and strong job security because they require skills that go well beyond the capabilities of AI.
Forest fire inspectors and prevention specialists
Job description: Judge fire hazards, investigate wildfire causes and enact prevention strategies.
Why they're AI-resistant: Fire prevention requires humans in the field and cannot be entirely automated.
Median pay: $71,420 a year
Flight attendants
Why they're AI-resistant: AI can’t serve meals. In-flight customer service requires a human touch.
Median pay: $68,370 a year
Lodging managers
Job description: Think “The White Lotus.” Oversee lodging operations, manage the staff and keep the guests happy.
Why they're AI-resistant: AI can’t unclog a guestroom toilet. You need people to provide the personal touch.
Median pay: $65,360 a year
Electricians
Why they're AI-resistant: AI can’t install your chandelier. Electrical work requires a human presence.
Median pay: $61,590 a year
Plumbers, pipefitters and steamfitters
Job description: Plumbers install and service water and gas systems in homes and businesses.
Why they're AI-resistant: Plumbing is unpredictable work. AI-controlled robots could handle some of it but not all.
Median pay: $61,550 a year
Industrial machinery mechanics
Job description: Maintain mechanical systems in industrial workplaces.
Why they're AI-resistant: AI would struggle with the real-time problem-solving demands of the work.
Median pay: $61,170 a year
Chefs and head cooks
Why they're AI-resistant: AI can’t taste the soup. Recipe development and food prep require a creative touch.
Median pay: $58,920 a year
Hearing aid specialists
Job description: Work with hearing aids and provide patient care.
Why they're AI-resistant: AI can’t handle the hands-on requirements of the job.
Median pay: $58,670
Personal service managers
Job description: Oversee wellness programs, event planning or luxury concierge services.
Why they're AI-resistant: The work requires personal interactions, emotional intelligence and decision-making that AI cannot handle.
Median pay: $57,570
Jobs with moderate AI risk
These careers involve tasks that eventually could be automated, Resume Now reports. But, for now, they still rely on human judgment and adaptability.
Maintenance workers, machinery
Job description: Close cousins to the industrial machinery mechanic, listed above, machinery maintenance workers perform routine upkeep on industrial machinery.
Why they're AI-resistant: Complex repairs require real-time problem-solving by humans.
Median pay: $61,170 a year
Insurance sales agents
Why they're AI-resistant: AI can handle some underwriting tasks, but this career requires personal service.
Median pay: $59,080 a year
Aircraft cargo handling supervisors
Why they're AI-resistant: AI can handle some aircraft cargo tasks, but you need human supervisors to handle the unexpected.
Median pay: $58,920
Security and fire alarm systems installers
Why they're AI-resistant: Installing and troubleshooting security and fire systems requires humans.
Median pay: $56,430 a year
| 2025-06-10T00:00:00 |
2025/06/10
|
https://www.sj-r.com/story/business/economy/2025/06/10/jobs-no-college-degree-ai-requirements/84112883007/
|
[
{
"date": "2025/06/10",
"position": 54,
"query": "job automation statistics"
}
] |
Data Quality Analyst job - Experis USA - 354697
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Data Quality Analyst job
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https://www.experis.com
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[] |
Collaborate with data engineers on unit testing views and stored procedures. Drive implementation and automation of business rules to support data quality ...
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Job Title: Data Quality Analyst
Work Schedule: Monday-Friday, 8 AM - 5 PM
Location: Columbus, OH
Work Arrangement: Remote
Compensation: $74hr
Candidates: US Citizen or GC Holders only. This role is not open to C2C
We are seeking a detail-oriented Data Quality Analyst to lead initiatives that ensure the accuracy, consistency, and reliability of enterprise data across various platforms and domains. This role involves proactive data validation, collaboration with cross-functional teams, and development of automated processes to improve data quality. The ideal candidate will have strong SQL skills, data governance experience, and a passion for driving data integrity.
Key Responsibilities:
Perform manual data validation, report testing, and research to identify data discrepancies. Collaborate with data engineers on unit testing views and stored procedures. Drive implementation and automation of business rules to support data quality objectives. Monitor and maintain data quality and integrity organization-wide. Identify and resolve data quality issues, assess business impact, and lead remediation efforts. Define and document data quality standards and goals in partnership with business stakeholders. Troubleshoot data anomalies and work across teams to implement preventative solutions. Support data governance, metadata management, and master data management initiatives.
Preferred Qualifications:
Strong SQL skills for writing and executing data validation queries.
Hands-on experience with domains such as SAP ECC, Finance, Salesforce, SuccessFactors, and Operations.
Familiarity with data governance frameworks and tools like Informatica EDC.
Experience using Power BI, data warehousing solutions, and data modeling techniques (e.g., star schema, data vault).
Proficient in MS Excel and Microsoft Office Suite.
Strong problem-solving and communication skills, with an ability to translate technical issues to business users.
Experience with agile methodologies, specifically SCRUM.
Education & Experience:
| 2025-06-10T00:00:00 |
https://www.experis.com/en/job/354697/data-quality-analyst
|
[
{
"date": "2025/06/10",
"position": 66,
"query": "job automation statistics"
}
] |
|
These human capabilities complement AI's shortcomings
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These human capabilities complement AI’s shortcomings
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https://mitsloan.mit.edu
|
[
"Brian Eastwood"
] |
The automation-related findings reflect AI replacing human labor. As for augmentation, the decline in employment stems from an increase in productivity, meaning ...
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The work tasks that AI is least likely to replace are those that depend on uniquely human capacities, such as empathy, judgment, ethics, and hope.
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What impact will AI and emerging technologies have on the U.S. labor force? Since the arrival of ChatGPT a little less than three years ago, such debates have typically fallen into one of two categories: ways AI can augment the workforce and ways AI-driven automation will disrupt the workforce.
AI at Work Research and insights powering the intersection of AI and business, delivered monthly. Email Leave this field blank
A new paper from MIT Sloan postdoctoral associate Isabella Loaiza and professor Roberto Rigobon takes a different approach, asking ,“What human capabilities complement AI’s shortcomings?”
The approach shifts the discussion from disruption and labor substitution toward human abilities. “In the future-of-work field, the focus is often on machines and not humans,” Loaiza said. “We wanted to focus on what humans can do. We don’t want to instill fear in people’s hearts. We wanted to show how AI is going to complement workers.”
To answer their question, the researchers developed a framework of human-intensive capabilities that gave rise to three key metrics:
A risk-of-substitution score
A potential-for-augmentation score
A score that indicates whether a task relies on certain human capabilities that AI lacks, among them ethics, creativity, and emotional intelligence.
The pair then applied each of these metrics to all U.S. tasks and occupations identified by O*NET, a U.S. Bureau of Labor Statistics database of occupational information that defines 19,000 tasks across approximately 950 types of jobs.
Their conclusion? Work that is dependent on human characteristics such as empathy, judgment, and hope is less likely to be replaced by machines.
AI is impacting higher-skilled work
Technological advances have always sparked worker concerns, but advances in mechanization, automation, and digitalization generally improved the quality of people’s jobs over time. This may not be the immediate case with artificial intelligence.
“AI feels different because it threatens to replace capabilities deeply connected to our cognitive ability,” Loaiza and Rigobon write, noting that AI is capable of brainstorming, content creation, and problem-solving.
“AI has been moving faster, and the effects we see in the labor force are different,” Loaiza said. “Previous waves of technology tended to negatively impact lower-skilled workers, while AI is impacting workers regardless of their education attainment.”
That said, AI has its limitations. It cannot make inferences from small datasets or extrapolate far beyond a training dataset. Problems with more than two viable solutions, and decisions based on shared experiences, pose a challenge for AI as well.
And, critically, AI struggles with subjective beliefs — which the researchers characterize as decisions based on a range of outcomes that differ from what the data suggest. “Some of the most transformative decisions in human history have been driven by beliefs that defied the status quo, even when prevailing data appeared to support it,” they write, citing women’s suffrage and the civil rights movement as cases where conviction took precedent over the status quo.
“[Humans sometimes] make decisions not because the data tells us it’s possible but because, out of principle, it should be done,” Loaiza said.
Humans can still do things AI cannot
To begin their study, Loaiza and Rigobon outlined five groups of human capabilities, represented by the acronym EPOCH.
Empathy and emotional intelligence. AI may be able to detect emotions, but humans can create a meaningful connection and share what the person is experiencing. Occupations such as social work and education illustrate this well.
AI may be able to detect emotions, but humans can create a meaningful connection and share what the person is experiencing. Occupations such as social work and education illustrate this well. Presence, networking, and connectedness. Roles in nursing and journalism reflect the importance of physical presence in building connections, fostering innovation, and collaborating with colleagues.
Roles in nursing and journalism reflect the importance of physical presence in building connections, fostering innovation, and collaborating with colleagues. Opinion, judgment, and ethics. Humans can navigate open-ended systems, such as the legal profession and the science industry, whereas AI struggles to grasp concepts like accountability and responsibility.
Humans can navigate open-ended systems, such as the legal profession and the science industry, whereas AI struggles to grasp concepts like accountability and responsibility. Creativity and imagination. Humor, improvisation, and “the visualization of possibilities beyond reality,” as the researchers put it, remain uniquely human abilities. They are especially valuable in design work and scientific work.
Humor, improvisation, and “the visualization of possibilities beyond reality,” as the researchers put it, remain uniquely human abilities. They are especially valuable in design work and scientific work. Hope, vision, and leadership. Grit, perseverance, and initiative further embody the human spirit. This means taking on a challenge despite long odds of success, such as starting a new company.
Armed with their framework, Loaiza and Rigobon used the O*NET data to study its nearly 19,000 work tasks in the context of automation and augmentation, and their relationship to human capabilities.
These so-called task statements are descriptive but specific to each occupation. That means that there is little overlap between different occupations’ task descriptions, which makes it difficult to identify similar tasks distributed across different roles.
If you’re aiming for disruptive innovation or truly transformative business, humans have a huge role to play. Isabella Loaiza Postdoctoral Associate, MIT Sloan Share
To overcome that limitation, Loaiza and Rigobon grouped the tasks into 750 clusters. One cluster, for example, consists of similar tasks associated with building sales websites in different occupations. Another comprises tasks associated with design review in fields as diverse as game design, engraving, and digital imaging.
The researchers then assigned each cluster of tasks three scores — the risk-of-automation score, the potential-for-augmentation score, and the EPOCH score, which indicates whether a task has related EPOCH capabilities that may protect that task against automation. From there, they compared those results against an aggregate change of employment in the U.S. labor force from 2016 to 2024.
The researchers found that tasks with a high risk of automation and/or augmentation came with a corresponding high risk of job loss. The automation-related findings reflect AI replacing human labor. As for augmentation, the decline in employment stems from an increase in productivity, meaning a firm can produce more without hiring additional workers. Augmentation isn’t necessarily partial automation, Loaiza noted; it simply means that workers can either complete tasks more quickly than before or perform tasks they couldn’t do before.
Human capabilities make a job more resilient
In contrast, all EPOCH capability groups were associated with employment growth, with the largest impact coming from the hope human capability and the second-largest from the opinion capability. These results, the researchers write, show a “shift towards a more human-intensive work not only in terms of the tasks performed in each occupation but also by the number of people employed in more human-intensive occupations.”
The positive impact of human capabilities was a pleasant surprise to Loaiza, but not necessarily an unexpected one. “Especially in developed economies, there are a lot of top-level managers in decision-making roles” who do work associated with high EPOCH scores, she said. “There’s a lot of value in human workers.”
For Loaiza, the findings reinforce the notion that AI strategy must emphasize augmenting workers rather than replacing them. It also gives enterprise leaders a road map for upskilling workers — with particular attention paid to “the fundamental qualities of human nature,” which are easy to overlook when training workers for a future driven by the use of AI.
“In a lot of fields workers can’t be fully replaced,” Loaiza said. “If you’re aiming for disruptive innovation or truly transformative business, humans have a huge role to play.”
Read next: When humans and AI work best together — and when each is better alone
| 2025-06-10T00:00:00 |
2025/06/10
|
https://mitsloan.mit.edu/ideas-made-to-matter/these-human-capabilities-complement-ais-shortcomings
|
[
{
"date": "2025/06/10",
"position": 72,
"query": "job automation statistics"
}
] |
Disrupted or displaced? How AI is shaking up jobs
|
Disrupted or displaced? How AI is shaking up jobs
|
https://www.afr.com
|
[
"Anjli Raval",
"Primrose Riordan",
"Sally Patten",
"Mandy Coolen",
"John Davidson",
"Nick Lenaghan"
] |
Recent examples of AI-driven lay-offs, from IBM to Duolingo, are fuelling questions about whether a slash and burn of white-collar roles is under way.
|
During Ocado’s most recent earnings call, chief executive Tim Steiner said the group’s advances in artificial intelligence and robotics had allowed it to fulfil online grocery shops at an ever faster pace.
In 2012, it took 25 minutes of human labour to pick a 50-item order. That is now down to 10. But Ocado’s technological progress means the company requires 500 fewer workers this year, after it already announced 2300 jobs would be at risk in 2023.
Loading...
Financial Times
| 2025-06-10T00:00:00 |
2025/06/10
|
https://www.afr.com/technology/disrupted-or-displaced-how-ai-is-shaking-up-jobs-20250610-p5m68p
|
[
{
"date": "2025/06/10",
"position": 9,
"query": "robotics job displacement"
}
] |
How AI is Reshaping Company Culture and Values
|
How AI is Reshaping Company Culture and Values
|
https://cerkl.com
|
[
"Penny Swift"
] |
“While AI adoption promises benefits such as enhanced efficiency, productivity, and innovation, it also presents significant challenges related to cultural ...
|
Key Insights Into AI in Company Culture AI is reshaping company culture : It’s no longer just about productivity. AI now influences values, behaviors, and engagement across the employee lifecycle.
It’s no longer just about productivity. AI now influences values, behaviors, and engagement across the employee lifecycle. Human-centered values remain essential: Mission, belonging, and transparency still drive performance and must guide AI integration to preserve trust and inclusion.
Mission, belonging, and transparency still drive performance and must guide AI integration to preserve trust and inclusion. AI personalizes the employee experience: From tailored content to intelligent recognition and nudges, AI helps employees feel seen, valued, and supported, wherever they work.
From tailored content to intelligent recognition and nudges, AI helps employees feel seen, valued, and supported, wherever they work. Impact can be measured in real-time: Metrics like eNPS, retention risk, and engagement depth, combined with dashboards and predictive insights, allow for fast, informed action.
Metrics like eNPS, retention risk, and engagement depth, combined with dashboards and predictive insights, allow for fast, informed action. Tools like Cerkl Broadcast scale culture with ease: By automating personalized content, segmenting audiences, and identifying behavior-based insights, Cerkl Broadcast empowers companies to grow a strong, AI-aligned culture without added complexity.
AI is no longer just a productivity tool. It’s becoming a quiet but powerful force in shaping company culture. AI-driven systems influence how values are modeled, decisions are made, and how employees engage with each other. However, successful AI integration also requires a culture that embraces change, continuous learning, and a mindset that views AI as a tool for enhancement rather than replacement.
The World Economic Forum (WEF) predicts that by 2030, 70% of the skills used in most jobs will have changed. In an article based on discussions at the 2025 WEF annual meeting, the COO of LinkedIn, Dan Shapero states that 80% of C-suite executives believe AI will kickstart a culture shift where teams are more innovative. 2025: the year companies prepare to disrupt how work gets done, highlights how the world of work has changed due to the impact of mobile devices, e-commerce, and social media. Now, AI is driving another wave of change that’s “creating demand for new jobs and skills, transforming roles and careers, and spurring productivity and innovation.” Furthermore, they predict AI will enable workers to focus on people-centric tasks rather than tedious administrative tasks.
“With this change, executives know they need to disrupt how their teams get work done. We are entering one of the largest change management exercises in history, and every business leader and professional will need to embrace it in order to unlock the value of AI. This will usher in a level of transformation that organizations and employees have never witnessed before.” Dan Shapero
As AI continues to reshape how work gets done, the foundation of a strong culture remains essential to navigate transformation. While AI brings unprecedented change, it’s these cultural anchors that help employees adapt, stay engaged, and find purpose amid evolving roles and expectations.
How AI and Company Culture Coexist in an AI-First Era
In today’s AI-first workplace, where algorithms assist with everything from content generation to employee engagement, it’s tempting to believe that technology alone can drive performance. But the reality is more nuanced. AI can support, but never replace, the human elements that define strong workplace cultures: shared purpose, values, and a sense of belonging.
Furthermore, a strong sense of belonging is a key component of a positive and inclusive company culture, where employees feel respected, valued, and supported. Feeling aligned with a company’s purpose and values is also a key driver of engagement, further reinforcing the importance of belonging.
Why Mission, Values, and Belonging Still Drive Performance
Even in an AI-driven environment, employees perform best when they feel connected to something greater than their individual tasks. Mission and values offer this sense of direction and meaning. They’re the cultural backbone that helps guide decision-making, foster ethical behavior, and unify diverse teams.
AI can amplify these core drivers, not replace them. For instance:
AI can highlight stories or recognition moments that reflect company values.
It can analyze internal content to ensure it reflects inclusive language.
It can personalize communication to ensure every employee feels seen and heard.
Belonging is especially crucial. AI can help scale belonging through targeted pulse surveys, personalized learning paths, and tools that support neurodiverse ways of working. However, AI must be grounded in a human-first strategy.
A lengthy McKinsey Digital report released in 2025 touches on “human centricity.” It emphasizes the need for diverse perspectives early in AI development and calls for transparent communication about how AI may impact jobs. However, it states that despite this, fewer than half of C-suite leaders involve nontechnical employees in the early stages of AI tool design. Yet human-centric development practices like agile pods and design-thinking, help ensure AI solutions are both ethical and widely accepted. Creating forums for employee input not only builds trust but reinforces a culture where people remain central to innovation.
Free Internal Communications Strategy Template to Elevate Your Approach Harness the power of AI to devise a strategy that will be a game-changer for your organization. Download NowLearn More Adapt to your organization’s needs
Customize for growth and success
Harness data-driven decisions
Drive engagement through tailored comms Download Free All fields are required.
How Hybrid Work and Digital Overload Strain Culture — and How AI Helps
Hybrid work has transformed flexibility into a norm, but it also creates silos, disconnects, and communication fatigue. The constant flood of messages and alerts on multiple platforms can quickly lead to digital overload, making it harder for employees to feel or become engaged or aligned.
Here’s where AI becomes a culture enabler:
Smart content distribution ensures employees get the right messages at the right time and channel, reducing noise.
ensures employees get the right messages at the right time and channel, reducing noise. AI-driven insights help managers detect disengagement or burnout early, based on behavioral data.
help managers detect disengagement or burnout early, based on behavioral data. Virtual assistants and chatbots reduce friction by helping employees find policies, tools, and resources quickly, in this way building trust through responsiveness.
In short, AI can’t “create” a workplace or company culture. But in the hands of intentional leaders, it can be a powerful tool to preserve, scale, and evolve it, especially as organizations navigate the complexities of hybrid, global, and digitally saturated workplaces.
Key AI Touchpoints Across the Employee Cultural Journey
AI is becoming embedded in every stage of the employee experience, not just boosting efficiency, but actively shaping and evolving company culture. When integrated with intention, AI can promote fairness, inclusivity, and an invaluable culture of continuous learning. From hiring and daily communication to development and ethical governance, AI’s touchpoints offer powerful opportunities to strengthen connection, clarity, and growth across the cultural journey.
Recruiting and Onboarding: Fair Starts and Smart Introductions
AI can help reduce bias in hiring by analyzing job descriptions and screening candidates more objectively. It can also offer a smoother onboarding experience through “culture concierge” chatbots that guide new hires, answer questions in real time, and reinforce organizational values from day one. This early alignment can successfully set the tone for inclusion and belonging.
Day-to-Day Communications: Personalization Without the Noise
AI-driven internal communication tools deliver personalized content via dynamic digests and channel-aware nudges, helping employees stay informed without feeling overwhelmed. These tools adapt to individual preferences and behaviors, creating a more respectful and responsive information culture. This is especially important in hybrid and deskless environments.
Learning and Growth: Fueling Curiosity and Community
AI encourages a culture of inquiry and discovery by tailoring development paths to each employee’s role, performance, and interests. From adaptive learning platforms to AI-powered mentor matching, these tools foster both individual growth and a sense of shared purpose. By automating repetitive tasks, AI frees up time for strategic thinking, collaboration, and creative problem-solving, in this way fueling the kind of innovation that keeps company culture vibrant.
Key Considerations for Successful AI Integration
To ensure AI strengthens rather than disrupts company culture, organizations must approach implementation thoughtfully and strategically. A successful rollout depends as much on mindset as on technology.
Companies should actively promote a learning-centric culture that encourages experimentation, accepts failure as part of innovation, and celebrates adaptability. This mindset helps reduce resistance and cultivates openness to evolving roles and processes. Comprehensive training and support are essential. Employees at all levels need access to ongoing learning, hands-on resources, and clear guidance to build AI fluency and confidence. Trust is another vital component. Leaders need to foster a culture of transparency and confidence by openly communicating how AI will be used, what data is going to be collected, and how decisions are made. This helps mitigate fear and encourages ethical collaboration between humans and machines. Organizations must take responsibility for the impact of AI. That means establishing ethical frameworks and clear usage guidelines, as well as regularly measuring outcomes to ensure AI is delivering on its promise to enhance productivity and engagement without compromising fairness, inclusion, or cultural integrity.
Free Internal Communications Strategy Template to Elevate Your Approach Harness the power of AI to devise a strategy that will be a game-changer for your organization. Download NowLearn More Adapt to your organization’s needs
Customize for growth and success
Harness data-driven decisions
Drive engagement through tailored comms Download Free All fields are required.
Why AI-driven Personalization Should Be at the Center of Organizational Culture
As workplaces grow more complex and distributed, a one-size-fits-all approach to internal communications and company culture simply no longer works. AI-driven personalization allows organizations to meet employees where they are — across roles, locations, languages, and preferences — while maintaining a cohesive cultural foundation.
Artificial Intelligence and Its Role in Shaping Organizational Work Practices and Culture by Obrain Murire discusses how profoundly AI is “transforming organizational landscapes, significantly influencing work practices and triggering cultural shifts.”
“While AI adoption promises benefits such as enhanced efficiency, productivity, and innovation, it also presents significant challenges related to cultural alignment, employee resistance, ethical concerns, and leadership communication. Effective leadership, transparent communication, and investments in skills development emerge as pivotal strategies for overcoming these obstacles and ensuring successful AI implementation.” Obrain Murire
The study highlights the importance of strategic alignment between AI initiatives and organizational culture as well as the value of leveraging AI capabilities like predictive analytics, automation, and personalization.
Tailored Content That Fosters Belonging
AI enables the delivery of hyper-relevant, personalized content to employees based on their roles, locations, interests, and previous engagements. Whether it’s safety updates for frontline workers or leadership insights for corporate teams, tailored messages make people feel seen and included. This sense of relevance fosters belonging, particularly in diverse, hybrid, or global organizations. It also helps reinforce shared values across every corner of the business.
Real-Time Insight and Action
AI-powered sentiment analysis and pulse surveys give leaders immediate insight into how teams feel and function. Rather than waiting for quarterly engagement reports, managers can receive instant prompts to act when morale dips or friction arises. This creates a more agile, empathetic culture where employee voices are heard and acted on in real-time — leading to higher retention rates.
Enhancing the Employee Experience Through Automation
By streamlining repetitive tasks, automating recognition, and personalizing workflows, AI helps reduce cognitive overload and frees employees to focus on meaningful, strategic work. This not only boosts morale and retention but also creates a workplace culture where productivity and well-being are mutually reinforcing. Employees who feel supported by smart systems are more likely to stay, grow, and contribute to long-term organizational success.
Specific processes used to improve efficiency via automation include:
Robotic process automation (RPA): AI-powered RPA tools automate repetitive tasks like data entry, document processing, and invoicing.
AI-powered RPA tools automate repetitive tasks like data entry, document processing, and invoicing. Workflow automation: AI can streamline workflows and processes, making them more efficient and less time-consuming.
Measuring Impact and Scaling Your AI Culture Strategy
As AI tools become integral to shaping workplace culture, organizations must take a deliberate, data-driven approach to measure their impact and refine strategies over time. It’s not enough to deploy AI. Rather, AI must be aligned with clear cultural goals and tracked for continuous improvement.
Core Metrics That Matter
To assess whether AI is enhancing culture, start by tracking key indicators such as:
eNPS (employee Net Promoter Score) to gauge satisfaction and advocacy
to gauge satisfaction and advocacy Engagement depth , including content interaction and platform usage
, including content interaction and platform usage Retention risk , identified through behavioral signals and turnover trends
, identified through behavioral signals and turnover trends Collaboration index, which tracks communication flows and cross-functional connectivity
AI doesn’t just report on the present—it helps organizations anticipate the future. Through predictive analytics, machine learning, and business intelligence, AI uncovers patterns in historical data that inform smarter, faster decisions and highlight emerging cultural trends before they escalate.
Predictive analytics: AI can analyze historical data to predict future trends, customer behavior, and market dynamics, allowing businesses to make more informed decisions.
AI can analyze historical data to predict future trends, customer behavior, and market dynamics, allowing businesses to make more informed decisions. Machine learning (ML): AI can analyze large datasets to identify patterns, trends, and insights that can inform business decisions.
AI can analyze large datasets to identify patterns, trends, and insights that can inform business decisions. Business intelligence (BI): AI can help businesses gain actionable insights from data, optimize operations, and reduce risks
Together, these metrics offer a holistic view of how AI influences both morale and performance.
Culture Health Dashboards and Feedback Loops
Once key metrics are defined, the next step is making them visible, actionable, and continuously relevant. AI-powered culture health dashboards give leaders real-time insights into how teams are feeling, collaborating, and evolving across departments, regions, and roles.
These dashboards are most effective when paired with a continuous improvement loop that turns data into action. By identifying trends in sentiment, engagement, or communication breakdowns, AI enables leaders to intervene early and with precision.
AI enhances this loop by:
Monitoring real-time sentiment from surveys, feedback, and communication tone
from surveys, feedback, and communication tone Identifying friction points or emerging risks in employee behavior or engagement
Suggesting targeted responses from manager nudges to policy tweaks, all based on predictive outcomes
Rather than reacting to problems after they’ve taken root, organizations can course-correct in the moment, fostering a more responsive, resilient, and people-centered culture.
A Scalable Roadmap: From Quick Wins to Future AI Use Cases
It stands to reason that you should start with quick wins like automated recognition, targeted content delivery, or AI-guided onboarding. Then you can work towards scaling to an enterprise rollout with a central culture hub, real-time analytics, and cross-platform personalization.
Looking ahead, generative AI can support even more advanced use cases from hyper-personalized learning experiences to predictive culture modeling, offering transformative potential when aligned with ethical and human-centered practices.
By grounding AI efforts in meaningful metrics and building on a strong cultural foundation, organizations can ensure that innovation scales with intention and success.
AI-Powered Culture at Scale with Cerkl Broadcast
In its 2025 AI Business Predictions, PwC states: “It’s now clear that AI can deliver value at scale.” But, to achieve that value, organizations must be strategic. They need to identify where AI can enhance outcomes, improve experiences, and drive culture without creating unnecessary complexity or cost. As PwC notes, AI may squeeze margins in one area while unlocking new opportunities in another, especially when hyper-personalization becomes a competitive advantage.
Dan Shapero, COO of LinkedIn, echoes this need for intentionality, emphasizing that scaling AI effectively requires the right tools and a workforce trained to get the most from them. That’s where Cerkl Broadcast comes in. The platform offers AI-powered internal communications designed to elevate employee experience and culture, no matter the size or structure of your organization.
Here’s How Cerkl Broadcast Supports AI-Driven Culture at Scale
By aligning AI with strategic communication goals, Cerkl Broadcast empowers companies to scale a culture that’s personalized, inclusive, and performance-driven without adding complexity for internal comms teams.
However, it’s important to set priorities for AI at scale by identifying what AI in company culture can do for you, your company, and your industry, as well as the implications of costs, value, and so on. We are happy to chat if you need more guidance.
What’s Next?
If your immediate priority is to transform and strategize your internal communications, we’ve got you covered. Our AI-powered internal communications strategy template is designed to simplify your communication planning process while aligning your strategies with organizational goals.
We will help you determine where your internal comms program is now, assess what you want to achieve, and establish the best ways to reach your goals. There’s advice on how to measure the level of success your new strategy has as well as the tools you need to support your plan.
It’s completely free, so why not download it now?
Free Internal Communications Strategy Template to Elevate Your Approach Harness the power of AI to devise a strategy that will be a game-changer for your organization. Download Now Download Free All fields are required.
FAQ
| 2025-06-10T00:00:00 |
2025/06/10
|
https://cerkl.com/blog/ai-in-company-culture/
|
[
{
"date": "2025/06/10",
"position": 70,
"query": "workplace AI adoption"
}
] |
Manufacturing Job Impact: How Many Roles Are Being Replaced?
|
Manufacturing Job Impact: How Many Roles Are Being Replaced?
|
https://patentpc.com
|
[
"Bao Tran",
"Patent Attorney"
] |
20 million manufacturing jobs could be displaced globally by 2030 due to automation. That's not a typo. Twenty million jobs—gone in the next ...
|
The manufacturing world is changing fast. Automation, robots, and artificial intelligence are transforming how things are made. While this means more efficiency and lower costs, it also means some jobs are disappearing. In this article, we’ll walk through the numbers and show you how many manufacturing roles are being replaced. But more importantly, we’ll talk about what you can do about it—whether you’re an employer, a worker, or somewhere in between.
1. 64% of manufacturing tasks could be automated with existing technology
Think about that for a second. Nearly two-thirds of what happens in a manufacturing plant today could be done by machines. That’s not a future prediction—it’s with the tech we already have today.
This includes tasks like welding, packaging, quality inspections, and even inventory movement.
What does this mean for the workforce? Jobs that involve routine and predictable physical tasks are most at risk. These are the roles where workers do the same thing over and over—tasks that machines can now do faster, with fewer mistakes, and without needing a break.
But it’s not all doom and gloom. There’s still a big gap when it comes to things like maintenance, complex assembly, and creative problem-solving. Machines are not replacing those anytime soon.
So, what’s the move? If you’re a worker in manufacturing, now is the time to upskill. Learn how to operate or maintain the machines replacing manual labor.
Employers should invest in training programs and reassign staff to more valuable roles—think machine supervision, diagnostics, or process optimization. The key is not to fight automation, but to move ahead with it.
2. 20 million manufacturing jobs could be displaced globally by 2030 due to automation
That’s not a typo. Twenty million jobs—gone in the next five years. This stat comes from global research and includes factories around the world, from small parts makers to large car manufacturers.
Asia is expected to be hit the hardest, especially countries like China that rely heavily on low-cost labor.
The reason is simple: as automation becomes cheaper and smarter, even countries that used to benefit from cheap labor will start using robots instead. They’re faster, more consistent, and ultimately cheaper in the long run.
So how can businesses prepare? If you’re a company leader, it’s time to do a serious assessment of which roles are at risk and which new ones you’ll need.
Start building a transition roadmap—maybe that includes hiring data analysts instead of line workers, or training technicians instead of replacing them outright.
And if you’re an employee? Stay flexible. Look at what roles are emerging in your company. Learn basic robotics, data input, or even something simple like machine troubleshooting.
Staying employed in manufacturing will require a mindset shift: from doing tasks to managing systems.
3. In the U.S., about 1.7 million manufacturing jobs have been lost to automation since 2000
This is not just a projection—it’s what already happened. Over the last two decades, automation quietly reshaped U.S. factories. Many people think jobs were lost due to outsourcing. While that’s partly true, automation played a much bigger role.
These were not just any jobs. They were high-paying, stable positions in places like Ohio, Michigan, and Pennsylvania. When they disappeared, entire communities felt the shock—economically, socially, and emotionally.
So what’s the solution? First, it’s time to rethink economic development. Local and state governments need to support programs that retrain laid-off workers.
Manufacturing companies should partner with community colleges and training centers to give workers a second shot in high-tech roles.
For individuals, don’t wait until your job is replaced. Look at trends within your plant. Are robots doing more of the work? Are new machines arriving? Ask your supervisor how you can get trained to work with the tech, not be replaced by it.
4. 42% of total manufacturing labor time is spent on repetitive tasks that are prime for automation
Almost half of all labor hours are used on things machines are perfect at—doing the same thing, over and over, with perfect consistency. Tasks like screwing in bolts, sealing packages, or checking part dimensions fall into this bucket.
That’s why automation is growing. It makes sense to offload these tasks to robots while humans focus on things that require judgment, decision-making, or creativity.
For business owners, this is an opportunity to redesign workflow. Instead of laying off workers, think about reorganizing teams. Let machines do the grunt work, and assign people to roles like quality improvement, innovation teams, or custom product development.
Workers, too, should start learning how to manage automated processes. If a robot is sealing boxes, someone needs to make sure it’s working correctly, troubleshoot errors, and perform maintenance. These are stable, high-paying roles that aren’t going anywhere soon.
5. The U.S. manufacturing sector has seen a 400% increase in robot usage since 1993
That’s an enormous jump. We’ve gone from barely using robots to making them a normal part of the factory floor. This has changed the entire dynamic of production—from how long things take to how many people you need.
It also means the types of workers needed have changed. We’re not just talking about engineers—machine operators, programmers, system techs, and even line supervisors need to understand how robotics fits into the picture.
Companies that have embraced robotics are generally seeing better output and lower error rates. But if they don’t train their people alongside the machines, they hit a wall. A fancy robotic arm is useless if no one knows how to reset it during a system glitch.
The big advice here is to invest in internal training. Set up weekend courses, cross-train staff, or bring in experts to mentor your teams. Workers should be encouraged to get familiar with robotic controls—even at a basic level.
A machine that can do the work of five people still needs someone to guide it.
6. 88% of manufacturing executives believe automation will increase in the next decade
This stat shows there’s little debate about where the industry is heading. Nearly nine out of ten manufacturing leaders agree: automation is only going to grow from here.
That tells us two things. First, automation is no longer an experiment. Second, those who don’t plan for it will fall behind.
If you’re in management, this is the time to make a long-term automation strategy. That doesn’t mean replacing everyone with machines.
It means taking a hard look at your operations and asking: where can machines take over repetitive work, and where do humans add the most value?
Talk with your teams. Don’t just drop in a new robot without explaining what it means for people’s roles. Use this shift to engage your staff—ask for their input, offer training, and make the transformation a shared goal.
For workers, this stat is a wake-up call. If you’re in a job that could be done by a robot, don’t panic—but don’t stand still. Ask your employer what training is available. Learn how to work with new tech. The future is being built in front of us, and those who adapt will thrive.
7. 25% of industrial robots installed globally are used in the automotive industry
One out of every four industrial robots in the world is found in car manufacturing.
That tells us something important: this sector leads the way in automation. It makes sense—building cars involves thousands of parts, precision welding, painting, and assembly lines that never stop.
Because of that, other industries often look to automotive as a model for how to adopt automation. So if you’re not in automotive, don’t assume you’re safe. What starts there often spreads elsewhere—consumer electronics, packaging, even food production.
If you’re a supplier to the auto industry, your clients are already using robots—and they expect the same efficiency from you. That means you need to keep up.
Consider upgrading old equipment, streamlining your workflows, or adopting semi-automated systems even in smaller plants.
For job seekers or students, this stat can be a guide. Learn what skills are in demand in the automotive world—robot programming, system monitoring, and advanced welding—and take those to other industries. Automation in automotive isn’t just a trend. It’s a signal of what’s coming everywhere else.
8. Each robot added to a manufacturing facility replaces approximately 1.6 human jobs
This number can feel personal. It puts a clear, human cost on automation. One robot doesn’t just help the team—it often takes over enough tasks to replace more than one person. That’s why the rise in automation can cause fear among workers.
But there’s another side to this. While jobs may be lost, new types of roles often emerge—robot maintenance, programming, system supervision. The key is whether the company helps workers make that leap.
For business owners, this is where leadership matters. Don’t let fear dominate your shop floor. Be upfront. If you’re adding robots, show employees where they can fit into the new system. Offer real options, not just words.
If you’re a worker, this is the moment to make yourself irreplaceable—not by doing what the robot does, but by doing what it can’t. Get trained to install, repair, or manage robotic systems. Even learning basic troubleshooting can set you apart.
This stat doesn’t have to be a threat—it can be a challenge to grow and evolve with the tools shaping the future.
9. 47% of manufacturing employers report difficulty in finding skilled human workers
Almost half of manufacturers say they can’t find the people they need. Not because there are no applicants, but because there are too few with the right skills. That’s a big problem when your factory depends on both machines and people.
The biggest shortages are in areas like CNC programming, mechatronics, machine maintenance, and robotics. These roles aren’t always taught in traditional schools, and many experienced workers are retiring, leaving a gap behind.
For employers, this is a wake-up call. If you can’t hire the talent you need, you have two choices: wait and hope, or build it yourself.
That means creating in-house training, partnering with local colleges, or setting up apprenticeships. Invest in the people already working for you. Upskill them before you’re forced to scramble.
For workers, this is an opportunity. If employers are desperate for people who can run or fix machines, that could be you. Even a six-month certificate course could double your chances of getting hired or promoted. The demand is high—you just need to meet it halfway.
10. By 2025, automation could displace 75 million jobs across all sectors, with manufacturing hit hardest
This is a big, global number. Seventy-five million jobs across all industries may vanish in just a few years—and manufacturing is expected to take the brunt of it. This doesn’t mean every job will disappear, but it does mean many will look very different.
The sectors most at risk involve physical, predictable work. That includes many roles on the manufacturing floor. If a task can be mapped out and repeated consistently, there’s a good chance a machine can do it soon.
So what should companies do? Think beyond just cutting jobs. Instead, redesign them. Blend human skills with machine speed. Use automation to remove the boring, repetitive parts of a job and keep the creative, problem-solving parts for people.
For individuals, take control of your future. You don’t need to become a robotics expert overnight. Start small—learn to use a digital dashboard, understand how production data is tracked, or get certified in industrial safety systems. Every bit of new knowledge adds value.
This stat is not about fear—it’s about preparing for change before it hits full speed.
11. 10% of all global employment is in manufacturing, making the impact of automation widespread
One out of every ten jobs in the world is in manufacturing. That’s huge. It means automation isn’t just a local issue—it’s global.
Every country, from industrial giants like Germany to emerging economies like Vietnam, is affected. And with that kind of scale, changes in this industry ripple far beyond the factory walls.
For governments and policy makers, this stat is a signal to get ahead of the curve. If millions of people could be out of work in a single sector, it’s essential to plan now.
That might mean funding reskilling programs, offering tax breaks for businesses that retrain workers, or building support systems for people in transition.
For business leaders, remember that your decisions don’t just affect your company—they affect entire communities. When automation replaces human workers, the ripple effect touches housing, schools, and local economies.
Consider strategies that keep people engaged, like shifting workers to new divisions or building automation teams internally instead of outsourcing.
If you’re a worker, you are part of something much larger. Use this moment to reassess your role. Ask yourself: is your job repetitive? Can it be taught to a machine?
If so, start learning adjacent skills—things close to your job but harder to automate. That could be quality assurance, logistics, or training others on safety procedures.
12. 84% of manufacturing firms plan to increase investment in automation post-2020
The pandemic changed a lot. One big shift? Companies realized how fragile their operations were when workers couldn’t come in.
That’s why more than four out of five manufacturers say they’re now spending more on automation. It’s not just about cost savings—it’s about staying resilient.
If you run a manufacturing firm, this is your chance to modernize. Use this momentum to review your processes. Which areas saw the most disruption during the pandemic? Where were you bottlenecked by labor shortages?
That’s where automation can help most. But don’t just buy machines—build a strategy around them. Plan for how they’ll be maintained, who’ll operate them, and how you’ll integrate them into your workflow.
For workers, this shift means it’s time to get tech-savvy. You don’t need a degree in engineering. But understanding how automation fits into your job will be a huge advantage. Get curious.
Ask how new machines work. Volunteer to be trained. Be the person who helps lead the change, not the one left behind.
And for smaller businesses that feel automation is too expensive—think small. Even a single semi-automated station or a smart sensor can make a big impact. You don’t need to go fully robotic overnight. Just start.
13. In China, up to 100 million low-skill manufacturing jobs are at risk of automation
China’s economy was built on manufacturing. Millions of people moved from rural areas to cities to work in factories. But now, even these jobs—once seen as safe for decades—are being threatened by machines.
The reason is simple: as Chinese wages go up, it becomes cheaper for companies to automate rather than hire more people. That’s why China is also one of the biggest investors in industrial robots. They’re racing to stay competitive.
If you’re operating a factory in China or sourcing from one, expect major changes in labor models. Don’t assume cheap labor will stay cheap. Automation is taking over fast, and suppliers will need to deliver higher productivity with fewer people.
For workers in similar positions elsewhere—India, Vietnam, Indonesia—this is your warning sign. Low-skill manufacturing roles are vanishing. Now is the time to upgrade your skills. Basic machine operation, reading digital panels, or even learning how to calibrate sensors can keep you employable.
This stat isn’t just about China. It’s a preview of what’s coming for every country that relies on low-cost labor in manufacturing. The world is moving to smart factories, and the only way to keep up is to get smarter with it.
14. Germany leads Europe in industrial robot density with 371 robots per 10,000 workers
Germany is a great example of how automation and jobs don’t always have to conflict. They have one of the highest robot-to-worker ratios in the world, and yet they also have strong employment and competitive wages in manufacturing.
So what’s the secret? It’s about balance. Germany invests heavily in vocational training and apprenticeships. Their workforce doesn’t just work next to robots—they know how to work with them. That’s a model worth copying.
If you’re a manufacturer in another country, look at what Germany does right. Don’t just drop machines on the floor and expect results. Train your people, build a pipeline of technical talent, and treat automation as a tool, not a replacement.
Workers should also take note. High robot density doesn’t mean fewer jobs—it means different jobs. Many German workers shift into technician, operator, or system analyst roles. The key is adaptability.
And if you’re a student or early-career worker? Learn from the German playbook. Combine hands-on skills with technical knowledge. You’ll be far more valuable than someone who knows only one side of the process.
15. 45% of manufacturers are implementing AI and machine learning in production processes
This isn’t science fiction anymore. Nearly half of manufacturers are using artificial intelligence to improve production. They’re not just adding robots—they’re adding smart robots. Systems that can predict when machines will break, adjust processes automatically, or even spot product defects using computer vision.
This is a big shift. Before, automation was about doing tasks. Now, it’s about thinking during tasks. That changes everything.
If you’re in management, this is your chance to build a smarter, leaner operation. AI can help you find waste, reduce downtime, and improve quality in ways that human observation never could. But it requires clean data and trained people. Make sure you invest in both.
Workers should see this as a new opportunity. AI doesn’t work on its own—it needs people to set it up, guide it, and interpret what it finds. Learn how to work with data. Even understanding basic production metrics or using dashboards can give you a huge edge.
And for businesses not yet using AI—don’t wait. You don’t have to start with a massive project. Try using AI-powered quality control cameras or predictive maintenance software. Once you see the ROI, scaling up becomes easier.
16. The global industrial robotics market is projected to reach $75 billion by 2030
That’s a massive number. It tells us one thing for sure—robots are big business, and they’re only getting bigger.
As more factories look to automate, demand for robots and the systems that run them will keep rising. That means not only will robots become more common, but also more advanced, more affordable, and more accessible to companies of all sizes.
For manufacturers, this projected growth should be a green light. If you’re not already exploring robotics, now is the time. Prices are coming down, and even small or mid-sized operations can start with simple automation tools.
It doesn’t have to be a full robotic assembly line—a single robotic arm or automated inspection station can have a real impact.
It’s also time to think long-term. As the market grows, competition will increase too. Companies that embrace robotics early will be in a better position to win contracts, reduce waste, and hit tighter deadlines.
The sooner you get comfortable with robotics, the easier it will be to stay ahead.
If you’re looking for a future-proof career, this stat points straight at robotics. Everything from design, to installation, to repair will need skilled people. Whether you’re on the floor or behind a computer, the opportunities are real. Get in now while the demand is growing.
17. Manufacturing productivity has increased 3x faster than job growth since 1990
This stat tells a story. Factories are making more—but they’re doing it with fewer people. Since 1990, manufacturing productivity has surged ahead while job growth has lagged far behind. That’s the power of automation: more output, less labor.
It sounds great from a business perspective. But it also explains why workers feel left out. If you’ve been in manufacturing for years, you’ve probably seen it. Fewer people on each shift. Shorter lines. More machines doing work people used to do.
So what’s the move here? If you’re in leadership, make sure your productivity gains aren’t just measured in numbers. Invest in the people who are still there. Give them tools to grow, not just survive.
More training, more cross-skilling, more chances to be part of continuous improvement efforts.
For workers, this is about learning how to contribute to productivity, not be replaced by it. Find ways to add value—can you suggest process improvements? Help with machine changeovers?
Assist with tracking output or errors? These things show you’re not just working in the system—you’re helping improve it.
Productivity doesn’t have to come at the cost of people. But only if people stay sharp and involved.
18. 60% of small-to-mid-sized manufacturers expect partial job replacement by 2027
It’s not just the big players. Even smaller manufacturers—companies with 50, 100, or 200 workers—are starting to adopt automation. And most of them already expect some jobs will be replaced in the next couple of years.
This tells us the trend is moving downmarket. You don’t need to be a global brand to automate anymore. Tools are cheaper, software is easier to use, and integration is more flexible. As a result, no shop is too small to be impacted.
If you’re a small business owner, the key is to be intentional. Don’t automate just because everyone else is. Look at your operations. Where are the bottlenecks? What tasks are hurting quality or taking too long? That’s where automation makes sense.
At the same time, communicate clearly with your team. Let them know your goals aren’t to replace them, but to improve efficiency—and help them grow into new roles. Be transparent, offer training, and involve employees in the transition.
If you’re working at a smaller firm, this is your window. Learn how to set up or operate whatever tech is being introduced. Smaller companies often promote from within, so being the go-to person for a new machine or system can move your career forward fast.
19. 70% of manufacturing CEOs cite automation as their primary strategy for cost reduction
Cost pressure is nothing new in manufacturing. But what’s changed is how companies plan to deal with it. Instead of cutting corners or outsourcing, CEOs are looking at automation to bring down costs while boosting reliability and quality.
That makes sense. Machines don’t take breaks, don’t get injured, and don’t need health insurance. But they do need maintenance, oversight, and smart programming—which still comes back to people.
If you’re a CEO or part of the C-suite, this stat means automation needs to be part of your cost strategy—not an experiment, but a pillar. Think long-term.
What investments today will reduce your labor and quality costs five years from now? How can you reallocate workers to higher-value roles instead of cutting them?
Also, don’t overlook the hidden costs of poor automation—downtime, integration issues, or poorly trained staff. The savings only happen when the tech is used properly. Make sure you build a plan that includes training and support, not just hardware.
If you’re on the ground, understand that your company is probably thinking this way. So how can you stay valuable? Get proactive. Ask how you can help with the automation strategy.
Learn to track production data, spot trends, or improve machine efficiency. You’re not a cost—they’re investing in the right people to help control them.
20. Robots in manufacturing have reduced injury rates by 72% in high-risk environments
This is one of the clearest benefits of automation—and one that often gets overlooked. When robots take over dangerous tasks like welding, lifting heavy items, or working with toxic materials, people stay safer. That’s a win for everyone.
Factories are full of risk. Sharp tools, hot surfaces, heavy machines—injuries happen, and they’re expensive. Not just in dollars, but in lost time, morale, and even reputation.
By moving risky tasks to machines, companies are protecting their workforce while improving uptime and quality.
If you’re an employer, this stat is a strong case for automation. Look at your injury reports from the last five years. Are there common patterns—lifting strains, repetitive motion injuries, burns, cuts? These are all areas where robotics or simple automation can help.
You don’t need to automate the whole process. Even something as simple as adding a robot to handle loading/unloading or packaging can drastically reduce risk.
For workers, this also means peace of mind. Yes, machines may shift the job, but they’re also making it safer. And someone still needs to supervise the system, ensure safety protocols are followed, and keep everything running smoothly. That could be your role.
Safety and automation can—and should—go hand in hand. Everyone wins when work becomes smarter and safer.
21. 38% of manufacturers say reskilling workers is a major challenge of automation adoption
Adding machines is easy. Changing mindsets? That’s the hard part. Over a third of manufacturers say the biggest challenge they face isn’t the tech—it’s getting their people ready for it.
This isn’t surprising. Learning new systems, adopting digital tools, or shifting from manual to machine oversight takes time, effort, and often a new way of thinking.
For employers, this is a critical reminder: buying new equipment without training is like giving someone a car without showing them how to drive. If you want your investment in automation to pay off, reskilling must be part of the plan from day one.
Start simple. Offer short workshops. Let workers shadow others using new tools. Build step-by-step guides or host lunch-and-learn sessions.
Learning doesn’t have to mean long hours in a classroom—it can be hands-on, fast, and directly tied to the new equipment on the floor.
For workers, don’t be afraid of the learning curve. Reskilling doesn’t mean going back to school full-time. It could be as small as learning how to monitor a machine dashboard, troubleshoot a simple error, or read automated reports.
These skills make you more valuable—not just to your current employer, but to future ones too.
The bottom line: automation without training leads to frustration. But with the right reskilling strategy, you can turn change into opportunity—for your team, your business, or your own career.
22. South Korea has the highest robot density in manufacturing: over 900 per 10,000 workers
No country uses robots in manufacturing more than South Korea. With more than 900 robots for every 10,000 workers, it has created one of the most advanced, efficient manufacturing systems in the world.
This doesn’t mean the country has no jobs—it means it has different kinds of jobs.
How did they get there? By investing in both technology and education. South Korean workers are highly trained, and many factories blend human skill with machine precision to get the best results. Automation is used as a partnership tool, not just a replacement tool.
For manufacturers in other countries, this is a clear roadmap. High robot density doesn’t have to lead to job loss—it can lead to smarter, faster, safer production when balanced with skilled labor.
The goal isn’t just more machines—it’s more capable people to work alongside them.
If you’re in the workforce, South Korea’s example proves that the more automated things get, the more skilled workers are needed. The key difference? They shifted from doing tasks to managing systems.
You can too. Learn how machines work. Dive into process control. Get familiar with quality metrics.
High robot density doesn’t mean low employment—it means high capability. That’s a future worth building.
23. 30% of U.S. manufacturing job losses from 1990 to 2007 were due to automation
This stat helps clear up a common myth. While offshoring often gets the blame for job losses in U.S. manufacturing, automation played a bigger role than many realize.
Nearly a third of job losses in that time period came not from jobs going overseas—but from machines taking over.
This is important because it shows the shift to automation isn’t new. It’s been happening for decades. What’s changed is the speed and the type of jobs affected. Before, it was mostly about physical labor.
Now, even jobs involving decision-making or monitoring are being automated with AI and smart systems.
For policymakers and leaders, this means we need new strategies. It’s not enough to try to “bring jobs back” if the jobs themselves no longer exist. Instead, focus on creating new types of roles—ones that work alongside automation, not against it.
For workers, it’s a reminder that waiting for the old jobs to come back might not be realistic. The better bet is to look forward. What roles are growing? What skills are in demand now?
Focus on those and put yourself in a position to lead the next phase of manufacturing, not just hold onto the past.
24. 52% of production roles are expected to transform significantly in the next 5 years
More than half of all production jobs will look very different by the end of this decade. That doesn’t mean they’ll vanish—it means the work inside them will change.
What used to be done by hand may now be managed by a system. What was once simple assembly may now involve digital tracking or machine interfacing.
This transformation is already happening. You might still have the same job title, but the tools, processes, and expectations are shifting under your feet.
For employers, the smartest thing you can do right now is prepare your team for this shift. Start with role audits. Look at each position and ask: What’s changing? What’s being automated? What new skills will this person need in one year? In three? Build your training plans around those answers.
For workers, embrace this as a chance to future-proof your career. You don’t need to start over—you need to adapt. Learn the tools that are entering your space.
Whether it’s a new scheduling system, a piece of software, or an updated inspection tool, getting familiar now gives you confidence and leverage.
Jobs are changing—but the people who change with them will always be needed.
25. The adoption of robotics is growing at 14% CAGR in the manufacturing sector
A 14% compound annual growth rate is no joke. It means that every year, more robots are being added to factory floors—and the pace is speeding up. This kind of sustained growth shows that automation isn’t a temporary trend. It’s becoming the norm.
If you run a business, this is a signal to start planning, not reacting. If you wait too long, you risk falling behind competitors who are producing faster, with better quality, and at lower cost.
That doesn’t mean automating everything at once. Start with one process, one department, or one pain point. Test, learn, and scale.
Also, be realistic. Robotics can be powerful, but they’re not plug-and-play. You’ll need to invest in integration, training, and ongoing maintenance. Having a clear roadmap makes all the difference.
For employees, this growth means more opportunities than you might think. Sure, some jobs go away—but many new ones show up. Robot operators, maintenance techs, system analysts, automation coordinators… these roles didn’t exist 10 years ago, and now they’re everywhere.
Look at where your company is investing. Are new systems being installed? Are workflows being changed? That’s your chance to get involved early. Learn on the job, ask questions, and be the person your company counts on to help drive the change.
26. 65% of manufacturers say automation has improved product quality and consistency
This stat points to one of the biggest benefits of automation that often goes unnoticed—better products.
Nearly two-thirds of manufacturers say that using machines has made their products more consistent, more accurate, and higher in quality. That matters, because no customer wants to deal with defects or delays.
Automation reduces human error. It ensures the same process happens the same way, every time. That might be applying adhesive, drilling holes, or checking dimensions. When a robot is programmed right, it doesn’t forget steps or get tired.
And that leads to fewer mistakes, fewer recalls, and happier customers.
If you’re managing a production line, this stat should catch your attention. Improved quality means fewer warranty claims, less rework, and better customer satisfaction. Use automation not just to lower labor costs, but to raise your product standards.
Automate where precision is needed most, and track the improvements.
For employees, there’s a hidden opportunity here. As product quality becomes a bigger priority, roles like quality control, calibration, testing, and oversight are more important than ever.
These are great areas to shift into, especially if you’re good at spotting issues or understanding process flow.
Quality is the backbone of brand reputation. With automation, you can help make sure it stays strong—and be a key player in keeping it that way.
27. In Japan, over 50% of factory operations are now fully automated
Japan has always been ahead when it comes to industrial efficiency. And now, more than half of its factory operations are completely automated. That means robots, sensors, and systems are handling entire processes—from start to finish—without human intervention.
This doesn’t mean there are no jobs. It means the jobs are different. Instead of standing at a conveyor belt, people are programming machines, monitoring systems, and analyzing performance.
Automation has become Japan’s solution to an aging workforce, and it’s working.
If you’re in manufacturing, this is a glimpse into the future. Fully automated operations aren’t just for massive companies anymore. With cloud software, plug-and-play robotics, and affordable sensors, even mid-sized businesses can go far down this path.
But success depends on people. A fully automated plant still needs system managers, process engineers, and rapid-response troubleshooters.
So if you’re an employee, don’t fear full automation—figure out how to fit into it. Learn system basics, understand how machines communicate, and position yourself as a go-to person when things need attention.
For companies, Japan’s example shows the value of planning and commitment. Don’t automate piece by piece without a plan—build toward a fully integrated system, where every part of the factory talks to each other. That’s how you reach true efficiency.
28. Manufacturing job displacement has contributed to a 20% wage stagnation in affected regions
There’s a tough side to this story too. In regions where manufacturing jobs have been displaced, wages have often stagnated. In some areas, they’ve even dropped.
This is especially true in towns where factories closed or automated heavily without a plan to retrain or redeploy workers.
Why does this happen? Because once those solid, middle-class jobs vanish, they’re replaced with lower-wage service jobs—or nothing at all. Without new training or opportunities, workers are stuck, and the local economy suffers too.
For policymakers, this stat should raise alarms. If you want strong communities, you need strong employment—and that means helping people shift, not sink. Fund training centers. Build apprenticeship programs. Incentivize companies to reskill, not just automate.
For employers, think long term. If automation is part of your plan, make wages and job quality part of it too.
Use automation to lift your team—not leave them behind. Higher-skilled roles deserve higher pay, and supporting worker transitions builds loyalty and community goodwill.
And for workers, remember: staying still is not a strategy. Wages grow when skills grow. Look for in-demand areas—robot operation, inspection systems, scheduling software. Every skill you add increases your earning potential and your options.
29. 76% of manufacturers believe hybrid human-robot teams are the future of the industry
Most manufacturers now believe the best model isn’t all-human or all-robot—it’s both. Hybrid teams are where machines handle the repetitive, risky, or precision tasks, and humans manage, adapt, and improve the processes around them.
This is a powerful approach. It allows companies to scale up without losing flexibility. And it makes employees part of the system, not pushed out by it.
If you’re leading a manufacturing operation, this is the model to aim for. Don’t look at automation as an end goal—look at it as a tool to build better teams. Train your people to work alongside the tech.
Set clear roles. Let robots handle the grind, and free your staff to focus on quality, innovation, and improvement.
For employees, this is encouraging news. You don’t have to be replaced—you can be enhanced. Learn how to collaborate with machines. Know when to let automation do its thing, and when to step in and make adjustments. This kind of balance is exactly what modern factories need.
And if you’re just entering the industry, focus your learning on systems thinking. Understand how workflows connect. How data moves. How machines and people support each other. These hybrid skills are in high demand—and they’ll open doors for years to come.
30. Up to 800 million jobs globally could be affected by automation by 2030, with manufacturing being the most exposed sector
Let’s end with the big picture. Eight hundred million jobs—nearly a quarter of the global workforce—could be affected by automation in the next few years. And no industry is more exposed than manufacturing.
That might sound overwhelming. But “affected” doesn’t always mean “eliminated.” Many of these jobs will evolve, shift, or become more tech-driven. The key difference between loss and evolution will be how businesses and workers prepare.
For governments, this is a call to create forward-thinking labor policies. For educators, it’s time to build training systems that match the speed of industry. For employers, it’s a responsibility to automate with care, and invest in the people behind the processes.
And for individuals, this is your moment. You don’t have to be a bystander. Learn something new. Stay curious. Talk to your manager about growth paths. Watch where the industry is heading and take steps to meet it there.
Manufacturing is transforming fast. But transformation isn’t destruction—it’s a rebuild. And everyone—from plant floor to executive suite—has a role in shaping what comes next.
wrapping it up
We are standing at a major turning point in the history of manufacturing. The numbers don’t lie—jobs are changing fast, and many roles as we know them today are being reshaped or replaced by machines. But this is not a story of loss. It’s a story of transition, of new possibilities, and of reinvention.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://patentpc.com/blog/manufacturing-job-impact-how-many-roles-are-being-replaced
|
[
{
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"query": "job automation statistics"
},
{
"date": "2025/06/15",
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] |
This A.I. Company Wants to Take Your Job - The New York Times
|
This A.I. Company Wants to Take Your Job
|
https://www.nytimes.com
|
[
"Kevin Roose"
] |
Mechanize, a San Francisco start-up, is building artificial intelligence tools to automate white-collar jobs “as fast as possible.”
|
Years ago, when I started writing about Silicon Valley’s efforts to replace workers with artificial intelligence, most tech executives at least had the decency to lie about it.
“We’re not automating workers, we’re augmenting them,” the executives would tell me. “Our A.I. tools won’t destroy jobs. They’ll be helpful assistants that will free workers from mundane drudgery.”
Of course, lines like those — which were often intended to reassure nervous workers and give cover to corporate automation plans — said more about the limitations of the technology than the motives of the executives. Back then, A.I. simply wasn’t good enough to automate most jobs, and it certainly wasn’t capable of replacing college-educated workers in white-collar industries like tech, consulting and finance.
That is starting to change. Some of today’s A.I. systems can write software, produce detailed research reports and solve complex math and science problems. Newer A.I. “agents” are capable of carrying out long sequences of tasks and checking their own work, the way a human would. And while these systems still fall short of humans in many areas, some experts are worried that a recent uptick in unemployment for college graduates is a sign that companies are already using A.I. as a substitute for some entry-level workers.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://www.nytimes.com/2025/06/11/technology/ai-mechanize-jobs.html
|
[
{
"date": "2025/06/11",
"position": 73,
"query": "AI employers"
},
{
"date": "2025/06/11",
"position": 71,
"query": "AI employers"
},
{
"date": "2025/06/11",
"position": 72,
"query": "AI employers"
},
{
"date": "2025/06/11",
"position": 72,
"query": "AI employers"
},
{
"date": "2025/06/11",
"position": 67,
"query": "AI employers"
},
{
"date": "2025/06/11",
"position": 15,
"query": "AI employers"
}
] |
Despite $2M salaries, Meta can't keep AI staff — talent reportedly ...
|
Despite $2M salaries, Meta can't keep AI staff — talent reportedly flocks to rivals like OpenAI and Anthropic
|
https://www.tomshardware.com
|
[
"Jowi Morales",
"Contributing Writer",
"Social Links Navigation"
] |
Meta has an over $2 million annual pay package for AI talent, but still keeps losing its people to OpenAI and Anthropic.
|
As companies pour billions of dollars into AI infrastructure, demand for AI talent to program and run these AI data centers is also greatly increasing. Deedy Das, a Venture Capitalist at Menlo Ventures and a former Google Search staff member, posted on X that Meta has an over $2 million annual pay package for AI talent, but still keeps losing its people to OpenAI and Anthropic. He said that he’s personally heard three such cases this week alone, which is major news given the size of Meta’s compensation.
Statistics say that for every 10.6 from DeepMind, 8.2 from OpenAI, and 2 from Hugging Face that move to Anthropic, it only loses one employee for each company. This movement shows that the startup is quickly growing and that many people from competing AI labs want to work for it. We don’t know how much the company offers, though, but we can safely assume that it’s at least on par or, more likely, substantially larger than what the competition pays.
According to the SignalFire research, beyond salary, Anthropic's edge is a unique culture that embraces "unconventional thinkers" and gives employees true autonomy, as well as flexible work options, a lack of title politics and forced management tracks. Furthermore, employees report an embrace of intellectual discourse and researcher autonomy, compared to bureaucracy elsewhere.
Meta is currently offering $2M+/yr in offers for AI talent and still losing them to OpenAI and Anthropic. Heard ~3 such cases this week.The AI talent wars are absolutely ridiculous.Today, Anthropic has the highest ~80% retention 2 years in and is the #1 (large) company top AI… pic.twitter.com/YSv5UNV5H2June 10, 2025
Aside from this, Das also mentioned that Anthropic has an unusually high two-year employee retention rate. The tech industry average is around 40% to 50%, with the retention rate dropping over the past couple of years due to layoffs. However, many AI startups’ two-year retention rates vary between 63% and 80%, with Anthropic holding the top spot. Next to it is Google’s DeepMind, which has a retention rate of 78%.
Many new employees who work in AI labs also come from major tech giants. It’s estimated that 5.4% of new hires come from Google’s non-AI divisions, while 4.3% are former Meta staffers. A further 3.2% used to work with Microsoft, and another 2.7% are ex-Amazon employees, while 2.1% were previously affiliated with Stripe, and 1.7% came from Apple. This accounts for nearly 20% of new employees in AI labs that have come from tech giants.
Massive tech companies used to be the dream job for many people, especially as they offered competitive pay, strong career growth, many opportunities within the industry, and prestige. However, 2024 was a bad year for the tech sector, with many companies laying off thousands of people. Intel was one of the biggest losers, with the company laying off 15% of its workforce (roughly 15,000 employees) after its disastrous August 2024 financial report. However, other companies like Amazon, Meta, Microsoft, Dell, and AMD were also hit with workforce reductions.
On the other hand, the continued growth in the AI sector is pushing many talented individuals towards these startups, as shown by the data. This will likely continue in the near future, as companies and nations continuously build more and more AI data centers.
Stay On the Cutting Edge: Get the Tom's Hardware Newsletter Get Tom's Hardware's best news and in-depth reviews, straight to your inbox. Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors
| 2025-06-11T00:00:00 |
2025/06/11
|
https://www.tomshardware.com/tech-industry/artificial-intelligence/despite-usd2m-salaries-meta-cant-keep-ai-staff-talent-flocks-to-rivals-like-openai-and-anthropic
|
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},
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"date": "2025/06/11",
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"date": "2025/06/11",
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] |
AI Engineer Salary in India [2025] – How Much Can You Earn?
|
AI Engineer Salary in India [2025] – How Much Can You Earn?
|
https://www.upgrad.com
|
[] |
Discover AI engineer salaries in India in 2025—freshers earn ₹5–7 LPA, while experienced pros can make ₹20+ LPA, depending on expertise and ...
|
Entry-Level AI Engineer Salaries (0-1 years)
AI career prospects in India are highly promising for engineering professionals. Entry-level engineers start at ₹5,00,000 per annum and earn up to ₹12,70,000 annually. Junior roles focus on building basic ML models and data processing. These positions provide hands-on experience with AI technologies. The average compensation across experience levels is ₹2,00,000 yearly. Below is a breakdown of entry-level AI engineer salaries in India:
Entry-level AI Engineer Salaries Role Average Annual Salary Range (₹) Additional Pay (per annum) Junior AI Engineer ₹2,00,000 - ₹7,00,000 Nil Data Analyst ₹4,00,000 - ₹8,00,000 ₹56,400
Source: Glassdoor
Curious about career paths beyond engineering? Explore top career options after PCB with salary insights.
Mid-Level AI Engineer Salaries (4-6 years)
With 4-6 years of experience, a mid-level AI professional's salary ranges from ₹7,00,000 to ₹15,60,000 annually. Their daily work focuses on building and improving AI systems for companies. They handle tasks like creating machine learning models and implementing AI solutions. Specializing in areas like natural language processing can lead to higher salaries. Deep learning expertise also commands better compensation in the market. These engineers help companies become more efficient through AI technology. Their work involves analyzing data and optimizing AI models for various projects.
Mid-level roles generally include positions like AI Developer or Machine Learning Engineer. Their work centers on integrating AI into existing company systems.
Mid-Level AI Engineer Salaries Role Average Annual Salary Range (₹) Additional Pay (per annum) AI Developer ₹9,00,000 - ₹14,90,000 ₹1,49,235 Machine Learning Engineer Salary ₹10,00,000 - ₹20,00,000 ₹2,00,000
Source: Glassdoor
Senior-Level AI Engineer Salaries (7-9 years)
AI engineers in India earn competitive salaries that increase with experience. Senior AI engineers with 7-9 years of experience earn between ₹8,00,000 and ₹20,50,000 lakhs annually. They often receive additional bonuses averaging ₹2,00,000 per year. This compensation reflects their advanced technical skills and leadership responsibilities.
As these engineers progress to higher roles like AI Architect or Head of AI, their work becomes more strategic. They focus on designing complex AI systems that solve business challenges. These senior professionals guide their teams to make careful decisions that align with company goals.
Senior-Level AI Engineer Salaries Role Average Annual Salary Range (₹) Additional Pay (per annum) AI Architect ₹23,90,000 - ₹26,20,000 per annum ₹3,00,000 Head of AI ₹61,000 - ₹65,000 per month NA
Source: Glassdoor
Artificial intelligence salary per month for engineers with over a decade of experience can exceed ₹1,00,000. At this level, positions like Lead AI Engineer involve broader responsibilities. They create large-scale AI solutions that impact the entire organization. These experienced professionals also develop strategic plans and work across different departments.
Top AI Certification Courses and Programs to Boost Your Career
Certification courses can accelerate your career if you're aiming for roles in machine learning, AI, or software development. Below is a table of various courses and certifications offered by top online providers to help you achieve a competitive AI developer income, including:
Course/Certificate Provider Duration Difficulty Level Job Opportunities Post Graduate Certificate in Data Science & AI (Executive) upGrad 12 months Intermediate Data Scientist, Machine Learning Engineer, AI Specialist, AI Engineer, Natural Language Processing Engineer, Gen AI Engineer, Software Developer, and Big Data Analyst Advanced Certificate Program in Generative AI upGrad 5 months Advanced Executive Post Graduate Program in Data Science & Machine Learning upGrad 13 months Advanced AI for Everyone Coursera 6 hours Beginner Introduction to Artificial Intelligence Coursera 13 hours Beginner Generative AI for Software Development Coursera 1 month Beginner
| 2025-06-11T00:00:00 |
2025/06/11
|
https://www.upgrad.com/blog/artificial-intelligence-salary-india-beginners-experienced/
|
[
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Unions will push AI regulation and pay at productivity summit - AFR
|
Unions will push AI regulation and pay at productivity summit
|
https://www.afr.com
|
[
"David Marin-Guzman",
"Primrose Riordan",
"Sally Patten",
"Mandy Coolen",
"John Davidson",
"Nick Lenaghan"
] |
White-collar groups want protections for workers disrupted by artificial intelligence while blue-collar ones are seeking wage rises through ...
|
Unions will push to regulate artificial intelligence in the workplace and for workers to gain a greater share of productivity benefits through higher pay in Prime Minister Anthony Albanese’s newly announced summit.
White-collar unions want the government to enforce a “digital just transition” for workers affected by AI, raising comparisons with measures for coal and gas-fired power jobs hit by the shift to renewables, while adopting requirements to compensate workers when their data is used to train artificial intelligence.
Loading...
| 2025-06-11T00:00:00 |
2025/06/11
|
https://www.afr.com/work-and-careers/workplace/unions-will-push-ai-regulation-and-pay-at-productivity-summit-20250611-p5m6k2
|
[
{
"date": "2025/06/11",
"position": 55,
"query": "artificial intelligence labor union"
},
{
"date": "2025/06/11",
"position": 58,
"query": "artificial intelligence labor union"
},
{
"date": "2025/06/11",
"position": 56,
"query": "artificial intelligence labor union"
},
{
"date": "2025/06/11",
"position": 89,
"query": "AI labor union"
},
{
"date": "2025/06/11",
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},
{
"date": "2025/06/11",
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},
{
"date": "2025/06/11",
"position": 89,
"query": "AI labor union"
},
{
"date": "2025/06/11",
"position": 90,
"query": "AI labor union"
},
{
"date": "2025/06/11",
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},
{
"date": "2025/06/11",
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"query": "artificial intelligence labor union"
},
{
"date": "2025/06/11",
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"query": "artificial intelligence labor union"
},
{
"date": "2025/06/11",
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{
"date": "2025/06/11",
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"position": 78,
"query": "AI labor union"
},
{
"date": "2025/06/11",
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"query": "artificial intelligence labor union"
}
] |
Fact Check Team: US companies cut jobs amid AI investments - KDBC
|
Fact Check Team: US companies cut jobs amid AI investments
|
https://cbs4local.com
|
[
"Janae Bowens",
"Fact Check Team",
"Https"
] |
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly ...
|
is showing signs of stability, but a wave of layoffs is sweeping through major companies, including Procter & Gamble, Microsoft, Citigroup, Walmart, Klarna, Disney, , and Warner Bros. Discovery, Despite a steady labor market, these companies are under pressure to reduce costs due to global uncertainty linked to tariffs imposed by President Donald Trump.
Experts indicate that while government job cuts are decreasing, companies are still feeling the financial strain. Many have resorted to raising prices, but layoffs remain a significant cost-cutting measure. Concerns persist about how tariffs and trade tensions might slow the economy overall.
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly due to AI investments. Shopify CEO Tobias Lütke told employees in April that they must justify why tasks cannot be performed by AI before requesting additional workers and resources.
Google is also making moves to free up cash for AI investments. The company is offering voluntary buyouts to U.S. employees across multiple teams, including search, ads, engineering, and research, as it plans to spend about $75 billion this year on capital expenses. This is part of Google's strategy to expand AI features like its new "AI Mode" search tool while maintaining cost efficiency, according to
Industry leaders warn that AI could trigger a "white-collar job apocalypse." The CEO of Anthropic told that AI could eliminate half of all entry-level white-collar jobs within 1 to 5 years, potentially causing unemployment rates to spike to 10 or even 20 percent. He is advocating for more transparency from AI companies and urging the government to prepare for the potential impact, suggesting a "token tax" on AI models to help redistribute wealth if job losses become severe.
The latest job report also highlights a challenging landscape for new graduates, with March and April job gains revised down by a combined 95,000 jobs. As companies slow hiring, current workers are opting to stay in their positions.
| 2025-06-11T00:00:00 |
https://cbs4local.com/news/nation-world/us-companies-cut-jobs-amid-ai-investments-artificial-intelligence-proctor-gamble-microsoft-citigroup-walmart-klarna-disney-paramount-warner-bros
|
[
{
"date": "2025/06/11",
"position": 76,
"query": "artificial intelligence layoffs"
},
{
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"query": "artificial intelligence layoffs"
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{
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"query": "artificial intelligence layoffs"
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{
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{
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{
"date": "2025/06/11",
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"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/11",
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"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/11",
"position": 68,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/11",
"position": 74,
"query": "artificial intelligence layoffs"
}
] |
|
Companies are overhauling their hiring processes to ... - Fortune
|
Companies are overhauling their hiring processes to screen candidates for AI skills—and attitudes
|
https://fortune.com
|
[
"Sage Lazzaro"
] |
Company leaders say they're now specifically considering candidates' proficiency with AI tools and sometimes prioritizing these skills over ...
|
As companies race to incorporate AI into their workflows, it’s not only models and tools they’re relying on for a competitive advantage but, increasingly, people. Across industries, 66% of business leaders said they would not hire someone without AI skills, according to the 2024 Work Trend Index Annual Report by Microsoft and LinkedIn.
Company leaders and professionals in the hiring space say they’re now specifically considering candidates’ proficiency with AI tools and sometimes even prioritizing these skills over professional experience. They’re also reimagining their hiring processes, developing new ways to screen for candidates’ familiarity with and ability to use AI tools. Their approaches range from focusing interview conversations on AI—providing an opportunity to gauge a person’s familiarity with and attitude toward the technology—to having candidates complete tasks with AI tools and observing how they use them.
“Every organization is—no matter what the skill set might be—looking to see if they can find someone that potentially has some experience with AI, and specifically generative AI, and now you’ve got agentic AI on the horizon, so they’re definitely looking for people who have experience in those areas,” said Thomas Vick, senior regional director for technology at talent and consulting firm Robert Half.
Skills take center stage
Vick said he noticed the emphasis on AI skills in hiring emerge about a year ago and continue to accelerate ever since. The clear trend is that AI skills are now deemed as important as experience and education.
In the LinkedIn and Microsoft report, which included insights from a survey of 31,000 people from 31 countries, 71% said they would hire a less experienced candidate with AI skills over a more experienced candidate without them. PwC’s 2024 AI Jobs Barometer states that skills sought by employers are changing at a 25% higher rate in occupations most able to use AI, such as developers, statisticians, and judges. Additionally, a study on hiring trends in the U.K. found that candidates with AI skills are landing wages 23% higher compared to those without, making a greater difference than higher degrees up until the PhD level.
Alyssa Cook, a senior managing consultant at hiring and staffing firm Beacon Hill, has also observed that hiring teams are more willing to hire candidates with AI skills. What’s more, she said, skills with specific AI tools a company is using or wants to adopt can even take precedence over an overall greater depth of experience with AI.
“Companies would rather hire a candidate who has hands-on experience with a particular tool they are implementing if they have the ability and interest to train up on other skills,” she said
The newfound focus on AI skills in hiring is happening across the various departments of companies. Vick said he’s seen it across accounting, finance, creative roles, and especially technical roles. According to job listing data cited by the Wall Street Journal, one in four U.S. tech jobs posted so far this year are looking for people with AI skills.
The AI test
Automation firm Caddi is one company where this is playing out across the organization. CEO Alejandro Castellano said interviewers regularly ask candidates about their experience using AI tools; for technical candidates, the firm encourages individuals to use AI coding assistants like Cursor, Claude Code, or Copilot during code analysis and technical exercises.
“We want to see how they work in real conditions,” said Castellano.
The approach flips on its head the way companies have traditionally tested candidates for software engineering jobs. Typically, coding tests have been designed to isolate candidates from their real workflows in order to assess their fundamental knowledge. In a world where AI tools are increasingly used to help employees accomplish particular tasks, however, this old approach hardly makes sense. In their day-to-day duties, developers and engineers must be able to work effectively with these systems to enhance their own productivity—not delve into the realm of theory and concepts.
“We’re moving toward exercises that reflect how engineers actually work, how they search, use AI suggestions, and debug. We care as much about how they solve a problem as we do about the end result,” Castellano said.
Ehsan Mokhtari, CTO of ChargeLab, a company that creates software for electric-vehicle charging, said encouraging candidates to use AI tools has become a formal part of the firm’s hiring process. The effort started a year ago after it was noticed that candidates were avoiding using AI tools, assuming they would be penalized for it. So the company revamped its hiring process and its broader operations to embrace AI tools, starting with restructuring take-home challenges for technical candidates and then rolling out the effort for positions across the company.
“We started with engineering, but we’re now pushing it org-wide. Sales came next—they were surprisingly fast to adopt AI. Tools like ChatGPT are now common for them for research and outbound comms. We’ve made AI literacy part of departmental OKRs,” Mokhtari said. “That means every function—support, product, sales, engineering, operations—is expected to include it in their hiring considerations.”
In working with clients on their hiring, Robert Half’s Vick has seen a variety of approaches to screen candidates for AI skills. Some companies are turning to their contractors, Vick says, asking those with AI experience to help them evaluate candidates during the interview process. One of the most popular techniques he’s seen is bringing job candidates into a “sandbox” environment and having them actually show how they would utilize AI within that environment to complete various tasks. It’s the same idea as the reimagined coding assessments, but applicable to any role in the organization.
Attitude goes a long way
While company leaders generally say they would hire a candidate who is proficient with AI over one who isn’t, they also stress that there’s more to it than skills: Attitude also plays a significant role.
ChargeLab’s Mokhtari explained that he looks at AI proficiency in two layers: skill set and mindset. While skill set is highly desirable, it can also be easily taught. Mindset, however—being proactive in using AI, curious about where it can add value, and not being combative toward it—“is harder to coach and more important long-term,” he said.
Castellano echoes this idea. He’s found that understanding how someone thinks about and works with AI is one of the strongest signals the company has found to gauge that person’s ability to keep delivering value in a fast-changing environment.
“We’re not just looking for people who know the tools,” he said. “We’re looking for those who are curious, adaptable, and thoughtful about how they use AI. That mindset makes the biggest difference.”
| 2025-06-11T00:00:00 |
2025/06/11
|
https://fortune.com/2025/06/11/ai-hiring-process-employee-skills-candidate/
|
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What jobs will AI replace & which are safe in 2025 [+ data]
|
What jobs will AI replace & which are safe in 2025 [+ data]
|
https://blog.hubspot.com
|
[
"Flori Needle"
] |
10 Jobs AI Will Likely Replace · 1. Telemarketers · 2. Bookkeeping Clerks · 3. Compensation and Benefits Managers · 4. Receptionists · 5.
|
Nearly 70% of all employees believe that AI will change 30% or more of their work.
And according to the World Economic Forum’s Future of Jobs Report 2025, it’s already happening. By 2030, 92 million jobs are expected to be displaced, even as 170 million new ones are created.
While it’s easy to fall down the rabbit hole of tools like “Will Robots Take My Job?” or Reddit threads, the more likely scenario is that AI will shift the skills we need. In fact, our workplaces are already adapting.
In this post, I’ll explore where AI is already disrupting the workforce, which jobs are most at risk due to AI, which jobs are least likely to be automated, and how to stay competitive in your career.
Table of Contents
The State of Artificial Intelligence in 2025 New research into how marketers are using AI and key insights into the future of marketing. Marketing AI Tools
Marketing AI Tools Practical Tips
Practical Tips Trends and Statistics
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Artificial intelligence disruption is already happening.
We’ve officially moved past asking if AI will change the workplace. According to McKinsey, 74% of organizations have adopted AI in at least one area of their business, and many are prioritizing tools that help their teams become even more effective.
The numbers from our 2025 State of AI Marketing report indicate an even higher adoption rate. Here’s how people are using AI in their current roles:
51% reported that their organizations encourage the use of AI in the workplace. Another 19% don’t have a stated policy for using AI.
64% of marketers are already using AI in their current role, and 91% agreed that teams at their company use AI to assist them in their jobs.
75% report their company’s AI investment has returned a somewhat or very positive ROI.
83% shared that their companies have invested in AI or automation tools for employees.
Of those, 80% use AI features built within broader tools (e.g., HubSpot’s Breeze AI), and 45% use standalone AI-driven platforms (e.g., ChatGPT).
Wondering how people are using AI? Here’s what our numbers show, ranked from 1-5 — of note, the numbers are similar across the board between B2B, B2C, and nonprofits:
Image and design generators like DALL-E and Midjourney (40% of marketers). General-purpose chatbots like Chat GPT, Copilot, and Claude (39%). SmartAI video or audio editing AI tools (37%). Voice or narration generators like Speechify, Murf, and Soundraw (34%). Video or animation generators like Sora and Synesthesia (31%).
How AI Is Already Reshaping Work
Some of the biggest shifts include:
Sales teams using AI to find new leads and analyze sales calls faster.
Content creators generating and repurposing multi-channel content faster than ever.
Customer service using AI chatbots to handle common questions before escalating issues to human representatives.
Finance teams are automating everything from invoice matching to fraud detection.
In fact, I’m working with a team to implement AI into sales/marketing alignment processes to feed warmer leads to salespeople so they spend less of their day on outbound email or cold calling. That same team is also exploring how to use AI to perform SEO optimizations at scale.
Such functions lead to greater ROI for businesses, and this is shooting the AI optimism off the roof, according to our latest State of AI report.
The numbers tell an optimistic story.
The WEF’s 2025 report shares its expectations that:
86% of companies will see AI-driven transformation by 2030.
92 million existing jobs will be displaced.
40% of current job skills will become outdated.
170 new jobs will emerge due to AI.
That’s a net growth of 78 million jobs! They’ll just look a little different from what we see today.
Leaders are still betting on human experience.
HubSpot co-founder and CTO Dharmesh Shah has a positive outlook on the future of AI. In fact, he thinks bots and AI will make us better at our jobs and more secure in our careers, not the other way around.
Brand builder Samyutha Reddy thinks similarly. Her teams regularly use generative AI, but she’s still actively hiring because AI can’t replace the human experience.
“We value writers in our society because they’re able to give us a thought-provoking human perspective on the world… It’s about humans sharing opinions on very real topics that help build your perspective on how you feel about something. So an AI could really never replace that human perspective,” she says.
And she’s right, in my opinion. That perspective — your perspective — is the value each of us brings. Additionally, it’s my belief that AI is raising the bar. Mediocre content and execution just don’t cut it anymore.
Source
What’s holding teams back from using AI?
No discussion of what jobs AI will replace is complete without exploring why people are resistant to implementing this evolving technology.
In our 2025 State of AI in Marketing report, marketers shared the biggest roadblocks holding back full-scale implementation.
Data privacy concerns (43%).
Training time and investment (39%).
Too many tools that do similar things but don’t integrate well (35%).
Integration challenges with existing or legacy systems (33%).
Preferring different tools from what their company invests in (26%).
Resistance to change within the organization (26%).
Job security concerns (25%).
Compliance concerns (22%).
I think it’s telling that resistance to change and job security concerns are relatively low on the list. More importantly, nearly 10% of people say they haven’t experienced barriers to adoption in the past year.
The State of Artificial Intelligence in 2025 New research into how marketers are using AI and key insights into the future of marketing. Marketing AI Tools
Marketing AI Tools Practical Tips
Practical Tips Trends and Statistics
Trends and Statistics And More! Get Your Free Report Learn more Get Your Free Report Download Free All fields are required. You're all set! Click this link to access this resource at any time. Download Now
What jobs can AI replace?
Even with this positive outlook, change can be scary. In fact, a 2025 PEW study shows that 52% of U.S. workers are worried about AI affecting their jobs, and 32% think it will lead to fewer opportunities.
So if you’re one of the more skeptical people wondering if your job is at risk of being replaced by AI, you’re in good company.
But instead of letting fear drive you, let’s use data and pattern analysis to start answering your question. Then, look for new ways to develop the skills you need to feel confident in your future prospects.
Assembly Lines and Algorithms — Oh my!
In the past, concerns over machines replacing human jobs rose with the automation of physical tasks common in factories and warehouses. Now, the same is happening with AI and the fear of it replacing human intelligence in the workplace.
“Think about your position and what your position will transform to in the next 12 to 24 months. Whether you like it or not, this is happening, and it is going to happen so fast that it will change the fabric of our society. If you haven’t already done so, search for whatever your title or your position is and look at all the [AI] tools that exist within that,” Duran Inci, CEO at Optimum7 says.
He’s not wrong. Before ChatGPT hit the mainstream in 2023, AI was already being integrated into business operations — especially by leaders looking to increase productivity through automation. It’s just that it was much more technical in nature.
Between 2012 and 2024, jobs with AI-specific skill requirements increased sevenfold. In contrast, regular jobs only doubled within the same timeframe. But while the demand for professionals with technical AI skills may be on the rise, traditional jobs with high exposure to AI may be left on the back burner.
What makes a job vulnerable to AI?
The more AI-exposed tasks your role includes, the greater the risk of automation.
While people often fear a future dominated by bots, most American workers aren’t there yet. The 2025 Pew report shows some different numbers from the HubSpot State of AI in Marketing report.
However, it’s my opinion that this can be explained by the fact that HubSpot’s numbers are largely from tech-forward companies more likely to adopt AI and new technologies. What’s more, the people we surveyed are primarily marketers who must embrace new technologies to help their companies maintain a competitive edge.
Here’s how the numbers from Pew and HubSpot compare:
Pew HubSpot Workers who say they rarely use AI in their jobs, if ever. 64% 34% Workers who say some work is done with AI. 16% 64%
Additionally, Pew’s data shows that:
31% of non-AI users say some of their jobs could be done with AI.
45% believe not much (or none) of what they do can be automated.
So even while adoption is growing, people’s exposure to AI depends widely on demographic factors that include age, industry, education, and job type. Check out the breakdown below.
Source
Those more likely to be exposed to AI have job tasks like:
Gathering and processing information.
Analyzing and evaluating data.
Making decisions or solving problems.
Adhering to compliance standards.
And these are the jobs that AI can replace. Not entirely, but to a significant extent, affecting 30% of current worked hours by 2030.
“I would say 40% to 50% of creative and generic positions are already 80% there, and you will lose millions of dollars in the next 10 to 20 years if you don’t already have the plus version of ChatGPT and if you don’t already use it,” Inci continues.
Common Traits of AI-Replaceable Jobs
Across industries, the jobs most vulnerable to AI tend to involve:
Repetitive tasks or clearly defined workflows.
Data-heavy decision-making that doesn’t require nuance.
Low levels of human interaction or emotional intelligence.
Standardized outputs that can be templated or scaled.
Of course, these roles won’t disappear overnight. But as automation becomes more capable and cost-effective, many of these functions may shift.
So, what kinds of jobs are on the chopping block? Let’s look at the top 10 most at-risk next.
10 Jobs AI Will Likely Replace Telemarketing Bookkeeping Clerks Compensation and Benefits Managers Receptionists Couriers Proofreaders Computer Support Specialists Market Research Analysts Advertising Salespeople Retail Salespeople
If your current role involves repetitive tasks, clearly defined workflows, or heavy data processing, this is your sign that it’s probably time to think ahead.
There’s some positive news — you don’t have to start from scratch (though you can, if you’re so inclined).
In my experience, your deep knowledge in your space means that you have a great deal of value to employers and clients. You just need to learn how to work with AI so you can shift into more advisory, strategic, or system-level roles.
Keep that in mind if you find your job on this list.
1. Telemarketers
Risk of Automation: Imminent
This role is almost entirely script-based, which makes it easy to replicate with voice AI and robocalling systems. With low conversion rates and high burnout, it’s one of the first places companies look to automate. Telemarketing jobs are expected to decline by 18.2% by the year 2032.
Ideas for next steps: If you’re a successful human telemarketer (and you’re definitely human if you’re looking for a career!), you’ve probably got great social perceptiveness and emotional intelligence. You might want to consider moving into a sales development or customer success role that requires a more human touch.
By learning how AI tools like chatbots or CRM assistants work, you can position yourself as the person who manages, not competes with, automation.
2. Bookkeeping Clerks
Risk of Automation: Imminent
Most bookkeeping is already being automated. Software like QuickBooks and Xero can categorize expenses, reconcile accounts, and generate reports with little to no oversight. Jobs in this role are expected to decline by 4.5% by 2032. If the bulk of your role currently handles manual, repetitive bookkeeping tasks, it’s a good time to expand your knowledge.
Ideas for next steps: You can become a great partner for clients and employers who need help understanding what to do with the information so they can make sound financial decisions. By learning how AI and automation tools work — and understanding the data they produce — you can become the person who interprets results and guides smarter business decisions.
3. Compensation and Benefits Managers
Risk of Automation: Moderate to High Risk
As companies grow, especially across multinational markets, a human and paper-based system can present more hurdles, time delays, and costs. That’s why there is a 64% chance of full automation within the next two decades. Automated benefits systems can save time and effort by providing benefits to large numbers of employees, and companies like Ultipro and Workday are already being widely adopted.
Ideas for next steps: Focus on the human side of HR — strategy, change management, and employee experience. You can also dig deeper into total rewards strategy, where contextual decision-making and cross-team collaboration are harder to automate.
4. Receptionists
Risk of Automation: Imminent
Pam predicted this back on The Office, but in case you’re not a fan, she said automated phone and scheduling systems can replace a lot of the traditional receptionist role — especially at modern technology companies that don’t have office-wide phone systems or multinational corporations.
Ideas for next steps: If you thrive in a people-facing role, consider pivoting into office management, employee experience, or event coordination — fields where emotional intelligence and logistics still require a human in the loop.
5. Couriers
Risk of Automation: High
Major companies are investing in drones and robots, so it’s only a matter of time until this space is dominated by automation altogether. (Can you imagine getting a pizza delivered by drone?)
Ideas for next steps: If the road is still the place for you, there are plenty of people who make a living as personal concierges, and that probably has some staying power due to the personal touch. Otherwise, getting into things like logistics and supply chain could be a great way to take what you’ve learned and future-proof yourself.
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6. Proofreaders
Risk of Automation: Imminent
AI writing assistants like Grammarly, Hemingway, and ChatGPT already handle grammar and clarity. Tone still is hit or miss in my experience. While you still need human oversight to make sure these are right, it’s likely these jobs will be going away soon.
Ideas for next steps: If you’re a skilled proofreader, lean into roles that require contextual editing — such as content strategy, editorial development, or brand voice. You can also offer human review of AI-generated content, where nuance still matters.
Gill Hill, an editor and brand voice specialist, feels secure in her role, saying that, at least for now, “AI can’t sound human. That’s why it gets tone so wrong. Tone is fundamentally the communication of emotion. And emotion is (for now) a purely human concept.”
7. Computer Support Specialists
Risk of Automation: Moderate to High
This is one of those tricky ones. The field is projected to grow by 6.2% by 2032. However, automation will play a big role here with AI-powered help desks, like those powered by ChatGPT, which can now resolve common Tier 1 tech issues without human involvement. Many support tickets are being triaged and sometimes even solved by bots.
Ideas for next steps: Want to stay in this space? Focus on getting the higher-tier skills to stay a few steps ahead of AI advancement so you can handle the trickier issues. Better yet, work towards complex problem-solving or systems-level IT. Roles in infrastructure, cybersecurity, or enterprise tech require judgment, interpretation, and cross-functional collaboration that AI can’t easily replicate.
8. Market Research Analysts
Risk of Automation: Moderate to High
Market research analysts play an incredibly important role in developing messaging, content, and products, but automated AI and surveys can compile this information more and more efficiently.
Ideas for next steps: While automated research tools can have a leg up in scale, speed, and accuracy, human researchers have hands-on knowledge and personal experience that an algorithm can’t develop. Human researchers who use automation tools can create a more effective process.
9. Advertising Salespeople
Risk of Automation: Moderate to High
As advertising shifts away from print and TV and towards web and social media landscapes, people simply aren’t needed to manage those sales for marketers who want to buy ad space.
Social media platforms make it easy for people to buy space through free application program interfaces (APIs) and self-serve ad marketplaces, removing the salesperson and making it faster and easier for users to run their ads.
Even the ads themselves are leaning more and more into artificial intelligence. When looking at actors or voice actors, we’re also seeing a trend of companies using AI voices for advertisements, like commercials.
Ideas for next steps: Specialize in campaign strategy, performance consulting, or brand partnerships, all of which are areas that require relationship-building and tailored insight. Understanding how AI optimizes ad performance can also position you as a smarter strategist.
10. Retail Salespeople
Risk of Automation: High
Companies are democratizing the shopping experience with features like self-checkout, and the modern buyer is much more internet-savvy and more likely to do internet research and make a buying decision on their own. I was blown away at Disneyland this year when I only had two interactions at their retail store: One when someone greeted me and asked if I needed help finding something, and the other when someone checked my receipt on the way out.
Ideas for next steps: On the other hand, the care a retail salesperson brings during a 1:1 interaction differs from automated and unemotional support, and many consumers prefer to interact with humans during the support process.
To truly uplevel your skills, focus on roles that deliver high-touch, human-centered service — like personal styling, experience design, or community engagement. The best retail workers can evolve into brand ambassadors or CX specialists that AI simply can’t replicate.
What jobs are safe from AI?
In addition to the millions of new jobs expected to result from AI and automation advances, certain roles remain largely immune to this. Going by the exposure scale, these jobs have medium to low exposure to AI.
This is because their most important work activities range from:
Training and teaching others.
Performing general physical activities.
Repairing and maintaining equipment.
Establishing and maintaining interpersonal relationships.
Resolving conflicts and negotiating with others.
Developing and building teams.
Selling or influencing others.
Judging the qualities of objects, services, or people.
There’s so much more. For example, it’s hard to replace firefighters with robots (though they could potentially make the job safer!).
These activities revolve around uniquely human traits, like emotional intelligence, contextual creativity, discernment, and manual labor that AI cannot replicate yet. This is also echoed by the World Economic Forum’s (WEF) top 10 work skills of 2025, some of which include:
Analytical thinking and innovation.
Critical thinking and analysis.
Creativity, originality, and initiative.
Leadership and social influence.
Complex problem-solving.
And this is true for business leaders and their hiring practices. At GuerrillaBuzz, co-founder Yuval Halevi says they rely mainly on human creativity to drive their marketing efforts. “We apply AI mainly to repetitive and time-consuming tasks, removing the monotony and freeing up time for more creative work.”
In the words of Ian Shine at the WEF, these jobs are safe because “one of the human brain’s biggest advantages over AI is the fact that it is attached to a real human body.”
Without further ado, here are some jobs that are safe from AI.
10 Jobs AI is Not Likely to Replace Human Resource Managers Sales Managers Marketing Managers Public Relations Managers Chief Executives Event Planners Writers Software Developers Editors Graphic Designers
1. Human Resources Managers
It’s kind of in the name, but your company’s Human Resources department will likely always need a human at the helm to manage interpersonal conflict with the help of non-cognitive and reasoning skills. Problem solving, contextual understanding, and unique business knowledge also make a human better equipped for this job.
The field is projected to grow 7.3% by 2032 as companies scale and need more robust structures for supporting and helping employees.
2. Sales Managers
Sales managers need high emotional intelligence to hit their monthly quotas, network and collaborate with customers, and motivate and encourage the larger sales team. Managers also have to analyze data and interpret trends. The high levels of intelligence required and the constant need to adapt to new situations make this role safe from automation.
3. Marketing Managers
Marketing managers have to interpret data, monitor trends, oversee campaigns, and create content. They also have to nimbly adapt and respond to changes and feedback from the rest of the company and customers, making this another human-forward career AI isn’t quite ready to replicate.
A unique contextual understanding and previous business experience make a human stand out from an automated system.
Interestingly, of the marketers we connected with for our 2025 State of Marketing Report, less than 20% saw AI taking over most marketers’ job duties. The vast majority agree that AI will help marketers do their jobs, either as a partner (50%) or by simply taking over the menial tasks (21%).
And those using platforms like HubSpot’s Breeze AI are likely to come out ahead.
4. Public Relations Managers
Successful public relations (PR) managers rely on a network of relationships and contacts to procure press placements and buzz for the companies they represent, making this another completely safe role.
PR managers who have to raise awareness around an issue or mission need a particularly human touch to raise funds or get people to participate in a campaign, too — and jobs are expected to grow 7.6% by 2032.
Most importantly, PR managers are often on the go, attending events and being on hand to provide support if need be — a computer will never be able to do this.
5. Chief Executives
It’s nearly impossible to automate leadership — after all, it’s hard enough to teach it. Chief executives must inform broad strategy, represent companies’ missions and objectives, and motivate huge teams of people working for them. Executives also have years of prior experience that make them successful.
Companies may answer to stakeholders and boards of directors, who likely wouldn’t want a robot giving them an earnings report, either.
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Trends and Statistics And More! Get Your Free Report Learn more Get Your Free Report Download Free All fields are required. You're all set! Click this link to access this resource at any time. Download Now
6. Event Planners
Event planning is a growing field, and if you ask anyone on our events team here at HubSpot, whether you’re planning an event for employees, customers, or an industry event with tens of thousands of attendees, the planning process has many, many moving parts involved.
Planners have to coordinate and negotiate with vendors, contractors, and freelancers to make things come together, and the organizational and people skills involved will make this another near-impossible role to automate.
Planners also attend events, ready to step in and troubleshoot on the spot. An automated tool simply couldn’t be present.
7. Writers
Writers have to ideate, create, and produce original written material, something that AI writing tools have not yet been able to replicate in the same way as humans (see recent BuzzFeed controversy about AI-generated travel articles).
There are also many types of writers, some of whom might be more at risk. An experienced journalist will likely be favored over AI, but a freelance copywriter may no longer be needed by a business that now uses AI to write Instagram captions. However, if they can shift to a more strategic role, they’ll be well-positioned.
As my friend Aly Goulet, a B2B copywriter and content strategist, says, “AI didn’t kill content. It challenged us to create better content.”
8. Software Developers
Software engineering and development is complex enough for human beings, and the time and skill investment needed to create applications, software, and websites will be tough to replicate — especially since developers need to execute perfectly to create great products for customers.
The field is expected to grow by 30.3% by 2032, so if you’re a software developer, you’re sitting pretty for now.
Source
9. Editors
While the automated proofreading technology mentioned previously can take a load off, editors have to review writers’ submissions for clarity, accuracy, comprehensiveness, and originality.
Some software can spot-check for clarity and scan for plagiarism, but the editor role must be carried out by a human in order to read work as another human would.
Today, 86.33% of marketers who use AI, for example, always make edits to the content that AI produces, according to our State of AI in Marketing survey.
10. Graphic Designers
Image generators like Midjourney and DALL-E make it easy for people to create what they want. And with ChatGPT’s new image capabilities, it’s getting easier. But I don’t see it replacing graphic design anytime soon. It’s an artistic and technical field best suited for someone with fine arts training and experience. Like writing, graphic design work must be original and tailored to a unique use case.
If a graphic designer is working with a business, building a relationship between both parties throughout the design process is also required. Plus, some image creators have been found to plagiarize artists’ work — a lawsuit is likely the last thing people want.
Navigating Artificial Intelligence
Kate O’Neill, the author of Tech Humanist and founder of KO Insights, explains that jobs that require emotional intelligence will be safer in the immediate future:
“This is going to be a continuously moving target, but for the time being, what AI can’t do well is use emotional intelligence, understand situational context, make judgment calls, and generally see nuance and meaning like we do.”
She goes on to add, “That means any kind of job that benefits from these kinds of human attributes is better off done by a human. A computer or robot may assist you in performing efficiently, but for now, you’re the one who adds the expertise on how to perform appropriately.”
Yuval Halevi echoes this from another angle: “Having knowledge that’s beyond the obvious is crucial. AI tools like ChatGPT and Gemini can sometimes provide inaccurate or misleading information.
“Without a deep understanding of your specific area, you might be misled or produce subpar work. Expertise ensures you can critically evaluate AI outputs and maintain high-quality standards.”
In short, expertise still matters. Your ability to think critically, question the output, and bring meaningful insights to the table is what sets you apart, especially in jobs where AI plays a supporting role.
How to Future-Proof Your Career
The good news is that AI isn’t eliminating jobs at the rate many people fear. While some of the roles I identified as most at-risk above will likely see some reduction in force, it’s likely that many of these fields will simply shrink by attrition. And remember, the WEF still expects to see a net gain of 78 million jobs by 2030.
The influence of AI on employment is growing. A study shows that 38% of companies will replace some roles with AI in 2025. It also showed that 87% consider AI experience beneficial, and 67% believe that employees with AI skills will have more job security.
On a Marketing Against The Grain Episode, Kipp Bodnar, HubSpot’s chief marketing officer, says, “As I think about the evolution of AI, I think about one of the things that’s going to go part and parcel with it is the need to be amazing at re-skilling our workforce all around the world.” (Listen to the full episode here.)
Re-skilling doesn’t mean preparing for the worst. Most jobs expect employees to engage in some sort of professional development, so this might be routine for you. According to the WEF study:
77% of employers say they plan to reskill their workforce to work with AI, not against it.
29% of workers are expected to be upskilled in their current roles.
19% will be redeployed into new opportunities at their company.
What does that look like for you?
You don’t need to abandon your field or career. Most jobs already expect employees to keep learning — and this is no different.
You might:
Explore AI tools that apply to your role (like HubSpot’s content assistants or Jasper for marketing).
Take a course to deepen your expertise in something adjacent to your current focus.
Learn how to work with AI instead of around it, whether that means testing tools that speed up routine tasks or helping your team adapt their processes.
And, if your job does shift over time? Re-skilling gives you leverage. You’ll have both domain expertise and modern tools at your disposal — and that’s the sweet spot in any evolving career landscape.
For example, you could explore different AI tools and how to leverage them in your day-to-day role. HubSpot’s AI tools, for instance, give you access to a suite of AI tools that could perform functions like content creation, data analysis, and even build a chatbot.
Or you could take a course to learn a new skill. As Shah said above, AI’s most significant impact will likely be helping us be more effective in our careers.
But, if your job is impacted, having taken the time to re-skill leaves you more prepared for future opportunities where you present yourself as a multifaceted candidate.
Learning from others is another great way to stay on top of the changing AI landscape and learn new skills. HubSpot has spoken to experts who have leaned into AI and incorporated it into their processes, and their insight is a valuable way to get ideas for re-skilling.
Here are some helpful resources:
Your experience is your greatest asset.
All technological revolutions impact how people work, and I think it likely that AI will simply follow this pattern. The biggest impact will be streamlining rote and mundane tasks to save us time.
People who adapt, stay curious, and keep learning will thrive. I’ve often said that the value you bring isn’t in the mechanics of what you do, but in the lived experience you bring to the table. So even if your job shifts due to AI, your best bet is to see AI as a collaborative partner that makes you better at what you do.
The more you learn to work with AI, the more irreplaceable you’ll become. To stay up to date on AI and the trends, HubSpot’s State of AI pillar houses all the information you need.
Editor's note: This post was originally published in June 2017 and has been updated for comprehensiveness.
| 2025-06-11T00:00:00 |
https://blog.hubspot.com/marketing/jobs-artificial-intelligence-will-replace
|
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|
Building a Future-Ready Workforce: Reskilling Strategies for the Age ...
|
Building a Future-Ready Workforce: Reskilling Strategies for the Age of Automation
|
https://hgwconsult.com.au
|
[
"H. G",
"W Consultants"
] |
The rise of automation and artificial intelligence (AI) is transforming the global workforce at a scale and speed never seen before. Jobs ...
|
Building a Future-Ready Workforce: Reskilling Strategies for the Age of Automation
Executive Summary
The rise of automation and artificial intelligence (AI) is transforming the global workforce at a scale and speed never seen before. Jobs are evolving rapidly, and organizations are being challenged to prepare their talent for a future defined by change, not stability. This article outlines strategic approaches to reskilling, explores global trends in workforce transformation, and offers insights into how organizations can build a future-ready workforce to remain competitive and inclusive in the digital age.
Introduction: Automation Is Rewriting the Rules of Work
By 2030, automation is projected to displace up to 375 million workers globally, according to McKinsey Global Institute. While automation will eliminate some jobs, it will also create demand for new roles requiring a different set of skills—especially those involving creativity, emotional intelligence, data literacy, and technological fluency.
The key question is no longer “Will automation replace jobs?” but rather “Are we prepared for the jobs that automation will create?”
The Case for Reskillin: Why Reskilling Matters:
Talent Shortages: The World Economic Forum (WEF) projects that 50% of employees will need reskilling by 2025 as adoption of technology increases.
Cost of Inaction: A 2023 IBM study found that organizations failing to reskill risk falling behind on innovation and customer expectations.
Social Impact: Reskilling fosters workforce inclusion and economic resilience, especially in underserved regions.
Reskilling vs. Upskilling: What’s the Difference?
Reskilling: Training employees for entirely new roles (e.g., a warehouse associate becoming a data analyst).
Upskilling: Enhancing skills to perform existing roles more efficiently (e.g., a marketer learning to use automation tools).
Both are essential in a long-term workforce strategy.
Future-Ready Skill Sets: What Organizations Should Focus On
Skill Category Examples
Digital Literacy Cloud computing, cybersecurity, AI
Cognitive Skills Problem-solving, data analysis
Social-Emotional Skills Leadership, empathy, collaboration
Adaptability Change management, resilience
Strategic Reskilling Framework for Businesses
1.Assess Current & Future Skills Gaps
Conduct skills audits
Forecast future talent needs based on strategic goals
2. Personalize Learning Paths
Use AI-driven learning platforms to match employees to relevant courses
Microlearning for busy professionals
3. Foster a Learning Culture
Incentivize continuous learning
Promote knowledge sharing and peer learning
4. Partner with Ecosystems
Collaborate with universities, tech providers, and governments
Tap into public programs and upskilling grants
5, Measure ROI
Track impact on employee retention, productivity, and innovation
Case Study: Siemens
Siemens launched a large-scale reskilling initiative through its “Siemens Learning World” platform, providing customized learning journeys across 160,000 employees. Within 18 months, over 3 million learning hours were logged, significantly boosting internal mobility and innovation.
Conclusion: Prepare Now or Fall Behind
Building a future-ready workforce requires a bold and structured reskilling strategy—one that empowers employees, transforms organizational culture, and prepares businesses to lead in a world reshaped by automation. It’s not a question of if businesses should act, but how fast they can adapt.
| 1926-02-14T00:00:00 |
1926/02/14
|
https://hgwconsult.com.au/1926-2/
|
[
{
"date": "2025/06/11",
"position": 77,
"query": "reskilling AI automation"
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By the numbers: Use AI to fill the IT skills gap - TechTarget
|
By the numbers: Use AI to fill the IT skills gap
|
https://www.techtarget.com
|
[
"Site Editor"
] |
According to the '2025 AI Skills Report' by Pluralsight, 65% of organizations had to abandon AI projects due to a lack of AI skills.
|
According to the '2025 AI Skills Report' by Pluralsight, 65% of organizations had to abandon AI projects due to a lack of AI skills. Don't be another statistic; invest in your staff.
AI is easily accessible for enterprises of all sizes. However, just because it is available doesn't mean your staff are prepared to use it. Without upskilling and staff involvement, AI projects might end up dead on arrival.
AI is well known to stoke employee fear of being replaced in the workplace. According to the "2025 AI Skills Report" by Pluralsight, 34% of tech professionals feel that it is extremely or very likely that they will be replaced by AI. However, that hasn't slowed workplace adoption -- the same report found that 84% of executives and IT professionals say using AI has made their lives easier.
IT pros can use AI in various areas, including the following:
AI cloud services management.
Data modeling and analysis.
Task automation.
Data library and software framework management.
Retrieval-augmented generation.
Natural language processing.
AI's success within the organization hinges on creating a collaborative journey with staff. According to "Winning the AI Race" by Great Place To Work, three out of four employees said they would be excited to use AI at work if their company were more transparent about how AI could improve their workflow. They are also very interested in being trained on AI tools.
By investing in comprehensive upskilling programs, enterprises can bridge the knowledge gap and empower employees to use AI tools effectively.
Kathleen Casey is site editor for SearchCloudComputing. She plans and oversees the site, and she covers various cloud subjects, including infrastructure management, development and security.
| 2025-06-11T00:00:00 |
https://www.techtarget.com/searchcloudcomputing/infographic/By-the-numbers-Use-AI-to-fill-the-IT-skills-gap
|
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|
Future of Work with AI Agents: Auditing Automation and ... - arXiv
|
Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce
|
https://arxiv.org
|
[] |
In this paper, we address this gap by introducing a novel auditing framework to assess which occupational tasks workers want AI agents to automate or augment.
|
Editor with 3-5 Years of Experience:
I proofread and copy-edit marketing materials, mostly in the travel and tourism sector. I also do some copywriting and script writing for different ad clients and some light design work.
I look through flyers, brochures, other marketing materials, and I do several passes for mistakes in grammar, in consistency, in flow and clarity. And I make changes on the document, usually a PDF, and send them back to the client. They fix them, they send me another version, and I do several more passes until we’ve spent enough time and got it perfect. I mainly use Adobe products, PDFs in Adobe Reader. I also use Microsoft Word and some Adobe Suite products, mostly Illustrator and InDesign.
For copy editing, I read through whatever material the client has sent me. I do a pass for basic grammar. I do a pass for clarity and flow, often changing the copy significantly to make it sound better. For proofreading, I go through the materials. Same thing, but with less of a view toward changing the copy and more toward finding errors in grammar and consistency and even design. For copywriting, I make an outline of my ideas for the project and complete a rough draft. Then I spend some time away from it and revise until I have a polished draft for the client.
I’m resistant to using AI in my daily workflow. If I’m forced to use it, I would use it for basic grammar editing, but I would check each suggestion against my own knowledge very carefully and give it full consideration before adopting it as a change.
Editor With More Than 10 Years of Experience:
So I work in a media company, [masked], and as an editor I make sure that all JavaScript that I’m going to print are formatted correctly, the colors are accurate, and there are no typos.
So a lot of what I do involves sitting at a computer using productivity tools like Adobe Creative Suite, Canva, QuarkXPress, and using the Google Enterprise. So for email, document sharing, I use Google Docs quite a bit for my editing purposes, but I’ll also receive files in PDF format. So just working with all the different tools on my computer to get my tasks done every day. So a lot of what I do involves sitting at a computer using productivity tools like Adobe Creative Suite, Canva, QuarkXPress, and using the Google Enterprise. So for email, document sharing, I use Google Docs quite a bit for my editing purposes, but I’ll also receive files in PDF format. So just working with all the different tools on my computer to get my tasks done every day.
So one of the most frequent pieces of software I use is Adobe Acrobat, and that is really great for editing PDFs. The next most frequent software I use would be Google Docs. Receiving files through Google Docs, that’s a great way to be able to provide updates and edits to annotate the files. And then I would say other tools like Adobe InDesign, Adobe Photoshop, QuarkXPress, Microsoft Publisher. Those are occasionally used, but that’s really about all four of the frequently used programs, I would say. So one of the most frequent pieces of software I use is Adobe Acrobat, and that is really great for editing PDFs. The next most frequent software I use would be Google Docs. Receiving files through Google Docs, that’s a great way to be able to provide updates and edits to annotate the files. And then I would say other tools like Adobe InDesign, Adobe Photoshop, QuarkXPress, Microsoft Publisher. Those are occasionally used, but that’s really about all four of the frequently used programs, I would say.
So, when I’m reviewing PDF documents, I will use the markup tool to add comments and highlight certain sections to make sure that the wording is accurate, or if there’s questions regarding the resolution of a photo, I can send that back to mark that up and say, hey, this needs to be a higher resolution photo, it won’t print out correctly. So it’s just a lot of manual review of every single file before it goes to print to make sure that everything is properly formatted, the colors are accurate, and it will reproduce correctly, just to make sure everything looks good for the customer. So, when I’m reviewing PDF documents, I will use the markup tool to add comments and highlight certain sections to make sure that the wording is accurate, or if there’s questions regarding the resolution of a photo, I can send that back to mark that up and say, hey, this needs to be a higher resolution photo, it won’t print out correctly. So it’s just a lot of manual review of every single file before it goes to print to make sure that everything is properly formatted, the colors are accurate, and it will reproduce correctly, just to make sure everything looks good for the customer.
So, I’m using AI right now when it comes to email, so with the Google suite, there are Google Gemini tools that help with formatting emails. I can take a very simple format for content for email and then using that to expand those topics and make it more of a wordy email. But I would like to be able to use AI more in my proofreading and editing than I am right now, so probably within the next couple months I should be able to do that.
Mathematician With 3-5 Years of Experience:
I do number theory and algebraic geometry, mostly around long-length programs or categorical long-length programs. [In my daily work, I] read papers and write papers.
Solving a math problem, I don’t know [whether there is any specific tool to use], just read papers and have an intuition of what the procedure of philosophy should be and work on it.
To be honest, I think [AI is] useless at this moment.
Mathematician With 3-5 Years of Experience:
I am studying geometric representation theory and categorical Langlands program. My work involves coming up the problem to work on and learning math tools to help me think of solutions.
I need to spend a lot of time reading papers and learning math tools. I also need to attend the seminar to find collaborators. Then I work on my problem. [In terms of tools,] I mainly use latex. I spend most of my time studying math. Papers in my field can have hundreds of pages - it takes a long time to understand and try to apply the technique.
At present, I don’t think AI has any use for mathematicians, at least for DeepSeek and ChatGPT. One core question I am interested in is whether AI can come up with new stuffs that haven’t been proposed before rather than solving problems people craft.
Mathematician With 6-10 Years of Experience:
I used to study number theory, in particular, p-adic Hodge theory in arithmetic geometry. Now, I work on the formalization of p-adic Hodge theory in Lean and also auto-formalization and auto theorem proving.
During formalization, I elaborate, generalize, and fill gaps in mathematical proofs. I design general fomalization frameworks and spend lots of time in Lean coding. Lean coding involves searching theorems, formalizing statements and filling in formalization details in the proof. The last part is the longest part. For auto formalization and formal theorem proving, I spend most of the time coding to establish the LLM’s training pipeline and preparing data for the training. I use the interactive theorem prover Lean. I also use LeanSearch and other tools related to Lean to accelerate. I use Python for LLM training and use DeepSeek for coding and debugging.
[Here is a concrete example of my workflow:] I formalized a famous number theory definition, called the period rings of Fontaine. I first wrote down a detailed version of the mathematical statements and proofs I need. Splitting the whole formalization project into several smaller goals. For each smaller goal, I generalize and design suitable definitions and lemmas for formalization. Then I begin actual formalization using Lean. I first write down the definitions and state the theorems in Lean without proof. After this, I fill in the proofs backwards, searching the library for existing theorems to use and design patterns to mimic. During formalization, I revise the natural language proof from time to time.
I think a primary AI tool could help me in filling in searching for theorems and design patterns during formalization. A stronger AI tool would do auto-formalization of theorem statements and provide suggestions in designs. An even stronger AI tool would be able to elaborate and fill gaps in mathematical proofs and autoformalize the human proofs. Additionally, an AI tool strong in coding, debugging, and software engineering would help a lot in coding.
Aerospace Engineer With 3-5 Years of Experience:
I am an aeronautic engineer. I work in the aircraft maintenance, repairs, design aircraft. I work with [masked].
We design aircraft, develop, test, and maintain aircrafts, and the systems that operate within Earth’s atmosphere, such as airplane, helicopters, drones, and missiles, though we’re not into missiles, though. Our work focuses on making aircraft, machines, very safe and efficient, capable of flight. We use Computer-Aided Design as a tool for Autodex, AutoCAD, Cartier, and Solidwork. And we use Computer Fluid Dynamics. It’s ANSYS Fluent, STAR-CNC-MM. What else? We use Finite Element Analysis. It’s a tool we use for Nastran.
[In my opinion,] AI is going to be very awesome and it’s going to make it very easier for us because most of the time, the main problem we have is detecting where the problem is in the engine, you know, so you have to do a lot of manual jobs and all that. So, but if we have AI, you can possibly tell in the dash cam or whatever, you know, you can possibly tell.
Aerospace Engineer With 1-2 Years of Experience:
I’m a current undergraduate senior and prospective master’s student in aerospace engineering, working in guidance, navigation, and controls, so like more simulation, computer programming side of aerospace engineering.
Most of what I do for work has traditionally been programming simulations to evaluate vehicle performance for orbital rockets. And so most of my tasks will be either building out a part of the simulation, programming new features or new testing, or kind of similar types of modeling of different subsystems of the rocket.
In general the tools or software that I use would be Visual Studio Code for the actual programming. The companies I’ve worked at have used project management tools like JIRA and Confluence. I think also just a lot of internet documentation is useful. And yeah, I very occasionally would use an AI tool like ChatGPT. Generally, my process would be to understand the requirements, which would involve talking to my manager, then kind of going about kind of like pre-reading or other types of information gathering necessary for the task, actual programming, and then like unit testing and other ways of forms of validation for the programming that I completed.
| 2025-06-11T00:00:00 |
https://arxiv.org/html/2506.06576v2
|
[
{
"date": "2025/06/11",
"position": 23,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 30,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 25,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 23,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 21,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 21,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 20,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 20,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 20,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 32,
"query": "future of work AI"
},
{
"date": "2025/06/11",
"position": 37,
"query": "job automation statistics"
}
] |
|
Dual impact of AI on economic growth and social disruption | Business
|
Dual impact of AI on economic growth and social disruption
|
https://www.devdiscourse.com
|
[] |
The model confirms that as AI is increasingly integrated into economic sectors, it automates tasks, reduces production costs, and improves ...
|
A recent international study has provided one of the most detailed examinations to date of how artificial intelligence (AI) affects economic growth across nations. Titled “The Impact of Artificial Intelligence on Gross Domestic Product: A Global Analysis” and published in the International Journal of Innovative Science and Research Technology, the research uses a mathematical model to quantify AI's multifaceted role in shaping national economies.
The study reveals a dual impact: AI drives significant productivity and innovation gains but simultaneously triggers job displacement and rising income inequality, challenging policymakers to balance progress with social stability.
How does AI accelerate GDP growth?
The study investigates whether and how AI adoption contributes positively to economic development. The authors built a mathematical model using panel data across multiple countries to measure the impact of AI integration on GDP growth, focusing on variables such as productivity, innovation, job losses, and income distribution.
Findings demonstrate that higher levels of AI adoption are significantly associated with increases in productivity and innovation - two key drivers of GDP. These gains are particularly visible in economies with advanced R&D infrastructures and high educational attainment. The model confirms that as AI is increasingly integrated into economic sectors, it automates tasks, reduces production costs, and improves decision-making efficiency, resulting in a measurable lift in economic output.
Additionally, AI opens new avenues for technological innovation. Entire industries, such as autonomous transportation, AI-based medical diagnostics, and personalized education, have emerged, fostering job creation in specialized sectors and stimulating economic dynamism. The study links these developments to a rise in national competitiveness, particularly in economies that lead in AI research and deployment.
What are the hidden economic risks of AI adoption?
While AI presents economic opportunities, the study emphasizes that its adverse consequences are equally critical. One of the most pressing risks identified is job displacement. As AI technologies automate routine, manual, or repetitive jobs, especially in manufacturing and service sectors, the demand for low-skilled labor drops, potentially increasing unemployment and reducing consumer spending.
The research also highlights how AI exacerbates income inequality. Wealthier firms and highly skilled workers stand to benefit disproportionately from AI's advantages, including profit margins and wage increases. Meanwhile, smaller businesses and low-skilled workers risk marginalization, widening the socio-economic gap.
Moreover, the deployment of AI technologies raises serious concerns around data privacy, cybersecurity, and ethical use. The extensive data collection required by AI systems introduces new vulnerabilities and legal complexities that, if left unaddressed, could undermine public trust and stall technological progress. These concerns contribute to a broader tension: while AI adoption is critical for growth, it could potentially hinder it if the negative effects are not strategically managed.
Can policymakers mitigate AI’s economic downsides?
The final component of the study proposes a robust framework for governments to maximize AI’s benefits while addressing its drawbacks. The authors urge targeted interventions across multiple fronts.
First, governments should invest in research and development to sustain AI-led innovation and bolster national competitiveness. Simultaneously, educational systems must pivot toward digital literacy, coding, data analytics, and adaptive learning models to prepare workers for an AI-dominated labor market. Training and reskilling programs are vital to help displaced workers transition into new roles.
Second, social safety nets must be strengthened to cushion the shock of job loss and economic dislocation. This includes unemployment insurance, subsidized retraining, and inclusive labor policies. Tax reforms and welfare programs may be necessary to counteract rising income inequality and ensure equitable growth.
Finally, regulatory frameworks need to be updated to address data privacy, algorithmic bias, and AI accountability. Ethical standards should be established to guide AI deployment in socially responsible ways. The study also recommends international cooperation to harmonize AI standards and ensure that its economic benefits are globally shared rather than geographically concentrated.
A complex but manageable economic force
If managed well, AI can be an unparalleled engine of growth, innovation, and global competitiveness. However, without deliberate policies to address its disruptive effects, it could deepen structural inequalities and destabilize labor markets.
This comprehensive research offers valuable empirical insights and a clear call to action. For governments, industries, and educational institutions, the message is clear: AI is neither inherently good nor bad for economic growth - it is powerful, and it must be steered with intention.
| 2025-06-11T00:00:00 |
https://www.devdiscourse.com/article/business/3453184-dual-impact-of-ai-on-economic-growth-and-social-disruption
|
[
{
"date": "2025/06/11",
"position": 59,
"query": "AI economic disruption"
},
{
"date": "2025/06/11",
"position": 61,
"query": "AI economic disruption"
},
{
"date": "2025/06/11",
"position": 61,
"query": "AI economic disruption"
},
{
"date": "2025/06/11",
"position": 61,
"query": "AI economic disruption"
},
{
"date": "2025/06/11",
"position": 45,
"query": "AI economic disruption"
},
{
"date": "2025/06/11",
"position": 71,
"query": "AI economic disruption"
},
{
"date": "2025/06/11",
"position": 54,
"query": "AI economic disruption"
},
{
"date": "2025/06/11",
"position": 55,
"query": "AI economic disruption"
}
] |
|
The Consequences of AI on Job Growth vs. Employment | INOP
|
The Consequences of AI on Job Growth vs. Employment
|
https://inop.ai
|
[
"Evelyn Theodorou"
] |
AI isn't replacing jobs—it's reshaping them. Discover how AI-driven workforce strategies can future-proof talent and boost business success.
|
Artificial Intelligence is no longer a futuristic concept—it’s here, actively reshaping industries at an unprecedented pace. From manufacturing floors to corporate boardrooms, AI is rewriting the rules of employment. While AI-driven automation is expected to displace some jobs, it is also creating new opportunities, requiring businesses and professionals to adapt, upskill, and rethink workforce strategies.
For CHROs, Talent Leaders, and Business Executives, this shift isn’t just about automation—it’s about how organizations attract, retain, and develop talent in an AI-driven economy. The challenge is clear: organizations that fail to integrate AI into their workforce strategies risk falling behind, while those that proactively adapt can gain a competitive advantage.
AI’s Influence on Employment: Evolution, Not Extinction
The World Economic Forum’s Future of Jobs Report 2025 paints a complex picture of AI’s impact on employment:
By 2030, AI and automation will create an estimated 170 million new jobs worldwide.
Simultaneously, around 92 million positions will be eliminated. Primarily those reliant on repetitive, routine tasks.
The fastest-growing industries will be those rooted in data analytics, AI & machine learning, cybersecurity, and environmental advocacy.
Traditional white-collar roles in HR, finance, legal, and IT will require a strategic overhaul as AI reshapes hiring, workforce planning, and talent management.
For years, AI adoption was most visible in blue-collar professions—automated assembly lines, self-operating warehouses, and predictive maintenance in factories. Now, with the rise of advanced AI agents from OpenAI and others, white-collar jobs are experiencing the same transformation. Highly skilled professionals who once believed they were immune to automation are finding their roles augmented or redefined by AI-driven decision-making and workflow automation.
Bridging the Skills Gap: A Critical Priority for CHROs
Despite the transformative potential of AI, many organizations lack a concrete plan to adapt. Research indicates that nearly 39% of job skills will transform by 2030, yet only half of employers have a reskilling strategy in place. For CHROs and business leaders, this presents a pressing question:
How can organizations reskill and upskill talent quickly enough to remain competitive?
Reskilling must be a boardroom priority. AI literacy and a culture of continuous learning are essential to future-proofing the workforce.
AI literacy and a culture of continuous learning are essential to future-proofing the workforce. Skills-based hiring is the new standard. Companies are prioritizing adaptability, problem-solving, and tech fluency over traditional degrees.
Companies are prioritizing adaptability, problem-solving, and tech fluency over traditional degrees. AI is enhancing—not eliminating—HR roles. AI-powered tools are freeing recruiters from repetitive screening tasks, allowing them to focus on strategic workforce planning, culture-building, and DEI initiatives.
A study by PwC’s AI Jobs Barometer 2024 shows that while AI-exposed professions such as IT, HR, legal, and teaching are still growing, their growth rate is slowing by 27% compared to lower-exposed roles. This underscores the urgent need for businesses to rethink talent strategies that integrate AI with human expertise.
The Ethical Imperative: AI & Workforce Transformation
AI in workforce planning isn’t just about efficiency—it’s about responsibility. Organizations must ensure AI adoption is ethical, transparent, and inclusive to avoid unintended biases and workforce inequities.
✅ Artificial Intelligence should complement, not replace, human intelligence – HR and talent leaders must champion AI tools that enhance decision-making rather than automate away critical human functions.
✅ Unbiased AI recruitment is non-negotiable – AI-driven hiring models must prioritize diversity, equity, and inclusion (DEI).
✅ AI literacy is key to retention – Companies must invest in AI training programs to keep employees competitive in an evolving job market.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://inop.ai/the-consequences-of-ai-on-job-growth-vs-employment/
|
[
{
"date": "2025/06/11",
"position": 66,
"query": "AI job creation vs elimination"
}
] |
Fact Check Team: US companies cut jobs amid AI investments - KATV
|
Fact Check Team: US companies cut jobs amid AI investments
|
https://katv.com
|
[
"Janae Bowens",
"Fact Check Team",
"Https"
] |
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly ...
|
is showing signs of stability, but a wave of layoffs is sweeping through major companies, including Procter & Gamble, Microsoft, Citigroup, Walmart, Klarna, Disney, , and Warner Bros. Discovery, Despite a steady labor market, these companies are under pressure to reduce costs due to global uncertainty linked to tariffs imposed by President Donald Trump.
Experts indicate that while government job cuts are decreasing, companies are still feeling the financial strain. Many have resorted to raising prices, but layoffs remain a significant cost-cutting measure. Concerns persist about how tariffs and trade tensions might slow the economy overall.
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly due to AI investments. Shopify CEO Tobias Lütke told employees in April that they must justify why tasks cannot be performed by AI before requesting additional workers and resources.
Google is also making moves to free up cash for AI investments. The company is offering voluntary buyouts to U.S. employees across multiple teams, including search, ads, engineering, and research, as it plans to spend about $75 billion this year on capital expenses. This is part of Google's strategy to expand AI features like its new "AI Mode" search tool while maintaining cost efficiency, according to
Industry leaders warn that AI could trigger a "white-collar job apocalypse." The CEO of Anthropic told that AI could eliminate half of all entry-level white-collar jobs within 1 to 5 years, potentially causing unemployment rates to spike to 10 or even 20 percent. He is advocating for more transparency from AI companies and urging the government to prepare for the potential impact, suggesting a "token tax" on AI models to help redistribute wealth if job losses become severe.
The latest job report also highlights a challenging landscape for new graduates, with March and April job gains revised down by a combined 95,000 jobs. As companies slow hiring, current workers are opting to stay in their positions.
| 2025-06-11T00:00:00 |
https://katv.com/news/nation-world/us-companies-cut-jobs-amid-ai-investments-artificial-intelligence-proctor-gamble-microsoft-citigroup-walmart-klarna-disney-paramount-warner-bros
|
[
{
"date": "2025/06/11",
"position": 92,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/11",
"position": 90,
"query": "artificial intelligence layoffs"
}
] |
|
Fact Check Team: US companies cut jobs amid AI investments
|
Fact Check Team: US companies cut jobs amid AI investments
|
https://fox28savannah.com
|
[
"Janae Bowens",
"Fact Check Team",
"Https",
"Www.Facebook.Com"
] |
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly ...
|
is showing signs of stability, but a wave of layoffs is sweeping through major companies, including Procter & Gamble, Microsoft, Citigroup, Walmart, Klarna, Disney, , and Warner Bros. Discovery, Despite a steady labor market, these companies are under pressure to reduce costs due to global uncertainty linked to tariffs imposed by President Donald Trump.
Experts indicate that while government job cuts are decreasing, companies are still feeling the financial strain. Many have resorted to raising prices, but layoffs remain a significant cost-cutting measure. Concerns persist about how tariffs and trade tensions might slow the economy overall.
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly due to AI investments. Shopify CEO Tobias Lütke told employees in April that they must justify why tasks cannot be performed by AI before requesting additional workers and resources.
Google is also making moves to free up cash for AI investments. The company is offering voluntary buyouts to U.S. employees across multiple teams, including search, ads, engineering, and research, as it plans to spend about $75 billion this year on capital expenses. This is part of Google's strategy to expand AI features like its new "AI Mode" search tool while maintaining cost efficiency, according to
Industry leaders warn that AI could trigger a "white-collar job apocalypse." The CEO of Anthropic told that AI could eliminate half of all entry-level white-collar jobs within 1 to 5 years, potentially causing unemployment rates to spike to 10 or even 20 percent. He is advocating for more transparency from AI companies and urging the government to prepare for the potential impact, suggesting a "token tax" on AI models to help redistribute wealth if job losses become severe.
The latest job report also highlights a challenging landscape for new graduates, with March and April job gains revised down by a combined 95,000 jobs. As companies slow hiring, current workers are opting to stay in their positions.
| 2025-06-11T00:00:00 |
https://fox28savannah.com/news/nation-world/us-companies-cut-jobs-amid-ai-investments-artificial-intelligence-proctor-gamble-microsoft-citigroup-walmart-klarna-disney-paramount-warner-bros
|
[
{
"date": "2025/06/11",
"position": 95,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/11",
"position": 93,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/11",
"position": 88,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/11",
"position": 86,
"query": "artificial intelligence layoffs"
}
] |
|
Fact Check Team: US companies cut jobs amid AI investments - WLOS
|
Fact Check Team: US companies cut jobs amid AI investments
|
https://wlos.com
|
[
"Janae Bowens",
"Fact Check Team",
"Http"
] |
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly ...
|
is showing signs of stability, but a wave of layoffs is sweeping through major companies, including Procter & Gamble, Microsoft, Citigroup, Walmart, Klarna, Disney, , and Warner Bros. Discovery, Despite a steady labor market, these companies are under pressure to reduce costs due to global uncertainty linked to tariffs imposed by President Donald Trump.
Experts indicate that while government job cuts are decreasing, companies are still feeling the financial strain. Many have resorted to raising prices, but layoffs remain a significant cost-cutting measure. Concerns persist about how tariffs and trade tensions might slow the economy overall.
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly due to AI investments. Shopify CEO Tobias Lütke told employees in April that they must justify why tasks cannot be performed by AI before requesting additional workers and resources.
Google is also making moves to free up cash for AI investments. The company is offering voluntary buyouts to U.S. employees across multiple teams, including search, ads, engineering, and research, as it plans to spend about $75 billion this year on capital expenses. This is part of Google's strategy to expand AI features like its new "AI Mode" search tool while maintaining cost efficiency, according to
Industry leaders warn that AI could trigger a "white-collar job apocalypse." The CEO of Anthropic told that AI could eliminate half of all entry-level white-collar jobs within 1 to 5 years, potentially causing unemployment rates to spike to 10 or even 20 percent. He is advocating for more transparency from AI companies and urging the government to prepare for the potential impact, suggesting a "token tax" on AI models to help redistribute wealth if job losses become severe.
The latest job report also highlights a challenging landscape for new graduates, with March and April job gains revised down by a combined 95,000 jobs. As companies slow hiring, current workers are opting to stay in their positions.
| 2025-06-11T00:00:00 |
https://wlos.com/news/nation-world/us-companies-cut-jobs-amid-ai-investments-artificial-intelligence-proctor-gamble-microsoft-citigroup-walmart-klarna-disney-paramount-warner-bros
|
[
{
"date": "2025/06/11",
"position": 97,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/11",
"position": 94,
"query": "artificial intelligence layoffs"
}
] |
|
Fact Check Team: US companies cut jobs amid AI investments - WBFF
|
Fact Check Team: US companies cut jobs amid AI investments
|
https://foxbaltimore.com
|
[
"Janae Bowens",
"Fact Check Team",
"Https",
"Www.Facebook.Com Foxbaltimore"
] |
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly ...
|
is showing signs of stability, but a wave of layoffs is sweeping through major companies, including Procter & Gamble, Microsoft, Citigroup, Walmart, Klarna, Disney, , and Warner Bros. Discovery, Despite a steady labor market, these companies are under pressure to reduce costs due to global uncertainty linked to tariffs imposed by President Donald Trump.
Experts indicate that while government job cuts are decreasing, companies are still feeling the financial strain. Many have resorted to raising prices, but layoffs remain a significant cost-cutting measure. Concerns persist about how tariffs and trade tensions might slow the economy overall.
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly due to AI investments. Shopify CEO Tobias Lütke told employees in April that they must justify why tasks cannot be performed by AI before requesting additional workers and resources.
Google is also making moves to free up cash for AI investments. The company is offering voluntary buyouts to U.S. employees across multiple teams, including search, ads, engineering, and research, as it plans to spend about $75 billion this year on capital expenses. This is part of Google's strategy to expand AI features like its new "AI Mode" search tool while maintaining cost efficiency, according to
Industry leaders warn that AI could trigger a "white-collar job apocalypse." The CEO of Anthropic told that AI could eliminate half of all entry-level white-collar jobs within 1 to 5 years, potentially causing unemployment rates to spike to 10 or even 20 percent. He is advocating for more transparency from AI companies and urging the government to prepare for the potential impact, suggesting a "token tax" on AI models to help redistribute wealth if job losses become severe.
The latest job report also highlights a challenging landscape for new graduates, with March and April job gains revised down by a combined 95,000 jobs. As companies slow hiring, current workers are opting to stay in their positions.
| 2025-06-11T00:00:00 |
https://foxbaltimore.com/news/nation-world/us-companies-cut-jobs-amid-ai-investments-artificial-intelligence-proctor-gamble-microsoft-citigroup-walmart-klarna-disney-paramount-warner-bros
|
[
{
"date": "2025/06/11",
"position": 97,
"query": "artificial intelligence layoffs"
}
] |
|
Artificial Intelligence in Education: Risks, Opportunities and ...
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Artificial Intelligence in Education: Risks, Opportunities and What’s Next
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https://www.the74million.org
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"Michael B. Horn"
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Their conversation explores the challenges and opportunities AI brings — particularly in developing curiosity as a critical habit for learners — and revisits ...
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Class Disrupted is an education podcast featuring author Michael Horn and Futre’s Diane Tavenner in conversation with educators, school leaders, students and other members of school communities as they investigate the challenges facing the education system in the aftermath of the pandemic — and where we should go from here. Find every episode by bookmarking our Class Disrupted page or subscribing on Apple Podcasts, Google Play or Stitcher.
In the last episode of the season, Michael Horn and Diane Tavenner come together, in person, to reflect on the arc of their artificial intelligence-focused series. They discuss key themes and takeaways, including the enduring importance of foundational knowledge, skepticism around the speed and impact of AI-driven change within traditional schools, and how transformative innovation is more likely to emerge from new educational models. Their conversation explores the challenges and opportunities AI brings — particularly in developing curiosity as a critical habit for learners — and revisits how their own perspectives shifted throughout the season.
Listen to the episode below. A full transcript follows.
Michael Horn: Hey, Diane, it is good to be with you in person.
Diane Tavenner: It’s really good to be in person. It’s a little funny where we are in person, but it’s kind of the perfect setting to end our A.I. you know, miniseries season six. We are at the air show. I think that’s what it’s called, the AI show in San Diego.
Michael Horn: I’m gonna take a selfie, as we say.
Diane Tavenner: We’re gonna send you a picture of this. So we’re. We’re recording here from the floor that is filled with educators and edtech companies and AI. AI. AI!
AI’s Educational Impact Outside Schools
Michael Horn: Because AI is the thing, which is perfect because our season this year has almost exclusively focused on the question of what will the impact of AI be in education? How do we shape that? What do we want it to be? All these questions, frankly, in ways that neither of us had imagined fully. I think when we started this and we did a first sort of rapid reaction.
Diane Tavenner: We did. Were we starting our kind of baseline assessment of what we thought and our knowledge and what we were curious about?
Michael Horn: Yep. And we’ve gone through this journey, and now today, we sort of get to tidy it up with our very sharp, insightful takes. No pressure on us.
Diane Tavenner: No pressure for those key headlines. But, you know, along the way, we interviewed a bunch of really interesting people, some skeptics, some really positive folks. And we benefited a lot from it.
Michael Horn: I learned a ton. My understanding of the space. I don’t know if I conveyed it on our prior episode, but I think it’s a lot deeper than it was when we started.
Diane Tavenner: For me, too. I really appreciate them. And then, you know, in true fashion, we just publicly processed out loud last episode.
Michael Horn: We do.
Diane Tavenner: And now we’re going to try to actually pull it together with some key takeaways. So that’s how we’re going to wrap it today. And so we kind of outlined, you know, three big categories here. And the first one is, I want to ask you what belief was confirmed for you as we made our way through this season?
Michael Horn: Yeah. So people obviously heard where we started, but I will confess, I’ve been struggling. I knew you were going to ask this question, and for days I’ve been wondering, what did it confirm for me? I think I will say two things. If that. And maybe that’s cheating. But it’s our podcast. Right. So, number one, I think it confirmed for me that foundational knowledge will still be important.
Diane Tavenner: Yes.
Michael Horn: And I think developing it into skills will still be important, just as Google did not change that reality, despite what a lot of educators and maybe more schools of education sadly were telling their students that became teachers. I don’t think AI will change that either. We had a long conversation in the last episode around the nature of expertise and who AI is useful for. I think the second thing that maybe hit harder for me but, but confirmed something that we talked about in the first episode was I think the most transformational use cases of AI in education will be in areas outside of the traditional schools with new models that leverage AI that wrap around it to do things very differently from business as usual, frankly. Like why you started public school is outside of the traditional. Right. I think the other piece of that is I’m somewhat skeptical that venture capital will be the thing that funds a lot of these new models that emerge.
Diane Tavenner: Say more about that. Why?
Michael Horn
Well, I could be very wrong in the latter. I’m just coming, we’re at this conference and I just coming from a place where a few people said no, we are funding these things. So I could be completely wrong. I guess my thoughts are that the time frames for explosive growth for VC are short; five to seven years.
Diane Tavenner: Yeah.
Michael Horn: The micro schools, the new emerging schooling models. I don’t even know if micro schools will be the word we use in five years from now. I’m not convinced those are like zero to a hundred thousand student businesses.
Diane Tavenner: Yeah.
Michael Horn: And so I don’t know, can you make a venture style business out of them? Venture might be funding the AI software that sort of makes those things go round and certainly the infrastructure that we’ve talked about.
Diane Tavenner: Right, right.
Michael Horn: But I, but I guess I think that’s going to be the really interesting hotbed of activity to look at. And we had this dichotomy on the first show, teacher facing versus student facing. I think that’s less present in my mind at the moment. But the student facing stuff I think will be in these new models, not the traditional ones.
Diane Tavenner: Fascinating.
Michael Horn: What about you?
Diane Tavenner: Well, I think that, you know, when.
Michael Horn: I feel free to disagree with me also I think.
Skeptical Optimism on Change
Diane Tavenner: Well, I think my confirmed belief is sort of a dimension of what you’re talking about, maybe the flip side of what you’re talking about or connected to it and I can’t decide if it’s in conflict with what you’re saying or not. So let me just put it out there and we’ll see. I will say that I think of myself almost as always an optimist, but I am a skeptic in one area and I believed coming into this that we weren’t going to hear that schools were being redesigned or that even had been. And so it sort of confirmed my belief that I don’t know what is going to bring about this kind of change. And so you are saying it’s going to happen outside of the. Yes, because that’s the only place that.
Michael Horn: It’s the only place for transformational use cases.
Diane Tavenner: And it may be yet.
Michael Horn: And it may be yet. And I think the confirmed belief for me at the moment, it’s great when you’re wrong and you learn something new. I will say. But at the moment, it confirmed my sense that it will, look at our field, they tend to be consumed with the hardest, most intractable problems at the center of the field. And this is gonna be the periphery. It’s not gonna be the bulk of it. So there’s a little bit of a cognitive dissonance if you.
Diane Tavenner: I think you’re right. And it’s. It’s so interesting. The story in America is truancy and absenteeism. So data tells a story along that. But if you’re processing that, that is the biggest problem. And then you’re creating, using AI to create a solution structure.
And what is happening in the school day is the problem. Families are voting with their feet.
Michael Horn: So it’s so interesting you say that. I’m rereading Bob Moesta’s book, Five Skills of Innovators. I almost mailed you a copy over the weekend. They’re solving a problem rather than asking, what is the system supposed to do and how do you tighten the variance around that? And as he says, you can solve the problem, but create five others. Or you say, what is the system supposed to do now? Yeah. And so that’s why I think we got to bust out. So let me ask you, Let me ask you the next question. Where did it change your mind or beliefs? Anything that we learned?
Diane Tavenner: Well, I do. I do think it changed my mind. And I’ll point to our episode with John Bailey. That’s how we kicked off this series. And I think I’ve talked to so many people who love that episode, and they’re like, oh, my gosh, I had no idea all the different ways that I could use ChatGPT or Claude or whatever AI I’m using. And it’s true. I mean, John, you know, talked about how we now have an expert in our pocket on every possible topic. And so it really pushed me to think about how I was using it in my life, both in.
In my personal life, in my professional life, and in our product. Now there’s Some challenges with this expert idea that I think came up for both of us.
Michael Horn: Yeah. And maybe that’s where I, maybe that’s where it changed my beliefs. I think I had a sense and you can read my quotes in newspapers and stuff like that. That or newspapers exist. Ed weeks, stuff like that. That. I think this series really gave me a much deeper set of questions around what kinds of students will actually be able to take advantage of these types of tools. I won’t go into it again. Did it the last episode around this novice expert, unknowing, knowing, sort of two by two.
Revising Views on AI Strategy
Michael Horn: And so I think that’s like something that I’m really wrestling and revising in my head coming out of this. I think along those lines, it gave me a much deeper concern over a lot of the things that could go wrong if we’re not super intentional and thoughtful about that game. But I think it’s like how we leaned into it. And I, I will say, I don’t know if this is a revision for me. You may tell me I’m leaving my principles behind, but I sort of scoffed a couple years ago when districts would say, we need an AI strategy. And I was like, no, that’s focusing on the inputs, not the outcomes you want. But I think I’ve revised my stance in that I do think that there needs to be more thoughtfulness around what are our beliefs and values and so forth in an era of AI, and what does that mean for what we think about teaching and learning? And maybe that’s your AI strategy.
Diane Tavenner: Well, and this harkens back to the episode with Rebecca Winthorp. Will AI provoke schools to go back and have the real conversations about what is the purpose of education? What are we trying to do? What matters now? How are we using this new, very powerful tool to further our purpose?
Michael Horn: Look, I would hope that they would, but, I mean, I think this is the answer, you know, see number one, where I think it’s more likely that these conversations happen in embryonic education communities than the traditional, despite how broken this could look in five years if we go down this road. But that’s, I left with a lot of concerns.
Diane Tavenner: Yeah. And I’m curious in my own use of AI, if I’m missing out or losing anything, because I’m not, like, processing some of my thinking and work in the way that I used to, like, no doubt more efficient, certain brain work during that process.
Michael Horn: So was it creating cognitive laziness that.
Diane Tavenner: I have no evidence that that’s true. But I do wonder.
Michael Horn: And on my other podcast, Future U, Jeff Salingo talked about how his daughter, one of his daughters, asked what you did when you didn’t have phones. And her visual image wasn’t like, oh, you memorized stuff and had to learn a lot. Her visual image was literally like, we have a phone in front of us, navigating us. We must have had a large fold out map. She couldn’t imagine that we would write down the directions and so forth and then. And occasionally you pulled over and had to recalibrate, but. And so he was like, oh, so this is an example of cognitive laziness. And I was like, I actually think that’s an example of freeing up the brain to do other things that I think is.
Curiosity’s Impact on Longevity
Diane Tavenner: Well, and in a whole other part of our lives. We both care a lot about longevity and the science and whatnot. And so there’s certainly some evidence over there that we are not helping our brains when we’re taking all those tasks out of our life. So I want to switch gears and name something else that it changed for me, and that’s curiosity. I think we both came to this. And for me, here was the big aha, like I have for years. Like, I built the summit model with the habits of success, and curiosity was one of the parts of that. But curiosity has always gotten sort of shortchanged, if you will, because everyone’s like, well, that’s great, but how do you teach it and how do you assess it? And it’s sort of sitting up there and to me, like, curiosity comes roaring back in.
It is having its shining moment.
Michael Horn: Like the habit.
Diane Tavenner: Yes.
Michael Horn: That you will need to be a thriving adult in this world. So you don’t take things on face value. So you are inquisitive, so you ask. So you’re always needing to use this, I think, to figure out what is truth, if you will. That’s perhaps a real skill that we will need to be better at developing.
Diane Tavenner: You know, I would probably call it more of a habit, but it is a skill. It’s one of those weird ones because I feel like we’re born naturally curious, not feel like there’s a lot of evidence of that. I sadly believe that our education system actually rings that curiosity out of us.
Michael Horn: It doesn’t reward it. Right?
Diane Tavenner: It doesn’t reward it. And you know what’s interesting? In my current work, you ask employers, you know, who would you provide job shadow opportunities for, who would you have as an intern, those sorts of things. And when you talk to them, curiosity rises to the top. What do they want? A young person who comes in, who’s a signal that you do have a growth mindset and you are interested in growing and you do want to learn and you’re just. Yeah, it’s just such an important quality, I think.
Michael Horn: Yeah, I think that’s right. And it. And it connects all these things. My own worry is that if people don’t have enough foundational knowledge, they’ll actually be far less creative in this world of AI where they’re just doing what is sort of told to them and unable to ask big questions. If I ask you to learn how to ask really big questions that break out of status quo systems and things of that nature.
Diane Tavenner: Exactly to that point. I think the other thing that I’ve been thinking differently about is throughout this series, as you know, my biological son is a history guy.
Michael Horn: Someone after my heart, I know, said.
Diane Tavenner: To me, the other one is obsessed with AI, so it’s an interesting combo.
Michael Horn: But yeah, the other one I have no chance of understanding.
Human Element in Innovation
Diane Tavenner: But yes, yeah, she said to me, you know, mom, because we’re talking about the speed of how the development of the innovation, but the human part is still really real. And so one of the things he said to me is, you know, do you know how long it took for America to fully adopt electricity after it was invented?
Michael Horn: It was like rebuilding of models around it that are native to that at the center.
Diane Tavenner: Yes. And I just think it’s so interesting. Like I had a conversation with ChatGPT about why did it take so long. And so some of the things I learned and my kiddo is like, there’s infrastructure. In the case of electricity, there was a cost. I would argue there’s like hidden costs to it.
Michael Horn: I think there’s huge costs. This is not the zero marginal cost world anymore of Silicon Valley.
Diane Tavenner: Right, right.
Michael Horn: It’s different.
Diane Tavenner: Right. There was a lack of immediate need or use. Why are you getting on AI like, and even the two of us saying, you know, we now almost never go on Google and search Google anymore because we’ve transformed our behavior over. But it took a minute even for us to sort of figure that out, change our behavior.
Michael Horn: Interesting. So this guy Horace Dediu, I was not going to go here until you just brought this up. Who runs the Asymco sort of community podcast, speaks a lot about Apple. He was with the Christensen Institute for a hot minute.
Diane Tavenner: OK.
Michael Horn: And he was doing his research around the adoption of refrigerators and dryers. Adoption of refrigerators was relatively fast, but the adoption of dryers was really, really slow. Oh, and dryers were really, really slow adoption because you had to change the component into which it fit in the house. Right.
Diane Tavenner: And so it requires a different plug.
Michael Horn: Infrastructure. Tells you how fast it will go.
Diane Tavenner: Yeah.
Michael Horn: And we don’t ever ask, have that conversation right around thinking about, you know, how much do you have to redesign huge parts to make really it useful.
Diane Tavenner: And I would assume the case with dryers to households across the country. And I. I think that when people look back on this moment in history, they’ll probably blur the time period it takes. But we’re going to live through, I think, a much longer time period.
Michael Horn: It’s interesting, a lot of my early funders at the Christensen Institute, people like Gisèle Huff, who I adore, they would get annoyed with me. I mean, when I said patience is going to be required because we have an install base, we have a system.
Diane Tavenner: Right.
Michael Horn: I, on the last one, expressed my belief that some of these dynamics could change around disruptive innovation actually now being welcomed for the first time.
Diane Tavenner: So I’m laughing at us a little.
Michael Horn: Bit because of our naivete.
Diane Tavenner: 2020 to do a little. Well, back in 2020, but then we thought we were going to do a little AI miniseries and then we’d figure it all out. But I think that as we wrap this season, season six, we actually have even more questions and curiosity ourselves.
Michael Horn: Well, and we’d love to hear from folks who are tuning in. This is a welcome invitation to just pester us less with your pitches and more with, like, what are you curious about?
Diane Tavenner: Yes.
Michael Horn: Who would you like to hear from? Not in your orbit, but, you know, people that would further both your understanding and ours.
Diane Tavenner: Yes. And what are you doing and what are you seeing and how can we sort of come along on this journey together?
Michael Horn: So let me end with this one question. Will AI have an impact on young people? If so, when and how?
Diane Tavenner: Yes.
Michael Horn: My answer to that question is like, despite what at least one of our guests said is, I can’t imagine it will not have a big impact on individuals. I think AI is going to be much more pervasive, in fact. And look, I’m not one of those people that says just because it’s in the working world, they need to use it now because we’re preparing them for that world.
Diane Tavenner: It’s already impacting them. So it is having an influence on the work that’s available to them. The way employers think about work. The what, what. Where it’s going to have an impact on.
High School: Experiential Learning Shift
Michael Horn: Particularly in high school, I think it’s going to be like the old world of like, here’s the curriculum. Go learn. It, I think, is massively thrown out the window. Right. Like, Maybe K through 8th is a little bit more constant because it is foundational. I, I don’t think it should change as much, but high school, I think, is different. It already should be much more experiential and exploratory in my view. But I, I think it’ll be, I think it should be extremely so now.
All right, let’s wrap. What are you reading, watching, listening to that I should be clued into.
Diane Tavenner: Well, I’m still on all of the ancient Greek fun, so I have gotten a lot of very polarized reactions to this, but hear me out. So Gavin Newsom has a new podcast.
Michael Horn: He does.
Diane Tavenner: I’ve been reading about it and lots of people have been reading about it. I live in California, as you know.
Michael Horn: So he’s your Governor.
Diane Tavenner: He is my governor. You have to listen to this. The first episode where he interviewed Charlie Kirk. And for those who don’t know, the premise is he’s talking to people who he really disagrees with. Here’s why I’m going to promote it. I love it. These are, they’re getting into the nuance of policy and how things work. And I am learning a lot and I want to be able to make my own decisions.
Diane Tavenner: So I want to hear the full scope of things and feel like. And I don’t. So this is the kind of conversation I want to exist out there.
Michael Horn: Well, so you’re learning from that and I’m learning from you. I, I am, I’m, I’m not just reading non fiction. I’ve also been embracing some fiction books. I’ll name one. Yeah, there you go. Right. I’ll name one which is Paradise. And I’m gonna mess up the author’s name.
Michael Horn: I’m gonna apologize, but Abdulrazak Gurnah. And I’m reading this book Paradise, because I’m, I’m learning from you that it’s nice to read fiction from the country where you’re about to travel. And as you know, I’m headed to Tanzania with Imagine Worldwide. I’m on the board there.
Diane Tavenner: Are you enjoying it?
Michael Horn: I’m still trying to make sense from it.
Diane Tavenne: Yeah.
Michael Horn: It’s less. The fiction that I read around Sierra Leone in particular was like very of the Civil War moment and like I could really figure out where that is. But in Paradise, there are a lot of currents going on in this book. I’m trying to sense make. And it’s really interesting.
Diane Tavenner: How beautiful.
Michael Horn: And thank you to all of our listeners once again. And thank you, of course, to the 74 for distributing this. And it’s how so many of our listeners connect with us. And so to all of you, we will see you next season on Class Disrupted.
| 2025-06-11T00:00:00 |
https://www.the74million.org/article/artificial-intelligence-in-education-risks-opportunities-and-whats-next/
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[
{
"date": "2025/06/11",
"position": 19,
"query": "AI education"
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Generative AI in the Classroom Guidance
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Generative AI in the Classroom
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https://education.delaware.gov
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This section provides optimal practices for integrating Generative AI within the classroom. It emphasizes applications and strategies to enhance educational ...
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Artificial intelligence (AI) is emerging rapidly across industries, including K-12 education. The Delaware Council on Educational Technology presents this guidance to support educators and education leaders in the appropriate, equitable, and inclusive use of AI in classrooms across the State of Delaware.
The full document as it was developed by the Council on Educational Technology in collaboration with the Generative AI Subcommittee.
Included in this Guidance
Delve into artificial intelligence, clarify its definition, weigh the advantages and disadvantages of its use in education, and dispel common myths surrounding its capabilities and limitations.
The comprehensive recommendations address crucial policy areas such as protecting student data, ensuring ethical, equitable, and effective Generative AI use, promoting equity, and enhancing learning through content and curricula that leverage Generative AI technologies. Best practices are provided with implementation strategies, suggestions for managing platforms, and tools for evaluating Generative AI products.
This section provides optimal practices for integrating Generative AI within the classroom. It emphasizes applications and strategies to enhance educational experiences, demonstrating how tools and applications support teaching and learning processes. The SAMR model includes illustrations to show how applications may align to each level. Also included are strategies for addressing academic dishonesty and ensuring that Generative AI tools are used responsibly in educational settings.
The final section suggests a framework for professional learning, listing essential topics and subject areas on which school districts and charter schools may choose to focus. Scalable training and delivery methods are recommended to guarantee that all educators and students across Delaware have equal opportunities to benefit from Generative AI technologies. This approach aims to equip teachers with the knowledge and skills necessary to integrate Generative AI into their teaching practices effectively, thereby enhancing the educational experience for students.
How to Use This Guidance
The guidance issued by the Delaware Council on Educational Technology on the adoption of Generative AI in educational settings serves as a comprehensive resource for educators and leaders across the state:
| 2025-06-11T00:00:00 |
https://education.delaware.gov/educators/academic-support/standards-and-instruction/digital-de/instructional-resources/generative-ai/
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[
{
"date": "2025/06/11",
"position": 25,
"query": "AI education"
}
] |
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UTEP Launches Master's in Education with Artificial ...
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UTEP Launches Master’s in Education with Artificial Intelligence Focus
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https://www.utep.edu
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UTEP Launches Master's in Education with Artificial Intelligence Focus. New degree will equip teachers with the skills to leverage AI in the classroom. EL PASO, ...
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UTEP Launches Master’s in Education with Artificial Intelligence Focus
New degree will equip teachers with the skills to leverage AI in the classroom
EL PASO, Texas (June 11, 2025) – A new master’s degree offered by The University of Texas at El Paso gives educators the tools they need to leverage artificial intelligence (AI) in the classroom.
UTEP's new Master of Arts in Education with a concentration in artificial intelligence was spearheaded by Olga Kosheleva, Ph.D., chair and associate professor of mathematics education at UTEP.
This first of its kind degree — the Master of Arts in Education with a concentration in AI — was spearheaded by Olga Kosheleva, Ph.D., chair and associate professor of mathematics education at UTEP.
“AI is becoming an integral part of how we interact with technology and that extends to the classroom,” said Kosheleva. “Teaching educators how to make practical use of AI will make them more efficient, from reducing the time needed to create lesson plans to creating more robust means of assessing students’ progression. The biggest value, however, will be the increased time that AI will give teachers to focus on working individually with students and forging the personal relationships that support academic growth.”
The degree will equip students with skills in instructional design, data science and systems thinking. Courses include “Foundations of Generative AI,” “Ethics, Culture, and Society in the Age of AI” and “Data Science for Educational Professionals.”
Kosheleva, a mathematician and director of the STEM education division at UTEP whose research on AI has been showcased across the globe, led the degree’s creation after seeing the UTEP Computer Science Department’s success in establishing an undergraduate program in AI.
“The ability to leverage AI is in high demand across the region and country and this degree addresses that need,” said Clifton Tanabe, Ph.D., dean of the College of Education. “This master’s degree reflects UTEP’s mission to transform the lives of its graduates and our community. In addition to being at the forefront of producing AI engineers, UTEP will also be a leader in teaching and learning about effective uses of AI in education.”
The new master’s degree is a fully online program that can be completed in 16 months. A GRE is not required to be considered for admission, and the program is open to any student with a bachelor’s degree.
Students will be prepared for careers as curriculum developers, school administrators, educational coordinators and policy analysts, among others.
The master’s degree program is currently taking applications for the Fall 2025 semester. Prospective students can apply to the program through the UTEP Graduate School. For more information, contact the UTEP Graduate School at (915) 747- 5491 or at [email protected].
About The University of Texas at El Paso
The University of Texas at El Paso is America’s leading Hispanic-serving university. Located at the westernmost tip of Texas, where three states and two countries converge along the Rio Grande, 84% of our 25,000 students are Hispanic, and more than half are the first in their families to go to college. UTEP offers 171 bachelor’s, master’s and doctoral degree programs at the only open-access, top-tier research university in America.
Last Updated on June 11, 2025 at 12:00 AM | Originally published June 11, 2025
By MC Staff UTEP Marketing and Communications
| 2025-06-11T00:00:00 |
https://www.utep.edu/newsfeed/2025/june/utep-launches-masters-in-education-with-artificial-intelligence-focus.html
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[
{
"date": "2025/06/11",
"position": 35,
"query": "AI education"
}
] |
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Ohio State University faculty skeptical, but hopeful about ...
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Ohio State University faculty skeptical, but hopeful about new AI education initiative
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https://www.wosu.org
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[
"Wosu Npr News"
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Ohio State University announced last week it will launch a new initiative to embed AI education into the core of every undergraduate curriculum.
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Ohio State University professors are skeptical, but hopeful that a new initiative to increase artificial intelligence fluency could help advance usage and understanding of the new technology in education.
The university announced last week it wants all 45,000+ undergraduate students to graduate with a fluency in using artificial intelligence. The university said all students will be required to learn to use A.I. in their coursework beginning with the fall semester.
OSU art professor Chris Coleman said he can get behind teaching students how to use AI, but he said the university will be on the cusp of figuring out whether AI becomes a helpful tool or a creative crutch.
"It's a big group of students to run this experiment with," Coleman said. "But I don't think you can ignore (AI) either. So I appreciate that it's bold and this is gonna be attempted."
Starting with the fall semester, Ohio State plans to embed AI education into the core of every class from computer science to agriculture. Students will also learn the ethics of using the tool.
Ohio State said in a news release last week that beginning with the class of 2029, every Buckeye graduate will be fluent in AI and how it can be responsibly applied to advance their field.
“Artificial intelligence is transforming the way we live, work, teach and learn. In the not-so-distant future, every job, in every industry, is going to be impacted in some way by AI,” Ohio State President Walter “Ted” Carter Jr. said in a statement.
Carter said Ohio State has an opportunity and responsibility to prepare students to keep up and lead the way in how AI impacts the workforce of the future.
“I’m so pleased that we are taking this bold step forward to set our students up for success and keep Ohio competitive for the long term. We have a strong foundation on which to build, and the AI Fluency initiative will only accelerate our momentum in mission-driven AI research and education," Carter said.
When it comes to art education, Coleman said students could use AI to help inspire physical art forms like sculpting and painting. When it comes to digital work like animation or computer coding, AI potentially could be used to do all the work, creating ethical concerns.
"We're quickly trying to figure out things like if it is still worth rendering frame-by-frame ultra real animation, or are we at the point where we can actually just sort of mock up the animation, plug that footage that quickly rendered footage into AI and get better results and actually save energy," Coleman said.
Coleman said he will be teaching a general AI arts class that he said will get people to think more creatively about using AI for art beyond just telling AI like ChatGPT to make a picture.
"What does it look like if you feed it the lyrics to a song, it turns that into an image. That image then re-inspires a new song, and then that song actually becomes food for another model which generates video," Coleman said. "I think there's some really interesting processes that can be built."
Coleman said he is concerned about how this technology may take away jobs. He pointed out when computers were first invented, jobs that were once done by mathematicians were taken over by computers.
Coleman gave the example of the women who helped run complex mathematical equations for NASA.
"For me, the most important thing is that students understand where AI comes from, how it works and how to make smart decisions about whether or not to use it for everything that they're doing in life," Coleman said. "That's the kind of literacy that I can get behind is being knowledgeable enough to make an intelligent choice about using AI."
Elizabeth Hewitt, chair of Ohio State's English department, said she likes to use the comparison of AI in academics to the debate over the calculator for math decades ago. Like the calculator, she said some view AI as a tool that can be used to help students improve and innovate their work.
Hewitt said professors discovered in 2022 that more students were using AI to help write their papers either partially or entirely. She said this led to an increase in complaints to Ohio State's Committee on Academic Misconduct.
Hewitt said at the time, AI was "hallucinating" or putting incorrect information and using language in odd ways. Since then, Hewitt has said AI has learned and become more sophisticated and much harder to detect.
"I think with the next generation, the algorithms have gotten better and more sophisticated. And so it's less easy to see. There never really were foolproof ways of detecting AI use in any case," Hewitt said.
Hewitt said in English, she wants students to know how to use it in the most ethical, responsible and productive way possible without using AI to do all their work for them.
Hewitt said she is skeptical of just how much AI will ultimately be able to replace the foundational knowledge taught in coursework for subjects like English and art.
"I do think that a lot of the (AI) advocates, the true believers, the people who are also going to make substantial amounts of money from it, they make it sound like this will be our new world in which that kind of knowledge isn't requisite. I have a hard time understanding what that will look like," Hewitt said.
Hewitt said Ohio State wants students to think critically about these hard questions about AI. She said this may determine how far AI usage can go without replacing the need for creativity and human thought.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://www.wosu.org/2025-06-11/ohio-state-university-faculty-skeptical-but-hopeful-about-new-ai-education-initiative
|
[
{
"date": "2025/06/11",
"position": 37,
"query": "AI education"
}
] |
AI in Education: 24/7 Learning Access for Students
|
AI in Education: 24/7 Learning Access for Students
|
https://technocratshorizons.com
|
[
"Tarvinder Singh"
] |
AI-powered learning provides global access to education and goes beyond that. Students can learn at their own pace and schedule without being bound by fixed ...
|
A few decades back, students had fixed classroom timings, so they had to wait for the next class schedule to resolve their queries. Moreover, students had to travel for hours, which would become tiresome for students living away from the educational institutions.
With the advancement in time and technology, the concept of eLearning came into existence, where students could connect with their educators virtually and easily access numerous courses. However, the eLearning platforms were also initially bounded with time as students could connect with educators at scheduled times. But, the introduction of AI in eLearning brought a drastic change.
AI changed the way eLearning platforms used to operate. Moreover, with the help of AI, students get 24/7 access to online courses. The 24/7 learning accessibility allowed students to learn at their desired time.
Of course, you might wonder how AI has made 24/7 accessibility possible for students. Well, it’s because of the amazing features of AI that it happened.
Today, this blog will tell you how AI in eLearning can provide students with 24/7 access to learning. Besides you will also learn about a powerful eLearning solution, Virtulearn, and how it simplifies online learning for students.
Role of AI in Education for Students
Since its evolution, AI applications in education have drastically transformed the education industry. From initializing virtual classrooms to enhancing learning flexibility to tracking individual progress, AI has eased students’ lives to a greater extent.
A few decades back, students had to think about the travel expenses to learn to access any course globally. But today, students can access any international course in the comfort of their space.
Moreover, AI-powered learning provides global access to education and goes beyond that. Students can learn at their own pace and schedule without being bound by fixed classroom schedules. The AI-powered chatbots and virtual assistants provide 24/7 assistance to students to ensure no query is left unsolved.
The ease of accessibility is why students adapt to AI-powered learning systems instead of traditional learning. The 24/7 accessibility of education opens learning opportunities for students who are doing part-time jobs and want to enhance their skills and knowledge. Besides, AI-powered learning becomes an effective learning medium for students who can’t afford to travel to foreign countries or states to pursue international courses.
Besides, there are numerous benefits through which AI allows students to access and learn their desired courses at any time.
How Does AI Help Students in Education to Access 24/7 Learning
The importance of AI in the education industry is increasing every day. And this is obvious to happen because of the 24/7 accessibility AI provides. However, online learning involves several challenges, such as students’ difficulty understanding complex concepts.
Moreover, students may struggle to go through uncountable courses irrespective of their required learning needs. Also, the absence of physical teachers would prevent students from knowing their individual growth and progress.
With an AI eLearning platform, students can easily overcome all these challenges without compromising. Let’s see in detail how AI can overcome barriers for accessing education for students.
Creating Personalized Learning Paths
Undoubtedly, every student has different learning abilities. Some students might grasp even the most complex topics quickly, while others might need time to grasp them perfectly. In the case of eLearning, several students face challenges in accessing the courses based on their abilities.
However, AI apps for education simplify this very easily. AI-powered data analytics tools help analyze individual students’ strengths and weaknesses. Based on the analytics reports, AI would generate personalized learning roadmaps for individual students to help them gain knowledge based on their abilities.
For instance, if a student has difficulty understanding a particular concept, AI analyzes it and designs quizzes, interactive models, and guides to help the student understand the complex concept easily. So, AI tools in education are simplifying learning through personalization.
Virtual Tutors and Assistants
Perfect learning becomes impossible in the absence of a teacher. However, in the case of eLearning platforms, students faced numerous difficulties as teachers could not be available 24/7 to support them. But thanks to AI, teachers are available 24/7 through virtual tutors and chatbots.
The virtual tutors would identify the areas where students faced challenges and recommend courses based on their needs. Moreover, the AI-powered virtual tutors act as emergency friends for students who are always there to resolve their doubts and queries whenever the students need them.
Besides, the chatbots can easily guide students in finding suitable courses based on their learning goals and budget. And the biggest benefit of these virtual chatbots is that, unlike physical teachers, these virtual bots are available 24/7.
So, artificial intelligence learning platforms not only simplify the accessibility of courses but also simplify the accessibility of teachers 24/7 through virtual assistants.
Simplifying Complex Learning Through Interactive Learning
Students, of course, will face challenges in understanding complex topics. Moreover, students’ biggest challenge is keeping themselves engaged while learning online. There can be topics where students might feel bored while learning them.
In the case of traditional classrooms, teachers would make the class interactive through real-time examples and conversations to create an interactive and engaging learning environment. However, in the case of eLearning, having an interactive environment is quite challenging. However, AI makes it happen by including numerous interactive elements like quizzes, games, and multimedia content.
Moreover, AI educational apps also offer 3D visuals of complex subjects to make it easy for students to understand them while keeping them engaged. So, creating an interactive and engaging eLearning environment is also one of the biggest advantages of AI in education because students easily grasp complex subjects.
Curating and Managing Courses As per Individual Needs
eLearning platforms offer hundreds of courses that would eventually confuse the students while choosing the courses. With such confusion in minds, students would make the wrong choice.
However, AI helps students access the right courses by evaluating their learning needs and curating the course content accordingly. Content curation not only helps students choose the right courses but also helps them save a lot of time wasted while going through numerous courses before making the choice.
Besides content curation, AI also helps students manage their courses at a centralized location. Accessing courses from different locations would sometimes confuse students, and in this confusion, students would forget to access various courses.
To simplify this situation, AI organizes every course material, including videos and text-based guides, in a unified way, ensuring students can easily access their courses without being overwhelmed.
Providing Real-Time Feedback and Assessment
Feedback plays a key role in the success of eLearning. Real-time feedback helps students acknowledge their progress. It also helps students identify where they are falling back and plan impactful strategies to overcome such regions.
In the case of eLearning platforms, providing real-time feedback to individual students becomes challenging due to the large number of students involved.
However, AI makes it easier through its precise progress-tracking tools. AI would analyze the individual learner’s progress and identify the areas where the students are falling back. Finally, based on the progress report, AI will give necessary and personalized feedback to individual students on time to ensure they progress quickly.
AI will provide real-time insights and course recommendations to help individual students to improvise effectively.
Flexibility Through Round the Clock Accessibility
There were days when classrooms had fixed timings, which restricted students from accessing learning at their own pace.
But today, AI education provides flexibility to students to understand not only from their comfort space but also from their schedule without worrying about the unavailability of teachers or courses.
You might be wondering how AI is making it possible. Well, it’s because of the 24/7 availability of AI learning resources like videos, tutorials, articles, and other interactive course content on the eLearning platforms. One of the biggest advantages of artificial intelligence in education is students can access a wide pool of learning resources anytime without worrying about their busy schedules.
How AI-Powered Virtulearn Becomes the Perfect Learning Partner for Students
The accessibility of AI in the education industry has helped students enhance their eLearning experiences. One such powerful AI eLearning platform is Virtulearn. From delivering personalized recommendations to having interactive learning elements to ease of accessibility, Virtulearn becomes a perfect eLearning solution for students to learn anytime at the comfort of their pace. Here are some amazing features of Virtulearn that make it an ideal platform for learners to upskill themselves.
24/7 Accessibility and Easy Enrollment
Virtulearn’s motto is to simplify students’ lives in every possible way. That’s why it involves a seamless enrollment process through which students can sign up quickly and easily and access numerous courses. Besides, Virtulearn offers round-the-clock accessibility to multiple learning resources, allowing students to learn anytime.
Real-Time Progress Tracking
Knowing your learning progress is very important to understand where you fall back and where you need to improvise. Our AI-powered analytics tools thoroughly analyze your progress and check completed assignments. With the detailed report, you can identify the areas where you need to work harder. Progress tracking also helps keep students motivated and helps them easily design their learning roadmaps.
Personalized Course Recommendations
With each student having unique learning needs and goals, our AI-powered Virtulearn performs a detailed analysis of individual learners’ needs and preferences and would recommend courses based on the evaluation. Moreover, Virtulearn also identifies individual learners’ strengths and weaknesses and creates customized learning paths that contribute to personal growth.
Diverse Range of Courses
Virtulearn offers various courses, from free to paid to hybrid, to ensure every student can access classes according to their needs and budget. However, diversity doesn’t mean compromising in terms of course quality. Virtulearn offers well-curated courses based on your needs while providing flexibility to ensure you invest your time and money in the right learning resources.
Easy Payment and Subscription Models
The AI learning app Virtulearn aims to simplify students’ lives, so it offers simplified payment and subscription models to ensure students can easily access the courses they need. This allows students to focus more on appropriate course selection rather than worrying about the payment complexities.
Conclusion
AI is transforming how eLearning platforms work, but it is not limited to this transformation only. It goes beyond by bringing an impactful revolution in the lives of students. The benefits of
Artificial Intelligence in education includes designing individual learning paths, introducing interactive elements, and providing accessibility through virtual assistants, real-time feedback, and flexibility. Artificial Intelligence applications in the education industry are creating impactful transformations by providing numerous benefits.
Virtulearn is an AI-powered eLearning platform that simplifies the lives of both educators and students. From automating building eLearning courses for students to providing 24/7 accessibility, Virtulearn intends to help online educators and learners have an interactive yet enjoyable learning environment.
Eager to learn more about Virtulearn? Book a free demo to learn how Virtulearn can benefit the eLearning industry through its impactful features and functionalities.
| 2024-06-19T00:00:00 |
2024/06/19
|
https://technocratshorizons.com/blog/how-ai-in-education-can-provide-24-7-access-to-learning-for-students/
|
[
{
"date": "2025/06/11",
"position": 64,
"query": "AI education"
}
] |
The Importance of AI in Education
|
The Importance of AI in Education
|
https://classplusapp.com
|
[
"Sanchita Pathak"
] |
The role of artificial intelligence in education is to make education more efficient, generate engaging content, and tailored to each student's needs.
|
Imagine a world where teaching and learning are not just a one-size-fits-all experience, but a journey tailored to each student’s unique needs and dreams. This is not something you need to imagine, it is the reality of education today, thanks to Artificial Intelligence (AI). The importance of AI in education cannot be ignored as it has transformed the way we teach and learn.
In this blog, we’ll explore how AI in education sector plays an important role and what is the importance of artificial intelligence in education.
What is AI?
AI in education refers to the use of artificial intelligence technologies to enhance the teaching and learning experience. This can involve different software and systems that adapt to individual student’s learning paces and styles, provide personalized learning experiences, assist teachers with grading and administrative tasks, and offer insights into students’ progress and challenges.
The importance of AI in education comes with AI tools such as educational apps, online tutoring systems, interactive games, and platforms that analyze data to improve educational outcomes. Essentially, the role of artificial intelligence in education is to make education more efficient, generate engaging content, and tailored to each student’s needs.
It also can analyze vast amounts of data and learn from interactions, allowing it to offer unique insights into each student’s learning process, meeting the educational experience to their individual needs and preferences.
Importance of AI in Education for Teachers
The role of Artificial Intelligence in education is continuously becoming a game-changer for teachers along with transforming the traditional form of teaching methods. Here’s a look at how the importance of AI in education is reshaping the role of teachers in education:
1. Personalized Learning
It can provide a personalized learning experience to every student along with analyzing the student’s learning pattern, strengths, and weaknesses.
Similarly, one of the uses of artificial intelligence in education can help teachers tailor the learning experience to meet the individual needs of each student.
2. Engagement
Moreover, AI-powered tools make the learning process more interactive for students, which can help keep students engaged. Different activities like games or simulations can be included to make the learning process more fun for your students.
3. Customized Feedback
Teachers can make use of AI in education to provide immediate and personalized feedback to students, helping them understand their mistakes and improve quickly.
AI can help teachers save their precious time by helping them with tasks that require manual efforts such as providing feedback, preparing lesson plans, classroom interactions, etc.
4. Automated Grading System
Teachers can make their task of grading their students an easy and smooth process. AI can grade assignments and exams quickly and consistently, allowing teachers more time to concentrate on lesson planning and individual student needs. This is one of the best uses of artificial intelligence in education.
5. Task Automation
Routine administrative tasks can be automated with AI, allowing teachers to dedicate more time to teaching and less to paperwork.
AI in education can streamline administrative tasks such as attendance, scheduling, and communication with parents, reducing the administrative burden on teachers.
Also read – What are Digital Classrooms?
6. Learning Gap Analysis
With the help of AI tools, teachers can easily identify learning gaps among students. Teachers can get access to detailed reports and insights into each student’s learning journey. By using these insights, educators can tailor their teaching strategies to meet the individual needs of their students, ensuring that no one is left behind.
AI’s ability to process vast amounts of data quickly and accurately means that these gap analyses are more comprehensive and detailed than what would be feasible manually.
7. AI in Examinations
The role of AI in examinations is multifaceted, offering benefits such as automated grading, plagiarism detection, and the generation of personalized feedback. Teachers can reduce their administrative workload, allowing them to dedicate more time to student engagement and instructional improvement.
8. Smart Content
The involvement of AI is transforming education by making teaching materials more interactive and personalized. Digital textbooks and online modules can adjust to each student’s learning pace, enhancing the importance of AI in education.
Moreover, these materials are always up-to-date, keeping content relevant and aligned with new standards. This capability is crucial for the future of AI in education, as it helps teachers deliver a dynamic learning experience that meets the expectations of today’s digital-native students.
9. Universal Access
Teachers can utilize the importance of AI in education to reach a wider audience and create an inclusive learning environment where every student has the opportunity to succeed. Furthermore, it can facilitate remote learning, which has become increasingly important, ensuring that education continues uninterrupted under any circumstances.
10. Enhanced Teaching
Teachers can analyze the effectiveness of teaching methods and offer suggestions for improvement, helping them to continually develop their skills.
By integrating these AI capabilities, teachers can enhance their teaching methods, provide better support to students, and manage their time more effectively.
Importance of AI in Education for Students
The importance of AI in education students is multifaceted and rapidly evolving. Here are some key points highlighting its impact:
Makes learning more accessible to students with special needs like voice-to-text transcribers, personalized learning interfaces, and software for adaptive learning.
Additionally, helps in identifying the learning patterns and preferences of individual students.
AI-powered language learning apps and tools can offer personalized language learning experiences for students.
Furthermore, AI systems can help in the early detection of learning disabilities or difficulties by analyzing student interaction and performance data, enabling timely intervention.
The main role of artificial intelligence in education offers a dynamic and evolving landscape that enhances the learning experience, making it more personalized, efficient, and accessible for students.
What Does the Future of AI in Education Look Like?
The future of AI in education looks like a fun and exciting adventure into a world of smart teaching and learning. AI tools will offer a helping hand, creating personalised learning experiences for every student.
Consequently, these tools could adapt to each student’s learning style, making sure that everyone understands the lesson, no matter if they are fast learners or slow learners.
Along with students, teachers will also benefit from AI advancements. They will get tools to ease their time-consuming tasks like grading papers, preparing lesson plans, or helping students directly.
Indeed, the future of AI in education is all about making teaching and learning more accessible, efficient, and enjoyable for everyone involved.
Conclusion
The importance of AI in education cannot be understated. AI will be a transformative force that will reshape how we teach and learn. It will make education more accessible, efficient, and engaging for teachers as well as students. The journey of artificial intelligence in education is just beginning, and its potential to revolutionize the field is limitless.
Importance of AI in Education FAQs
Q1. What is the importance of AI in education? A1. AI offers automation of repetitive tasks, personalized experiences, enhanced data analysis, improved efficiency, and innovative solutions in various sectors including healthcare, education, as well as businesses Q2. How is artificial intelligence in education used? A2. In education, AI is used for personalized learning, automated grading, intelligent tutoring systems, data-driven insights for curriculum development, etc. Q3. Is AI good or bad? A3. AI, like any technology, is neither inherently good nor bad; its impact depends on how it’s used. Q4. Is AI better for the future of education? A4. AI has the potential to greatly enhance education by personalizing learning and improving accessibility. However, it should complement, not replace, traditional teaching methods. Q5. How is AI changing education? A5. AI is revolutionizing education by personalizing learning experiences as well as providing adaptive feedback, enhancing student engagement and outcomes.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://classplusapp.com/growth/importance-of-ai-in-education/
|
[
{
"date": "2025/06/11",
"position": 66,
"query": "AI education"
}
] |
Introduction - Artificial Intelligence in Teaching & Learning
|
Artificial Intelligence in Teaching & Learning
|
https://camosun.libguides.com
|
[
"Derek Murray"
] |
This guide is intended to provide an introduction to AI and its current implications for teaching and learning in higher education.
|
Artificial intelligence (AI) is rapidly altering the landscape of higher education. While artificial intelligence has been around for decades, it was in the fall of 2022 when tools like ChatGPT took the education world by storm. This guide is intended to provide an introduction to AI and its current implications for teaching and learning in higher education.
There is so much information out there right now about AI and its implications for education that wading through it can be a daunting task. We've tried to do some of that work for you and collect some resources and recommendations here to support you in developing your own thoughtful approach to AI in teaching and learning.
If you have any questions about the contents of this guide, or suggestions for additions or improvements, please contact [email protected] or complete our feedback form. CETL faculty are available for consultations with individual faculty or to facilitate a conversation in your department. Please reach out! We have real humans (not chat bots) ready to help.
Photo by Possessed Photography on Unsplash
What is AI?
Artificial intelligence "refers to computer systems that undertake tasks usually thought to require human cognitive processes and decision-making capabilities." (EDUCAUSE, 2017) ChatGPT, an example of generative AI (Gen-AI), "generates coherent and complex responses, based on statistical recognition of existing textual patterns in a large corpus of sources. GenAIs can also produce images, numeric data and references, based on similar types of predictive algorithms." (CRADLE, 2023) You may have heard the phrase "human-like responses" from people describing what ChatGPT does. To be clear: ChatGPT does not think like a human. It is also not a "copy and paste" program. It uses existing patterns in a body of text to predict a probable response to a user prompt.
GenAI tools can do much more than produce text outputs based on text inputs!
The range of GenAI tools available is continually expanding. There are tools now that can read not only text, but also images, video, and entire documents. GenAI tools can provide text, but also audio, video, images, slide shows, infographics, etc.
| 2025-06-11T00:00:00 |
https://camosun.libguides.com/ai/introduction
|
[
{
"date": "2025/06/11",
"position": 68,
"query": "AI education"
}
] |
|
Must-Attend AI in Education Conferences in 2025 and 2026
|
Top AI in Education Conferences to Watch in 2025-2026
|
https://flypix.ai
|
[
"Flypix Ai Team"
] |
Discover leading AI in education conferences in 2025 and 2026 covering innovation, tools, research, and policy for educators and tech leaders.
|
Artificial intelligence is rapidly changing how we teach, learn, and manage educational systems. From smart tutoring platforms and adaptive assessments to ethical concerns and AI literacy, the use of artificial intelligence in education is expanding. In 2025 and 2026, a series of international conferences will gather experts, educators, researchers, and policymakers to explore the challenges and opportunities of this shift. These events provide a platform to share research, discover new tools, and discuss the future of learning shaped by AI. Whether you’re a teacher, academic, developer, or decision-maker, these conferences offer valuable insights into how AI is being integrated into education worldwide.
1. ISTELive 25
The ISTELive 25 and ASCD Annual Conference will take place from June 29 to July 2, 2025, in San Antonio, Texas, at the Henry B. Gonzalez Convention Center. This combined event offers both in-person and virtual participation, with a schedule that includes mainstage sessions, hands-on workshops, and preconference institutes starting June 28. The program focuses on topics such as artificial intelligence in education, digital learning environments, instructional design, and educational leadership.
The conference includes interactive presentations and demonstrations, with a dedicated expo open from June 30 to July 2 showcasing educational technologies and tools. Participants can use a mobile app for session planning, networking, and tracking continuing education credits. The event integrates content from ISTE and ASCD, offering a wide range of professional development opportunities for educators across K–12 settings.
Key Highlights:
Annual gathering focused on innovation in educational technology
Features a wide range of sessions, interactive workshops, and exhibition spaces
Includes dedicated content tracks exploring AI tools, teaching strategies, and ethical considerations
Target Audience:
K-12 and higher education teachers and instructional leaders
Ed-tech developers and innovators
School administrators and district-level decision-makers
Contact Information:
Website: conference.iste.org/2025
LinkedIn: www.linkedin.com/company/ISTEofficial
Twitter: x.com/ISTEofficial
Facebook: www.facebook.com/ISTEofficial
Instagram: www.instagram.com/isteconnects
2. Thirty-Second International Conference on Learning
The 32nd International Conference on Learning will be held from July 8 to 10, 2025, at the University of Granada, Spain. The conference will address the theme “Human Learning and Machine Learning: Challenges and Opportunities for Artificial Intelligence in Education.” It is organized in a blended format, allowing both in-person and online participation. The event includes research presentations, thematic workshops, and structured discussions across several streams, including pedagogy, curriculum, assessment, and educational technology.
A plenary panel focused on AI in education will be part of the official program. The conference will also feature activities such as a conference dinner, local tours, and a showcase of work by emerging scholars. Participants will explore practical and theoretical challenges of integrating AI into educational settings.
Key Highlights:
Annual gathering by the Learner Research Network
2025 theme: “Human Learning and Machine Learning”
Combines in-person and online sessions with peer-reviewed presentations
Target Audience:
Researchers in education, learning sciences, and pedagogy
Teachers and institutional leaders
Developers of learning technologies and AI systems
Contact Information:
Website: thelearner.com/2025-conference
Address: 60 Hazelwood Drive, Champaign, IL 61820 USA
Phone: +1-217-328-0405
Email: [email protected]
LinkedIn: www.linkedin.com/company/common-ground-research-networks
Twitter: x.com/CGRNetworks
Facebook: www.facebook.com/thelearnerresearchnetwork
Instagram: www.instagram.com/commongroundresearchnetworks
3. AIME Conference
The AIME Conference, organized by the Artificial Intelligence in Measurement and Education special interest group of NCME, will take place from October 27 to 29, 2025, in Pittsburgh, Pennsylvania. It focuses on the role of artificial intelligence in educational assessment and measurement, bringing together researchers, developers, and practitioners. The conference includes paper and poster sessions, as well as workshops addressing the technical and ethical aspects of AI in testing environments.
Key topics at AIME include automated scoring, adaptive testing, natural language processing for educational content, and the design of transparent and fair AI assessment systems. The event also explores the use of generative AI in learning evaluation and the policy implications of its deployment in education. The conference is structured to facilitate interdisciplinary exchange and emphasizes both technical development and responsible implementation of AI in educational measurement.
Key Highlights:
Hosted by NCME’s AI in Measurement and Education special interest group
Focuses on AI-driven assessment, fairness, explainability, and innovation
Features paper presentations, poster sessions, and workshops
Target Audience:
Psychometricians, measurement specialists, data scientists
Educational researchers and policy experts
Developers of AI assessment tools
Contact Information:
Website: ncme.org/event/special-conferences/aime-conference
Address: 9 Mantua Road, Mt. Royal, NJ 08061
Phone: (856) 284-3700
E-mail: [email protected]
4. MIT AI and Education Summit 2025
MIT AI & Education Summit 2025 is the second annual event organized by MIT’s RAISE initiative, scheduled for July 16-18, 2025, in Cambridge, Massachusetts. They convene researchers, educators, students, and industry professionals to explore AI’s role in teaching and learning. The program includes invited talks, paper presentations, poster sessions, youth-led tracks, and hands-on workshops, including multilingual sessions in Portuguese and Spanish.
The Summit features a student-centered component where youth authors present position papers and posters on topics like AI tools in classrooms and policy considerations. It also incorporates collaborative events such as the Global AI Hackathon and a Climate Challenge aimed at applying AI to sustainability issues. Activities extend into networking events and site tours across MIT facilities .
Key Highlights:
Organized by MIT’s RAISE initiative
Explores AI literacy, personalized learning, and responsible AI use
Includes keynote talks, paper presentations, hackathons, and policy panels
Target Audience:
Academics, researchers, and graduate students
Ed-tech developers and education policymakers
Institutional leaders and curriculum designers
Contact Information:
Website: raise.mit.edu/events/mit-ai-and-education-summit-2025
Address: Bldg NE49-2nd Floor, Cambridge, Massachusetts 02139 USA
E-mail: [email protected]
5. ITHET 2026
The IEEE International Conference on IT in Higher Education and Training (ITHET 2026) is scheduled for May 20-22, 2026, in Oslo, Norway. It addresses the integration of information technology and artificial intelligence in higher education, featuring full and short papers as well as specialized workshops on topics like peer review systems, interactive learning technologies, and learning analytics.
Organized under the auspices of IEEE, ITHET 2026 features peer-reviewed proceedings that will be published through IEEE Xplore. The technical committee includes international representatives from institutions like the University of Technology Sydney and Sorbonne Paris Nord, with keynote addresses focusing on AI-enhanced learning environments. The event serves as a platform for presenting research on AI-driven instructional design, personalization, and campus-wide educational technologies
Key Highlights:
Explores the role of AI and IT in higher education
Covers topics like generative AI, smart learning, and ethics
Accepted papers are published in IEEE Xplore
Target Audience:
Higher education faculty and instructional technologist
Educational researchers and graduate students
Academic leaders focused on digital transformation
Contact Information:
Website: ithet.net
Phone: +61425220252
E-mail: [email protected]
6. AI and the Future of Education Conference (APUS)
AI and the Future of Education is a virtual conference hosted by the APUS (American Public University System). It will take place on October 16-17, 2025. The event brings together educators, scholars, and AI-tool developers to examine the role of artificial intelligence in higher education, with particular attention to its application in classroom instruction, student support systems, and faculty development.
The conference program includes sessions covering topics like AI-driven tutoring, personalized learning, ethical implications of AI use in academia, and strategies for enhancing accessibility. Participants are offered free access to live presentations and subsequent on-demand recordings. The event is open to the public and aims to support attendees in integrating AI practices into postsecondary education.
Key Highlights:
Explores AI applications in teaching, learning, and academic services
Covers AI ethics, equity, accessibility, and student support systems
Includes peer‑reviewed proposals featuring presentations, panels, and posters
Target Audience:
Postsecondary educators and instructional designers
Higher education administrators and decision‑makers
Researchers in AI, pedagogy, and educational technology
Contact Information:
Website: apus.edu/academic-community/conferences/ai-and-the-future-of-education
Address: 111 W. Congress Street, Charles Town, WV 25414
Phone: 877-755-2787
Twitter: x.com/APUSPRteam
Facebook: www.facebook.com/apusuniversity
Instagram: www.instagram.com/apusalumni
7. AIED 2025
AIED 2025 is the 26th International Conference on Artificial Intelligence in Education, scheduled from July 22 to 26, 2025 in Palermo, Italy. It brings together researchers, practitioners, and policymakers focused on AI-supported learning systems and cognitive science approaches. The conference features a main track along with workshops, tutorials, a doctoral consortium, late-breaking results, and a dedicated track for global perspectives on AI in education.
The event includes interactive components such as workshops on intelligent textbooks and a standalone workshop on fairness in algorithmic decision-making in education. The doctoral consortium supports PhD candidates through mentoring sessions, while the WideAIED track encourages contributions addressing underrepresented regions and ethical challenges. It is organized by the International AIED Society and hosted by the University of Palermo.
Key Highlights:
26th installment of the flagship AIED conference
Covers adaptive learning, inclusive AI, cognitive modeling, and ethics
Includes technical papers, demonstrations, and workshops
Target Audience:
Researchers in AI and education
Designers of intelligent learning systems
Graduate students and academic professionals
Contact Information:
Website: aied2025.itd.cnr.it
E-mail: [email protected]
8. TCEA AI for Educators Conference
AI for Educators Conference is an online event organized by TCEA, set to take place from July 22 to 24, 2025. It focuses on practical uses of artificial intelligence in K-12 teaching, offering sessions on lesson planning, instructional design, and student engagement using AI tools. The event includes live presentations and workshops, with additional on-demand access to all content after the event.
Participants will explore topics such as the ethical use of AI in classrooms, prompt development, and classroom-ready applications of AI. The event supports continuing education requirements and includes tools for networking among educators. All registrants receive a one-year membership to TCEA and access to the recorded sessions until the end of August 2025.
Key Highlights:
Hands-on event focused on practical AI tools for K-12 educators
Covers lesson planning, productivity, classroom engagement, and micro-credentials
Delivered virtually with interactive sessions and expert panels
Target Audience:
K-12 teachers and instructional coaches
School administrators and ed-tech coordinators
District-level technology and curriculum staff
Contact Information:
Website: tcea.org/events/ai
Address: 3100 Alvin Devane Blvd, Building B, Austin, TX 78741
Phone: 512-476-8500
E-mail: [email protected]
Twitter: x.com/tcea
Facebook: www.facebook.com/tcea.org
Instagram: www.instagram.com/tcea_org
9. AIFE Japan
AIFE Japan is an international event focused on the intersection of artificial intelligence and K-12 education. The conference is scheduled for April 10-12, 2026 in Yokohama, Japan. It is designed for educators, school leaders, and AI-tool developers to engage in dialog about AI’s role in classroom instruction and educational leadership. The program features workshops, hands-on sessions, and model-lesson demonstrations that emphasize applying AI tools responsibly within school settings.
AIFE Japan highlights collaborative engagement by bringing together diverse stakeholders – educators, EdTech providers, and researchers – to exchange practical strategies and technical guidance. The event’s activities include think tanks, hackathons, toolbox-sharing sessions, and facilitated research presentations, aimed at supporting AI literacy and pedagogy in K-12 classrooms.
Key Highlights:
Organized by AIFE, in partnership with 21st Century Learning, with a focus on AI in K-12 education
Offers practical workshops, research sessions, and hands-on model lessons
Addresses AI literacy, ethics, classroom applications, and future trends
Target Audience:
K-12 teachers, instructional coaches, and school leaders
Educational policymakers and curriculum developers
Ed-tech startups, product designers, and academic researchers
Contact Information:
Website: www.21c-learning.com/events/aife-japan
E-mail: [email protected]
Twitter: x.com/21cli
Facebook: www.facebook.com/21CLInternational
10. EAAI-26
EAAI-26 is the 16th Symposium on Educational Advances in Artificial Intelligence, scheduled for January 24 to 26, 2026, in Singapore. It runs in conjunction with AAAI-26 and focuses on teaching and learning in artificial intelligence at various educational levels. The conference accepts peer-reviewed papers and includes tracks such as AI for education, teaching AI in K-12, and assignments for AI courses. Submission deadlines for papers were in August 2025, with notifications issued in November.
The event is organized by the Association for the Advancement of Artificial Intelligence (AAAI). Specific organizers for EAAI-26 are not yet confirmed. It features invited talks, including a session by the recipient of the AI Educator Award, and panels on integrating AI into education. Sessions cover the design of teaching materials, empirical classroom studies, and technical tools for instruction. The symposium also hosts a workshop for sharing model assignments used in AI courses.
Key Highlights:
Academic symposium dedicated to improving how artificial intelligence is taught and learned
Offers peer-reviewed sessions, invited talks, and hands-on teaching resources
Covers classroom research, curriculum design, and technical tools for instruction
Target Audience:
University faculty and course instructors in computer science and AI
Curriculum developers and education researchers
Graduate students and professionals designing AI education programs
Contact Information:
Website: eaai-conf.github.io
E-mail: [email protected]
Conclusion
The integration of artificial intelligence into education is accelerating, influencing everything from classroom instruction to institutional policy. Conferences focused on this intersection provide essential spaces for collaboration, showcasing emerging tools, research findings, and discussions on responsible use.
Whether the goal is to explore adaptive learning technologies, understand the ethical implications of AI in teaching, or implement practical solutions in schools and universities, these events offer valuable insights. As AI continues to evolve, staying connected to these conversations will be key for anyone shaping the future of education.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://flypix.ai/blog/ai-in-education-conferences/
|
[
{
"date": "2025/06/11",
"position": 89,
"query": "AI education"
}
] |
AI in Hiring: Can it Be Trusted?
|
AI in Hiring: Can it Be Trusted?
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https://employeejustice.com
|
[] |
AI can make hiring and workplace decisions more efficient, but it must be used responsibly. Employers should not rely on algorithms as a substitute for human ...
|
AI systems are only as fair as the data and algorithms behind them. When poorly designed or implemented without oversight, these tools can lead to:
Discrimination: If an AI system is trained on biased historical data (for example, data that favors male candidates), it may replicate and even worsen that bias.
If an AI system is trained on biased historical data (for example, data that favors male candidates), it may replicate and even worsen that bias. Lack of transparency: Many AI tools operate as “black boxes,” making it difficult for workers to understand how decisions are made—or to challenge them.
Many AI tools operate as “black boxes,” making it difficult for workers to understand how decisions are made—or to challenge them. Privacy violations: Constant AI monitoring can infringe on employees’ rights to privacy and create a culture of surveillance.
In fact, multiple lawsuits and investigations have already highlighted how AI tools can unintentionally discriminate against protected groups under laws such as Title VII of the Civil Rights Act and the Americans with Disabilities Act.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://employeejustice.com/blog/ai-in-hiring-can-it-be-trusted/
|
[
{
"date": "2025/06/11",
"position": 22,
"query": "AI employers"
},
{
"date": "2025/06/11",
"position": 7,
"query": "artificial intelligence employers"
},
{
"date": "2025/06/11",
"position": 14,
"query": "artificial intelligence employment"
}
] |
Strategy – AI+BI Platform for Enterprises
|
Strategy – AI+BI Platform for Enterprises
|
https://www.strategysoftware.com
|
[] |
Strategy, formerly MicroStrategy, is an award-winning AI+BI software company pioneering AI advancements for business intelligence.
|
Built to meet you where you are
Our tools are designed with flexibility and versatility at their core, adapting to your unique roles, industries, and needs.
| 2025-06-11T00:00:00 |
https://www.strategysoftware.com/
|
[
{
"date": "2025/06/11",
"position": 61,
"query": "AI employers"
}
] |
|
Placer.ai: Location Intelligence & Foot Traffic Data Software
|
Location Intelligence & Foot Traffic Data Software – Placer.ai
|
https://www.placer.ai
|
[] |
Grow Your Business With Placer.ai. Try our dashboard for free. Sign Up. Solutions. Retail. Optimize store performance with rich consumer behavior insights · CRE.
|
If I can use Placer in its most simple form and sign leases with it, then that says a lot for the rest of the product, where I can utilize void analysis and more. But at its most simple form, if I can convince one little tenant to lease 1,500 feet, that’s a great thing.
| 2025-06-11T00:00:00 |
https://www.placer.ai/
|
[
{
"date": "2025/06/11",
"position": 63,
"query": "AI employers"
}
] |
|
The HubSpot Blog's AI Trends for Marketers Report [key ...
|
The HubSpot Blog’s AI Trends for Marketers Report [key findings from 1,000+ marketing pros]
|
https://blog.hubspot.com
|
[
"Erica Santiago"
] |
In China, companies must register AI models with the government. For multinational companies, these regulations mean that AI marketing practices that work ...
|
With 66% of marketers globally using artificial intelligence in their roles, it's no longer a matter of if you should use AI but how. How are other marketers (and your competition) using AI? How can you use AI ethically?
To understand AI in marketing in 2025, we surveyed over 1,000 marketing and advertising professionals worldwide.
Our findings will help us decipher AI trends, find the current most common use cases for AI, and learn how to navigate the technology as new laws and guardrails address its usage.
Table of Contents
The State of Artificial Intelligence in 2025 New research into how marketers are using AI and key insights into the future of marketing. Marketing AI Tools
Marketing AI Tools Practical Tips
Practical Tips Trends and Statistics
Trends and Statistics And More! Get Your Free Report Learn more Get Your Free Report Download Free All fields are required. You're all set! Click this link to access this resource at any time. Download Now
Key Findings: The Current State of AI
1. AI is no longer an experiment — it's integral to marketing workflows.
When AI first began gaining traction, we often encouraged marketers to experiment with AI and see if/how they can adopt it into their workflow.
Well, the days of just experimentation are over, and while the robots aren't taking our jobs, they are now embedded in our workflows.
Here are the facts:
91% of marketing leaders say employees/teams at their organization use AI to assist them in their jobs.
82% of marketers say either they or their company invested in automation tools for employees to leverage in their roles.
66% report that their organization builds internal AI tools specifically for marketing teams.
As you can see from these stats, marketers have fully embraced AI as an assistive tool. So, if you're still not embracing AI within your organization, best believe your competition likely is.
2. Though AI has become commonplace in marketing, barriers still keep more marketers from adopting new AI tools.
While AI has become crucial and normalized in marketing, many marketers are still hesitant to integrate new AI tools into their workflows. Here are the Top 3 concerns I've pulled from our survey:
Data Privacy — The majority of marketers in our survey (42%) say data privacy concerns have prevented their team from adopting new AI tools in the past year.
— The majority of marketers in our survey (42%) say data privacy concerns have prevented their team from adopting new AI tools in the past year. Training and Time Investment — 39% of marketers said investing in time and training on new AI tools created a barrier against adopting them.
— 39% of marketers said investing in time and training on new AI tools created a barrier against adopting them. Too Many Similar Tools — 35% of marketers say there are too many AI tools that all do the same thing but don't connect to one another, creating more complications when adopting new ones into their workflow.
3. AI is not taking our jobs.
While I understand marketers worry about their job security in the age of AI, our latest data shows that 65% of marketers champion using AI as an assistive tool while encouraging others not to become overly reliant on it. Why? Because AI is evolving, but it isn’t perfect.
AI-related challenges, like inconsistencies and bias, still exist today.
We haven’t lost the power of the human touch.
Right now, most industry professionals are using AI to support job functions, not replace them entirely. So, you can go ahead and exhale.
Now that AI is the norm, are there regulations on the use of AI in marketing?
In the United States? No. Currently, no comprehensive federal legislation or regulations address the use or development of AI in the U.S.; however, other countries and regions have restrictions and stipulations.
For example, the European Union passed the AI Act, which enforces strict rules on AI transparency, bias, and high-risk applications, such as algorithms. In China, companies must register AI models with the government.
For multinational companies, these regulations mean that AI marketing practices that work in the U.S. could be banned in other regions, thus locking them out of major global markets.
So, if you‘re a strictly U.S.-based company, you don’t have much to worry about, right? Not exactly. While there aren't any set AI laws to adhere to, many consumers are divided in their feelings toward how companies use AI.
For instance, Coca-Cola faced harsh backlash from consumers who called the company's AI Christmas 2024 ad a “creepy dystopian nightmare.”
Audiences were turned off by the ad's choppy appearance and criticized the ethics (or lack thereof) of using AI instead of working solely with artists and animators.
Consumers also worry about how personal data is used in AI training and the chances of a data breach that jeopardizes their privacy.
So, how can marketers address these concerns? The key is to be transparent. Let your consumers know if and how AI is being used in your marketing strategy, whether it's in content creation, data gathering, or communication.
For example, many media outlets include a note at the start or end of their blog posts clarifying that the post was created entirely or with the help of AI.
The State of Artificial Intelligence in 2025 New research into how marketers are using AI and key insights into the future of marketing. Marketing AI Tools
Marketing AI Tools Practical Tips
Practical Tips Trends and Statistics
Trends and Statistics And More! Get Your Free Report Learn more Get Your Free Report Download Free All fields are required. You're all set! Click this link to access this resource at any time. Download Now
General Attitudes Towards AI
The Marketer’s Perspective
I mentioned earlier that most marketing pros believe in AI's benefits but don’t want to rely on it 100%.
They believe AI supports two core personal areas: time and productivity.
We found that 79% of marketers agree that AI and automation tools can help them spend less time on manual tasks, and 73% agree that they can spend more time on the most important parts of their role.
66% of marketers say AI and automation tools can help them spend more time on the creative aspects of their jobs.
You know what that means? Less busy work and more time for big thinking. I’d consider that a win.
And if you want to see for yourself how AI can boost productivity and save time, experiment with tools like Breeze Copilot, which uses generative AI and CRM data to elevate work and execute tasks quickly.
The Organizational Perspective
At the organizational level, 50% of marketing leaders say their organization at least somewhat supports marketers using AI in their roles, 30% say their organization does not encourage the use of AI, and the remaining 20% say their company has no specific policy regarding AI.
Meanwhile, 75% of leaders whose organizations have invested in AI say that the investment has yielded a positive ROI. Only 4% say the investment returned a negative ROI and roughly 20% report neither a positive nor a negative ROI.
In addition to productivity, marketing leaders believe AI has had the most impact on making employees more effective at their jobs and helping them make data-driven decisions.
How Marketers Are Using AI at Work
As I said at the beginning, most marketing professionals in our survey report using AI in their current role. Let’s see what they’re using and where they’re seeing the most ROI.
Most Popular AI Tools for Marketers
Image and design generators like DALL-E and Synthesia are the top choice for 40% of marketers. Chatbot tools like ChatGPT, Google Gemini, and Copilot are second, with 39% of marketers using them.
Finally, in third place are SmartAI video or audio editing AI tools, such as ones with automatic editing features or AI light, noise, voice, and/or color correction.
ROI of AI-Powered Marketing Channels
As a blogger, it surprised me that email was the #1 channel that marketers are using AI to create for. But as a blogger, I’m also probably biased.
The majority (50.77%) use AI for email marketing and other newsletter platforms. This percentage is just a hair above the 50% who use AI text-based social media.
In third place, 47% of marketers use AI to create blog posts, articles, and other long-form content.
For all of these channels, they’re seeing a worthwhile ROI:
Email: 63% are seeing at least a somewhat positive ROI.
63% are seeing at least a somewhat positive ROI. Social Media: 67% are seeing at least a somewhat positive ROI.
67% are seeing at least a somewhat positive ROI. Blog/Long-Form: 68% are seeing at least a somewhat positive ROI.
Generative AI and Marketing
Content Creation
The most common task that marketers are using generative AI for is text-based content creation (52%), such as blogs, eBooks, and marketing email copy. For marketers who use GenAI to create content, here’s what they’re doing with it:
53% are using it for content quality assurance, such as spellcheck, accessibility review, or writing recommendations.
such as spellcheck, accessibility review, or writing recommendations. 50% are writing copy for marketing content like blogs and emails.
like blogs and emails. 48% creating images for marketing content with AI art tools.
However, AI isn’t the full answer to content creation. Only 4% of marketers are using AI to write entire pieces of content for them. The vast majority are using it for inspiration or to give them an outline and a few paragraphs to build on.
And 46% are only somewhat confident that they would know if the information GenAI produces is inaccurate.
Additional GenAI Use Cases
48% of marketers use generative AI to conduct various research, such as market research, finding datasets, and summarizing articles.
Automating direct brand messaging or conversational marketing. (DMs, conversational emails, SMS)
GenAI is also useful for branded communication, with 41% of marketers using it to automate direct brand messaging and conversational marketing.
For all of these use cases, the majority of marketing professionals say AI is very effective, and they’re saving an average of one to two hours in their work day as a result.
The Future of AI for Marketers
We don’t have a crystal ball, but we do have the top AI predictions from over a thousand marketing professionals.
Here’s what marketers are saying about the future of AI:
65% of marketing leaders say their team plans to increase their investment in AI and automation tools over the course of 2025.
65% of marketing directors believe that most software they use will have AI or automation capabilities built in by 2030.
67% of marketers agree that by 2030, most people will use a generative AI tool like ChatGPT to assist them in their jobs.
67% of marketers believe that AI will significantly impact how they (and other marketers) do their jobs in 2025.
As the AI landscape changes, we’ll keep tabs on how it evolves and how it’s being used in the workplace.
Keep an eye out for more insights that will allow you to use AI to unleash your human potential — and leave the busy work to the robots.
| 2025-06-11T00:00:00 |
https://blog.hubspot.com/marketing/state-of-ai-report
|
[
{
"date": "2025/06/11",
"position": 66,
"query": "AI employers"
}
] |
|
AI for local news: 5 lessons from the Lenfest AI Fellows ...
|
AI for local news: Five lessons from our first Lenfest AI Fellows gathering
|
https://www.lenfestinstitute.org
|
[] |
Tips for building a more inclusive and effective AI ecosystem for local journalism that benefits organizations of all sizes.
|
Guide AI for local news: Five lessons from our first Lenfest AI Fellows gathering By David Chivers Attendees at the first Lenfest AI Fellows gathering. Share
As AI adoption accelerates across the media industry, one of the most important questions remains: How do we ensure small and mid-sized newsrooms — not just the largest players — can benefit from these developments and leverage AI meaningfully?
In March, the Lenfest AI Collaborative and Fellowship Program – in collaboration with OpenAI and Microsoft – brought together our fellows, grantee organization executives, AI practitioners, researchers, and academics for our first in-person convening at The Walter Cronkite School of Journalism at Arizona State University. Here are five lessons we learned about building a more inclusive and effective AI ecosystem for local journalism:
1. Peer learning unlocks the most meaningful progress
Throughout the convening, a striking theme emerged: Peer-to-peer learning drove the deepest engagement.
Hearing journalists and technologists like those from The Minnesota Star Tribune and The Philadelphia Inquirer candidly discuss their AI product journeys demystified implementation and inspired attendees to accelerate their own AI adoption plans.
The Minnesota Star Tribune shared its Agate AI framework along with some of its early experiments and challenges as it innovates on its AI infrastructure. The team quickly recognized that reorganizing existing content around geographic markers could enhance local relevance and deliver greater value to readers, building on the organization’s statewide ambitions.
shared its Agate AI framework along with some of its early experiments and challenges as it innovates on its AI infrastructure. The team quickly recognized that reorganizing existing content around geographic markers could enhance local relevance and deliver greater value to readers, building on the organization’s statewide ambitions. The Philadelphia Inquirer shared the latest iteration of its AI-powered “Research Assistant” to help journalists quickly access and synthesize decades of archival reporting, using semantic search and natural language queries to streamline research, improve citation accuracy, and unlock the value of historical content. The tool aims to strengthen reporting, save time, and serve as a model for how AI can support local reporting while preserving editorial standards. You can learn more about how The Inquirer built the tool here.
Lesson: Technical skills matter — but trust, shared experiences, and community matter even more. Structured peer cohorts and case-study exchanges are more effective than top-down instruction alone.
2. You don’t need engineers to innovate
Several successful local news AI experiments have come from individuals without technical backgrounds: Speaking at the convening, Dorinne Mendoza, the product and partnership lead of The American Journalism Project’s Product & AI Studio, highlighted how important it can be to have newsroom partners willing to experiment. A couple of examples she shared were:
Centro de Periodismo Investigativo in Puerto Rico used OpenAI’s API to build a custom translation tool — without a single full-time developer.
used OpenAI’s API to build a custom translation tool — without a single full-time developer. Boyle Heights Beat, a neighborhood publication in Los Angeles, quickly adopted AI for wildfire communication, starting from scratch with simple prompts and templates.
Lesson: Curiosity, not coding, is the first requirement for newsroom AI innovation. With AI tools becoming increasingly more accessible, newsrooms — regardless of technical prowess or size — can quickly create tangible value with the simplest of tools.
3. “Small wins” can have outsized impact
Group discussions highlighted projects where seemingly modest applications of AI created major ripple effects and/or build outsized momentum within your organization:
Chicago Public Media ’s AI fellow created a Slack-integrated AI tool to summarize traffic updates for WBEZ on-air hosts, saving time for the host and representing a lightweight utility that had immediate impact with minimal complexity.
’s AI fellow created a Slack-integrated AI tool to summarize traffic updates for on-air hosts, saving time for the host and representing a lightweight utility that had immediate impact with minimal complexity. CalMatters translated emergency information during wildfires, which allowed the outlet to reach more Californians who needed immediate help.
translated emergency information during wildfires, which allowed the outlet to reach more Californians who needed immediate help. San Antonio Report streamlined a decade-old sales process using AI-generated slides.
Lesson: Local publishers should prioritize tools that solve real, everyday problems — both in the newsroom and on their business teams. These “small wins” can catalyze broader newsroom transformation and unlock revenue growth.
Several news leaders said they’re inundated with pitches from AI vendors, tools, and startups — many of which promise transformative results. Small organizations, however, often lack the internal capacity to vet these vendors effectively, leading to wasted time and resources.
Lesson: There is an urgent need for clear vendor evaluation templates, landscape analyses, and trusted peer-reviewed directories to help news organizations make informed decisions about AI adoption. To get started, the Center for Cooperative Media created a free resource for publishers that recommends AI tools based on what tasks they are best suited for.
At The Lenfest Institute, we’re working to support our cohort with practical tools — and, through partnerships with Arizona State University and others, extend these solutions to benefit the broader journalism industry.
5. Seek to learn from innovators in other industries
Engaging with AI innovators outside of journalism drives meaningful idea generation and opportunities to learn from novel approaches in other industries. For instance, many participants in our March gathering learned much from Arizona State University’s work in “principled innovation.” Ted Cross, an executive director at ASU, collaborates with university leaders to embed principles of integrity, curiosity, and civic purpose to help shape a university culture focused on human flourishing.
The institution’s framework of “principled innovation” centers on asking not only what works, but what’s right, for whom, and at what cost. From designing more inclusive interfaces to asking how tech reshapes social norms (like replacing human interaction with automation), ASU urges its teams to pause, reflect, and map the full ecosystem of stakeholders impacted.
For news organizations, the parallel is clear. If we build AI tools that only optimize for efficiency or cost reduction, we may unintentionally undermine equity, trust, or community relevance. As ASU emphasized, the real innovation lies beyond what AI simply can do. Instead, whether it advances human dignity, community connection, and democratic accountability. Sound familiar?
Lesson: Innovation must be grounded in values, not just capabilities. Once rooted in shared principles, we can adapt and transfer breakthroughs from other industries in service to journalism’s mission.
The path forward: From early cohorts to an industry ecosystem
The breakthroughs happening now — from civic transcription to multilingual reporting — point to a future where AI can strengthen and expand local journalism’s reach and resilience, not replace it. For local newsrooms looking to integrate AI, our Fellows have demonstrated that the path forward involves:
Starting small.
Solving real problems.
Centering the mission.
Sharing what works (and what doesn’t).
Perhaps the most important insight from this convening is that early experiments are laying the groundwork for a more robust and resilient AI-journalism ecosystem.
But this requires sustained effort.
At The Lenfest Institute AI Collaborative and Fellowship Program, our aim is to support projects that create scalable, ethical models for AI adoption — tools that can strengthen financial sustainability in local news and be rapidly adopted by peers. We’re also deepening co-development of AI solutions that engage new audiences, unlock revenue opportunities, and enhance news organizations’ efficiency through personalization and augmentation, all while upholding the core values of public-service journalism.
This AI convening was just a first step. As we continue fostering this burgeoning AI-journalism ecosystem alongside our partners, we hope to advance the following priorities:
Publish accessible case studies and playbooks showcasing practical applications of AI — including open-source code when possible — to make the projects as easy as possible to replicate.
showcasing practical applications of AI — including open-source code when possible — to make the projects as easy as possible to replicate. Create bridges between AI-focused innovation programs and broader journalism networks.
between AI-focused innovation programs and broader journalism networks. Design lightweight, adaptable AI tools that small and mid-sized organizations can easily integrate into their workflows.
that small and mid-sized organizations can easily integrate into their workflows. Invest in peer-to-peer convenings and regional cohorts to extend knowledge beyond early adopters.
To learn more about the work emerging from The Lenfest Institute AI Collaborative and Fellowship Program — or to share your own perspective on AI in local news — contact David Chivers at [email protected].
| 2025-06-11T00:00:00 |
https://www.lenfestinstitute.org/solutions-resources/ai-for-local-news-five-lessons-lenfest-ai-fellows-gathering/
|
[
{
"date": "2025/06/11",
"position": 37,
"query": "AI journalism"
}
] |
|
Fact Check Team: US companies cut jobs amid AI ...
|
Fact Check Team: US companies cut jobs amid AI investments
|
https://komonews.com
|
[
"Janae Bowens",
"Fact Check Team",
"Https",
"Www.Facebook.Com Komonews"
] |
A wave of layoffs is sweeping through major companies, including Procter & Gamble, Microsoft, Citigroup, Walmart, Klarna, Disney, Paramount, and Warner Bros. ...
|
is showing signs of stability, but a wave of layoffs is sweeping through major companies, including Procter & Gamble, Microsoft, Citigroup, Walmart, Klarna, Disney, , and Warner Bros. Discovery, Despite a steady labor market, these companies are under pressure to reduce costs due to global uncertainty linked to tariffs imposed by President Donald Trump.
Experts indicate that while government job cuts are decreasing, companies are still feeling the financial strain. Many have resorted to raising prices, but layoffs remain a significant cost-cutting measure. Concerns persist about how tariffs and trade tensions might slow the economy overall.
Artificial intelligence is also playing a growing role in these layoffs. Klarna's CEO revealed that the company has cut 40% of jobs partly due to AI investments. Shopify CEO Tobias Lütke told employees in April that they must justify why tasks cannot be performed by AI before requesting additional workers and resources.
Google is also making moves to free up cash for AI investments. The company is offering voluntary buyouts to U.S. employees across multiple teams, including search, ads, engineering, and research, as it plans to spend about $75 billion this year on capital expenses. This is part of Google's strategy to expand AI features like its new "AI Mode" search tool while maintaining cost efficiency, according to
Industry leaders warn that AI could trigger a "white-collar job apocalypse." The CEO of Anthropic told that AI could eliminate half of all entry-level white-collar jobs within 1 to 5 years, potentially causing unemployment rates to spike to 10 or even 20 percent. He is advocating for more transparency from AI companies and urging the government to prepare for the potential impact, suggesting a "token tax" on AI models to help redistribute wealth if job losses become severe.
The latest job report also highlights a challenging landscape for new graduates, with March and April job gains revised down by a combined 95,000 jobs. As companies slow hiring, current workers are opting to stay in their positions.
| 2025-06-11T00:00:00 |
https://komonews.com/news/nation-world/us-companies-cut-jobs-amid-ai-investments-artificial-intelligence-proctor-gamble-microsoft-citigroup-walmart-klarna-disney-paramount-warner-bros
|
[
{
"date": "2025/06/11",
"position": 79,
"query": "AI layoffs"
}
] |
|
I Used ChatGPT to Plan a Career Pivot, and Found It ...
|
I Used ChatGPT to Plan a Career Pivot, and Found It Empowering
|
https://www.aol.com
|
[
"Aol Staff",
"Amanda Smith",
"June",
"At Pm"
] |
Use ChatGPT as a brainstorming buddy. You can also chat through how to negotiate a raise, write a cover letter and resume, find a new job and use it as a ...
|
Oscar Wong/Getty Images
The future of work, and the very concept of a career, is on shaky ground. While technologists and business leaders prophesize over the most likely economic impact of AI, workers are left wondering where their place and purpose will be in the decade ahead.
AI Atlas
With a tough job market, the cost of living, the rise of AI and global uncertainty, it's a good time to contemplate your career. You can do this with the help of an AI chatbot, which can talk through your options and come up with a plan.
If you can't handle another week of Sunday scaries, you're experiencing a career calling in another direction or simply want a backup plan if robots take over, use ChatGPT as a brainstorming buddy. You can also chat through how to negotiate a raise, write a cover letter and resume, find a new job and use it as a career coach.
(Disclosure: Ziff Davis, CNET's parent company, in April filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.)
Career change, here I come
This exercise isn't for a role shift or moving around within the same industry. Rather, it's to help guide your thinking if you're considering a complete career change.
Quick caveat: Don't make any important life decisions using only AI. Sit with the chatbot's suggestions, talk to the people in your life, do your own research and ponder on what a new profession might look like.
While I'm happy with my job as a freelance writer (yes, even in the age of AI), I'll use myself as an example of how to walk through the process.
According to the World Economic Forum, there will be 92 million displaced jobs, but 170 million new jobs in the next decade. Meaning the next era will be more about career changes than job losses. I downloaded the 290-page document so I could upload it into ChatGPT to interpret and use in its career shift suggestions. You want to pick lucrative career paths that are on the rise, not in decline.
Log into ChatGPT so it has all the context about you from any previous times you've used it for questions or advice. You might need to feed it more information about your interests, goals and life vision, though. If you tried the "dream day in the life" trend, this is good information to use.
You likely have more of an idea about what you want to do with your life now than you did when you were 18. Use your life experiences and learnings to guide ChatGPT. If you have an idea of the area you'd like to move into next, tell ChatGPT.
If not, start here: "I'm currently a [role] at [company] and have been working in [industry] for [number of years]. I'm interested in [X], [Y], [Z]. What are some different career paths and industries that could be suitable? Use everything you know about me, as well as this attached report to identify lucrative career options."
For me, ChatGPT provided some writer-adjacent career options, like a communications director, policy writer, workshop facilitator or marketing manager. These were still within the communications realm, so I had to specify in my second prompt that I was looking for a complete career change.
But I didn't love what it suggested:
ChatGPT/Screenshot by CNET
I told ChatGPT that I'd be willing to upskill and get another degree.
It came up with a behavioral scientist, human-centered AI ethicist, a role in urban design and policy, and a mental health innovator. All of these roles were still very "techy" and not really what I'd be into.
I gave that feedback to ChatGPT.
While the non-tech, high-demand job suggestions were a little closer, nothing excited me. ChatGPT kept trying to push me into sustainability and education. Two noble paths, but neither light me up.
New career suggestions
This time, I told ChatGPT that I have a growing interest in women's health and fertility, after going through IVF. I asked: What are some lucrative, fast-growing career paths in this sector?
ChatGPT/Screenshot by CNET
ChatGPT laid out a few possible paths, with training options and earning potential. For example, a fertility coach, patient advocate, policy advocate, head of content for a fertility brand, editorial director for a women's health publication or founder for a women's health venture.
ChatGPT/Screenshot by CNET
Now we're talking!
Next, I said I'd slowly transition into this field over the next five years and would be happy to do more study, then asked for more recommendations and a timeline to work toward.
Here's the suggested roadmap:
ChatGPT/Screenshot by CNET
ChatGPT/Screenshot by CNET
I asked ChatGPT to tweak the timeline, based on a few changes, and it gave me another updated five-year transition plan. While the plan wasn't perfect, it was 80% there. ChatGPT gave me ideas I hadn't thought of and provided some pretty convincing stats, like what the fastest-growing job categories will be, predicted employment rates, wage potential and median salary.
This was an empowering exercise that everyone should do. It's always good to have a plan B in place. Remember you'll probably have to hold the AI chatbot's hand before it will reach the right path for you -- and then it'll be able to give you ideas and information on what you need to do to get the rest of the way there. Just make sure you also talk to some real people before committing to anything.
| 2025-06-11T00:00:00 |
https://www.aol.com/used-chatgpt-plan-career-pivot-190000319.html
|
[
{
"date": "2025/06/11",
"position": 20,
"query": "ChatGPT employment impact"
}
] |
|
Opinion: How College Grads Use AI Has Implications for ...
|
Opinion: How College Grads Use AI Has Implications for Employers
|
https://www.govtech.com
|
[
"Parmy Olson",
"Bloomberg Opinion"
] |
Recently, the chief executive officer of AI firm Anthropic predicted AI would wipe out half of all entry-level white-collar jobs. The reason is simple.
|
(TNS) — Companies are eliminating the grunt work that used to train young professionals — and they don’t seem to have a clear plan for what comes next.AI is analyzing documents, writing briefing notes, creating Power Point presentations or handling customer service queries, and — surprise! — now the younger humans who normally do that work are struggling to find jobs. Recently, the chief executive officer of AI firm Anthropic predicted AI would wipe out half of all entry-level white-collar jobs. The reason is simple. Companies are often advised to treat ChatGPT “like an intern,” and some are doing so at the expense of human interns.This has thrust college grads into a painful experiment across multiple industries, but it doesn’t have to be all bad. Employers must take the role of scientists, observing how AI helps and hinders their new recruits, while figuring out new ways to train them. And the young lab rats in this trial must adapt faster than the technology trying to displace them, while jumping into more advanced work.Consulting giant KPMG, for instance, is giving graduates tax work that would previously go to staff with three years of experience. Junior staff at PriceWaterhouseCoopers have started pitching to clients. Hedge fund Man Group Plc tells me its junior analysts who use AI to scour research papers now have more time to formulate and test trading ideas, what the firm calls “higher-level work.”I recently interviewed two young professionals about using AI in this way, and perhaps not surprisingly, neither of them complained about it. One accountant who had just left university said he was using ChatGPT to pore over filings and Moody’s Ratings reports, saving him hours on due diligence.Another young executive at a public-relations firm, who’d graduated last year from the London School of Economics, said tools like ChatGPT had cut down her time spent tracking press coverage from two and a half hours to 15 minutes, and while her predecessors would have spent four or five hours reading forums on Reddit, that now only takes her 45 minutes.I'm not convinced, however, that either of these approaches is actually helping recruits learn what they need to know. The young accountant, for instance, might be saving time, but he’s also missing out on the practice of spotting something fishy in raw data. How do you learn to notice red flags if you don’t dig through numbers yourself? A clean summary from AI doesn’t build that neural pathway in your brain.The PR worker also didn’t seem to be doing “higher-level work,” but simply doing analysis more quickly. The output provided by AI is clearly useful to a junior worker’s bosses, but I’m skeptical that it’s giving them a deeper understanding of how a business or industry works.What’s worse is that their opportunities for work are declining overall. “We’ve seen a huge drop in the demand for ‘entry-level’ talent across a number of our client sets,” says James Callander, CEO of a Freshminds, a London recruitment firm that specializes in finding staff for consultancies. An increasing number of clients want more “work ready” professionals who already have a first job under their belt, he adds.That corroborates a trend flagged by venture capital firm SignalFire, whose “State of Talent 2025” report pointed to what they called an “experience paradox,” where more companies post for junior roles but fill them with senior workers. The data crunchers at LinkedIn have noticed a similar trend, prompting one of its executives to claim the bottom rung of the career ladder was breaking.Yet some young professionals seem unfazed. Last week, a University of Oxford professor asked a group of 70 executive MBA students from the National University of Singapore if Gen Z jobs were being disproportionately eroded by AI. Some said “no,” adding that they, younger workers, were best placed to become the most valuable people in a workplace because of their strength in manipulating AI tools, recounts Alex Connock, a senior fellow at Oxford’s Saïd Business School, who specializes in the media industry and AI.The students weren’t just using ChatGPT, but a range of tools like Gemini, Claude, Firefly, HeyGen, Gamma, Higgsfield, Suno, Udio, Notebook LM and Midjourney, says Connock.The lesson here for businesses is that sure, in the short term you can outsource entry-level work to AI and cut costs, but that means missing out on capturing AI-native talent.It's also dangerous to assume that giving junior staff AI tools will automatically make them more strategic. They could instead become dependent, even addicted to AI tools, and not learn business fundamentals. There are lessons here from social media. Studies show that young people who use it actively tend not to get the mental health harms of those who use it passively. Posting and chatting on Instagram, for instance, is better than curling up on the couch and doom-scrolling for an hour.Perhaps businesses should similarly look for healthy engagement by their newer staff with AI, checking that they’re using it to sense-check their own ideas and interrogating a chatbot’s answers, rather than going to it for all analysis and accepting whatever the tools spit out.That could spell the difference between raising a workforce that can think strategically, and one that can’t think beyond the output from an AI tool.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://www.govtech.com/education/higher-ed/opinion-how-college-grads-use-ai-has-implications-for-employers
|
[
{
"date": "2025/06/11",
"position": 39,
"query": "ChatGPT employment impact"
}
] |
Artificial Intelligence
|
Law and the Workplace
|
https://www.lawandtheworkplace.com
|
[
"Guy Brenner",
"Jonathan Slowik",
"Dixie Morrison",
"Margo Richard",
"June",
"April",
"January",
"November",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class"
] |
As the nation prepares for a second Trump Administration, and employers increasingly interested in adopting and using AI tools, one question on employers' minds ...
|
In the latest lawsuit of its kind, the American Civil Liberties Union recently filed a complaint with the Colorado Civil Rights Division and the Equal Employment Opportunity Commission (“EEOC”) alleging an AI interviewing tool discriminated against a deaf and Indigenous employee at Intuit seeking a promotion.
According to the complaint, when the employee applied for
| 2025-06-11T00:00:00 |
https://www.lawandtheworkplace.com/category/artificial-intelligence/
|
[
{
"date": "2025/06/11",
"position": 58,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/11",
"position": 44,
"query": "artificial intelligence labor union"
}
] |
|
Banking on AI: The Coolest Tech Jobs You Haven't ...
|
Banking on AI: The Coolest Tech Jobs You Haven’t Considered
|
https://www.iit.edu
|
[] |
Tech banking is emerging as one of the coolest jobs in tech for graduates with skills in AI and machine learning.
|
Big tech isn’t the only sector pushing to attract workers with skills in artificial intelligence and machine learning. The banking and financing industries have invested heavily in AI trends, technology, and research, and workers are flocking to opportunities with banks and other financial institutions.
The Wall Street Journal reports that colleges and universities across the country are seeing more graduates in technology-focused fields accepting employment with banks and financial services over the last four years. These industries have aggressively sought talent across the AI stack including those with skills in neural networks, model building, training, and fine-tuning. At the same time, big tech companies have announced rounds of layoffs.
With the stability of working for a banking institution more appealing to college graduates rather than taking a chance with a tech company navigating changes, banking could be one of the coolest jobs in tech today.
Here’s what you should know:
Why are banks investing in AI—and in graduates of AI and computer science programs?
Banks and other financial institutions say that they feel that AI has transformational power and can be part of every product and service they build, and they are invested in showing talented candidates that they can work on meaningful problems.
And banks and other financial institutions are backing this up: they are hiring for roles in machine learning engineering, data engineering, generative AI, responsible AI, and information security.
In particular, they have been on a hiring spree for AI developers and computer scientists. These areas are where colleges and universities are not currently graduating enough students with these needed skills and who are ready to tackle problems in banking.
How is AI and other technology skills used in banking?
There are a multitude of ways that people with technology- and AI-focused skills are being utilized by banks and other financial institutions.
One such example is Illinois Tech graduate Ismail Iyigunler (Ph.D. AMAT ’12).
As director of global markets risk analytics at Bank of America, Iyigunler develops and maintains mathematical and algorithmic models that outline the best-case and worst-case scenarios—and everything in between, which allow bank officials to make valuable risk management decisions.
“My main focus is to ensure that the models are adequate, stable, and fit for risk management,” he says. “The modeling is a complex statistical analysis. It’s the math-related aspect of risk management.”
Iyungler’s knowledge of the models helps him communicate how the model works so that those relying on them understand how the results are determined.
“Stakeholders need to understand, and be comfortable with, the computations and limitations of the models,” he says. “You must be able to defend your model. You must prove that the model is fit for purpose.”
What skills do I need to get into these tech careers?
According to the United States Bureau of Labor Statistics, there are a handful of general skills that banks and other financial institutions are looking for:
Analytical skills : You will evaluate a range of information in finding profitable investments.
: You will evaluate a range of information in finding profitable investments. Communication skills : You must be able to clearly explain your recommendations to clients.
: You must be able to clearly explain your recommendations to clients. Computer skills : You must be adept at using software to analyze financial data and trends, create portfolios, and make forecasts.
: You must be adept at using software to analyze financial data and trends, create portfolios, and make forecasts. Decision-making skills : You must reach conclusions so that you can recommend whether to buy, hold, or sell a security.
: You must reach conclusions so that you can recommend whether to buy, hold, or sell a security. Detail oriented : You must pay attention to every detail, as even small issues may have large implications.
: You must pay attention to every detail, as even small issues may have large implications. Math skills: You will use mathematics to estimate the value of financial securities.
But you must also have the technical know-how to maximize what technologies such as generative and responsible AI, machine learning engineering, data engineering, and information security.
Illinois Tech’s College of Computing incorporates AI techniques into all of its computing degree programs, which can help equip the next generation of tech bankers and innovators. Just some of the programs that Illinois Tech offers includes:
Explore how you can kickstart a tech-driven career in banking or another field today.
Key Takeaways: AI Trends and Careers in Tech Banking
As artificial intelligence trends continue to reshape industries, tech banking is emerging as one of the coolest jobs in tech for graduates with skills in AI and machine learning. With big tech facing layoffs, top talent is increasingly choosing the stability and innovation offered by tech banks and financial institutions, which are investing heavily in AI-driven roles such as machine learning, engineering, data science, and responsible AI. These tech bank careers offer graduates the chance to work on meaningful, high-impact projects, making banking one of the most exciting and rapidly growing AI career paths today.
| 2025-06-11T00:00:00 |
https://www.iit.edu/blog/tech-banking-ai-jobs
|
[
{
"date": "2025/06/11",
"position": 83,
"query": "artificial intelligence employment"
}
] |
|
European Broadcasting Union and NVIDIA Partner on ...
|
European Broadcasting Union and NVIDIA Partner on Sovereign AI to Support Public Broadcasters
|
https://blogs.nvidia.com
|
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"Richard Kerris",
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"Class",
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] |
The work will create better foundations for public service media to benefit from European cloud infrastructure and AI services that are exclusively governed by ...
|
The EBU to bring sovereign AI frameworks that prioritize local governance and public trust to more than 110 member organizations across 50+ countries.
In a new effort to advance sovereign AI for European public service media, NVIDIA and the European Broadcasting Union (EBU) are working together to give the media industry access to high-quality and trusted cloud and AI technologies.
Announced at NVIDIA GTC Paris at VivaTech, NVIDIA’s collaboration with the EBU — the world’s leading alliance of public service media with more than 110 member organizations in 50+ countries, reaching an audience of over 1 billion — focuses on helping build sovereign AI and cloud frameworks, driving workforce development and cultivating an AI ecosystem to create a more equitable, accessible and resilient European media landscape.
The work will create better foundations for public service media to benefit from European cloud infrastructure and AI services that are exclusively governed by European policy, comply with European data protection and privacy rules, and embody European values.
Sovereign AI ensures nations can develop and deploy artificial intelligence using local infrastructure, datasets and expertise. By investing in it, European countries can preserve their cultural identity, enhance public trust and support innovation specific to their needs.
“We are proud to collaborate with NVIDIA to drive the development of sovereign AI and cloud services,” said Michael Eberhard, chief technology officer of public broadcaster ARD/SWR, and chair of the EBU Technical Committee. “By advancing these capabilities together, we’re helping ensure that powerful, compliant and accessible media services are made available to all EBU members — powering innovation, resilience and strategic autonomy across the board.”
Empowering Media Innovation in Europe
To support the development of sovereign AI technologies, NVIDIA and the EBU will establish frameworks that prioritize independence and public trust, helping ensure that AI serves the interests of Europeans while preserving the autonomy of media organizations.
Through this collaboration, NVIDIA and the EBU will develop hybrid cloud architectures designed to meet the highest standards of European public service media. The EBU will contribute its Dynamic Media Facility (DMF) and Media eXchange Layer (MXL) architecture, aiming to enable interoperability and scalability for workflows, as well as cost- and energy-efficient AI training and inference. Following open-source principles, this work aims to create an accessible, dynamic technology ecosystem.
The collaboration will also provide public service media companies with the tools to deliver personalized, contextually relevant services and content recommendation systems, with a focus on transparency, accountability and cultural identity. This will be realized through investment in sovereign cloud and AI infrastructure and software platforms such as NVIDIA AI Enterprise, custom foundation models, large language models trained with local data, and retrieval-augmented generation technologies.
As part of the collaboration, NVIDIA is also making available resources from its Deep Learning Institute, offering European media organizations comprehensive training programs to create an AI-ready workforce. This will support the EBU’s efforts to help ensure news integrity in the age of AI.
In addition, the EBU and its partners are investing in local data centers and cloud platforms that support sovereign technologies, such as NVIDIA GB200 Grace Blackwell Superchip, NVIDIA RTX PRO Servers, NVIDIA DGX Cloud and NVIDIA Holoscan for Media — helping members of the union achieve secure and cost- and energy-efficient AI training, while promoting AI research and development.
Partnering With Public Service Media for Sovereign Cloud and AI
Collaboration within the media sector is essential for the development and application of comprehensive standards and best practices that ensure the creation and deployment of sovereign European cloud and AI.
By engaging with independent software vendors, data center providers, cloud service providers and original equipment manufacturers, NVIDIA and the EBU aim to create a unified approach to sovereign cloud and AI.
This work will also facilitate discussions between the cloud and AI industry and European regulators, helping ensure the development of practical solutions that benefit both the general public and media organizations.
“Building sovereign cloud and AI capabilities based on EBU’s Dynamic Media Facility and Media eXchange Layer architecture requires strong cross-industry collaboration,” said Antonio Arcidiacono, chief technology and innovation officer at the EBU. “By collaborating with NVIDIA, as well as a broad ecosystem of media technology partners, we are fostering a shared foundation for trust, innovation and resilience that supports the growth of European media.”
Learn more about the EBU.
Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://blogs.nvidia.com/blog/european-broadcasting-union-sovereign-ai/
|
[
{
"date": "2025/06/11",
"position": 93,
"query": "artificial intelligence labor union"
}
] |
How AI is rocking the future of jobs | Opinion
|
How AI is rocking the future of jobs
|
https://www.deseret.com
|
[
"Brent Orrell"
] |
From engineers to fast food, how AI is rocking the future of jobs. The future is here, and it requires robust AI literacy and support for workers and families.
|
Artificial intelligence development and deployment is accelerating, and so are the ironies. A recent report by Great Learning found that a growing number of Indian engineers — a group deeply involved in creating and deploying AI — are pessimistic about how it will affect their careers. Far from irrational pessimism, this is an early indicator of what my recent research calls the “de-skilling” of the knowledge economy — AI’s slow but accelerating erosion of middle-skill technical and cognitive work.
The concern Indian engineers express is increasingly visible across global labor markets. U.S. labor unions are calling for AI legal protections. Fast food chains are testing AI-driven voice ordering and robotic kitchen equipment that could displace thousands of teenage and other entry-level workers. The technologies that seem novel today are rapidly becoming commonplace, creating broad unease about the future of work.
While production workers are not exempt from AI impacts, the most exposed jobs are held by millions working in middle-skill, middle-income “knowledge” economy jobs. Many of these jobs are made up of the types of tasks that are especially well suited to AI automation because, like the factory jobs of the past, they are repetitive, “codable” and subject to technological substitution.
The compression of middle-skill employment is already visible in sectors like software development. Routine front-end coding tasks are increasingly being handled by generative AI. More experienced coders — those who can manage complex system integration and lead cross-functional teams — are still in demand. But the base of the coding professional pyramid is narrowing. This is classic skills-biased technological change: those with the right combination of technical and noncognitive skills benefit greatly, others must reskill, and many are squeezed out of their current jobs altogether.
“Workers need to know that, as a society, we have their backs if AI displaces them. If we fail to prepare, we are inviting even more of the economic and social turmoil that we’ve experienced in the past decade.”
What’s striking in the new reports is how widespread the effects are becoming. In fast food, AI is reducing the need for human cashiers and kitchen staff — roles traditionally filled by young people seeking their first work experience. These aren’t knowledge economy jobs per se, but they serve as training grounds for “master skills” — like teamwork, time management and communication — that future AI-enhanced jobs increasingly demand.
As AI systems become capable of handling not just repetitive tasks but also judgment-heavy work like customer service, legal document review and financial risk analysis, even highly credentialed professionals are exposed. Automating brain work is likely to have effects similar to automating “muscle” work. Productivity growth means we will still need workers, but those workers will need a different blend of technological and human-facing capabilities.
The extreme uncertainty we face means starting now in designing an automation adjustment assistance system with the scale and flexibility required for potentially sweeping labor market changes. As I will outline in a forthcoming report, such a system would have four core elements: better jobs data, worker-controlled transition support, broad AI literacy programs and, as a hedge for the future, greater investment in child, family and community stability.
Our existing “rearview mirror” labor market information systems need recalibration toward understanding the impact of technological change. Without locally and regionally focused “headlight” data, it’s difficult, if not impossible, to effectively target re-skilling and education investments. When it comes to AI impacts, harnessing the power of predictive analytics is the foundation for finding and supporting the workers most exposed to automation.
A second key need is to develop more flexible and worker-driven employment transition systems. Tools like Individual Training Accounts (ITAs) can empower workers to choose their own upskilling pathways, while a reimagined version of Trade Adjustment Assistance — tailored for the effects of automation — could offer broader, more effective transition support that would cushion change for those in need of long-term reskilling.
AI literacy is also critical in the same way reading and math are. This means integrating exposure to AI tools and concepts into K-12 education, higher ed, corporate retraining programs and workforce development.
Crucially, we need to invest in people to build the human attributes required for learning and work in an AI-driven economy. The challenge is that these skills — sometimes referred to as noncognitive or soft skills — are often shaped very early in life. That means increasing investment in family stability, early childhood development and other initiatives that promote healthy communities.
We’ve seen this movie before in the automation revolution of the past 40 years. Workers need to know that, as a society, we have their backs if AI displaces them. If we fail to prepare, we are inviting even more of the economic and social turmoil that we’ve experienced in the past decade. And, this time, we will have only ourselves to blame.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://www.deseret.com/opinion/2025/06/11/artificial-intelligence-ai-changing-future-jobs/
|
[
{
"date": "2025/06/11",
"position": 29,
"query": "future of work AI"
}
] |
Balancing AI and the Human Touch in HR
|
AI and the Future of Work
|
https://www.lifelabslearning.com
|
[] |
In this LifeLabs Learning blog, we dig into AI and the future of work and how humans and artificial intelligence can best join forces in the workplace.
|
Summary: As AI becomes more embedded in HR, the challenge for People leaders isn’t whether to use it, it’s when. This blog explores how to strike the right balance between AI automation and human leadership. You’ll learn where AI can increase efficiency and accuracy, and where human skills like empathy, communication, and critical thinking are essential. Plus, get practical tips for building a future-ready HR function that’s both tech-powered and people-centered.
Know When to Use AI and When Not To
The key to keeping human resources human-centered while tapping into AI’s capabilities is finding the sweet spot where high-tech complements the human touch. Savvy leaders collaborate with AI, striking a balance between efficiency and authentic connections. This combined approach empowers everyone to be on their A-game while ensuring employees are happy, engaged, and ready for whatever the future throws at us.
AI can speed up decisions and automate manual tasks. But not every HR task benefits from automation. Use AI for consistency, scale, and speed. Rely on people for connection, nuance, and trust.
Here’s a breakdown of when to use each.
Use AI When: You Need to Speed Up Repetitive or Data-Heavy Tasks
AI is a powerful tool for processing large amounts of information quickly and accurately. It excels at tasks like:
Resume screening and shortlisting
Sentiment analysis in surveys or reviews
Scheduling and logistics
Identifying trends in employee feedback
Recommending learning pathways based on skill data
These tasks benefit from AI’s speed and pattern recognition. In fact, studies show that AI can cut time-to-hire in half!
What to watch for: AI lacks context. If it’s left unchecked, it can reinforce bias or overlook what makes someone a strong cultural fit. That’s where human oversight is critical.
Use Human Skills When: The Situation Calls for Judgment, Emotion, or Trust
AI can surface data, but it can’t lead a tough conversation, coach through a conflict, or read between the lines of body language. That’s where human-centered leadership comes in.
Some examples where the human touch is essential:
Performance reviews and career development conversations
Conflict resolution and team dynamics
Organizational change and restructuring
Supporting mental health or personal challenges
Making final hiring or promotion decisions
These are the moments that shape trust and culture, and they require empathy, intuition, and clear communication. According to Deloitte, jobs requiring soft skills are growing 2.5x faster than others and will make up two-thirds of all roles by 2030.
What to watch for: Over-automating these human moments can backfire. Employees want to feel heard, understood, and supported, not processed by a system.
Use Both When: You’re Designing HR Systems for Scale
The most effective HR leaders don’t choose between AI and people; they design systems that use both. AI does the heavy lifting; humans handle the high stakes.
Examples of effective AI + human collaboration:
Use AI to flag potential flight risks, then have a manager follow up with a stay conversation.
Let AI recommend learning content, then discuss it in 1:1s to make it actionable.
Automate the basics of onboarding, then pair new hires with a human mentor or buddy.
This approach lets you scale impact without sacrificing the personal connections that drive engagement and retention.
What You Can Do Now
Want to strike the right balance between tech and touch? Here’s where to start:
Audit your HR workflows. What’s repetitive and rules-based? That’s likely a good fit for AI.
What’s repetitive and rules-based? That’s likely a good fit for AI. Invest in people skills. Coach your managers on how to lead with empathy, provide feedback, and navigate change— skills that AI can’t replicate.
Coach your managers on how to lead with empathy, AI can’t replicate. Communicate the “why” behind AI. Employees are more likely to embrace AI tools when they understand how it benefits them and when humans stay involved.
Need help equipping managers with the right skills? LifeLabs Learning manager training programs focus on the people side of leadership, so your team is ready to lead through change, coach effectively, and build strong, connected teams.
Great HR Is Tech-Powered and People-Centered
AI can do a lot, but it can’t replace human leadership. The most effective HR teams will be those that use technology to enhance, not erase, the human experience. By pairing AI with strong people skills (we call them Tipping Point SkillsTM), you’ll build a culture that’s more efficient, more responsive, and more human than ever.
Want more? Download our AI for HR Playbook
Watch ‘AI Meets HR: Leveraging AI in People Operations’ On-Demand!
FAQs:
| 2025-06-11T00:00:00 |
https://www.lifelabslearning.com/blog/ai-and-the-future-of-work-balancing-tech-and-the-human-touch
|
[
{
"date": "2025/06/11",
"position": 80,
"query": "future of work AI"
}
] |
|
How Automation is Driving the Reshoring Revolution
|
Boosting Domestic Production: How Automation is Driving the Reshoring Revolution
|
https://wes-tech.com
|
[] |
Statistics show that automation creates more jobs than it displaces. The World Economic Forum's “Future of Jobs Report” projected that although automation ...
|
The conversation about reshoring in the U.S. has evolved from a theoretical strategy to a pressing necessity. Since pandemic-era supply chain meltdowns and the increasing cost of overseas labor, American manufacturers are under pressure to secure domestic supply chains and create future-ready operations.
At the heart of the shift toward domestic production is automation. Automation enhances efficiency, improves product quality, and helps manufacturers fortify against future disruptions. Most importantly, automation makes reshoring economically viable and cost-effective.
Here’s how companies who invest in automation today can lead the charge toward automation-fueled reshoring for tomorrow.
Automation Reduces Labor Costs Without Eliminating Jobs
One concern that often arises regarding automation is that it will cut jobs. However, contrary to popular belief, automation only changes how labor is used. The work isn’t replaced; the energy is merely redirected.
Workers are freed from repetitive, dangerous, and physically demanding applications so they can focus on higher-value tasks. Often, these new roles require more decision-making, oversight, and creativity.
A good way to think of reshoring is an evolution from the rigid assembly line of Henry Ford. In today’s high-flex, tech-driven environment, teams have options. In the past, workers built cars piece by piece on a factory line. Today, small teams using automation and robotic systems can make better, safer, and even more complex vehicles in a fraction of the time.
The shift in productivity enhances output and quality; the win-win is that the work also offers employees enhanced job satisfaction and safety!
Enhancing Quality and Consistency Through Automation
Precision in manufacturing is no longer the elite bar; instead, it’s become the baseline requirement. One that allows manufacturers to go head to head in an increasingly global market where they can’t afford the luxury of variability, waste, and rework.
Automation essentially eliminates these risks to guarantee precision. Integrating advanced sensors, AI-driven inspections, intelligent tooling, vision systems, and mistake-proofing (poke-yoke), automation shores up every critical control point and eliminates gaps.
In the automotive industry, for example, the level of consistency provided by automation allowed an auto factory in the U.S. to produce identical, high-quality vehicles to their counterparts’ work in Japan. Better still, vehicle components could be made every 30 seconds with the same safety, performance, and customer satisfaction levels.
When the average cost of vehicles rises, reshoring helps bring the price back down. Thanks to the role of automation in standardizing production, domestic products have a better lifetime value—they last longer, perform better, and require fewer repairs!
Additionally, every element of automotive production can be measured, adjusted, and optimized in real time. Vision systems inspect for defects that would be invisible to the human eye. Torque tooling helps ensure proper fastening. AI-powered algorithms catch micro-pattern deviations well before they snowball into major concerns.
Quality isn’t just about preventing failure; it’s also about predictability. Reliability gives domestic manufacturers the edge in reshoring efforts where reputation and repeat customers are everything. Automation enables the creation of superior products at competitive prices. Consumers enjoy safety, performance, and comfort thanks to the relentless approach to quality control!
Customization and Market Demand Driving Automation
While safety, performance, and comfort are paramount, today’s consumers have also come to expect variety. People want choices, from 30 hot sauce flavors to dozens of trim package options on a single-car model. The demand for customization on a mass level is a significant driver of automation tech.
The ability to pivot is critical in food production, automotive manufacturing, and agriculture industries. Automation lets manufacturers stay nimble with high-mix, high-volume product lines without compromising speed or accuracy. Higher volumes are now possible with products from bread to toilet paper to SUVs.
What’s more? These modern automation systems aren’t one-trick ponies. They are configurable, intelligent, adaptable, and created for variability.
Farmers can now use vision-based systems to eliminate weeds with lasers—no chemicals required! We’re moving into a future where organic produce is available to the average consumer. Accessibility and sustainability improve, the impact on the environment lowers, and competitive pricing is possible—it’s the power of automation in action.
Addressing the Job Loss Myth: Automation as a Job Creator
As previously mentioned, automation doesn’t eliminate jobs; it expands the field and changes how labor is used.
Of course, there’s still the old fear-mongering around “robots replacing humans.” At this point, it’s a laughable idea unsupported by data. Statistics show that automation creates more jobs than it displaces.
The World Economic Forum’s “Future of Jobs Report” projected that although automation and technology could displace approximately 85 million jobs, it would simultaneously create 97 million new roles. These new positions are in exciting fields like artificial intelligence and data analysis, and with reskilling and workforce development, automation is a net positive for employment.
In other words, as a Brookings Institution article notes, automation often creates as many jobs as it displaces. Even as specific tasks are automated the overall impact of automation leads to increased productivity and the creation of new job categories to support new technologies.
Gone are the days when workers had to wrestle a 125-pound water heater into place, risking their physical health and the product’s safety. Now, machines can literally do the heavy lifting as employees manage the workflow and oversee the process.
This shift moves manufacturing roles towards more engaging, safer opportunities, opening doors for upskilling and career growth.
Key Automation Technologies for Reshoring Success
Successful reshoring is dependent on several automation technologies working together in harmony. These technologies may include:
Robotics for assembly, handling, and packaging
for assembly, handling, and packaging AI-driven quality control offering real-time defect detection
offering real-time defect detection Vision systems for precision inspection and guidance
for precision inspection and guidance Material handling systems for heavy, hazardous, or repetitive movement
Yes, the upfront investment is required, but the long-term cost advantages and rewards make it worth the change. Manufacturers have greater control, increased output, and fewer disruptions for an ROI that outweighs the initial cost.
More importantly, automation offers companies security and self-sufficiency. Domestic companies can respond, pivot, adapt, and scale when the next global supply chain shock inevitably occurs.
Overcoming the Biggest Reshoring Challenges
One of our biggest reshoring hurdles is dependence on raw foreign materials. Nearly every product on the market, from smartphones to pens, contains materials sourced from overseas. Reshoring doesn’t quite mean complete independence…yet.
A prime example is the chip shortage that highlighted American vulnerability. Major automakers were forced to halt production (even stripping chips from dishwashers to finish vehicles).
The electronics and semiconductor industries are crucial to reshoring efforts, and automation will play a central role in mitigating shortages and ensuring future supply security.
The solution to our dependence on foreign raw materials is multi-layered. We must invest in domestic mining, expand our refining capacity, and rethink how products are sourced and designed. Even the most advanced automation systems can’t function at full potential without focusing on domestic raw material extraction and processing.
The most strategic approach for automation integration is to start small–introduce automation gradually in one area, track and measure the value as proven, and scale gradually. The key is in strategic investment, not only focusing on today’s products but on tomorrow’s expanded line as well.
The Future of Reshoring: A Golden Age of Automation?
We’re standing at the edge of a manufacturing renaissance—but only if companies act! If we act today, rather than holding back, investing in automation, we futureproof our companies to be more resilient tomorrow.
Those who invest in automation today will be far more resilient tomorrow. If we look back at the lessons from the COVID-19 pandemic, we learn from the backlog of cargo ships anchored outside the port of Los Angeles.
In November of 2021, 114 ships were anchored or loitering offshore in L.A.; 86 were container ships holding products that Americans needed but couldn’t get to. The situation highlighted the vulnerabilities in the global supply chain. Many companies vowed to reform, but as time has passed some businesses have reverted to old practices, chasing the cheapest suppliers. Again, prioritizing cost savings over preparation and resilience planning while the risk of future disruptions looms large.
The sharp manufacturers are those who are reshoring now, not waiting around for another wake-up call. Automation is the path forward.
Hard-tooled, single-purpose machines are no longer feasible. Today’s automation platforms are modular, reconfigurable, and built for speed. Whereas 30 years ago, manufacturers relied on a machine to make a specific item, robotic systems can pivot from one product to another with minimal downtime. Flexibility is essential in high-mix environments.
The challenge lies in remaining forward-thinking. Manufacturers must budget for future needs rather than staying focused on today. For example, a 4% initial investment in automation could add years of usability and adaptability, but lean margins and short-term thinking can get in the way of futureproofing.
The time for action is now. Reshoring isn’t just possible; it’s also profitable and practical. Automation is the engine that drives durable goods and production back to American soil. Companies that invest strategically will gain a competitive edge. Automation boosts efficiency, empowers the workforce, enhances quality output, and builds supply chain resilience.
So the real question isn’t if a company should automate but when. Move now and prepare for the next disruption; don’t wait and get stuck in the past.
If you’re ready to futureproof your manufacturing operations, reach out to Wes-Tech today. Let’s talk about what automation can do for your business today and tomorrow.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://wes-tech.com/boosting-domestic-production-how-automation-is-driving-the-reshoring-revolution/
|
[
{
"date": "2025/06/11",
"position": 22,
"query": "job automation statistics"
}
] |
How Has The Engineering Job Market Changed in 2025?
|
How Has The Engineering Job Market Changed in 2025?
|
https://www.ssipeople.com
|
[] |
This data suggests that the engineering job market is experiencing sustained ... automation industries, among others. Coupled with the addition of AI ...
|
Over the past six months, the engineering industry has given job seekers mindful optimism due to the rapid evolution of AI, shifting employer hiring practices and demands, and rising opportunities for work. The engineering job market is a significant source of optimism, aligning with the Bureau of Labor Statistics, which projects architecture and engineering occupations will grow faster than the average of all occupations over the next decade.
So, what are the engineering job market trends that are driving this growth through 2025 and beyond?
The Business of Engineering
In their first quarterly report for 2025, The American Council of Engineering Companies (ACEC) reported that on average, 9% of engineering positions remain unfilled, which is one point more than the previous quarter. Additionally, the ACEC predicted that 75% of engineering firms will experience an increase in hiring over the next 12 months. This data suggests that the engineering job market is experiencing sustained growth and opportunity, which is especially reassuring for engineers searching for jobs amidst the rapidly evolving industry changes.
Adding to the optimism in the engineering job market, there has been a notable demand for engineers across various industries. For example:
According to the World Economic Forum’s Future of Jobs Report 2025, some of 2025’s fastest-growing roles include Fintech Engineering, Environmental and Renewable Energy Engineering, and Electrotechnology Engineering
Companies involved in automation, including those that utilize machinery and robotics, are also aggressively searching for engineers with technical skills as more products are being manufactured with AI and other smart technologies.
A Shift in Employer Demands
Amidst increased return-to-office work policies in place and a shift to hybrid work environments. There are some segments of the engineering field, such as mechanical engineers, that still see their fair share of remote work. Additionally, many job postings for engineers that require hands-on, in-person work will specify hybrid or on-site days, indicating a growing trend of allowing engineers to set flexible schedules.
Along with where engineers sit for work, there has been a significant trend among IT and engineering employers this year in prioritizing hiring skills rather than qualifications. Particularly for technology-based engineers, skills-based hiring has enabled tech companies to address their talent shortages by opening the door to candidates who may not possess all the qualifications but exhibit the necessary skills required for the job.
This hiring strategy doesn’t always directly correlate to traditionally trained engineers. However, there has been a growing trend for engineers to complement their strong foundational skills in engineering with crossover skills from other disciplines, augmented explicitly with technical skills. It is known as the T-Shaped Skills Model, and engineering is a prime example of having specialized knowledge as your base, with overarching supplemental skills to support that knowledge and the work at hand.
Not only does skills-based hiring widen the talent pool, but it also allows companies to fill skill gaps, enabling them to leverage their technical innovation and gain a competitive advantage over competitors as they rapidly develop new products. This means that aspiring tech and IT engineers have an opportunity to secure a competitive position, as employers are shifting their emphasis from traditional qualifications to the various skills candidates possess.
AI Impacts the Engineering Job Market
There is significant discussion revolving around artificial intelligence (AI) and its many implications for the job market. Fortunately for engineers, AI presents an opportunity for more work and job positions. Specifically, the ACEC noted in their first quarterly report for 2025 that the rise of data centers will have a positive effect on engineering jobs soon. The development of private data centers will require talent to support the growing capabilities of AI, with the engineering job market reaping the positive benefits of these recent developments.
Moreover, in the same quarterly report, the ACEC found that 78% of engineering firms believe that AI will have a positive impact on their firm in the coming year. Tech and engineering employers are anticipating prosperity due to AI and will be more likely to increase their hiring of engineers within the next six months to a year.
The engineering job market is well-positioned for a period of positive growth, not only in the tech and IT industry, but also in the finance, environmental, and automation industries, among others. Coupled with the addition of AI, the engineering job market will experience new job opportunities in a developing field. As we head into the second half of 2025, the field of engineering is forecasted to have optimistic sentiments, providing candidates searching for a position positive reassurance and those already in the industry an opportunity to grow.
| 2025-06-11T00:00:00 |
https://www.ssipeople.com/how-has-the-engineering-job-market-changed-in-2025/
|
[
{
"date": "2025/06/11",
"position": 97,
"query": "job automation statistics"
}
] |
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