title_s
stringlengths 2
79
| title_dl
stringlengths 0
200
| source_url
stringlengths 13
64
| authors
listlengths 0
10
| snippet_s
stringlengths 0
291
| text
stringlengths 21
100k
| date
timestamp[ns]date 1926-02-14 00:00:00
2030-07-14 00:00:00
| publish_date_dl
stringlengths 0
10
| url
stringlengths 15
590
| matches
listlengths 1
278
|
|---|---|---|---|---|---|---|---|---|---|
The Organizational Cultural Impact of the Social use of AI ...
|
The Organizational Cultural Impact of the Social use of AI and Bots on Remote Employees Returning to the Workplace
|
https://managementworld.online
|
[
"Michael A. Jones",
"Rome Business School",
"Bright O. Asonye"
] |
The findings show that many employees struggled with face-to-face interaction because they were used to the assistance of AI in workplace communication and ...
|
Artificial intelligence (AI) mediated communication tools supported collaboration, maintained production and even provided emotional support during remote work. Employees thereby expected workplace engagement to rely more heavily on AI-driven efficiency. Although COVID-19 did not lead to the creation of AI, the COVID-19 pandemic did drive AI to the vanguard of corporate operations through the growth of digital innovation. This research explored workplace reintegration and AI adoption using a qualitative approach to understand employee experience of AI adoption and workplace reintegration. In-depth interviews with employees who had experienced this transition were the data source. The AI dependency, how to develop interpersonal skills, and where leadership lies in managing the balance between AI and human-centered work culture were thematically analyzed to determine the main patterns and concerns. The findings show that many employees struggled with face-to-face interaction because they were used to the assistance of AI in workplace communication and decision-making, which used to be effective. A psychological and behavioral change was needed for the transition to in-person collaboration. While AI chatbots helped employees schedule, troubleshoot, and get answers to their questions more expeditiously than in the traditional workplace, traditional workplace where interactions are now slower and less efficient.
| 2025-06-11T00:00:00 |
https://managementworld.online/index.php/mw/article/view/1145
|
[
{
"date": "2025/06/11",
"position": 39,
"query": "workplace AI adoption"
}
] |
|
How software giant Workday got 79% of its employees to ...
|
How software giant Workday got 79% of its employees to embrace AI
|
https://fortune.com
|
[
"John Kell"
] |
“Everyday AI” was developed with the goal of boosting AI adoption across the company by 20% from the baseline set at the beginning of 2025. Workday says the ...
|
Leadership at business software giant Workday wanted employees to embrace artificial intelligence, but after conducting some internal research, they uncovered a few barriers.
Their study found that 43% of Workday’s employees—known as “Workmates”—said they lacked sufficient time to explore AI. More than a third of them also expressed uncertainty about how to use these new tools and worries about reliability and accuracy.
“Here we are wanting them so badly to explore, but they don’t feel that they have that time or that permission,” says Ashley Goldsmith, chief people officer at Workday. “What we’re working on is really changing the mindset.”
To encourage greater use across the organization, Workday held a splashy, all-hands meeting in April that prominently featured AI use case testimonials from across the workforce. Workday also set up a digital academy to promote AI upskilling and hosted a “prompt-a-thon” where employees could brainstorm problems they think can be solved with AI and develop prompts to best leverage large language models.
In another nudge this year, senior leadership for the first time mandated that all 19,300 employees establish personal goals for how they will use AI to improve their work and learn new skills. Their progress will be assessed by managers at the end of the year.
Workday says these “Everyday AI” initiatives were built on internal analysis of the company’s workforce that uncovered that peer-to-peer guidance was more compelling than C-suite technologists evangelizing the benefits of AI. The company has also sought to reassure employees that experimentation is highly encouraged and that doing work faster with AI is always preferred over not using those tools.
“Everyday AI” was developed with the goal of boosting AI adoption across the company by 20% from the baseline set at the beginning of 2025. Workday says the increase was a better-than-anticipated 37% through May, with 79% of all workers now using AI. The tools used now range from the company’s own AI chatbot Workday Assistant to AI features from vendors including Zoom, Google, and Slack to generative AI-specific tools to support specific functions like customer support and coding assistant GitHub Copilot for developers.
Jim Stratton, who recently became Workday’s senior vice president of technology and architecture after serving as chief technology officer from 2018 until May this year, says his own approach to generative AI has evolved over the past few years.
Historically, the company would roll out fresh new features to all customers globally at the same time. But innovation is moving too quickly for AI—and some customers want to see early versions of AI-enabled tools before they are more broadly launched. That’s led to a staged rollout process for generative AI features, including at Workday, where early adopters get access to new tools first.
He’s focusing more on measuring the return on investments for generative AI, which can be easier to track for AI tools that assist customer support specialists or software developers using AI to generate code or bug fixes. But Stratton says ROI can be more difficult to quantify for other use cases, including when used to more accurately predict sales forecasts or when to help craft a pitch to a customer.
“Increasingly, in probably the last 18 months or so, there’s a real focus on measured ROI out of those investments,” Stratton says about AI and machine learning advancements. “Both in terms of what we do internally and also the products that we now go build.”
Workday says it has put extra emphasis on the company’s responsible AI principles, which include testing, risk assessments, and documentation, all work that’s especially critical for a software company whose tools are used to recruit and onboard talent, performance management, and onboarding. Some workplace tasks associated with this work, like decisions around compensation or promotions, should remain with workers.
“There’s certain critical steps that for a very long time, I think humans will absolutely still be the decision makers,” says Stratton.
While that may be some comfort to human resource employees, fresh fears of AI’s impact on the workplace have increased in recent weeks, encapsulated by Anthropic CEO Dario Amodei’s warning that AI could eliminate around 50% of all entry-level, white-collar jobs. Workday itself generated headlines along those lines when it announced in February that it would lay off 1,750 workers, or 8.5% of its staff, as the company prioritized investments like AI.
With developer productivity improving by 20% or more, Stratton acknowledges the fears workers may have that companies will need fewer employees to do the same amount of work. “That could be true,” he says. “But the way we view it, particularly on the development side of things, we can get more done with the same number of people so we can just go faster in terms of delivering more product.”
Goldsmith says there could be cases in which the technology completely takes over the work a person does, but ultimately she espouses AI’s benefits to both the business and workers. This is the tough sell that all businesses are confronting: encouraging workers to use AI to complete more tasks, while assuaging concerns that doing so won’t put them out of a job.
“We can reinvest those dollars in our technology and do more to advance the support and work for our customers,” says Goldsmith. “That’s how we talk to our employees about it. It is about super charging them, not replacing them.”
John Kell
Send thoughts or suggestions to CIO Intelligence here.
Introducing Fortune AIQ AI is reshaping work. What does it mean for your team? Fortune has unveiled a new hub, The “people” aspect of AI, explores various aspects of how mastering the “human” element of an AI deployment is just as important as the technical details. Companies are overhauling their hiring processes to screen candidates for AI skills —and attitudes. Read more
—and attitudes. Read more ‘AI fatigue’ is settling in as companies’ proofs of concept increasingly fail. Here’s how to prevent it. Read more
Here’s how to prevent it. Read more AI is changing how employees train—and starting to reduce how much training they need. Read more
Read more AI is helping blue-collar workers do more with less as labor shortages are projected to worsen. Read more
as labor shortages are projected to worsen. Read more Everyone’s using AI at work. Here’s how companies can keep data safe. Read more AI is reshaping work. What does it mean for your team? Fortune has unveiled a new hub, Fortune AIQ , dedicated to navigating AI’s real-world impact. Fortune has interviewed and surveyed the companies at the front lines of the AI revolution. In the coming months, we’ll roll out playbooks based on their learnings to help you get the most out of AI—and turn AI into AIQ. The first AIQ playbook,, explores various aspects of how mastering the “human” element of an AI deployment is just as important as the technical details.
NEWS PACKETS
Why Morgan Stanley built an AI tool to modernize legacy software. While many businesses are embracing AI coding tools from vendors like Anthropic and OpenAI, Morgan Stanley took a somewhat unique approach to assist the financial giant as it modernizes legacy software. The bank told the Wall Street Journal that it built an in-house tool known as DevGen.AI on OpenAI’s GPT models that can translate legacy code into plain English specs, which developers can use to rewrite the code. This approach has saved developers 280,000 hours this year, says Morgan Stanley, which also claims that AI-powered coding tools are better at writing new code than older programming languages or those customized for any given organization. “We found that building it ourselves gave us certain capabilities that we’re not really seeing in some of the commercial products,” Mike Pizzi, global head of technology and operations at Morgan Stanley, told the news outlet.
Meta mulls a multibillion-dollar investment in an AI startup. Facebook’s parent company is considering an investment in Scale AI that could exceed $10 billion in value, reports Bloomberg, in what would be Meta’s largest external AI investment. Scale AI, which provides data labeling services to customers including Microsoft and OpenAI, was last valued at about $14 billion in 2024 in a funding round that included Microsoft and Meta. If the Meta-Scale AI deal were to be inked, it would put the social media giant more in line with big tech rivals that have poured billions into AI startups, including Microsoft’s investment in OpenAI and Anthropic’s funding from the likes of Amazon and Google’s parent company Alphabet.
Alphabet says it will keep hiring engineers at least into 2026. Sundar Pichai, CEO of Google-parent Alphabet, told Bloomberg that the tech giant’s engineering talent base will continue to grow even as its AI investments accelerate. “I just view this as making engineers dramatically more productive, getting a lot of the mundane aspects out of what they do,” says Pichai. He added that while AI excels in some tasks like coding, large language models continue to make some basic mistakes. Alphabet remains a strong AI bull: It recommitted to plans to spend $75 billion this year to add data center capacity. Alphabet’s engineering hiring forecast is more optimistic than what some others have said about the technology’s impact on the future job prospects for workers. Just last week, OpenAI CEO Sam Altman said AI today is like an intern that can work for a couple hours but at some point, “it’ll be like an experienced software engineer that can work for a couple of days.”
ADOPTION CURVE
CEOs are more aware of responsible AI concerns than CIOs and CTOs. Consumers are far more likely to express concerns about the accuracy, privacy, explainability, and accountability of AI than any of the seven C-suite leadership roles surveyed by consulting giant EY. For consumers, the average percent of respondent concern across nine responsible AI principles was 53%, versus 38% for CEOs. CIOs were ranked fifth out of the seven C-suite roles (26%) and CTOs were last (23%).
EY explains that this is likely due to 50% of CEOs saying they have primary responsibility for AI at their organizations and that they tend to be more customer-facing than most other C-suite leaders, with the possible exception of the chief marketing officer. To get their C-suite peers to champion responsible AI, EY recommends that CEOs encourage other C-suite leaders to spend more time with customers and more closely study their feedback and surveys, go beyond regulatory regulations to address the risks of AI, and more loudly explain their organizations’ responsible AI principles to customers.
JOBS RADAR
Hiring:
- New York State Technology Enterprise Corporation is seeking a CTO, based in Albany, NY. Posted salary range: $215K-$300K/year.
- Shake Shack is seeking a VP of digital and restaurant technology, based in New York City. Posted salary range: $245.6K-$328.9K/year.
- Chanel is seeking a senior group director, CIO office portfolio management and performance, based in New York City. Posted salary range: $175K-$210K/year.
- Alto Pharmacy is seeking a CTO, based in Texas. Posted salary range: $280K-$400K/year.
Hired:
- Genesys named Trevor Schulze as CIO, joining the call center software provider to oversee the global IT organization to further bolster the company’s AI and cloud strategies. Schulze joins Genesys from software provider Alteryx, where he was most recently SVP and chief digital and information officer. He also held CIO and senior leadership roles at RingCentral and Micron Technology.
- Neurocrine Biosciences announced Lewis Choi as CIO, to oversee the neurological-focused biopharmaceutical company’s technology initiatives. He joins Neurocrine after a 13-year career at Thermo Fisher Scientific, most recently as the VP of AI automation and data. Prior to that, he held IT roles at Life Technologies, which was acquired by Thermo Fisher, and SysGroup and ADP.
- Washington Post promoted Sam Han as chief AI officer, a new role after serving in various leadership positions at the news organization for nearly eight years. Han will lead the development of the Post's AI suite of products and he previously served as a VP at hotel operator Marriott. He also held leadership roles at Persistent Systems and Sears Holdings.
- Imprint named Will Larson as CTO, joining the provider of co-branded credit cards to oversee the engineering team and all technology development. Prior to Imprint, Larson was CTO at software provider Carta and served as CTO at mental healthcare app Calm. He also previously led the engineering teams at Stripe and Uber.
- LogicMonitor appointed Garth Fort as chief product officer, overseeing product strategy for the provider of IT infrastructure monitoring. Previously, Fort served as CPO and SVP of software provider Splunk, which was acquired by Cisco for $28 billion last year. Fort also previously held manager roles at Amazon Web Services and Microsoft.
- Thunder announced Paul Kersey as CTO, joining the Salesforce consultancy as it deepens capabilities in Mulesoft software, the AI platform Agentforce, and data cloud technologies. Previously, Kersey was VP of technology at IT service and consultancy NTT Data and as CTO at Apisero, which NTT acquired in 2022.
- Simplilearn announced that Jitendra Kumar will return to the company as CTO, where he will spearhead efforts to embed AI technologies into the company’s online learning courses. Kumar was previously CTO at the Blackstone-backed education company from 2013 through 2019. During the subsequent six years, Kumar was co-founder and CEO at two companies, HappyCredit and ReelOn.
- Napier AI named Noel King as CTO, where he will oversee the development of the company’s AI-enabled financial crime compliance software. Most recently, King was CTO at software firm Implement Technologies and IT services provider Shipyard Technology Ventures.
- MindBridge promoted Rachel Kirkham to CTO, after she was most recently SVP of AI and product at the financial risk software provider. Kirkham has been promoted several times by MindBridge since joining in 2020. Previously, she spent nine years at the UK National Audit Office, an independent public spending watchdog, including serving as head of data analytics research.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://fortune.com/2025/06/11/how-software-giant-workday-got-79-of-its-employees-to-embrace-ai/
|
[
{
"date": "2025/06/11",
"position": 42,
"query": "workplace AI adoption"
}
] |
AI for Workforce Engagement Management
|
AI for Workforce Engagement Management
|
https://blog.intermedia.com
|
[
"Mark Sher",
".Block-Author .Name",
"-Webkit-Margin-Before",
"-Webkit-Margin-After",
"Margin-Top",
"Margin-Bottom",
"Font-Family",
"Dinotregular",
"Helvetica-Neue",
"Helvetica"
] |
Beyond AI alone, productivity also rises with better collaboration infrastructure. The chart below shows that as organizations adopt more collaboration tools, ...
|
The modern workforce is more dynamic, distributed, and digitally connected than it has been in the past. With remote and hybrid teams becoming the norm, businesses face new challenges in keeping employees engaged, aligned, and productive across time zones and locations.
AI plays a direct role in how organizations manage, support, and empower their people. From optimizing schedules and communication to identifying burnout risks and streamlining collaboration, AI tools are transforming workforce engagement into a smarter, more strategic process.
This article explores how AI is reshaping workforce engagement management and what it means for businesses looking to stay agile, efficient, and employee-focused.
Quick Takeaways
AI automates workforce management tasks like scheduling, resource allocation, and shift optimization, saving time and reducing manual errors.
Personalized insights from AI help improve employee engagement, monitor wellness, and identify burnout risks early.
Personalized insights from AI help improve employee engagement, monitor wellness, and identify burnout risks early. AI-powered tools enhance collaboration and communication across remote and hybrid teams with real-time updates and intelligent workflows.
Predictive analytics and performance tracking allow businesses to adjust staffing and training in response to changing needs.
Combining AI with cloud-based systems streamlines workforce operations and supports better decision-making across departments.
Understanding AI Workforce Management
AI workforce management refers to the use of artificial intelligence to optimize and automate various aspects of managing a team, from scheduling to performance monitoring. Traditional workforce management often involves manual processes that can be time-consuming and prone to errors. However, AI enhances these methods by automating routine tasks and providing data-driven insights.
AI-driven systems can handle complex tasks such as:
Predicting workforce needs
Optimizing resource allocation
Managing shift schedules with precision
These systems are also designed to integrate seamlessly with communication platforms, ensuring that teams can collaborate efficiently, regardless of location.
By improving operations and decision-making, AI-driven technologies are not only optimizing processes, but also freeing employees to focus on more creative, strategic tasks. The chart below highlights the top benefits businesses report from adopting AI:
Core Benefits of AI for Workforce Engagement
AI offers several key benefits that improve workforce engagement and management efficiency:
Enhanced Employee Engagement
AI helps businesses create personalized work experiences by analyzing engagement data. It can provide tailored feedback, track employee performance, and recommend proactive measures to improve satisfaction and retention.
Automated Scheduling and Resource Allocation
AI tools ensure that the right resources are allocated at the right time. They optimize staffing levels by predicting demand and adjusting schedules to prevent both overstaffing and understaffing, which leads to greater operational efficiency.
Efficient Communication
AI enhances communication by providing real-time updates and facilitating smooth interactions among team members, especially in remote or hybrid environments. These tools can analyze communication patterns to identify potential bottlenecks and suggest improvements.
Real-Time Collaboration
AI-powered platforms help teams collaborate more effectively by automating workflows and reducing manual errors. In fact, a recent survey found that businesses using AI extensively reported a 72% increase in high productivity levels, compared to 55% for those with limited AI use.
Beyond AI alone, productivity also rises with better collaboration infrastructure. The chart below shows that as organizations adopt more collaboration tools, the percentage of employees reporting high productivity increases from just 38% with no tools to 80% with seven.
This highlights how the combination of AI and robust collaboration tools creates a work environment that supports higher output, especially in distributed or hybrid teams.
AI and Employee Wellness Monitoring
Engaged employees are productive, supported, balanced, and less likely to burn out. AI tools are helping businesses take a more proactive approach to workforce wellness by identifying early signs of stress and disengagement before they escalate into bigger issues.
By analyzing behavioral data such as changes in communication patterns, task completion rates, or time spent across platforms, AI can detect subtle shifts that may indicate workload imbalance or burnout risk. Some platforms also integrate with wellness check-ins or sentiment analysis tools to flag teams or individuals that may need support.
This kind of real-time insight gives managers the ability to act early, whether it’s adjusting workloads, offering wellness resources, or checking in personally. Over time, AI-driven wellness monitoring contributes to better retention, higher morale, and a workplace culture where employees feel seen and supported.
AI Use Cases in Workforce Engagement Management
AI’s impact on workforce engagement management is becoming increasingly evident across various industries. In fact, 57% of business leaders predict that AI will substantially transform their businesses within the next three years, demonstrating the far-reaching potential of AI in workforce management.
Here are a few practical use cases where AI-driven solutions improve workforce management:
AI-Driven Analytics for Staffing Optimization: Predictive analytics powered by AI can forecast staffing needs based on historical data, trends, and real-time variables. This allows businesses to adjust their workforce in response to fluctuating demands, ensuring optimal staffing levels at all times.
Predictive analytics powered by AI can forecast staffing needs based on historical data, trends, and real-time variables. This allows businesses to adjust their workforce in response to fluctuating demands, ensuring optimal staffing levels at all times. Communication Pattern Analysis: AI tools can analyze how teams communicate across different platforms, identifying patterns that impact productivity. By recognizing bottlenecks or breakdowns in communication, AI systems offer actionable insights to improve collaboration and streamline workflows.
AI tools can analyze how teams communicate across different platforms, identifying patterns that impact productivity. By recognizing bottlenecks or breakdowns in communication, AI systems offer actionable insights to improve collaboration and streamline workflows. Training and Development Opportunities: AI-powered platforms can track employee performance and engagement, identifying skill gaps and recommending tailored training programs. By analyzing employee behavior, these tools suggest development paths that boost morale, increase retention, and enhance overall productivity.
These use cases demonstrate how AI transforms workforce management, enabling businesses to remain agile and responsive to changing work dynamics.
Integrating AI with Cloud-Based Communication Tools
The integration of AI with cloud-based communication tools is a game-changer for managing remote and hybrid workforces. By combining the power of AI with the flexibility of cloud-based systems, businesses can ensure:
Seamless communication
Real-time collaboration
Improved operational efficiency
AI tools help manage distributed teams by providing real-time updates, automating administrative tasks, and facilitating seamless collaboration across different locations. These platforms also support unified communication systems that allow for better coordination and workflow management, ensuring that teams stay aligned on goals and deadlines.
Cloud-based communication tools, enhanced with AI capabilities, create an environment where managers can monitor performance, track productivity, and foster engagement without the need for constant manual oversight. This combination of AI and cloud technology offers businesses a powerful solution for optimizing workforce engagement and management.
Overcoming Common Barriers to AI Adoption in Workforce Management
While the benefits of AI are clear, many organizations still face obstacles when it comes to implementation. Concerns about data privacy, employee resistance, and lack of in-house expertise often hold businesses back from getting the most out of AI-driven tools.
One of the biggest challenges is trust. Employees may worry that AI is being used to monitor them too closely or replace their roles. Clear communication about the purpose of AI (augmenting work, not replacing it) goes a long way in building confidence and buy-in. Involving teams early in the adoption process also helps them understand how AI can improve their daily experience.
Data privacy and compliance are also top of mind, especially for businesses handling sensitive information. Choosing AI tools with built-in security features, access controls, and transparent data use policies ensures compliance while protecting employee trust.
Finally, the learning curve can feel steep. Businesses that succeed with AI often start small by rolling out pilot programs, offering training, and selecting tools with intuitive interfaces. With the right approach, even smaller teams can adopt AI effectively and see real operational gains.
Turn Workforce Data into Action Today with Intermedia
AI is changing the way businesses engage, support, and manage their teams, from automating scheduling to enhancing communication and even monitoring employee well-being. With the right tools in place, organizations can build more efficient operations and more connected, productive workforces.
Ready to improve how your team works and connects? Explore how Intermedia’s AI-enhanced communication and management tools can help you boost engagement, streamline operations, and support your workforce—wherever they are. Request a demo today.
| 2025-06-11T00:00:00 |
2025/06/11
|
https://blog.intermedia.com/ai-for-workforce-engagement-management/
|
[
{
"date": "2025/06/11",
"position": 79,
"query": "workplace AI adoption"
}
] |
Artificial Intelligence in Employment: 2025 Regulatory Update
|
Artificial Intelligence in Employment: 2025 Regulatory Update
|
https://blog.dciconsult.com
|
[
"Dave Schmidt",
"Authors"
] |
The integration of artificial intelligence (AI) into employment decision-making processes for organizations continues to accelerate, as does the ...
|
By Dave Schmidt and Sarah Layman
The integration of artificial intelligence (AI) into employment decision-making processes for organizations continues to accelerate, as does the evolution of the legal and regulatory environment. This post provides information on key happenings in state and federal regulatory activity as of June 2025.
State Activity Adds to the Patchwork of Requirements
Colorado, California, and Virginia have seen significant activity in 2025 related to the use of AI that employers need to know about:
Colorado
Status: Passed, In Effect on February 1, 2026
The Colorado Consumer Protections in Interactions with Artificial Intelligence Systems (CPIAIS) is currently the most comprehensive state law addressing the development and use of AI in high-impact contexts (including employment decision-making). Despite significant debate and last-minute calls by Governor Polis to delay or revise the legislation, including efforts to ease compliance burdens and refine the definition of “algorithmic discrimination,” the law is set to take effect on February 1, 2026.
CPIAIS applies to high-risk AI systems, defined as any AI that makes—or plays a substantial role in making—a “consequential decision,” such as hiring, promotion, or termination. Both developers and deployers of such systems are held to a duty of reasonable care to avoid algorithmic discrimination. If discrimination is discovered, it must be reported to the Colorado Attorney General within 90 days. Reasonable care is presumed if specific compliance steps are taken (e.g., Risk management programs, Annual impact assessments, Transparency and notices). For additional details, see our blog or download DCI’s Colorado Artificial Intelligence Act Cheat Sheet.
California
Status: Pending Approval by Office of Administrative Law
Although early efforts to pass broad, stand-alone AI laws in California stalled (see DCI’s blogs here and here), state regulators have instead moved forward with targeted amendments to the California Fair Employment and Housing Act (FEHA). The new regulations, titled “Employment Regulations Regarding Automated-Decision Systems” were finalized in March 2025 and could take effect as early as July 2025 (pending approval from the Office of Administrative Law). Most notably, the amended text provides:
Clear definitions of “automated-decision systems (ADS),” “algorithm,” “artificial intelligence,” and “machine learning”;
A mandate to retain all relevant employment records, including ADS data and documentation, for at least four years;
Notice requirements obligating employers to inform applicants and employees when an ADS is used in employment decision-making;
A requirement to provide reasonable accommodations for individuals with disabilities or religious needs, particularly when ADS tools evaluate attributes (e.g., reaction time, vocal tone) that may be impacted;
Explicit language stating that practices resulting in adverse impact are unlawful unless the criteria used are job-related, consistent with business necessity, and there is no less discriminatory alternative available that would serve the employer’s goals as effectively;
Recognition that, in the event of a legal claim, evidence (or lack thereof) of proactive anti-bias testing and mitigation efforts—including the quality, effectiveness, scope, and response to such testing—will be relevant.
.
In the meantime, California continues to propose and consider bills related to the use of artificial intelligence that could impact employment decision-making, such as AB-1018
Virginia
Status: Vetoed by Governor
This spring Virginia’s legislature narrowly passed House Bill 2094, the High-Risk Artificial Intelligence Developer and Deployer Act, bore a close resemblance to Colorado’s CPIAIS. However, Governor Glenn Youngkin subsequently vetoed the bill, citing its “burdensome” regulatory framework. The bill could technically still become law via a two-thirds override in both chambers, but given the initial vote tallies, this outcome is unlikely.
Federal Updates
There have also been developments related to AI use and oversight at the federal level in early 2025, including new guidance from the Office of Management and Budget (OMB) for federal agencies and the withdrawal of previously provided guidance by some federal agencies.
OMB Guidance for Federal Agencies
At the federal level, the White House Office of Management and Budget issued two significant memoranda in April of 2025 addressing AI use and procurement across federal agencies, officially rescinding and replacing Biden-era guidance.
The primary directive, OMB Memo M-25-21-"Accelerating Federal Use of AI through Innovation, Governance, and Public Trust", establishes comprehensive standards for the governance, risk management, and oversight of AI in government operations. According to the White House Fact Sheet, these policies mark a fundamental shift from the previous administration by emphasizing rapid and responsible AI adoption (e.g., “Agency Chief AI Officer roles are redefined to serve as change agents and AI advocates, rather than overseeing layers of bureaucracy”), efficient and effective acquisition of American AI systems (the focus of OMB Memo M-25-22), and practical applications that “work for the American people.”
While these requirements are centered on federal agencies, their reach is likely to extend to private-sector vendors and contractors providing AI tools and services to the government, shaping wider expectations for responsible AI use and procurement.
Withdrawn Guidance
Under the Trump Administration, a range of previously issued AI-related guidance documents have been formally removed from federal websites including:
Artificial Intelligence and Worker Well-being: Principles and Best Practices for Developers and Employers (DCI Blog)
Guide on Artificial Intelligence and Equal Employment Opportunity for Federal Contractors (DCI Blog)
Joint Statement on Enforcement of Civil Rights, Fair Competition, Consumer Protection, and Equal Opportunity Laws in Automated Systems (DCI Blog)
It is yet to be seen if revised guidance documents related to the use of AI in employment decision-making will be issued.
Looking Ahead: Preparation and Monitoring Needed
As the legal landscape surrounding AI in employment evolves, it is prudent for employers to not only track legislative changes, but also proactively create robust governance teams and frameworks to address the challenges and risks associated with using AI-driven hiring processes. DCI will continue to monitor developments and provide timely updates with practical insight for employers.
For more information, register for DCI’s upcoming webinar covering the key updates in AI regulations for employers.
| 2025-06-12T00:00:00 |
https://blog.dciconsult.com/ai-in-employment-2025-regulatory-update
|
[
{
"date": "2025/06/12",
"position": 49,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 45,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 41,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 42,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 44,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 45,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 49,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 42,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 43,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 43,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 41,
"query": "AI regulation employment"
},
{
"date": "2025/06/12",
"position": 42,
"query": "AI regulation employment"
}
] |
|
NY to track AI-related layoffs - LinkedIn
|
NY to track AI-related layoffs
|
https://www.linkedin.com
|
[] |
New York just became the first state to track whether layoffs are the result of artificial intelligence, adding a new checkbox to its Worker Adjustment and ...
|
The World Economic Forum characterized the coming change in the job market because of AI to be more of a shift than a loss. The report released shows that Artificial Intelligence will replace humans in the workplace to the tune of 75 million jobs. They also report that 133 million new jobs will be created because of AI for a gain of 58 million jobs. That is great news because the dystopian future where AI takes our employment and we have no way to secure income sounds terrible. Projections of how companies will operate when AI replaces human workers have coalesced around several areas of employment that would be most affected by the change. 🤖 Routine and repetitive tasks such as manufacturing 🤖 Data entry, bookkeeping, and administrative or clerical work 🤖 Customer service, telemarketing, and market research 🤖 Paralegals and proofreading While this potential sunset of these careers will be a huge impact on not just the workplace, but on education to attain these roles, support systems in place for these workers, and social systems built on these identities. But what about the new jobs that will be created? Yes, McKinsey & Company projects (https://lnkd.in/gCXTpSdA) job creation in the following areas as a reaction to more prolific AI adoption: 🆕 AI Trainers and Teachers 🆕 Data Analysts and Scientists 🆕 Human-Machine Teaming managers 🆕 AI Ethics and Policy Specialists This is where my concern stems from. There is a glaring gap in skills and knowledge between the jobs being phased out in favor of AI, and the jobs being created to support AI. The jobs that are being replaced are the jobs that require the least amount of experience, creativity, problem solving skills, and critical thinking. The new jobs are highly specialized and require training and experience. So, we are making the baseline requirement for entering the workforce much higher by eliminating many jobs that allow employment without specialized training or experience. Those that are unwilling to train for new skills or change career paths may find themselves unable to find work. A shift of this magnitude requires coordination and communication. We need to consider the impact of eliminating a percentage of jobs in favor of AI and replacing them with jobs that the newly unemployed cannot necessarily get or do. https://lnkd.in/g_DbJ5Vv
| 2025-06-12T00:00:00 |
https://www.linkedin.com/news/story/ny-to-track-ai-related-layoffs-6901441/
|
[
{
"date": "2025/06/12",
"position": 13,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/12",
"position": 80,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/12",
"position": 62,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/12",
"position": 22,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/12",
"position": 21,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/12",
"position": 53,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/12",
"position": 51,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/12",
"position": 70,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/12",
"position": 19,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/12",
"position": 94,
"query": "artificial intelligence layoffs"
}
] |
|
Udemy Launches New AI Fluency Packages to Accelerate ...
|
Udemy Launches New AI Fluency Packages to Accelerate Workforce Transformation
|
https://investors.udemy.com
|
[] |
Udemy Launches New AI Fluency Packages to Accelerate Workforce Transformation ... SAN FRANCISCO --(BUSINESS WIRE)--Jun. 12, 2025-- Udemy (Nasdaq: ...
|
New packages help professionals and organizations upskill and reskill in AI to drive business outcomes
SAN FRANCISCO --(BUSINESS WIRE)--Jun. 12, 2025-- Udemy (Nasdaq: UDMY), a leading AI-powered skills development platform, today announced a suite of new AI Packages designed to help organizations and professionals develop AI fluency. The new packages provide end-to-end skills development to accelerate an organization’s AI transformation, underscoring Udemy’s position as a trusted upskilling and reskilling partner to nearly 80 million learners and more than 17,000 organizations across the globe.
“At Udemy, we believe that building the right AI skills, sharpening them over time, and keeping them up to date is the key to driving stronger business performance,” said Hugo Sarrazin , President & CEO at Udemy . “As AI adoption surges across industries globally, Udemy’s dynamic marketplace enables us to respond to technological shifts with unprecedented agility. While traditional publishers struggle to keep pace, our platform can introduce and update cutting-edge AI reskilling/upskilling journeys in real-time, ensuring learners and organizations are equipped with the most relevant skills.”
In today’s uncertain global labor market, and with the rapid pace of change in AI, organizations need expert guidance and support to assess their current skills gaps and develop a clear path forward to drive better outcomes. That’s exactly what Udemy’s new AI Packages are designed to support. Customers can get access to:
The AI Readiness Package for building foundational AI skills. The AI Readiness Package includes 50 curated courses along with a structured introduction to AI concepts. Learners will also have access to the Udemy AI Assistant which provides real-time coaching and in-course support to help the learner better understand the concepts and tools being taught. Additionally, learners can develop real-world soft skills through immersive, instructor-designed conversation simulations with Udemy’s AI-powered Role Play.
The AI Growth Package to support teams who want more specialized AI skills. The AI Growth Package is targeted at teams looking to develop advanced, role-specific skills including generative AI for performance and productivity, neural networks for data science and engineering, and agentic AI. It builds on the AI Readiness Package and includes an additional 30+ curated learning paths, multilingual support, and certification prep, along with access to both the Udemy AI Assistant and Udemy AI Skills Mapping, which helps leaders develop personalized and targeted learning paths at scale.
These new packages build on the success of Udemy’s standalone AI courses, which have already surpassed 11 million enrollments, and the recently launched “AI for Business Leaders” program designed to help senior leaders build core AI skills as they guide their teams toward developing an AI habit and, ultimately, AI fluency.
To complement the new offerings and support more advanced use cases, including broader digital transformation initiatives, Udemy continues to offer a comprehensive Enterprise Plan that includes access to 30,000+ premium courses and 200+ certification paths as well as a full suite of Professional Services that gives learning and business leaders access to expert help so that they can develop a fully customized, end-to-end program aligned with business objectives.
With the introduction of these new packages, Udemy continues to expand its suite of AI offerings – spanning both content and platform – delivering on its mission to transform lives through learning and drive AI fluency for organizations and professionals worldwide.
To learn more about Udemy’s unique approach to AI upskilling and reskilling, visit https://business.udemy.com/spotlight/ai-upskilling.
About Udemy
Udemy (Nasdaq: UDMY) is an AI-powered skills development platform transforming how companies and individuals across the world build the capabilities needed to thrive in a rapidly evolving workplace. By combining on-demand, multi-language content with real-time innovation, Udemy delivers personalized experiences that empower organizations to scale workforce development and help individuals build the technical, business, and soft skills most relevant to their careers. Today, thousands of companies, including Ericsson, Samsung SDS America, On24, The World Bank , and Volkswagen , rely on Udemy Business for its enterprise solutions to build agile, future-ready teams. Udemy is headquartered in San Francisco , with hubs across the United States , Australia , India , Ireland , Mexico and Türkiye.
View source version on businesswire.com: https://www.businesswire.com/news/home/20250612135088/en/
Media Contact
Risha Tyagi
Senior Global Corporate Communications Manager
[email protected]
Investor Contact
Dennis Walsh
Vice President, Investor Relations
[email protected]
| 2025-06-12T00:00:00 |
https://investors.udemy.com/news-releases/news-release-details/udemy-launches-new-ai-fluency-packages-accelerate-workforce
|
[
{
"date": "2025/06/12",
"position": 95,
"query": "AI workforce transformation"
},
{
"date": "2025/06/12",
"position": 96,
"query": "AI workforce transformation"
},
{
"date": "2025/06/12",
"position": 100,
"query": "AI workforce transformation"
},
{
"date": "2025/06/12",
"position": 89,
"query": "AI workforce transformation"
},
{
"date": "2025/06/12",
"position": 92,
"query": "AI workforce transformation"
}
] |
|
AI literacy: What it is, what it isn't, who needs it and why it's ...
|
AI literacy: What it is, what it isn’t, who needs it and why it’s hard to define
|
https://theconversation.com
|
[
"Arne Bewersdorff",
"Daniel S. Schiff",
"Marie Hornberger"
] |
AI literacy refers to a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI.
|
It is “the policy of the United States to promote AI literacy and proficiency among Americans,” reads an executive order President Donald Trump issued on April 23, 2025. The executive order, titled Advancing Artificial Intelligence Education for American Youth, signals that advancing AI literacy is now an official national priority.
This raises a series of important questions: What exactly is AI literacy, who needs it, and how do you go about building it thoughtfully and responsibly?
The implications of AI literacy, or lack thereof, are far-reaching. They extend beyond national ambitions to remain “a global leader in this technological revolution” or even prepare an “AI-skilled workforce,” as the executive order states. Without basic literacy, citizens and consumers are not well equipped to understand the algorithmic platforms and decisions that affect so many domains of their lives: government services, privacy, lending, health care, news recommendations and more. And the lack of AI literacy risks ceding important aspects of society’s future to a handful of multinational companies.
How, then, can institutions help people understand and use – or resist – AI as individuals, workers, parents, innovators, job seekers, students, employers and citizens? We are a policy scientist and two educational researchers who study AI literacy, and we explore these issues in our research.
What AI literacy is and isn’t
At its foundation, AI literacy includes a mix of knowledge, skills and attitudes that are technical, social and ethical in nature. According to one prominent definition, AI literacy refers to “a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace.”
AI literacy is not simply programming or the mechanics of neural networks, and it is certainly not just prompt engineering – that is, the act of carefully writing prompts for chatbots. Vibe coding, or using AI to write software code, might be fun and important, but restricting the definition of literacy to the newest trend or the latest need of employers won’t cover the bases in the long term. And while a single master definition may not be needed, or even desirable, too much variation makes it tricky to decide on organizational, educational or policy strategies.
Who needs AI literacy? Everyone, including the employees and students using it, and the citizens grappling with its growing impacts. Every sector and sphere of society is now involved with AI, even if this isn’t always easy for people to see.
Exactly how much literacy everyone needs and how to get there is a much tougher question. Are a few quick HR training sessions enough, or do we need to embed AI across K-12 curricula and deliver university micro credentials and hands-on workshops? There is much that researchers don’t know, which leads to the need to measure AI literacy and the effectiveness of different training approaches.
Measuring AI literacy
While there is a growing and bipartisan consensus that AI literacy matters, there’s much less consensus on how to actually understand people’s AI literacy levels. Researchers have focused on different aspects, such as technical or ethical skills, or on different populations – for example, business managers and students – or even on subdomains like generative AI.
A recent review study identified more than a dozen questionnaires designed to measure AI literacy, the vast majority of which rely on self-reported responses to questions and statements such as “I feel confident about using AI.” There’s also a lack of testing to see whether these questionnaires work well for people from different cultural backgrounds.
Moreover, the rise of generative AI has exposed gaps and challenges: Is it possible to create a stable way to measure AI literacy when AI is itself so dynamic?
In our research collaboration, we’ve tried to help address some of these problems. In particular, we’ve focused on creating objective knowledge assessments, such as multiple-choice surveys tested with thorough statistical analyses to ensure that they accurately measure AI literacy. We’ve so far tested a multiple-choice survey in the U.S., U.K. and Germany and found that it works consistently and fairly across these three countries.
There’s a lot more work to do to create reliable and feasible testing approaches. But going forward, just asking people to self-report their AI literacy probably isn’t enough to understand where different groups of people are and what supports they need.
Approaches to building AI literacy
Governments, universities and industry are trying to advance AI literacy.
Finland launched the Elements of AI series in 2018 with the hope of educating its general public on AI. Estonia’s AI Leap initiative partners with Anthropic and OpenAI to provide access to AI tools for tens of thousands of students and thousands of teachers. And China is now requiring at least eight hours of AI education annually as early as elementary school, which goes a step beyond the new U.S. executive order. On the university level, Purdue University and the University of Pennsylvania have launched new master’s in AI programs, targeting future AI leaders.
Despite these efforts, these initiatives face an unclear and evolving understanding of AI literacy. They also face challenges to measuring effectiveness and minimal knowledge on what teaching approaches actually work. And there are long-standing issues with respect to equity − for example, reaching schools, communities, segments of the population and businesses that are stretched or under-resourced.
Next moves on AI literacy
Based on our research, experience as educators and collaboration with policymakers and technology companies, we think a few steps might be prudent.
Building AI literacy starts with recognizing it’s not just about tech: People also need to grasp the social and ethical sides of the technology. To see whether we’re getting there, we researchers and educators should use clear, reliable tests that track progress for different age groups and communities. Universities and companies can try out new teaching ideas first, then share what works through an independent hub. Educators, meanwhile, need proper training and resources, not just additional curricula, to bring AI into the classroom. And because opportunity isn’t spread evenly, partnerships that reach under-resourced schools and neighborhoods are essential so everyone can benefit.
Critically, achieving widespread AI literacy may be even harder than building digital and media literacy, so getting there will require serious investment – not cuts – to education and research.
There is widespread consensus that AI literacy is important, whether to boost AI trust and adoption or to empower citizens to challenge AI or shape its future. As with AI itself, we believe it’s important to approach AI literacy carefully, avoiding hype or an overly technical focus. The right approach can prepare students to become “active and responsible participants in the workforce of the future” and empower Americans to “thrive in an increasingly digital society,” as the AI literacy executive order calls for.
The Conversation will be hosting a free webinar on practical and safe use of AI with our tech editor and an AI expert on June 24 at 2pm ET/11am PT. Sign up to get your questions answered.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://theconversation.com/ai-literacy-what-it-is-what-it-isnt-who-needs-it-and-why-its-hard-to-define-256061
|
[
{
"date": "2025/06/12",
"position": 29,
"query": "AI education"
}
] |
AI education – News, Research and Analysis
|
AI education – News, Research and Analysis – The Conversation – page 1
|
https://theconversation.com
|
[
"Kentaro Toyama",
"Simon Knight",
"Lisa M. Given",
"Kieran Hegarty",
"John Preston",
"Kalervo Gulson",
"Casey Fiesler",
"Mark Sanderson",
"Kirsty Kitto",
"Teresa Swist"
] |
Generative AI can help personalise learning and improve student engagement. But teacher training is essential if AI is to serve, not undercut, the goals of a ...
|
DNY59/E+ via Getty Images June 12, 2025 AI literacy: What it is, what it isn’t, who needs it and why it’s hard to define President Trump’s executive order calling for AI literacy highlights its importance. The order also underscores its amorphous nature. Here’s how to develop and measure effective AI literacy programs.
Khanchit Khirisutchalual/Getty Images January 24, 2025 Navigating deepfakes and synthetic media: This course helps students demystify artificial intelligence technologies Students learn skills to help them distinguish fact from fiction in the world of AI.
mayam_studio December 10, 2024 Reconciling technology with humanism: the future of education in the age of generative AI Generative AI can help personalise learning and improve student engagement. But teacher training is essential if AI is to serve, not undercut, the goals of a humanistic education.
Pavel_Chag/iStock via Getty Images January 3, 2024 AI is here – and everywhere: 3 AI researchers look to the challenges ahead in 2024 Artificial intelligence is everywhere, and the tech industry is racing along to develop ever more powerful AIs. Three scholars look ahead to the next chapter in this technological revolution.
Flickr/sergio m mahugo, Edited by The Conversation September 3, 2023 Google turns 25: the search engine revolutionised how we access information, but will it survive AI? It’s hard to remember life before Google, when the closest thing to it was your local librarian. Soon the search engine will be offering AI-based summaries in its search results.
Jacob King/PA/AAP November 21, 2021 Algorithms can decide your marks, your work prospects and your financial security. How do you know they’re fair? A UK controversy about school leavers’ marks shows algorithms can get things wrong. To ensure algorithms are as fair as possible, how they work and the trade-offs involved must be made clear.
vectorfusionart/Shutterstock November 9, 2021 Facebook, the metaverse and the monetisation of higher education The metaverse may change how profit is made in higher education.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://theconversation.com/topics/ai-education-73971
|
[
{
"date": "2025/06/12",
"position": 58,
"query": "AI education"
}
] |
Join Artificial Intelligence in Education
|
Registration
|
https://www.edweb.net
|
[] |
Artificial Intelligence in Education is a free professional learning community where educators can hear from a range of experts, developers, and solution ...
|
Join Artificial Intelligence in Education
Artificial Intelligence in Education is a free professional learning community where educators can hear from a range of experts, developers, and solution providers on this fast-developing new technology that will have an unprecedented impact on human life. We'll hear, in particular, about the many ways AI is transforming education.
Keep up with our latest programs in AI here.
Watch our Most Recent edWebinar Guiding AI Policy in Schools: An Ethical and Systems Thinking Approach
Presented by Dr. Karen Rezach, Director of The Ethics Institute, Kent Place School (NJ); and Lisa Yokana, Co-Founder and COO, Next World Learning Lab
Browse all edWebinars and Podcasts on AI
View Reports and Resources on AI
| 2025-06-12T00:00:00 |
https://www.edweb.net/AI
|
[
{
"date": "2025/06/12",
"position": 69,
"query": "AI education"
}
] |
|
AI in Education FAQs - Camara Education
|
Ai in Education FAQs
|
https://camara.org
|
[] |
Ai in Education FAQs As artificial intelligence becomes more prevalent in classrooms and learning environments, educators, parents, and students have many ...
|
AI Is Already Shaping the World
AI powers tools students use every day from search engines to social media algorithms to digital assistants. Teaching AI helps students understand how their digital world works.
2. It’s a Key Job Skill
AI literacy is becoming essential in many careers, not just in tech, but in healthcare, finance, media, and more. Early exposure can give students a competitive edge.
3. It Builds Critical Thinking
By learning how AI systems work (and where they fail), students develop skills in:
Bias detection
Data literacy
Ethical reasoning
Problem solving
4. It Promotes Responsible Use
Teaching AI in schools helps reduce misuse, like cheating with generative AI tools by encouraging ethical engagement and transparency.
AI education isn’t about turning every student into a programmer, it’s about making informed, thoughtful digital citizens. Like we teach math or science to understand the world, teaching AI helps students navigate the future safely and smartly.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://camara.org/ai-in-education-faqs/
|
[
{
"date": "2025/06/12",
"position": 88,
"query": "AI education"
}
] |
California's No Robo Bosses Bill Could Restrict Employers' ...
|
California’s No Robo Bosses Bill Could Restrict Employers’ Use of AI
|
https://ogletree.com
|
[] |
The California Senate approved a bill, called the “No Robo Bosses Act,” that would restrict when and how employers can use automated decision-making systems ...
|
Quick Hits
The proposed “No Robo Bosses Act” (SB 7) in California aims to regulate the use of automated decision-making systems in employment by mandating human oversight and written notice to employees about such systems.
The bill would prohibit employers from relying primarily on automated systems for critical employment decisions and allows affected employees to appeal those decisions within a specified timeframe.
The No Robo Bosses Act, Senate Bill (SB) No. 7, introduced by California Senator Jerry McNerney on March 6, 2025, and backed by some major labor unions, would require employers to provide written notice that an “automated decision system” (ADS) is used to make employment-related decisions and require human oversight when ADSs are used to guide employment-related decisions.
The bill, which is now under consideration in the California Assembly, would further prohibit employers or vendors engaged by employers from using such systems to “predict” workers’ future behavior or performance.
“The Senate’s passage of SB 7 … sends a strong message: The use of AI in the workplace needs human oversight to ensure that California businesses are not operated by robo bosses,” Senator McNerney said in a statement. “AI must remain a tool controlled by humans, not the other way around.”
SB 7 comes amid employers’ widespread implementation of automated decision-making and AI technologies to conduct business and improve efficiency. While these products have benefits and are often implemented in routine ways, California is leading in regulating these technologies and their impact on workers, mainly whether they can result in unlawful employment discrimination.
Here is what California employers need to know about the new proposal.
What Would SB 7 Do?
SB 7 would clarify that employers or vendors may not use an ADS in ways that violate federal, state, or local labor laws. Employers would be prohibited from relying “primarily” on an ADS to make hiring, promotion, discipline, or termination decisions and require a “human reviewer to conduct its own investigation and compile corroborating or supporting information for the decision.”
The bill would prohibit using an ADS to conduct “predictive behavior analysis,” defined as “any system or tool that predicts or infers a worker’s behavior, beliefs, intentions, personality, emotional state, or other characteristics or behavior.” ADS would not be allowed to “inform compensation” decision unless the employer “can clearly demonstrate that any differences in compensation for substantially similar or comparable work assignments are based on cost differentials in performing the tasks involved” and the data used “was directly related to the tasks the worker was hired to perform.”
Further, employers would be prohibited from discriminating or retaliating against workers for exercising their rights under the bill.
Is Notice of an ADS Required?
This bill would add a provision to the California Labor Code requiring an employer (or vendor) to provide written notice of the presence and use of an ADS to make employment-related decisions. Specifically, the bill would require employers to maintain a list of all ADSs in use in the workplace and provide written notice to employees of the presence and use of an ADS.
If employers do use an ADS to make an employment-related decision, they would be required to provide written notice to the affected employees informing the employee: (1) that the employer or vendor used an ADS; (2) a human to contact, including evidence found and collected by a human reviewer for the decision; (3) that the worker has the right to appeal; (4) that the worker has a right to correct any errors in the input or output data; (5) a form or link to where the employee can find more information on data used; and (6) that the employer is prohibited from retaliating against the worker.
What Can Affected Employees Do?
SB 7 would allow employees affected by an employment-related decision informed by ADS to appeal the decision within thirty days. Employers would have to respond to such an appeal within fourteen days and designate a human reviewer who can look at all the evidence and has the discretion and authority to overturn the decision.
Are There Unanswered Questions?
There continues to be ambiguity around what actually constitutes an automated decision system or tool. While SB 7 excludes “a spam email filter, firewall, antivirus, software, identity and access management tools, calculator, database, dataset, or other compilation of data,” innumerable AI-driven tools might technically be deemed an ADS. Further, the notice required by the statute includes undefined information such as “[t]he logic used in the ADS, including the key parameters that affect the output of the ADS, and the type of outputs the ADS will produce.”
It is unclear whether, for example, a chatbot that directs employees to one resource over another could be swept into the bill. The bill also sets forth a wide range of impermissible uses that are vaguely defined, including, for example, a prohibition against using an ADS that “relies on individualized worker data as inputs or outputs to inform compensation, unless the employer can clearly demonstrate that any differences in compensation for substantially similar or comparable work assignments are based on cost differentials in performing the tasks involved, or that the data was directly related to the tasks the worker was hired to perform.”
Does this mean that an employer is subject to the rule if it seeks the assistance of an AI tool to create a compensation scheme if the information used includes what employees are being paid? These and many other questions loom large.
Are There Other AI Proposals?
SB 7 is one of many AI proposals in California. The concern with SB 7 and many other bills is whether they have adequately considered competing proposed rules and regulations. In May 2025, the California Civil Rights Department (CRD) advanced new comprehensive regulations concerning employers, which define automated decision-making and other essential terms in ways that overlap with SB 7. The CRD, the agency responsible for enforcing California’s privacy laws, is undertaking a similar effort.
Moreover, California is one of a handful of states and jurisdictions, including Colorado, Illinois, and New York City, that are scrutinizing how these technologies impact workers and employer processes. With the Trump administration shifting federal policy toward removing barriers to the development of AI technology, states and local jurisdictions are expected to continue to look at this issue.
Next Steps
SB 7 is still in its early stages, and amendments could be made to the legislation as it progresses through the state legislature. Notably, California Governor Gavin Newsom has expressed concerns with potential overregulation. In September 2024, Governor Newsom vetoed an AI safety bill that would have required developers of large AI models to avoid causing critical harm and would have given whistleblower protections for employees to come forward with safety concerns.
Additionally, federal lawmakers are considering measures to stop states from regulating AI. The U.S. House of Representatives, as part of its recent budget measure, H.R. 1, approved a ten-year moratorium prohibiting states and local jurisdictions from enforcing any laws or regulations “limiting, restricting, or otherwise regulating artificial intelligence models.” However, the U.S. Senate is reportedly considering a watered-down measure that would condition jurisdictions’ ability to receive part of a $500 million allocation of federal funding for AI infrastructure on pausing any AI regulations.
Ogletree Deakins’ Technology Practice Group will continue to monitor developments and will provide updates on the California and Technology blogs as additional information becomes available.
Follow and Subscribe
LinkedIn | Instagram | Webinars | Podcasts
| 2025-06-12T00:00:00 |
https://ogletree.com/insights-resources/blog-posts/californias-no-robo-bosses-bill-could-restrict-employers-use-of-ai/
|
[
{
"date": "2025/06/12",
"position": 21,
"query": "AI employers"
}
] |
|
Surprise! Employers are using AI to interview you
|
Surprise! Employers are using AI to interview you
|
https://www.computerworld.com
|
[
"More This Author",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img",
"Height Auto Max-Width",
"Vertical-Align Bottom .Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow .Wp-Block-Co-Authors-Plus-Avatar",
"Vertical-Align Middle .Wp-Block-Co-Authors-Plus-Avatar Is .Alignleft .Alignright"
] |
One in five US and UK employers now use generative AI tools for initial candidate interviews, making AI-based hiring mainstream.
|
One-in-five employers in the US and the U.K. now use generative AI (genAI) tools to interview candidates, according to the results of a TestGorilla survey of 1,084 organizations in the two countries. TestGorilla, an Amsterdam-based pre-employment testing platform company, found that these kinds of hiring tools are no longer experimental, they are embedded in everyday HR and hiring operations.
Twenty-one percent of organizations in the US and 20% of those in the UK use genAI to conduct at least initial interviews with prospective hires, TestGorilla’s State of Skills-Based Hiring 2025 report showed.
Organizations are now refocusing on quality of hiring — and the use of genAI to aid in those efforts, according to TestGorilla and others.
| 2025-06-12T00:00:00 |
https://www.computerworld.com/article/4005351/surprise-employers-are-using-ai-to-interview-you.html
|
[
{
"date": "2025/06/12",
"position": 24,
"query": "AI employers"
}
] |
|
AI and Jobs Part 1: The (Job) Sky Is Falling. No, Seriously
|
AI and Jobs Part 1: The (Job) Sky Is Falling. No, Seriously — Connecting the ...
|
https://www.boneconnector.com
|
[
"Leslie Boney"
] |
AI could wipe out half of all entry-level white collar jobs, particularly in technology, finance, law and consulting, and could boost overall unemployment to ...
|
· Already-hired entry-level workers aren’t safe either. LinkedIn’s “chief economic opportunity officer,” Aneesh Raman noted that AI is breaking “the bottom rungs of the career ladder – junior software developers…junior paralegals and first-year law firm associates” as well as young retail associates and customer service reps.
· And it’s not just entry-level workers. Mark Zuckerberg told Joe Rogan in January that by the end of this year “we at Meta, as well as the other companies that are basically working on this, are going to have an AI that can effectively be a sort of mid-level engineer that you have at our company that can write code.” Not long after that Meta made an announcement: it’s cutting its workforce by 5%.
· Microsoft is cutting about 3% of its workforce, many of them engineers.
· Wal-Mart has cut 1500 corporate jobs in anticipation of the shift ahead.
· Crowdstrike, a cybersecurity firm, is cutting 5% of its workforce, noting we are at “a market and technology inflection point, with AI reshaping every industry.”
So far the big changes are coming in technical areas, but as technology spreads, it won’t stop there. A San Francisco company called Mechanize isn’t the least bit shy about sharing its ambition: “We’ll only truly know we’ve succeeded once we’ve created A.I. systems capable of taking on nearly every responsibility a human could carry out at a computer, the company wrote in a recent blog post. “Our goal is to automate all work,” says 29-year-old Tamay Besiroglu, one of the company’s co-founders, told the New York Times.
“We want to get to a fully-automated economy, and make that happen as fast as possible.”
Besiroglu and his other co-founders disagree only on how fast that is -- 10, 20 or 30 years?
OK big deep breath. Let’s admit it is part of the culture of AI co-founders seeking huge amounts of cash and computing power is to sell big stories, to “fake it till you make it,” to overpromise, then underdeliver. So let’s say that there’s only a 50% chance that Amodei is right about half of entry level jobs going away in 1-5 years and there’s only a 10% chance that Mechanize is right that all jobs involving a computer will be automated in 10-30 years. Either way, it’s completely irresponsible for us not to be having big, ugly, ongoing, thoughtful discussions about what the future of work will look like.
So who’s thinking about the implications of the job change that is coming?
Not the Trump administration: they’re focused on making sure the US wins the AI race with China, and the “Big Beautiful Bill” in its current form actually prohibits any state from putting any restrictions on AI. Their perspective: we need to win first, figure out how to deal with the consequences later. But Trump’s own supporters warn that they ignore the jobs issue at their own peril: as Steve Bannon noted recently on his “War Room” podcast, “We have to get ahead of this or we’re going to have mass unemployment, particularly among entry-level people under 30.” MAGA activist Charlie Kirk echoed that idea: “What you are going to see is one of the most dramatic job displacements, and it’s going to be a top issue in the 2028 campaign.” Mass unemployment will be politically inconvenient.
Not the folks at Mechanize or most AI companies. They’re focused on building ever-smarter, ever-more-powerful AI that enables them to hit their goals of a fully-automated workplace. In an interview with the New York Times, co-founder Matthew Barnett made clear why he thinks the tradeoff was worth it.
“If society as a whole becomes much wealthier, then I think that just outweighs the downsides of people losing their jobs.” Mr. Barnett said.
| 2025-06-12T00:00:00 |
https://www.boneconnector.com/writings/work-ai-entrylevel
|
[
{
"date": "2025/06/12",
"position": 67,
"query": "AI employers"
},
{
"date": "2025/06/12",
"position": 41,
"query": "ChatGPT employment impact"
},
{
"date": "2025/06/12",
"position": 90,
"query": "future of work AI"
}
] |
|
Why AI companies don't want journalism to exist
|
Why AI companies don’t want journalism to exist
|
https://open.spotify.com
|
[] |
In this week's episode of Media Confidential, Alan and Lionel are joined by Karen Hao, journalist and author of Empire of AI.Karen talks about being banned ...
|
In this week's episode of Media Confidential, Alan and Lionel are joined by Karen Hao, journalist and author of Empire of AI.Karen talks about being banned from returning to OpenAI, after being embedded there to write a profile of the company in its early days.She charts the rise of AI companies and the three discuss why journalists and newsrooms should be wary of making deals with “a company or industry that fundamentally doesn't want you to exist”.Karen’s book ‘Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI’ is available now Hosted on Acast. See acast.com/privacy for more information.
| 2025-06-12T00:00:00 |
https://open.spotify.com/episode/2T4roQrTLRUkjAHNOpd2Ut
|
[
{
"date": "2025/06/12",
"position": 64,
"query": "AI journalism"
}
] |
|
Google's Genesis AI: Revolutionizing Journalism or ...
|
Google's Genesis AI: Revolutionizing Journalism or Threatening Its Integrity?
|
https://opentools.ai
|
[] |
Google is taking journalists on new journeys with Genesis, an AI tool designed to assist in news writing. While some hail its potential to boost ...
|
Mixed Reactions from News Organizations
The unveiling of Google's AI tool "Genesis" to the world has resulted in a flurry of reactions from various sectors of the news industry. According to a report by The Wall Street Journal, the tool aims to aid journalists by automating certain routine tasks, potentially enhancing efficiency. This innovation, however, has been met with a spectrum of emotions. On one hand, some news organizations view it as a groundbreaking development that could revolutionize how news is created, offering journalists the freedom to focus on more complex reporting tasks. On the other hand, the tool has raised alarms about the possible erosion of journalistic quality and integrity, with fears that the reliance on AI might lead to superficial reporting or even misinformation. These mixed reactions underscore the apprehensions surrounding the evolving relationship between technology and traditional journalism [source].
| 2025-06-12T00:00:00 |
https://opentools.ai/news/googles-genesis-ai-revolutionizing-journalism-or-threatening-its-integrity
|
[
{
"date": "2025/06/12",
"position": 67,
"query": "AI journalism"
},
{
"date": "2025/06/12",
"position": 54,
"query": "artificial intelligence journalism"
}
] |
|
Adobe Newsroom
|
Adobe Newsroom
|
https://news.adobe.com
|
[] |
Adobe Firefly Revolutionizes Creative Ideation with New Mobile App, Multimedia Moodboarding and Expanded AI Models ... media content creators, video pros, and ...
|
CNBC
“AI is a massive tailwind in each one of those businesses - Document Cloud and Creative Cloud - both with respect to attracting a whole new set of customers as well as the ability to have additional value and therefore being able to monetize.”
Read more
| 2025-06-12T00:00:00 |
https://news.adobe.com/
|
[
{
"date": "2025/06/12",
"position": 86,
"query": "AI journalism"
}
] |
|
Maybe AI Will Replace Your Job, but Such Predictions Are ...
|
Maybe AI Will Replace Your Job, but Such Predictions Are Hard to Make
|
https://www.aei.org
|
[
"Sarah Jeddy",
"James Pethokoukis"
] |
But his forecast of 10–20 percent unemployment deserves scrutiny, not panic. ... AI-driven employment collapse ahead. Their latest 10-year forecast shows ...
|
Anthropic boss Dario Amodei’s buzzy warning that artificial intelligence could vaporize half of entry-level white-collar jobs within five years makes for gripping headlines. But his forecast of 10–20 percent unemployment deserves scrutiny, not panic. Worries of a robopocalypse for the labor market should take comfort from a new Dallas Federal Reserve study. Researchers there offer evidence that Silicon Valley’s latest doomsday prediction is more likely hysteria than economic history in the making.
In the recent analysis “Will AI replace your job? Perhaps not in the next decade,” economists Mark A. Wynne and Lillian Derr examine what happened to jobs that experts deemed “computerizable” a decade ago. Their findings should humble today’s gloomy AI prophets.
Nearly half of American jobs faced obsolescence by machines, according to a widely referenced 2013 Oxford University study in which economists meticulously assessed the computerization risk of 700 occupations. They famously concluded that 47 percent of America’s workforce was vulnerable to its silicon competitors. Tax preparers, insurance underwriters, and data entry clerks were supposedly on the chopping block.
Yet a decade later, the correlation between computerization risk and actual job losses? From the paper:
… we looked at whether changes in the growth rate of occupations from 2003–13 to 2013–23 were correlated with Frey and Osborne’s measures of computerizability. … Again, there is no correlation between an occupation’s change in growth rate and its susceptibility to computerization … [pointing] to the idea that Frey and Osborne’s computerizability concerns did not have as significant an effect on the workforce as perhaps anticipated.
Fast forward to 2025: AI anxiety has shifted to teachers, architects, and political scientists, according to a new study that attempts to identify occupations most vulnerable to automation from generative AI. Only telemarketers appear on both death lists.
Yet the Bureau of Labor Statistics—whose employment projections “have historically been fairly accurate, according to the Dallas Fed researchers—sees no AI-driven employment collapse ahead. Their latest 10-year forecast shows many AI-exposed occupations actually growing. Statisticians, despite being prime AI targets, are projected to thrive.
True, the BLS assumes “technological progress will be in line with historical experience.” They’re not modeling a scenario where AI achieves superintelligence capable of replacing all remote work, much less super-capable robots handling physical tasks. If AI truly breaks out and moves to full human capabilities or beyond, all bets are off (although I still think there will be lots for human workers to do). But that’s precisely why the Fed study matters. It shows how consistently we’ve overestimated technology’s disruptive power.
From the report’s conclusion:
There is very little evidence of artificial intelligence taking away jobs on a large scale to date. Correlation between AI exposure and the projections of job growth or decline over the next decade remains low. Furthermore, just 10 years ago, our concerns about which jobs were at risk were quite different from the ones we are concerned about today, demonstrating that such considerations evolve. Many jobs once feared to be at risk did not end up showing major decline in employment data. AI is such a rapidly changing field, we do not know much about its ability to one day have a large overall workforce impact, especially as current studies are mainly speculative. However, like the many technological changes that came before it, AI is a tool. Though rapid improvements in AI capabilities could lead to large workforce effects, over the next decade that worry can be tempered by the current data and the fact that concerns about technological unemployment are not new and rarely come to pass as first anticipated.
So for now, best to stick with the lessons of history, which are clear. Steam engines didn’t create permanent mass unemployment. Computers didn’t eliminate half of jobs. ATMs didn’t end bank tellers. Each technology transformed work rather than destroying it. AI will likely follow suit, augmenting human capabilities rather than replacing humans wholesale. The Dallas Fed’s analysis offers a sobering reminder: When Silicon Valley predicts a job market disruption, bet on evolution, not extinction.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://www.aei.org/economics/maybe-ai-will-replace-your-job-but-such-predictions-are-hard-to-make/
|
[
{
"date": "2025/06/12",
"position": 6,
"query": "AI unemployment rate"
},
{
"date": "2025/06/12",
"position": 15,
"query": "job automation statistics"
}
] |
Is ChatGPT Safe for Business? 8 Security Risks & ...
|
Is ChatGPT Safe for Business? 8 Security Risks & Compliance Guide 2025
|
https://www.metomic.io
|
[] |
When employees input sensitive data to ChatGPT, they may not consider the ChatGPT privacy implications when seeking quick solutions to business problems.
|
Is ChatGPT Safe for Business? 8 Security Risks & Compliance Guide 2025
TL;DR
ChatGPT security risks are significant for businesses, with major credential exposures on dark web markets while the platform processes over 1 billion daily queries. Most organizations lack proper ChatGPT security visibility and controls, creating substantial vulnerabilities. New AI regulations like the EU AI Act are creating compliance requirements with penalties up to €35 million, with key provisions taking effect in 2025.
Are ChatGPT Security Risks a Major Threat to Businesses in 2025?
With artificial intelligence adoption accelerating across enterprises—now used by 92% of Fortune 500 companies—ChatGPT security concerns have become more critical than ever. As we move through 2025, security teams face unprecedented ChatGPT risks, confronting AI security threats they may never have encountered before.
ChatGPT continues to be used by employees worldwide, with over 800 million weekly active users processing more than 1 billion queries daily. While it provides almost-instant answers and productivity benefits, the ChatGPT security implications have become far more complex in the enterprise environment.
How Serious Are ChatGPT Security Threats in 2025?
ChatGPT security risks have evolved significantly since its initial release. Recent studies show that 69% of organizations cite AI-powered data leaks as their top security concern in 2025, yet nearly 47% have no AI-specific security controls in place.
Understanding ChatGPT Data Security Risks
The primary ChatGPT security threats come from the information employees input into the system. When employees input sensitive data to ChatGPT, they may not consider the ChatGPT privacy implications when seeking quick solutions to business problems.
According to updated research, sensitive data still makes up 11% of employee ChatGPT inputs, but the types of data being shared have expanded to include:
Traditional PII and PHI
Proprietary source code (as demonstrated in the Samsung ChatGPT incident)
Internal meeting notes and strategic documents
Customer data for "analysis" purposes
Financial projections and business intelligence
Copying and pasting sensitive company documents into ChatGPT has become increasingly common, with employees often unaware of ChatGPT GDPR risks under new AI regulations.
What Are the 8 Biggest ChatGPT Security Risks in 2025?
1. ChatGPT Data Leakage and Retention
The most significant ChatGPT security risk involves employees sharing sensitive data through the platform. While OpenAI has implemented stronger data protection measures, ChatGPT retains chat history for at least 30 days and can use input information to improve its services.
New in 2025: Enterprise ChatGPT versions offer improved data handling, but the default consumer version still poses significant ChatGPT business risks.
2. ChatGPT Account Security and Unauthorized Access
A significant ChatGPT security breach resulted in over 225,000 OpenAI credentials exposed on the dark web, stolen by various infostealer malware, with LummaC2 being the most prevalent. When unauthorized users gain access to ChatGPT accounts, they can view complete chat history, including any sensitive business data shared with the AI tool.
3. ChatGPT Data Transmission Vulnerabilities
ChatGPT security vulnerabilities during data transmission pose significant risks. Sensitive information shared with ChatGPT could be intercepted during transmission, giving malicious actors opportunities to misuse business data or intellectual property.
Researchers discovered CVE-2024-27564 (CVSS 6.5) in ChatGPT infrastructure, with 35% of analyzed organizations at risk due to misconfigurations in security systems.
4. AI-Generated Misinformation and ChatGPT Deepfakes
ChatGPT security concerns now include sophisticated AI-generated content risks. In 2025, cybersecurity researchers observe that AI-generated phishing emails are more grammatically accurate and convincing, making ChatGPT-powered social engineering attacks harder to detect.
5. ChatGPT-Enabled Social Engineering Attacks
Bad actors now use ChatGPT to create highly convincing email copy and messages that imitate specific individuals within organizations. Recent research shows ChatGPT phishing attacks are more convincing, and AI is used to craft deepfake voice scams, with 2025 predictions warning of AI-driven phishing kits bypassing multi-factor authentication.
6. ChatGPT Prompt Injection Attacks
Prompt injection represents a new category of ChatGPT security threats where malicious actors craft prompts designed to trick ChatGPT into revealing sensitive information or bypassing safety guardrails. Research shows that by prompting ChatGPT to repeat specific words indefinitely, attackers could extract verbatim memorized training examples, including personal identifiable information and proprietary content.
7. Shadow ChatGPT Usage and Unauthorized AI Tools
"Shadow ChatGPT"—unauthorized or unmonitored ChatGPT usage within enterprises—affects nearly 64% of organizations that lack ChatGPT visibility. This creates significant blind spots for security teams managing ChatGPT business risks.
8. ChatGPT Compliance and Regulatory Violations
New ChatGPT regulations like the EU AI Act create significant compliance requirements with prohibitions starting February 2, 2025, and full ChatGPT compliance required by August 2026. California's updated CCPA now treats ChatGPT-generated data as personal data. 55% of organizations are unprepared for AI regulatory compliance, risking substantial fines and reputational damage from ChatGPT non-compliance.
What Can We Learn from Real-World ChatGPT Security Incidents?
Samsung ChatGPT Security Incident (2023): Engineers from Samsung's semiconductor division inadvertently leaked confidential company information through ChatGPT while debugging source code. According to a company-wide survey conducted by Samsung, 65% of respondents expressed apprehension regarding ChatGPT security risks associated with generative AI services.
Recent ChatGPT Data Exposures (2024-2025): Multiple significant ChatGPT security incidents occurred, including a bug in the Redis open-source library used by ChatGPT that allowed certain users to view the titles and first messages of other users' conversations.
Is ChatGPT Actually Safe for Business Use Right Now?
The answer regarding ChatGPT business safety in 2025 is nuanced. While ChatGPT itself has implemented stronger security measures, including enhanced encryption, regular security audits, bug bounty programs, and improved transparency policies, the primary ChatGPT risks come from how organizations and employees use the tool, particularly without proper governance frameworks.
Current ChatGPT threat assessment: There are confirmed dangers associated with sharing sensitive data in unsecured AI environments, including risks of data breaches, reputational damage, and financial losses. The National Cyber Security Centre continues to warn that AI and Large Language Models could help cybercriminals write more sophisticated malware and conduct more convincing phishing attacks.
What Do the New 2025 AI Regulations Mean for Your Business?
EU AI Act ChatGPT Compliance Requirements
The EU AI Act ChatGPT compliance categorizes AI applications by risk level, from prohibited uses to minimal risk categories. High-risk ChatGPT applications in sectors like law enforcement and employment face stricter compliance standards. Non-compliance with the EU AI Act will result in maximum financial penalties of up to EUR 35 million or 7 percent of worldwide annual turnover, whichever is higher.
US State-Level ChatGPT Regulations
California's updated CCPA ChatGPT compliance now treats ChatGPT-generated data as personal data, while other states are introducing their own AI regulations, creating a complex ChatGPT compliance landscape.
ChatGPT Regulatory Preparedness
52% of leaders admit uncertainty about navigating ChatGPT regulations, making compliance a critical business risk for 2025. Only 18% of organizations have an enterprise-wide council authorized to make decisions on responsible AI governance.
How Can You Secure ChatGPT in Your Organization?
1. Implement ChatGPT Governance and Security Policies
Establish a ChatGPT governance council with representatives from IT, legal, compliance, and risk management
with representatives from IT, legal, compliance, and risk management Develop a codified ChatGPT security policy outlining acceptable use and security protocols
outlining acceptable use and security protocols Create role-specific ChatGPT training addressing unique departmental risks
2. Deploy ChatGPT Security Controls
Implement ChatGPT Data Loss Prevention (DLP) solutions designed for AI interactions
designed for AI interactions Use enterprise ChatGPT versions with enhanced security features (OpenAI Enterprise, Microsoft Azure OpenAI)
with enhanced security features (OpenAI Enterprise, Microsoft Azure OpenAI) Deploy AI-driven security solutions to detect suspicious ChatGPT patterns and high-risk prompts
3. ChatGPT Employee Security Training
Updated for 2025: Conduct regular ChatGPT security training sessions covering:
Recognition of sensitive information types
Techniques for sanitizing ChatGPT prompts before submission
Understanding of ChatGPT-specific threats like prompt injection
Awareness of new ChatGPT regulatory requirements
4. Implement ChatGPT Technical Safeguards
Zero Trust architecture with strict verification for all ChatGPT interactions
with strict verification for all ChatGPT interactions Multi-factor authentication for all ChatGPT tool access
for all ChatGPT tool access Network monitoring for unusual ChatGPT-related behaviors
for unusual ChatGPT-related behaviors Content filtering to prevent harmful or sensitive data sharing through ChatGPT
5. Establish ChatGPT Data Handling Policies
Never share customer data through public ChatGPT tools
through public ChatGPT tools Use anonymized examples or fictional scenarios instead of real data in ChatGPT
or fictional scenarios instead of real data in ChatGPT Implement approval processes for ChatGPT use in sensitive contexts
for ChatGPT use in sensitive contexts Define consequences for ChatGPT policy violations
6. Continuous ChatGPT Security Monitoring and Assessment
Conduct regular ChatGPT risk assessments aligned with frameworks like NIST AI RMF
aligned with frameworks like NIST AI RMF Implement behavioral analytics to detect unauthorized ChatGPT manipulation
to detect unauthorized ChatGPT manipulation Maintain AI Bill of Materials (AIBOM) for ChatGPT supply chain transparency
for ChatGPT supply chain transparency Establish incident response plans specific to ChatGPT security events
Where Is AI Security Heading in 2025 and Beyond?
Key ChatGPT security trends shaping 2025 and beyond:
Increased ChatGPT regulatory scrutiny with global AI governance frameworks
with global AI governance frameworks Rise of ChatGPT-enabled cyberthreats requiring new defensive strategies
requiring new defensive strategies Growing emphasis on ChatGPT transparency and explainable AI systems
and explainable AI systems Integration of ChatGPT security into existing cybersecurity frameworks
What Are the Key Takeaways for 2025?
Bottom Line: While ChatGPT and similar AI tools offer tremendous productivity benefits, the ChatGPT security landscape has become significantly more complex. Organizations must balance innovation with ChatGPT security through:
Proactive ChatGPT governance rather than reactive policies Employee education on evolving ChatGPT threats Technical controls specifically designed for ChatGPT interactions ChatGPT regulatory compliance preparation for expanding AI laws Continuous monitoring of ChatGPT usage across the organization
The organizations that succeed in 2025 will be those that treat ChatGPT security not as a barrier to innovation, but as an enabler of responsible AI adoption that builds trust with customers and stakeholders while protecting valuable business assets.
Ready to Secure Your AI Usage?
Don't let ChatGPT security risks compromise your business. Metomic's advanced AI Data Security Solution provides the visibility and control needed to safely harness AI productivity while protecting sensitive data.
Schedule a demo today to see how Metomic can help you:
Detect and prevent sensitive data sharing
Maintain compliance with evolving regulations
Build a comprehensive AI security strategy
Download our comprehensive ChatGPT Security Guide for detailed implementation strategies and risk assessment templates.
| 2025-06-12T00:00:00 |
https://www.metomic.io/resource-centre/is-chatgpt-a-security-risk-to-your-business
|
[
{
"date": "2025/06/12",
"position": 81,
"query": "ChatGPT employment impact"
}
] |
|
Hacks/Hackers launches new lab to empower newsrooms ...
|
Hacks/Hackers launches new lab to empower newsrooms to build AI tools
|
https://www.hackshackers.com
|
[] |
Hacks/Hackers' Newsroom AI Lab transforms information sharing for independent journalists, equipping lean newsrooms with the skills to adopt AI responsibly. Our ...
|
Hacks/Hackers is launching a Newsroom AI Lab to support smaller newsrooms in evaluating, adopting and implementing large language models and other recent technologies, supported by a $300,000 grant from the Patrick J. McGovern Foundation . The Hacks/Hackers Newsroom AI Lab will build lasting technical capacity in participating newsrooms through hands-on collaboration, structured technical support and development of new AI tools and templates designed specifically for journalism that can be used by any newsroom.
“Small newsrooms are the voice of communities everywhere, providing a platform for local stories to be heard. Hacks/Hackers’ Newsroom AI Lab transforms information sharing for independent journalists, equipping lean newsrooms with the skills to adopt AI responsibly. Our partnership with Hacks/Hackers reflects our commitment to ensuring small newsrooms can continue to be trusted sources of information, deepening connections within the audiences they serve,” said Vilas Dhar, President, Patrick J. McGovern Foundation.
Hacks/Hackers Newsroom AI Lab participants will be guided by technical advisors Jake Kara and Paige Moody , who bring years of experience working closely with reporters, editors and product teams to build tools that solve real newsroom problems, most recently at The Washington Post. Kara spent over a decade as a data reporter and editor before becoming a software engineer full time, motivated to find and build the tools he wished were available in the newsrooms he worked in. Before leading the Reporting Tools engineering team at The Post, Moody wrangeld data at Mapbox, a provider of custom online maps, to enable visualization and analysis of complex geospatial information.
“The Newsroom AI Lab isn’t about quick hacks or one-off tools — it’s about building lasting muscle within newsrooms to navigate the fast-moving world of AI,” said Paige Moody. “We’re here to help teams gain confidence in asking the right questions, scoping smart experiments, and managing technical projects with clarity and purpose. Just as importantly, we will empower newsrooms to identify when AI isn’t an appropriate part of the solution. Newsroom partners will walk away not just with a prototype, but with the skills and frameworks to keep experimenting, evaluating and building long after the lab concludes.”
“Participating Newsroom AI Lab partners should be curious about if and how AI can fit responsibly into journalism’s mission, and also should be able to focus on a project that allows experimentation, learning and big thinking,” said Jake Kara. “We’re looking for smaller newsrooms that want to thoughtfully and critically engage with AI to adapt to and harness its potential. That also includes learning to identify when AI tools are or are not the right tool for the job."
Over the course of a year, Hacks/Hackers technical advisors will guide small cohorts of newsrooms, with each newsroom focusing on its own AI-powered project. Newsroom AI Lab advisors will help identify a challenge or opportunity where AI might play a role in a sustainable solution, and then collaboratively scope and execute a prototype tailored to their unique workflows, team and audience. The Newsroom AI Lab will also create a public “Implementation Playbook” that highlights prototyped tools and approaches that can be adapted by others. Additionally, the Lab will produce a “build-vs-buy” evaluation framework to help newsrooms assess when to adopt off-the-shelf solutions vs develop their own.
“We want to enable journalists to supercharge their work with new technologies like large language models, especially smaller newsrooms where this can have an outsized impact in how they provide information and connect with their communities” said Burt Herman, Hacks/Hackers co-founder, who, together with Hacks/Hackers Strategic Advisor Paul Cheung, will manage the Newsroom AI Lab. “While the fast-paced, continuous evolution of these technologies is both exciting and daunting, we can use AI itself to help these newsrooms adapt and leverage emerging technologies. This will also support AI developing in ways to augment human abilities in alignment with democratic ideals.”
"The misuse of AI damages audience trust and wastes chances to better inform local communities," said Paul Cheung. "The Hacks/Hackers Newsroom AI Lab, with support from the Patrick J. McGovern Foundation, will help smaller newsrooms experiment with AI tools that match their resources and goals."
Criteria for participating in the Hacks/Hackers News AI Lab includes:
Small-to-medium-sized newsroom, ideally independently operated
Demonstrated need and interest in AI/technical project development
Capacity to participate in program and decision-making
Comfortable with public facing documentation of partnership work
Work with the Newsroom AI Lab
The Hacks/Hackers Newsroom AI Lab expects to onboard its first cohort of newsrooms this summer. If your news organization is interested in working with the lab, please fill out this form.
For more information about Hacks/Hackers, contact: Burt Herman , [email protected]
The Patrick J. McGovern Foundation (PJMF) is a philanthropic organization dedicated to advancing artificial intelligence and data science solutions to create a thriving, equitable, and sustainable future for all. PJMF works in partnership with public, private, and social institutions to drive progress on our most pressing challenges, including digital health, climate change, broad digital access, and data maturity in the social sector.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://www.hackshackers.com/hacks-hackers-launches-new-lab-to-empower-newsrooms-to-build-ai-tools/
|
[
{
"date": "2025/06/12",
"position": 81,
"query": "artificial intelligence journalism"
}
] |
SAG-AFTRA Strikes Back: Files ULP Over AI Darth Vader ...
|
SAG-AFTRA Strikes Back: Files ULP Over AI Darth Vader in Fortnite
|
https://www.laborrelationsupdate.com
|
[
"Michael Lebowich",
"Joshua Fox",
"Shanice Z. Smith-Banks",
"June",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img"
] |
... AI to replace union-covered voice work without negotiation. This case highlights the growing tension in entertainment over AI's role in replicating or ...
|
On May 19, 2025, the Screen Actors Guild‐American Federation of Television and Radio Artists (“SAG-AFTRA”)—the union representing actors, voice artists, and other media professionals—filed an unfair labor practice charge against Llama Productions, a subsidiary of Epic Games, over the use of an AI-generated voice for Darth Vader in Fortnite. The heart of the dispute isn’t the technology itself, but the process: SAG-AFTRA alleges that Llama Productions unilaterally changed employment terms by deploying AI-generated voices without notifying or bargaining with the union.
The controversy centers on the iconic Darth Vader character, whose voice in Fortnite is now generated by an AI model trained on the late James Earl Jones’ voice. Although Jones reportedly signed over the rights to his voice before his death in 2024, SAG-AFTRA argues that Epic Games sidestepped its collective-bargaining obligations by using AI to replace union-covered voice work without negotiation.
This case highlights the growing tension in entertainment over AI’s role in replicating or replacing human performers, especially for legacy characters and posthumous performances. The outcome could set a major precedent for how AI is used in video games and other media, shaping the future of labor rights in the digital age.
At the core of disputes like this is the language found in collective bargaining agreements—particularly in management-rights clauses. The contractual language can either permit or restrict employers from introducing new technologies, such as AI, into the workplace. The clarity and specificity of these clauses are increasingly vital as technology continues to reshape the workplace.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://www.laborrelationsupdate.com/2025/06/sag-aftra-strikes-back-files-ulp-over-ai-darth-vader-in-fortnite/
|
[
{
"date": "2025/06/12",
"position": 26,
"query": "artificial intelligence labor union"
}
] |
Risk Management in the Modern Era of Workplace ...
|
Risk Management in the Modern Era of Workplace Generative AI
|
https://www.employeebenefitsblog.com
|
[
"Marjorie C. Soto Garcia",
"Brian Casillas",
"Rebecca L. Richard",
"Marjorie C. Soto Garcia Provides Strategic Advice To Employers Of All Sizes",
"Fortune Companies To Startups",
"On The Full Spectrum Of Labor Union",
"Organizing Matters",
"Class Action Employment Litigation",
"Workplace Compliance Issues. She Advises Organizations Across Many Industries",
"Including Healthcare"
] |
... labor union and organizing matters, class action employment litigation, and workplace compliance issues. She advises organizations across many industries ...
|
As human resources (HR) leaders plan to expand the use of generative artificial intelligence (GenAI) in the workplace, nearly a dozen states have enacted or are considering legislation to regulate its use in employment practices. Additionally, courts are seeing class actions involving alleged disparate impact discrimination and wage and hour violations related to GenAI. Implementing GenAI technologies without understanding their algorithms or data usage can expose employers to legal risks such as potential class actions based on privacy, AI regulations, and employment claims.
Read more here.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://www.employeebenefitsblog.com/2025/06/risk-management-in-the-modern-era-of-workplace-generative-ai/
|
[
{
"date": "2025/06/12",
"position": 52,
"query": "artificial intelligence labor union"
}
] |
Alex Bores - Assemblymember
|
Alex Bores - Assembly District 73
|
https://assembly.state.ny.us
|
[] |
Video ; Training and Use of AI Frontier Models ; Modernizing the Uniform Commercial Code ; AI Protections ; Requiring Exit Surveys for Resigning State Employees.
|
Training and Use of AI Frontier Models
Modernizing the Uniform Commercial Code
AI Protections
Requiring Exit Surveys for Resigning State Employees
Protect Passover Act
Recognizing AAPI Heritage Month in NY
The Use of Virtual Credit Cards by Insurers
Increasing Number of State Supreme Court Justices in New York
Safer Weapons Safer Homes Act Passes in the Assembly
Federal Workers Day of Action
Protecting Consumers from Unethical Business Practices
Bores Questions Union Representatives During Joint Budget Hearing on Labor and Workforce
Commissioners Testify During Joint Budget Hearing on Labor and Workforce
Commissioners Testify During a Joint Budget Hearing on Economic Development
Chief Administrative Judge Testifies During Joint Budget Hearing on Public Protection
Joint Budget Hearing on Public Protection
State Officials Testify at Joint Budget Hearing on Public Protection
DFS & Health Commissioners Testifiy During Joint Budget Hearing on Health
Healthcare Advocates Testify During Joint Budget Hearing on Health
Transportation Commissioner Testifies During Joint Budget Hearing on Transportation
MTA Chairman Testifies During Joint Budget Hearing on Transportation
DMV and Thruway Representatives Testify During Joint Budget Hearing on Transportation
Bores EBike Safety Bill Signed into Law
Authorizes the Legislature to Increase the Number of Justices
Enacting the Safer Weapons, Safer Homes Act
Stop Civil Discrimination Act Passes the Assembly
Restaurant Reservation Anti-Piracy Act Passes the Assembly
Requiring Limited Use Motorcycles to be Registered at Point of Sale
Bores Urges Passage of the No Cap Act
Requiring Accident Reports to Specify Where E-Bikes and E-Scooters are Involved
Safeguarding Election Integrity Provision Included in Budget Bill
Commissioners Testify During Budget Hearing on Mental Hygiene
Cloud Computing Bill Passes the Assembly
State Commissioners Testify During Budget Hearing on Economic Development
Chief Judge of Court Administration Testifies During a Joint Budget Hearing on Public Protection
DCJS And State Police Officials Testify During a Joint Budget Hearing on Public Protection
Homeland Security Commissioner and CI Officer Testifies During a Joint Budget Hearing on Public Protection
MTA Chairman Testifies During Joint Budget Hearing on Transportation
DMV Commissioner and Thruway Authority Director Testify During a Joint Budget Hearing on Transportation
Bores Questions Government Officials During Budget Hearing on Health
Healthcare Advocates Testify During Budget Hearing on Health
Bores Questions Healthcare Advocates During Budget Hearing on Health
PEF Representatives Testify During a Hearing on the Impact of Artificial Intelligence on the Workforce
AI Advocates Testify During a Hearing on the Impact of Artificial Intelligence on the Workforce
RWDSU Representative Testifies During a Hearing on the Impact of Artificial Intelligence on the Workforce
Human Resource Expert Testifies During a Hearing on the Impact of Artificial Intelligence on the Workforce
NY SAG-AFTRA Director Testifies During a Hearing on the Impact of Artificial Intelligence on the Workforce
NYCLU Representative Testifies During a Hearing on the Impact of Artificial Intelligence on the Workforce
Teamsters Representative Testifies During a Hearing on the Impact of Artificial Intelligence on the Workforce
Protecting Workers’ Intellectual Property
Increasing the Number of Judges Throughout New York State
Establishing Computer Science Education Week in NY
Compensating Franchised Motor Vehicle Dealers for Warranty Service Agreements
DFS Superintendent Testifies at Hearing on Cryptocurrency Industry in NY
NYS Attorney General's Office Testifies at Hearing on Cryptocurrency Industry in NY
Cryptocurrency Experts Testify During Hearing on Cryptocurrency Industry in NY
Public Hearing on Cryptocurrency Industry in NY
Bores Urges Passage of the No Slavery in NY Act
Addressing the Shortage of Optometrists in New York State
Changing Dates of Petition Filings
Raising the Fine for Violators of the Do Not Call Registry
Spousal Liability Coverage on Motor Vehicle Insurance
Health Commissioner Testifies During Budget Hearing on Health
Addiction Services & Support Commissioner Testifies During Budget Hearing on Mental Hygiene
Chief Administrative Judge Testifies During a Budget Hearing on Public Protection
Joint Legislative Budget Hearing on Public Protection
MTA CEO Testifies During Budget Hearing on Transportation
Amending the NYS Constitution in Relation to Equal Protection
Welcome to the Assembly
| 2025-06-12T00:00:00 |
https://assembly.state.ny.us/mem/Alex-Bores/media/
|
[
{
"date": "2025/06/12",
"position": 76,
"query": "artificial intelligence labor union"
}
] |
|
Future of work
|
Future of work
|
https://www.techuk.org
|
[] |
Technology is powering a growth in flexible work across the economy, whilst emerging technologies such as robotics and AI are set to become common place.
|
Sue Daley OBE
Sue leads techUK's Technology and Innovation work.
This includes work programmes on cloud, data protection, data analytics, AI, digital ethics, Digital Identity and Internet of Things as well as emerging and transformative technologies and innovation policy.
In 2025, Sue was honoured with an Order of the British Empire (OBE) for services to the Technology Industry in the New Year Honours List.
She has been recognised as one of the most influential people in UK tech by Computer Weekly's UKtech50 Longlist and in 2021 was inducted into the Computer Weekly Most Influential Women in UK Tech Hall of Fame.
A key influencer in driving forward the data agenda in the UK, Sue was co-chair of the UK government's National Data Strategy Forum until July 2024. As well as being recognised in the UK's Big Data 100 and the Global Top 100 Data Visionaries for 2020 Sue has also been shortlisted for the Milton Keynes Women Leaders Awards and was a judge for the Loebner Prize in AI. In addition to being a regular industry speaker on issues including AI ethics, data protection and cyber security, Sue was recently a judge for the UK Tech 50 and is a regular judge of the annual UK Cloud Awards.
Prior to joining techUK in January 2015 Sue was responsible for Symantec's Government Relations in the UK and Ireland. She has spoken at events including the UK-China Internet Forum in Beijing, UN IGF and European RSA on issues ranging from data usage and privacy, cloud computing and online child safety. Before joining Symantec, Sue was senior policy advisor at the Confederation of British Industry (CBI). Sue has an BA degree on History and American Studies from Leeds University and a Masters Degree on International Relations and Diplomacy from the University of Birmingham. Sue is a keen sportswoman and in 2016 achieved a lifelong ambition to swim the English Channel.
| 2025-06-12T00:00:00 |
https://www.techuk.org/shaping-policy/future-of-work.html
|
[
{
"date": "2025/06/12",
"position": 85,
"query": "future of work AI"
}
] |
|
Workflow Automation Statistics & Trends in 2025
|
Workflow Automation Statistics & Trends in 2025
|
https://www.cflowapps.com
|
[] |
The robotic process automation market was valued at 1.4 billion USD in 2019 and is expected to grow at a CAGR of 40% between 2020 and 2027. Enterprise companies ...
|
Brief Introduction to Workflow Automation
Workflow automation is taking over key business processes, this doesn’t come as a surprise for those who know the benefits of workflow automation. Modern business workflows are full of tedious tasks that are time-consuming but do not require brainpower. Here is where workflow automation comes into the picture. Workflow automation helps employees focus on important and strategic activities, rather than repetitive mundane work.
What is workflow automation?
Workflow automation is an approach to standardizing the flow of tasks, information, and documents across work-related functions to perform independently as per predefined business rules. Successful implementation of workflow automation for key everyday tasks improves the overall productivity of the organization. There are several statistics illustrating automation benefits.
Process workflows are a series of activities executed to complete a task. Automating these workflows shifts the performance of these activities from humans to software programs. The first step in automation is to identify all the steps that make up the job. The next step is to create the rules and logic that decide the sequence of these tasks. The automation software programs predefined business logic and rules. The business rules and logic are in the form of If-Then statements that act like instructions that tell the program what actions to undertake and how to move from one task to another.
All types of business functions made up of repetitive tasks can be effectively automated. In addition to improving productivity, workflow automation frees employees from performing mundane tasks. They get more time to focus on strategic project-related activities, while automation software takes care of repetitive tasks.
The main steps in workflow automation are listed below:
Identify the processes that would benefit from automation
Map the processes by breaking them into steps/tasks
Define business goals clearly
Choose the workflow automation software wisely
Implement the software for the chosen processes
Train employees about the new changes
Establish KPI metrics for measuring the success of implementation and adjust automated workflows to meet the goals
Enable continuous improvement by gathering employee feedback combined with KPIs
Cflow is a workflow automation platform that enables quick and easy automation of key business workflows. Just anyone can use the visual form builder in Cflow to create automated workflows to streamline the process. Procurement, finance and accounting, HR, and Admin workflows can be effectively automated by Cflow.
Also Read: top three ways to improve work performance
Why is it Important for a Business?
Automated workflows offer several benefits to the business. From improving productivity to easing out day-to-day operations, there are several ways in which process automation can improve business results.
Why is workflow automation important for the business?
Workflow automation increases job efficiency and allows staff to focus on non-automated activities
Automation of process workflows improves visibility across various tasks in the process
Workflow automation increases productivity by automating repetitive and redundant tasks
Automating process workflows increases the precision of the process. The inaccuracies and inconsistencies in manual processes are eliminated through automation.
The accountability of all the stakeholders is increased by automating the process. Each stakeholder is clear about their role in the automated workflow.
Work satisfaction of employees increases considerably by automating labor-intensive and mundane tasks.
Automated client-facing processes improve the quality of customer service.
Tasks in an automated process communicate seamlessly with each other. The exchange of information between tasks is seamless in an automated process.
Choosing to automate key process workflows is a major business decision. The software you choose to automate your processes is key to the success of implementation. Cflow is a complete workflow automation solution that works for any type and scale of business.
Workflow Automation Statistics to Consider for 2023
The decision to automate business processes must be supported by relevant automation statistics that throw light on the performance, benefits, and industry-wise relevance of the automation software. We have gathered relevant workflow statistics that help businesses make the right decisions regarding workflow automation implementation.
Automation statistics have been categorized into the rise of workflow automation; automation benefits statistics; industry-wise automation data; and challenges in workflow automation. These statistics help businesses see why automation is the way forward in several industries. The sources for these statistics are highly reliable and trusted companies across the globe.
According to Signavio, 62% of the organizations have modeled up to 25% of their businesses, but a meager 2% have all their processes modeled.
Business process automation (BPA) and business process management are closely related terms but with different meanings. BPM is an organizational methodology for effective and efficient management of processes.
BPM can be used to improve the outcome of process automation by identifying and eliminating flawed routines from manual processes. Although BPM is a business practice, organizations implement it by using specialized BPM tools to model their processes and optimize, automate, and measure them.
BPA is both a complementary and interdependent process that automates recurring tasks that require decision-making. Robotic process automation and digital process automation are the two main forms of automation that are taking off.
Rise of Workflow Automation
These statistics focus on how workflow automation has been adopted by various industries. The percentage of businesses that use automation, the percentage of business processes that are automated, and the adoption of workflow automation, are some of the areas of focus of these statistics.
31% of businesses have fully automated at least one key business function.
13% of the surveyed organizations say that they are implementing intelligent automation solutions at scale; 23% are implementing, and 37% are piloting automation
41% of the respondents say that they are using automation extensively across multiple functions.
94% of the business professionals at enterprises prefer a unified automation platform for all their applications and building workflow automation rather than relying on disparate systems.
The market for workflow automation and related technologies is growing at the rate of 20% per year and is poised to reach 5 billion USD by the year 2024.
Among various workflow automation technologies, robotic process automation (RPA) is at the top with 31% adoption and AI is the least at 18%.
AI-driven automation is gaining traction among companies with 74% of the current users saying that their organization will increase their AI investment in the next 3 years.
57% of the respondents say that their organization is in the piloting stage of workflow automation in one or more business units or functions.
38% of the respondents admit that their organizations have not taken the first step in automation. Half of these respondents plan to take up automation within the next year.
Out of the total market revenue for RPA, the on-premises sector accounted for the largest revenue share of over 90% of the automation market in 2020.
Grandview Research valued the RPA market at 1.4 billion USD in 2019 and forecasted a market growth of 40.6% CAGR between 2020-27.
Larger organizations claim to use automation in their business (40%) than smaller organizations (25%).
The robotic process automation market was valued at 1.4 billion USD in 2019 and is expected to grow at a CAGR of 40% between 2020 and 2027. Enterprise companies have been the largest adopters of RPA.
Digital process automation (DPA) was valued at 7.8 billion USD in 2019 and is poised to reach 16 billion USD by 2025 to Morder Research.
More than 80% of the business leaders report having been speeding up work process automation and expanding the use of remote work.
43% of businesses say that they plan to reduce their workforce by adopting automation technologies.
36% of organizations are already implementing BPM software for automating workflows.
As many as 50% of business leaders say that they were planning to increase the automation of repetitive tasks within their organization.
As per Gartner, 69% of managerial work will be automated by 2024.
A Deloitte survey says that 29% of companies are planning to implement BPM software soon.
48% of organizations are working towards installing process automation solutions for managing manual tasks better.
67% of organizations are implementing BPA solutions for increasing visibility across different systems.
According to a survey by Salesforce, 47% of IT leaders claim that the greatest ROI from process automation is from the operations function.
29% of organizations intend to implement low-code workflow automation. 24% of the organizations are already implementing low-code workflow automation software.
Business process automation is a team activity that requires the contribution and involvement of each stakeholder.
45% of business teams have important roles to play in building automation.
Statistics around Impact and Benefits of Automation
One of the main reasons why companies automate their process workflows is to improve the productivity of the process. The inaccuracies and inconsistencies of manual processes are eliminated through automation. Automating repetitive steps in a process saves a considerable amount of time. In addition to improving company revenue, automation also improves employee skills and work satisfaction. These statistics ought to provide answers to companies with questions in their mind regarding the benefits of automation.
90% of executives expect their automation investments to improve the capacity of their workforce over the next 3 years.
73% of the leaders in the IT industry attribute 10-50% of time savings in performing tasks to automation.
As many as 51% of automation initiatives aim at boosting business efficiency.
57% of IT leaders say that automation technology saves 10-50% on the business costs associated with manual processing.
More than half of the respondents agree that automation minimizes human error.
42% of business leaders agree that automation accelerates the completion of repetitive tasks.
As many as 85% of business leaders believe that automation of their workload will enable them and their employees to focus more on strategic goals that are important for business success.
78% of business leaders believe that automating tasks in the organization increases the productivity of all stakeholders.
31% of business leaders believe that automation helps reduce labor costs.
Organizations that have implemented an automation solution indicated a 12% workforce capacity increase.
Companies are increasingly optimistic about automation adoption. It was expected that in 2020, the organization’s automation strategies would drive a 15% increase in its revenue.
83% of US executives and 70% of Indian executives report faster adoption of automation during the COVID pandemic when compared to other countries.
Organizations that have implemented automation have reported a cost reduction of 24%, which is up from 19% in 2019.
52% of business leaders believe that 10-30% of their daily tasks can be automated.
Process automation is the building block for digital transformation. 97% of IT decision-makers say that process automation is vital for successful digital transformation.
Artificial Intelligence and machine learning have fueled the growth of workflow automation by enabling commercially viable products and services that can be used to automate several routine business processes.
Leading SaaS solution providers have simplified the process of automating manual processes and workflows within an organization and across businesses. Cflow from Cavintek is a trusted cloud-based SAAS workflow automation platform that simplifies the automation of key business workflows. Here are some of the AI-related workflow automation statistics.
25% of organizations are currently using AI in their process automation, and 53% intend to implement it soon.
Surveyed executives in countries like China and the United Kingdom showed a preference for using AI automation to free up workers for higher-value tasks.
As many as 43% of AI high performers are led by a clear vision and strategy.
62% of organizations are using AI to support IT operations and 54% use it for improving business process efficiency.
45% of companies use AI to bring down business costs.
66% of the companies have increased their revenue by deploying AI technology.
Organization budgets for AI technology have increased by 55% year on year.
38% of the companies are implementing machine learning to reduce costs, 34% use it for improving customer experience, and 17% want to use it to increase conversion rates.
Statistics on industry-wise Adoption of Process Automation
Statistics around the adoption of automation have shown that several organizations have adopted process automation for reasons varying from improving productivity to minimizing errors to reducing labor costs.
In this section, we have gathered statistics on how each industry or business function has adopted BPA. Big data, Human resource management, Sales and Marketing, Customer Service Automation, Finance, Supply chain management, social media, Cyber Security, Insurance, Manufacturing, Advertising, and Health Industry.
Automation in Big Data
Automating Big Data helps organizations manage data better and recognize patterns in behavior and performance. Deeper insights from big data enable organizations to make informed business decisions. Here is how the automation streak in Big Data is going on:
95% of businesses need to manage unstructured data out of which 40% are required to do this regularly.
30% of organizations are investing in Big Data to modernize their IT systems, while 23% are looking at cost savings, and 20% plan to use it for accelerating business growth.
45% of the companies using big data operate at least a part of the data on the cloud.
Human Resource Management and Automation
Managing human resources in an organization can be challenging when done via manual processes. Manual processes are prone to delays, inconsistencies, and human biases. Several organizations are adopting BPA solutions to automate key HR functions like employee onboarding, pre-employment screening, background checks, etc.
45% of the HR departments are focusing on intelligent process automation over the next year.
One out of 10 business leaders agrees that hiring automation is a straightforward process.
18% of IT leaders say that the highest ROI from automation occurs in the HR department.
70% of business leaders believe that they can build more effective teams through automation.
54% of business leaders believe that automation software will bring down the need for HR staff and hiring managers.
61% of decision-makers believe that automation technology could help to hire managers to pick the right talent for the job.
56% of HR departments have increased their revenue by adopting AI technology.
10% of companies are already using AI to optimize talent management.
Automation in Sales and Marketing
Sales and marketing teams are using automation technology to improve their conversion rates, lead capture, and deal closing. Automating the buyer journey improves customer engagement and interaction. These statistics throw light on how organizations are building their sales and marketing automation strategy.
Survey respondents have credited AI-driven automation with increasing revenue in pricing, customer service analytics, and prediction of the likelihood of buying.
• 55% of global marketers plan to increase spending on overall marketing technology in the next year.
• Automation in sales boosts productivity within the department by 14.5% and brings down marketing costs by 12.2%.
• Using Salesforce, sales teams have been able to close 30% more deals, reduce the sales cycle by 18%, and save 14% of admin time.
• Sales automation in lead nurturing has shown a 200% increase in conversions for different brands.
• B2B marketers have seen an average increase of 10% in their sales pipeline by automating their sales.
• The quality of leads can be significantly improved through marketing automation.
• As many as 67% of marketing leaders are making use of marketing automation platforms.
• Marketing automation spending is expected to surpass 25 billion USD by 2023 with a growth rate of 14%.
• Sales personnel can automate offers based on browsing history to provide personalized offers. Personalized offers are known to increase sales by 20%.
• Sales personnel that use marketing automation to improve the quality of leads have been able to generate 3 times more leads every month.
• 74% of the marketers surveyed agree that the main reason why business owners and marketers use automation is to save time and costs.
• Conversions are estimated to increase by 77% by spending on marketing automation solutions.
• As many as 91% of online marketers see automation as a critical factor in the success of their marketing campaigns.
• 50% of sales time is wasted on unproductive prospecting.
• 92% of marketing automation users say that the main use of automation is to improve lead generation, increase customer retention, and lead nurturing.
• 75% of companies use marketing automation software.
• 83% of marketing departments automate social media posting, 75% automate email marketing, 58% automate social media advertising, and 36% automate social media engagement.
In automated email marketing, the welcome sequence is the most common automated email marketing technique. The second most common strategy is lead nurturing emails.
End-to-end workflow automation Build fully-customizable, no code process workflows in a jiffy. Try for Free Schedule a Demo →
Most businesses agree that automated landing pages are effective in lead generation.
• Only a meager 2% of companies are using sales automation for sales order fulfillment.
• 39% of companies automate sales prospecting.
• 36% of companies use automation to generate sales quotes.
• 18% of IT leaders say that their marketing departments are seeing the highest ROI from automation.
• More than half of the businesses surveyed claim that sales and marketing automation increases their conversion rates significantly.
• 58% of companies use chatbots for sales or marketing.
• 39% of organizations say that their marketing automation system produces higher-quality leads.
• According to a survey, 59% of Fortune 500 companies are using marketing automation and this number is steadily increasing.
• A study conducted by Omnisend revealed that omnichannel sales automation is likely to create customer retention of 90% and an increase of 250% in engagement and purchase rates.
Automation in Advertising
Google automated bidding is among the most used advertising automated software. Let us look at some statistics about automation in advertising.
• 70% of the advertisers used Google’s automated bidding software in 2019.
• A whopping 130,000 USD can be saved by businesses annually by automating advertising strategies.
• Programmatic advertising accounted for more than 80% of digital display marketing in the US in 2018.
• It is expected that by the end of 2020, 90% of mobile display advertisements will be negotiated automatically via programmatic ads.
• From 32% of automated display ads in 2016, the figure doubled to 64% in 2019.
• Most companies will be able to automate 80% of their advertising processes by 2023.
Automation in Finance
The finance and accounting function is a critical one in any business. The highest levels of accuracy and consistency are required while handling financial transactions. Automating key finance and accounting processes improves the accuracy and efficiency of transactions and data handling.
Here are some of the finance and accounting automation statistics for your reference.
• The Global Market for Accounting Software is forecasted to grow at a CAGR of 8.02% from 2018 to 2026, with the market value poised to reach 20.4 billion USD by 2026.
• According to a McKinsey survey, CEOs could save 20% of the time they spend on financial tasks via automation.
• Bot interactions with humans in the banking sector are expected to reach a 90% success rate by 2023.
• McKinsey predicted automation and AI to take up 10-25% of banking tasks.
• ICICI bank has deployed more than 750 robots to handle daily transactions and interactions with these robots have recorded 100% accuracy. Also, the bank was able to cut down on the time spent on complaint redressal about ATM cash disbursal from 12 hours to 4 hours via automation.
• 19% of automation falls under order to cash.
• Since the outbreak of COVID-19, financial services and technology sectors have adopted automation rampantly, with as many as 88% and 76% of the executives reporting increased implementation.
• More than 50% of CEOs of banking and financial organizations are focusing on simplifying their products and operations by adopting process automation.
Insurance Statistics
Insurance is another business sector that is prime for automation. Several tasks in insurance transactions require quick turnaround and verification of data. Manual execution of these tasks is a laborious task that is prone to inaccuracies. Automating tedious insurance tasks provides relief from repetitive work for insurance personnel. Some statistics explore the effectiveness of automation in insurance.
A report predicts that customer service and claims adjustment tasks will see a 16% displacement. The report also predicts that by 2030, the United States forecasts 46% of its insurance claims processing jobs to be automated.
2017 Deloitte insights predicted a loss of 22.7 million insurance jobs in the US because of automation and the creation of 13.6 new ones over the next 10 years.
In 2018, claims review was considered one of the biggest processes to be automated. 30% of the insurers were considering robotic process automation as a popular choice for automating this process.
Automation was able to bring down the quote generation time for insurance agents in California to 4 minutes as opposed to 14 days. The form that adopted this automation solution saw a 70% increase in sales.
Automation in the Manufacturing sector
The manufacturing sector is among the earliest adopters of automation. Large manufacturing units can improve the quality of products and save time and costs through automation. Some of the statistics reiterate the usefulness of automation in the manufacturing sector.
• IT and Engineering Industries account for nearly 40% of an organization’s automation.
• A McKinsey Report estimates that automating manufacturing tasks can increase annual productivity by 1.4%.
• As per a report, 749 billion work hours can be saved by automating 64% of manufacturing tasks.
• An Oxford report estimates that implementing automation in the manufacturing sector could reach 4.9 trillion USD by 2030.
• According to a report by the World Economic Forum, 42% of the time spent on manufacturing tasks will be automated by robots by 2023.
• A PwC study states that the use of automation in 3 key industries will contribute 15.7 trillion USD to the global economy.
Customer Service Automation Statistics
Automating tasks in customer service has a direct impact on customer engagement and experience. It boosts the company’s reputation and improves customer retention.
• 34% of the organizations are using guided self-service.
• 65% of customer service experts say that automating feedback gathering helps analyze customer behaviour effectively.
• 38% of customer representatives say that their organizations use chatbots to automate customer service interactions.
Automation in Healthcare
The global healthcare automation market is expected to grow at the rate of 8.415% in 7 years. Growing at this rate, this industry is expected to show profits amounting to 63 million USD by 2026.
A McKinsey report states that to match the demand for assisted care, the purchase of medical robots has risen by 50% every year. The Global Medical Robotics Market is expected to reach 20 billion USD by 2023.
Statistics around Challenges or Roadblocks to the Adoption of Workflow automation
31% of the respondents are concerned about labor displacement and job loss that could be caused by automation and artificial intelligence.
7-24% of currently employed women and 8-28% of men may need to transition to different skill sets due to changes in labour demand due to automation.
Conclusion
The automation statistics listed above are compelling enough for businesses to consider the automation of key functions of their company. The benefits of automation outnumber the challenges. Implementing a cloud workflow automation solution like Cflow enables organizations to streamline their operations, which in turn improves productivity.
Users can easily customize workflows according to the unique requirements of their business. The no-code workflow automation software creates workflows within minutes using a visual form builder. Your search for a customizable workflow solution ends with Cflow. Explore more by signing up for a free trial today.
| 2025-06-10T00:00:00 |
2025/06/10
|
https://www.cflowapps.com/workflow-automation-statistics/
|
[
{
"date": "2025/06/12",
"position": 13,
"query": "job automation statistics"
}
] |
Understanding Agentic AI in workforce management - Sona
|
Understanding Agentic AI in workforce management: Essential insights for enterprise leaders
|
https://www.getsona.com
|
[
"Cristina Maria"
] |
Machine Learning (ML) serves as the backbone for many Workforce Management (WFM) tools today, including Sona's own forecasting engine. ML excels at sifting ...
|
Artificial Intelligence (AI) is everywhere. From executive offices to employee lounges, the buzz about AI transforming the workplace is inescapable. However, for many enterprise leaders managing extensive frontline workforces, discussions about AI often seem either overly technical or frustratingly vague. You might find yourself wondering:
What exactly is Agentic AI?
Is it just another buzzword?
How does it stand apart from traditional AI, machine learning, chatbots or workflows?
📌 Addressing common misconceptions about Agentic AI
Before we proceed further, let's clear up some frequent misunderstandings:
"Agentic AI is just a chatbot."
"Agentic is another name for machine learning."
"Agentic sounds impressive, but it won't be practical for years to come."
These concerns are understandable, yet they don’t accurately portray how Agentic AI functions in real-world applications. Let’s explore why.
🗝️ Machine Learning vs. Agentic AI: The key difference
Machine Learning (ML) serves as the backbone for many Workforce Management (WFM) tools today, including Sona’s own forecasting engine. ML excels at sifting through extensive data to predict future demand, staffing requirements, or attrition risks. It's excellent at answering questions such as: "How many employees will I need next Thursday at 2 PM?"
Unlike older forecasting models, Sona's next-gen solution enhances predictions by being location-specific and responsive to real-time variables like holidays, weather changes, and, soon, promotions and local events. This enables businesses to create more accurate and flexible schedules - but that is still Machine Learning.
Whilst accurate forecasts can be incredibly powerful, they don't create value alone. That’s where Agentic AI steps in.
Agentic AI takes predictions processed via Machine Learning and acts on them. An agent doesn’t just display information to a human; it can take autonomous actions within parameters set by managers. For high-value or sensitive tasks, such as major schedule adjustments or compliance-critical modifications, managers are prompted to review and approve suggested actions before execution.
Examples of agent actions requiring manager approval (high-risk or sensitive tasks):
Proactively filling open shifts when it detects staffing gaps.
Adjusting labour distribution across locations in response to real-time demand.
Alerting managers to compliance risks before they occur.
Proposing optimised schedules that balance costs, compliance, and employee satisfaction.
Responding to queries related to allergen policies or regulatory-sensitive information.
Examples of agent actions managers can automate (low-risk, routine tasks):
Answering HR policy questions about holiday leave and remaining entitlements.
Providing reminders about upcoming shift start times or required certifications.
🔄 In summary: Machine Learning informs. Agents act.
💬 Why Agentic AI is more than a chatbot
Many associate AI with conversational interfaces, and chatbots have indeed added value in various sectors. However, it's useful to see chatbots as just one possible application of agentic technology. While chatbots generally wait for human prompts, agents take it further: they monitor, detect, plan, and execute tasks proactively on your behalf, always within the guardrails set beforehand.
🔄 In simple terms: Chatbots are spoken to. Agents start the conversation.
❓ How does Agentic AI differ from workflows?
A common question among enterprise leaders is: "Isn't this just another form of workflow automation?"
While both workflows and Agentic AI aim to improve efficiency, they operate very differently.
Workflows are rule-based, static processes designed for predictable, repetitive tasks. For instance, a workflow might automatically route an approved time-off request to payroll or notify a manager when a shift swap is submitted. These workflows follow predefined paths and don’t adapt when circumstances change.
Agentic AI, in contrast, is dynamic and decision-oriented. Agents can analyse real-time data, evaluate multiple factors (like staffing shortages, compliance rules, or weather disruptions), and generate context-specific solutions. Crucially, agents can handle unforeseen situations that aren’t explicitly pre-programmed.
🔄 To summarise: workflows follow fixed sequences; agents analyse, plan, and act based on real-time conditions.
👎 The limitations of legacy WFM systems
Most traditional WFM platforms were designed for an era before the widespread availability of real-time data and AI. While some have incorporated predictive analytics or chat interfaces, they fundamentally remain highly manual, rules-based systems that depend on:
Static templates
Manager intervention
Lengthy back-and-forth communication between teams
Even when they include ML-powered forecasting, these systems expect managers to manually translate forecasts into schedules, fill gaps, and manage exceptions.
Agentic AI completely transforms this model. Instead of simply making forecasts "available," it immediately puts them to use. Managers become supervisors instead of manual operators.
It’s like having an operator who never sleeps and never misses a signal - essentially, your best manager is now in every setting.
Practical applications: Agentic AI at work
🏨 Hospitality:
A large hotel chain uses Agentic AI to dynamically adjust schedules based on unexpected events (e.g., a large conference or canceled group booking). The agent reallocates (subject to manager approval where necessary) housekeeping and front desk shifts to maintain service standards without overspending on labour.
💚 Social Care:
A residential care provider uses Agentic AI to ensure every shift meets required qualifications. When a team lead calls in sick, the agent automatically sources a compliant replacement, maintaining safe staffing levels without risking care quality. The agent’s interconnected data awareness ensures it won't suggest filling a shift with someone lacking the right skills or unnecessarily assign a higher-cost senior staff member when a qualified junior can cover the role, optimising both compliance and labour costs.
👉 Starting with Agentic AI
If you’re interested but unsure how to adopt Agentic AI, here are some a few initial steps:
1. Evaluate your data readiness
Agents depend on clean, accurate workforce data (scheduling, time & attendance, workforce metrics, etc.). The better your data quality, the more effective the agent.
2. Select an AI-native WFM platform
Retrofitting older platforms for Agentic AI is challenging. Look for solutions that are designed from the ground up to support autonomous agents.
3. Begin with high-impact use cases
Focus initial deployments on areas where agentic automation offers quick returns on investment: shift-filling, overtime management, compliance monitoring, HR queries, and real-time schedule optimisation.
4. Train and empower managers
Agentic AI changes the manager’s role from operator to supervisor. Investing in training ensures your managers trust the system and can switch seamlessly to focusing on high-value tasks.
📘 Glossary: Key Agentic AI terms
Agent: An autonomous software entity that monitors, plans, and takes actions on your behalf.
Autonomy: The agent’s ability to make decisions and act independently within defined guardrails.
Guardrails: Pre-set rules and permissions that control what actions the agent can take.
Forecasting: The use of machine learning to predict future staffing needs based on historical data.
Orchestration: The agent’s ability to coordinate multiple actions across systems to achieve optimal outcomes.
Supervisor Role: The new manager function overseeing and guiding the agent's actions.
Responsibilities: A set of operator-defined goals that guide the agent's actions. Unlike priorities, which follow a strict order, responsibilities allow the agent to consider multiple objectives simultaneously. This enables the agent to process complex trade-offs and present managers with a single, optimised recommendation—rather than dozens of suggestions the manager would still need to sift through.
Interconnected Systems Awareness: The agent operates across all interconnected systems (such as Sona Scheduling, HR, and Payroll), consuming data from each. This ensures that any recommendations fully respect qualifications, certifications, cost considerations, and policies. For example, it won't suggest filling a shift with an unqualified employee or assign a higher-cost senior employee when a qualified junior team member is available, helping optimise labour costs automatically.
🎯 Conclusion: The future of WFM is Agentic
For organisations with large, complex frontline workforces, Agentic AI is not a distant vision — it’s happening today. By combining the predictive power of machine learning with the autonomous execution of agents, enterprise leaders can unlock new levels of efficiency, resilience, and employee satisfaction.
At Sona, we believe Agentic AI represents the next major evolution in workforce management, building on the foundations of digitisation. Organisations that explore it early are positioning themselves for stronger efficiency, resilience, and long-term advantage.
🚀 See Sona’s Agentic AI in action
Curious how this could work for your business? Visit our dedicated Agentic AI page to learn more, book a demo, and experience the future of of the frontline firsthand.
| 2025-06-12T00:00:00 |
https://www.getsona.com/blog/understanding-agentic-ai-in-workforce-management
|
[
{
"date": "2025/06/12",
"position": 8,
"query": "machine learning workforce"
}
] |
|
The AI Revolution at Work: Beyond Replacement to ...
|
The AI Revolution at Work: Beyond Replacement to Transformation
|
https://www.insightsfromanalytics.com
|
[] |
Recent research from Harvard Business Review reveals a troubling paradox at the heart of AI adoption. While generative AI demonstrably boosts task ...
|
How AI is reshaping the workplace faster than we imagined — and what it means for your career and organization.
Three years ago, I confidently proclaimed, "AI will not replace people; people who use AI will replace people who do not use AI." Today, I need to refine that statement. The reality unfolding before us is more nuanced and more urgent than I initially anticipated.
AI is replacing people in specific roles, such as customer support, data entry, and basic content creation. However, what's fascinating is that those same displaced workers can leverage AI to accomplish far more than they ever could in their previous roles. The question isn't whether AI will change work, it's whether you and your organization will adapt fast enough to thrive in this transformation.
The Productivity Paradox: More Efficient, Less Motivated
Recent research from Harvard Business Review reveals a troubling paradox at the heart of AI adoption. While generative AI demonstrably boosts task performance, making emails warmer, performance reviews more analytical, and content creation faster, it comes with an unexpected psychological cost.
Workers who collaborate with AI on one task and then transition to unassisted work report an 11% drop in intrinsic motivation and a 20% increase in boredom. This isn't a minor side effect; it's a fundamental shift in how we experience work itself.
The culprit? AI removes the cognitively demanding aspects of tasks—the very elements that make work engaging and personally fulfilling. When AI generates the bulk of a performance review, the human feels disconnected from the creative process. When we return to solo work, everything feels slower, more tedious, less inspiring.
The New Workplace Reality: Exponential Technology Meets Human Uncertainty
We're living through what technology leaders are calling "exponential IT"—a period where multiple transformative technologies are converging simultaneously. Agentic AI, which can set strategic goals and autonomously determine the steps to achieve them, represents the next leap beyond simple generative AI tools.
Consider this real-world example: A software developer recently used AI tools to build a complex insurance underwriting system in four months for $400,000—a project that traditionally would have required two teams, a year, and $2-3 million. The developer employed Claude for specialized software development, Cursor AI as a coding co-pilot, and Devin AI as a virtual junior developer. While he went to lunch, AI agents continued coding, sending updates via Slack when they needed input or hit obstacles.
This isn't science fiction—it's happening now. One development team logged 14,000 lines of code in a single day, with 13,800 lines written by AI and only 200 by humans.
The Skills That Matter: Beyond Technical Expertise
In this rapidly evolving landscape, the most valuable skill isn't Python programming or data analysis—it's adaptability. Organizations need employees who can integrate AI outputs, analyze patterns AI might miss, and critically evaluate AI-generated content.
The new premium skills include:
Integration and Analysis: While AI excels at pattern recognition, humans excel at finding unexpected connections and challenging AI's conclusions. AI is prone to "reward hacking"—finding the most straightforward path to meet its targets, which may not align with real-world objectives.
AI Partnership: The most productive approach treats AI as a thought partner rather than a replacement. Instead of simply asking AI to "create a budget," effective users ask, "What should I consider when building this budget? What might I be overlooking?"
Metacognitive Oversight: As AI handles routine tasks, human work increasingly requires higher-level thinking about thinking—evaluating AI outputs, questioning assumptions, and maintaining strategic oversight.
The Leadership Imperative: Navigate or Be Displaced
For leaders, the message is clear: organizations that strategically embrace AI will outperform those that don't. But this isn't just about deploying tools—it's about fundamental transformation.
The most successful companies are following a deliberate progression:
Start with transparency: Acknowledge uncertainty while communicating a clear vision for how AI will eliminate low-value work, not jobs. Engage employees in the journey: The Syntax company, with 2,500 employees, began with generative AI training before advancing to AI agents, involving employees in hackathons and innovation initiatives. Focus on augmentation first: Rather than using AI to cut costs, leading companies use it to free employees from "time confetti"—the endless small tasks and notifications that fragment productive work.
The Immediate Action Plan
Based on current technology trajectories and organizational needs, here's what individuals and companies should prioritize:
For Individuals:
Develop comfort with AI tools as thinking partners
Focus on skills that complement AI: creativity, strategic thinking, and relationship building
Practice adaptability, the ability to learn and pivot quickly as technology evolves
For Organizations:
Invest in AI literacy across the workforce, not just technical teams
Create sandbox environments where employees can experiment with AI safely
Redesign workflows to alternate between AI-assisted and independent tasks
Build governance around AI use while avoiding the trap of saying "no" to innovation
The Bottom Line: Adaptation Is Non-Negotiable
The research is clear: AI collaboration initially reduces workers' sense of control, but transitioning back to solo work restores autonomy while highlighting the contrast in efficiency and capability. The solution isn't to avoid AI—it's to thoughtfully integrate it in ways that preserve human agency and engagement.
We're not just witnessing the automation of routine tasks; we're seeing the emergence of a new form of work where humans and AI systems work in continuous collaboration. The organizations and individuals who master this collaboration first will define the competitive landscape for the next decade.
The choice is stark: evolve into an AI-augmented professional and organization, or risk being displaced by those who do. The technology exists today. The only question is whether you'll use it to transform your work before someone else uses it to replace you.
| 2025-06-12T00:00:00 |
2025/06/12
|
https://www.insightsfromanalytics.com/post/the-ai-revolution-at-work-beyond-replacement-to-transformation
|
[
{
"date": "2025/06/12",
"position": 99,
"query": "workplace AI adoption"
}
] |
New York Requiring Companies to Reveal If AI Caused Layoffs
|
New York Requiring Companies to Reveal If AI Caused Layoffs
|
https://www.entrepreneur.com
|
[
"Sherin Shibu"
] |
New York has become the first state to require companies filing WARN notices to disclose if layoffs are due to AI, robotics, ...
|
It's the first official statewide move towards understanding AI's effect on the labor market.
New York is taking an unprecedented step by asking companies to disclose whether artificial intelligence is the reason for their layoffs.
The move applies to New York State's existing Worker Adjustment and Retraining Notification (WARN) system and took effect in March, Bloomberg reported. New York is the first state in the U.S. to add the disclosure, which could help regulators understand AI's effects on the labor market.
The change takes the form of a checkbox added to a form employers fill out at least 90 days before a mass layoff or plant closure through the WARN system. Companies have to select whether "technological innovation or automation" is a reason for job cuts. If they choose that option, they are directed to a second menu where they are asked to name the specific technology responsible for layoffs, like AI or robots.
Related: Morgan Stanley Plans to Lay Off 2,000 Workers, Replacing Some with AI
New York Governor Kathy Hochul first proposed the change in her January 2025 State of the State address.
At the time of writing, no companies filing WARN notices in the state have said the layoffs were due to AI.
So far, no other states have followed New York's lead in adding the AI disclosure to their WARN notices, but some experts suggest "it signals growing concern among regulators," one CEO told Bloomberg.
Related: AI Is Dramatically Decreasing Entry-Level Hiring at Big Tech Companies, According to a New Analysis
There is increasing concern about AI's effect on the labor market. Last month, Anthropic CEO Dario Amodei predicted that AI would wipe out half of all entry-level, white-collar jobs within the next five years and cause unemployment to rise 20%.
In March, Amodei said that AI would take over all coding for software engineers within a year.
Meanwhile, Victor Lazarte, general partner at venture capital firm Benchmark, said in April that AI was "fully replacing people" in professions like law and recruitment. He predicted that AI would automate routine tasks and take over entry-level jobs.
| 2025-06-13T00:00:00 |
2025/06/13
|
https://www.entrepreneur.com/business-news/new-york-requiring-companies-to-reveal-if-ai-caused-layoffs/493267
|
[
{
"date": "2025/06/13",
"position": 84,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/13",
"position": 81,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 77,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 94,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/13",
"position": 82,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 81,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 81,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 84,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/13",
"position": 80,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 81,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 84,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/13",
"position": 63,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 82,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 96,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/13",
"position": 56,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 71,
"query": "AI layoffs"
},
{
"date": "2025/06/13",
"position": 88,
"query": "artificial intelligence layoffs"
}
] |
Can AI Graphic Design Tools Compete with Human Creativity?
|
Can AI Graphic Design Tools Compete with Human Creativity?
|
https://blog.1invites.com
|
[
"Divanshi Harkhani"
] |
The answer is no. AI is changing how graphic design works, but it's not taking over the role of designers. Instead, AI tools help with tasks that can be ...
|
Technology is changing quickly, and AI is a big part of it. You’ve probably seen AI-created art, logos, and marketing materials and wondered if AI is replacing human designers. The answer is no. AI is changing how graphic design works, but it’s not taking over the role of designers. Instead, AI tools help with tasks that can be automated, letting designers focus more on creativity and making unique designs.
Why AI in Graphic Design Matters
Graphic design has always been a mix of creativity and strategy. With AI stepping in, the industry is changing rapidly. AI-powered tools can generate logos, suggest fonts, and create branding kits in minutes. But does this mean AI is replacing designers? Not quite. Instead, it’s helping them by automating repetitive tasks, allowing them to focus on creativity and storytelling.
The Growing Role of AI in Design
AI should be available to everyone so they can make high-quality designs. Want a quick logo or social media post? It can be designed instantly. This helps some startups and small businesses that cannot afford full-time designers. But, AI works on patterns. It doesn’t think outside the box like a human. It increases workflow speed but can never fill in what a skilled designer brings in terms of depth and originality.
The Challenges Designers Face
Designers often face the challenge of tight deadlines, repetitive tasks, and creative burnout. AI can help by automating routine work such as resizing images or generating layouts, freeing up time for more innovative projects. However, AI lacks human intuition—it cannot interpret vague client feedback or craft unique, emotionally resonant designs. That’s where human creativity still shines.
What Businesses Need
The demand of businesses for affordable, quality designs is now satisfied by AI with its quick and cost-effective means. Although graphics generated through AI are helpful, they can rarely be unique. Brands seeking individuality, though, need human creativity to make their designs unique and evoke emotion.
How AI Graphic Design Tools Work
Graphic design is a field driven by human creativity, artistic intuition, and experience. However, with the emergence of artificial intelligence, design processes have become faster, more accessible, and highly automated. AI-powered tools are transforming the way people create visuals, making design more efficient while still requiring a human touch for originality and emotion.
Automation vs. Creativity
AI graphic design tools use machine learning and algorithms to analyze existing designs, identify patterns, and generate new visuals. They can rapidly produce aesthetically pleasing designs but lack human creativity, intuition, or emotional depth.
AI is great at mimicking styles, optimizing layouts, and enhancing efficiency but can’t push the creative boundaries or create deeply meaningful artwork. It is a very powerful assistant in design processes but still relies on human input for originality and artistic vision.
Key Features of AI Design Tools
Most AI-powered design platforms offer many features to assist users in making professional-looking graphics with minimal effort. Some of the most common tools include:
Template-Based Design: AI-driven platforms provide ready-made layouts that users can customize to fit their needs. These templates make it easy to create flyers, social media posts, and marketing materials without requiring advanced design skills.
AI-driven platforms provide ready-made layouts that users can customize to fit their needs. These templates make it easy to create flyers, social media posts, and marketing materials without requiring advanced design skills. Logo & Branding Generators: Many AI Design Generators are used to create logos based on specific user inputs on color schemes, fonts, or industry-specific words. While they look cool, sometimes their originality in design can miss the touch that a human brings.
Many AI Design Generators are used to create logos based on specific user inputs on color schemes, fonts, or industry-specific words. While they look cool, sometimes their originality in design can miss the touch that a human brings. Photo Editing & Enhancement: AI-driven editing tools can make changes to lighting quickly, enhance image quality, remove background, and insert filters, which eliminates time-consuming processes.
AI-driven editing tools can make changes to lighting quickly, enhance image quality, remove background, and insert filters, which eliminates time-consuming processes. Color and Font Suggestions: AI analyses all the trends around design and suggests color schemes and fonts for use that just work well, so the final composition will turn out great visually.
Where AI Excels
AI design tools offer several key advantages, particularly when it comes to efficiency and accessibility:
Speed & Efficiency: It can make graphics in just seconds, thus providing an optimal requirement for companies and content creators that have to produce good visual images promptly. In today’s digital landscape, where strong social signals, such as likes, shares, and comments, can significantly boost a brand’s visibility, having eye-catching graphics ready in moments is a major advantage. Whether it’s a post on social media a marketing flyer or a banner of an ad, AI works considerably faster than its human counterparts.
It can make graphics in just seconds, thus providing an optimal requirement for companies and content creators that have to produce good visual images promptly. In today’s digital landscape, where strong social signals, such as likes, shares, and comments, can significantly boost a brand’s visibility, having eye-catching graphics ready in moments is a major advantage. Whether it’s a post on social media a marketing flyer or a banner of an ad, AI works considerably faster than its human counterparts. Automating Repetitive Tasks: Resizing images, making adjustments to layouts to fit various AI design platforms, and keeping brand consistency through multiple designs is very monotonous. AI automates such tasks so designers can focus more on the creative aspects of their work.
Resizing images, making adjustments to layouts to fit various AI design platforms, and keeping brand consistency through multiple designs is very monotonous. AI automates such tasks so designers can focus more on the creative aspects of their work. Providing Design Inspiration: Even pro designers sometimes need a creative nudge. AI will even suggest design elements, layouts, and styles that may never have crossed the designer’s mind and inspire new ideas.
Can AI Replace Human Graphic Designers?
AI is changing graphic design, making it faster and more accessible. But while AI can automate tasks and generate visuals, it does not have the creativity, emotional intelligence, or strategic thinking that human designers bring. Instead of replacing designers, AI is a powerful tool to augment their work.
The Limitations of AI
Despite its great capabilities, AI has some limitations that make it impossible to fully replace human designers:
Lack of Original Thought: AI generates visuals based on patterns it has learned from existing designs. It can replicate styles and suggest layouts, but it cannot create something truly original or groundbreaking. Every AI-generated design is based on past data, limiting its ability to think outside the box.
AI generates visuals based on patterns it has learned from existing designs. It can replicate styles and suggest layouts, but it cannot create something truly original or groundbreaking. Every AI-generated design is based on past data, limiting its ability to think outside the box. Limited Emotional Intelligence: Great design goes beyond aesthetics to storytelling and even evoking the emotions of others. Human designers understand cultural contexts, audience psychologies, and brand narratives which enable them to craft designs to resonate with other people on another level. Artificial intelligence lacks emotional insight making its designs lack a personal and generic feel.
Great design goes beyond aesthetics to storytelling and even evoking the emotions of others. Human designers understand cultural contexts, audience psychologies, and brand narratives which enable them to craft designs to resonate with other people on another level. Artificial intelligence lacks emotional insight making its designs lack a personal and generic feel. Customization Challenges: AI tools are awesome for creating templates and basic designs, but fail in cases of deeper customization. A human designer can refine, tweak, and adapt designs in ways an AI simply cannot, ensuring that each piece is tailored to a brand’s unique identity and goals.
Where Humans Outshine AI
AI may accelerate a design workflow but has qualities, no matter how minute, that are irreplaceable in a creative process as such:
Conceptual Thinking: A good designer doesn’t make images; instead, they conceive identities for a brand, plan strategies, and even come up with creative ideas in line with the business needs. AI generates designs, yet lacks strategic thought behind building one single brand entity.
A good designer doesn’t make images; instead, they conceive identities for a brand, plan strategies, and even come up with creative ideas in line with the business needs. AI generates designs, yet lacks strategic thought behind building one single brand entity. Emotional Connection: People bond to stories, emotions, and meaning behind a design. Whether it’s a logo, a website, or an advertisement, human designers infuse their work with storytelling elements that AI cannot replicate.
People bond to stories, emotions, and meaning behind a design. Whether it’s a logo, a website, or an advertisement, human designers infuse their work with storytelling elements that AI cannot replicate. Customization & Adaptability: Human designers, unlike AI, can adapt, refine, and experiment infinitely because they operate outside set parameters. They can interpret client feedback, make creative decisions on the fly, and adapt their designs to unique needs and evolving trends.
When to Use AI Graphic Design Tools
AI-powered design tools have made it easier than ever to create professional-quality visuals in minutes. However, though they give speed and convenience, not every design may be right for the job. Knowing when to use the AI and when to use a human can help you get the best results for your project.
Best Use Cases for AI
AI design tools are ideal in situations where efficiency, affordability, and simplicity are key. Some of the best use cases include:
Small Businesses & Startups: Most small businesses and startups have a limited budget for design work. AI tools enable them to quickly create logos, social media posts, flyers, and marketing materials without hiring a professional designer.
Most small businesses and startups have a limited budget for design work. AI tools enable them to quickly create logos, social media posts, flyers, and marketing materials without hiring a professional designer. Marketers & Social Media Managers: Content creation is always in demand for marketers and social media managers. AI tools help by generating eye-catching visuals, resizing images for different platforms, and automating repetitive design tasks, saving both time and effort.
Content creation is always in demand for marketers and social media managers. AI tools help by generating eye-catching visuals, resizing images for different platforms, and automating repetitive design tasks, saving both time and effort. Non-Designers Needing Professional Graphics: Not everyone will have the time or expertise for professional design. AI gives non-designers an easy way to create sleek and polished visuals in pre-made templates and smart recommendations for design.
AI is a great answer when speed and accessibility are more important than deep personalization or originality. But sometimes, a human designer is the better option.
When to Hire a Human Designer
Although AI can handle a lot of the design work, there are certainly limits. When a project requires a lot more emotional or complicated thought, the human designer will do better. Instead, the best AI tools help with tasks that can be automated, letting designers focus more on creativity and making unique designs. Consider hiring a designer if:
A unique brand identity : Branding is not just a logo; it involves strategic thinking, consistency, and an understanding of the company’s values and audience. A human designer can develop a brand identity that is truly unique and aligned with your vision.
: Branding is not just a logo; it involves strategic thinking, consistency, and an understanding of the company’s values and audience. A human designer can develop a brand identity that is truly unique and aligned with your vision. Highly customized visuals : the AI system functions based on templates and certain predetermined styles. Thus, customization options might be limited. For intricate designs where precision to the smallest detail matters, a professional designer can do the job and focus on delivering something unique.
: the AI system functions based on templates and certain predetermined styles. Thus, customization options might be limited. For intricate designs where precision to the smallest detail matters, a professional designer can do the job and focus on delivering something unique. Emotionally driven storytelling: Wonderful design is a human thing: it speaks directly to people. Whether it’s a campaign with a great narrative, a brand that inspires trust and loyalty, or visuals reflecting the values of a company, a human designer can create designs that resonate in ways AI can’t.
What’s the Future of AI in Graphic Design?
AI is evolving in graphic design, enhancing creativity rather than replacing designers. As AI tools advance, they’ll automate tasks and offer smart suggestions, but human creativity and storytelling will remain at the core of great design.
AI as a Collaborative Tool
Instead of being afraid of AI, designers are now seeing it as a powerful assistant that enhances their work. AI can handle many of the time-consuming, repetitive aspects of design, such as:
Resizing images for different platforms: AI can instantly adjust images for social media, websites, and print without the need for manual tweaking.
Generating design variations: AI can generate various designs with a change in color schemes, fonts, or layouts for designers to test more ideas rapidly.
Automate mundane tasks: From background removal to auto-aligning elements, AI eliminates many manual adjustments, freeing up designers to focus on creativity.
By embracing AI, designers can make their work more efficient, free from repetitive tasks, and concentrate on the conceptual and artistic sides of design, which are indeed the parts that require human intuition and emotion.
The Evolution of AI in Design
AI-powered design tools are expected to become even more sophisticated in the coming years. Some key developments to watch for include:
More Advanced Automation: AI will continue improving at automating design elements, allowing for faster, smarter, and more intuitive workflows. Expect AI tools to generate designs that better align with user preferences based on past projects and real-time feedback.
Better Personalization: Future AI design tools may offer hyper-personalized recommendations based on industry trends, audience preferences, and brand awareness, making AI-assisted designs feel more unique and relevant.
Integration with Other Creative Tools: AI is increasingly integrated into current design tools, including DesignWiz and 1Invites, so it becomes a seamless part of the workflow, unlike a separate tool.
Enhanced Collaboration: That is where AI Design Platforms come in, allowing for more effective collaboration between designers and clients through generative design drafts based on briefs, intelligent feedback, and even predicting possible changes a client might request.
Should You Use an AI Graphic Design Tool?
When deciding whether to use an AI graphic design tool, evaluating your specific needs and the type of design project you’re working on is important. AI graphic design software is powerful and convenient, but they are best suited for certain situations. To determine if they’re the right fit for you, consider these stages:
Awareness Stage: Understanding Your Needs
Before jumping into AI design tools, take a step back and assess what you need for your project. Ask yourself the following questions:
Do you need quick, simple designs? If you need something fast and straightforward, an AI tool for Design can generate designs in minutes, making them ideal for social media posts, flyers, and ads.
If you need something fast and straightforward, an AI tool for Design can generate designs in minutes, making them ideal for social media posts, flyers, and ads. Is budget a primary concern? AI tools are cost-effective compared to hiring a professional designer, making them a great option for businesses or individuals working with limited budgets.
AI tools are cost-effective compared to hiring a professional designer, making them a great option for businesses or individuals working with limited budgets. Do you prefer hands-on customization? If you like customizing every design aspect, you may find AI tools more limiting. While they offer templates and quick edits, a professional designer provides a level of refinement and personal touch that AI can’t match.
Consideration Stage: Exploring Options
Once you’ve established your needs, it’s time to explore the available AI-powered design tools. Several platforms offer user-friendly features, depending on what you’re looking for:
1Invites – A specialized tool designed for creating stunning digital invitations, event graphics, and personalized greeting cards. Acting as an AI greeting card generator, 1Invites helps users craft elegant and visually appealing invitations and greeting cards with ease, eliminating the need for complex design skills.
– A specialized tool designed for creating stunning digital invitations, event graphics, and personalized greeting cards. Acting as an AI greeting card generator, 1Invites helps users craft elegant and visually appealing invitations and greeting cards with ease, eliminating the need for complex design skills. DesignWiz – AI Flyer Generator helps users create professional flyers quickly and effortlessly. By simply entering your purpose or theme, the AI generates a high-quality flyer in seconds. Plus, with seamless integration with ChatGPT, you can enhance your flyer design even further using the DesignWiz AI flyer Generator, making it a convenient and powerful tool for all your flyer creation needs.
– AI Flyer Generator helps users create professional flyers quickly and effortlessly. By simply entering your purpose or theme, the AI generates a high-quality flyer in seconds. Plus, with seamless integration with ChatGPT, you can enhance your flyer design even further using the DesignWiz AI flyer Generator, making it a convenient and powerful tool for all your flyer creation needs. Canva – Known for its intuitive drag-and-drop interface, Canva is perfect for non-designers who need to create polished visuals quickly.
Each platform has its strengths, so it’s worth exploring them to find the one that aligns best with your goals.
Decision Stage: Making the Right Choice
For businesses or individuals with little design experience, AI tools for Design can be a game-changer. They provide an accessible way to create visually appealing content without the need for advanced design skills. If your goal is to quickly produce visuals for marketing or social media, AI tools are a great option.
However, if you’re looking for a unique, highly customized, and memorable brand identity, investing in a human designer may still be the best choice. Human designers bring originality, strategic thinking, and a personal touch to the creative process, ensuring that your brand stands out in a crowded marketplace.
Ultimately, the right choice depends on your specific design needs, budget, and the level of customization required. AI tools are perfect for efficiency and accessibility, but when it comes to crafting a lasting and distinct brand, human creativity remains irreplaceable.
Conclusion
AI is revolutionizing graphic design by making it faster, more accessible, and highly efficient, but it cannot replace human creativity. While tools like DesignWiz and 1Invites streamline workflows, automate repetitive tasks, and provide design inspiration, they lack originality, emotional intelligence, and strategic thinking. Instead of replacing designers, AI serves as a powerful assistant, allowing professionals to focus on storytelling, branding, and unique visual identities. The future of design lies in balancing AI’s efficiency with human creativity, ensuring that designs are not only visually appealing but also meaningful and impactful.
| 2025-06-13T00:00:00 |
2025/06/13
|
https://blog.1invites.com/impact-of-ai-graphic-design-tools-on-designers/
|
[
{
"date": "2025/06/13",
"position": 80,
"query": "artificial intelligence graphic design"
}
] |
Chipotle CEO Scott Boatwright: AI 'Ava Cado' cut hiring time by 75%
|
Chipotle CEO says AI has cut hiring time by 75%—it ‘ensures we have the best talent that’s available’
|
https://www.cnbc.com
|
[
"Ashton Jackson"
] |
Specifically, the AI hiring platform is built for "chatting with candidates, answering their questions about Chipotle, collecting basic ...
|
Land a job interview with Chipotle and you may meet a team member named Ava Cado, who can answer your questions about the company or onboarding. She might email you if your interview goes well.
Ava isn't a real person. "Ava Cado" is the name of Chipotle's artificial intelligence hiring platform, built by a recruiting software firm called Paradox. Since its implementation last year, Chipotle has reduced its hiring time by 75%, CEO Scott Boatwright told Fortune on Monday.
"This not only helps us keep our restaurant staffed, but ensures we have the best talent that's available in the industry," Boatwright said. The hiring speed could help Chipotle open more than 300 locations this year, a new one "almost every 24 hours," he added.
Specifically, the AI hiring platform is built for "chatting with candidates, answering their questions about Chipotle, collecting basic information, scheduling interviews for hiring managers, and sending offers to candidates who are selected by managers," according to a Chipotle press release published on October 22.
Boatwright took the helm of Chipotle in November, after previously serving as its COO. His company isn't alone: 25% of U.S. organizations use artificial intelligence tools for HR-related purposes, according to a 2024 survey from the Society of Human Resource Management.
DON'T MISS: How to use AI to be more productive and successful at work
Some of the upsides seem clear: You can automate boring or rote administrative tasks, and provide job seekers with instant answers to their most commonly asked questions.
The practice may come with downsides, though. If you're a job seeker hoping to chat with a recruiter, and you're met by a chatbot that can't answer your specific question, you could walk away with a sour impression of the company, notes a Nov. 4 Indeed blog post.
AI-enabled hiring could also come with increased cybersecurity risk, if the platform you use retains interview recordings, resumes or other pieces of personal information from job candidates to help the AI learn. Companies that create hiring platforms tend to take data security pretty seriously — but any data transmitted over the internet from your organization to an AI's learning database has the potential to be exposed.
And, as Boatwright noted, using AI to grow a business means little if your product or service isn't good enough for humans to buy.
"We don't look to replace the human experience, we look to remove waste and expand or enhance the team member experience," he said, adding: "Customers come to Chipotle for the food."
Want to up your AI skills and be more productive? Take CNBC's new online course How to Use AI to Be More Successful at Work. Expert instructors will teach you how to get started, practical uses, tips for effective prompt-writing, and mistakes to avoid.
Plus, sign up for CNBC Make It's newsletter to get tips and tricks for success at work, with money and in life, and request to join our exclusive community on LinkedIn to connect with experts and peers.
| 2025-06-13T00:00:00 |
2025/06/13
|
https://www.cnbc.com/2025/06/13/chipotle-ceo-scott-boatwright-ai-ava-cado-cut-hiring-time-by-75-percent.html
|
[
{
"date": "2025/06/13",
"position": 42,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 32,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 26,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 36,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 24,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 26,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 36,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 28,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 26,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 38,
"query": "AI hiring"
}
] |
Chipotle: AI Hiring Platform Cuts Hiring Time by 75% | PYMNTS.com
|
Chipotle: AI Hiring Platform Cuts Hiring Time by 75%
|
https://www.pymnts.com
|
[] |
An artificial intelligence hiring platform has reportedly cut hiring time at Chipotle by 75%. The quick-service restaurant chain has been using the platform.
|
An artificial intelligence hiring platform has reportedly cut hiring time at Chipotle by 75%.
By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions .
Complete the form to unlock this article and enjoy unlimited free access to all PYMNTS content — no additional logins required.
The quick-service restaurant chain has been using the platform — which was built by recruiting software firm Paradox and dubbed “Ava Cado” by Chiptole — for a year, CNBC reported Friday (June 13).
“This not only helps us keep our restaurant staffed, but ensures we have the best talent that’s available in the industry,” Chipotle CEO Scott Boatwright told Fortune in an interview posted Monday (June 9), according to the CNBC report.
Twenty-five percent of U.S. companies use AI tools to perform tasks related to human resources (HR), the report said, citing data from the Society of Human Resource Management.
This technology enables companies to automate repetitive tasks and provide instant answers to job seekers’ frequently asked questions, according to the CNBC report.
At the same time, AI could leave a bad impression with candidates who had hoped to talk with a recruiter and could create a cybersecurity risk when companies store or transmit job candidates’ data, per the report.
When Chipotle announced in October that it was rolling out the AI hiring platform to its 3,500 restaurants, the company said the system would free its restaurants’ general managers from spending time on collecting candidates’ basic information, scheduling interviews and other administrative tasks.
“Paradox operates as if we’ve hired additional administrative support for all our restaurants, freeing up more time for managers to support team members and provide an exceptional guest experience,” Chipotle Chief Human Resources Officer Ilene Eskenazi said at the time in a press release.
When Paradox announced its acquisition of people analytics platform Eqtble in February, Paradox CEO Adam Godson said that “AI is the future of talent acquisition.”
“We’ve always believed that conversations are the UI of the future, and we see an opportunity to create a data foundation that powers people intelligence in a conversational, frictionless way,” Godson said at the time in a press release.
Many experts see AI as a valuable tool for employers that could lead to faster hiring processes and potentially reduced costs in talent acquisition, PYMNTS reported in August.
“Recruiters use AI-based HR tech to find the best candidates, and job seekers can benefit too,” Cliff Jurkiewicz, vice president of global strategy at Phenom, told PYMNTS at the time.
| 2025-06-13T00:00:00 |
2025/06/13
|
https://www.pymnts.com/artificial-intelligence-2/2025/chipotle-ai-hiring-platform-cuts-hiring-time-by-75percent/
|
[
{
"date": "2025/06/13",
"position": 46,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 46,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 46,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 40,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 49,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 43,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 41,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 45,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 41,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 42,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 76,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 79,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 79,
"query": "AI hiring"
}
] |
Top 10 Best AI Recruiting Platforms (2025) - Hiring Guide - Qureos
|
Top 10 Best AI Recruiting Platforms (2025)
|
https://www.qureos.com
|
[
"Nawal Malik"
] |
Top 10 AI Recruiting Platforms · 1. Iris by Qureos · 2. HireVue · 3. Manatal · 4. Workable · 5. LinkedIn Recruiter · 6. FactoHR · 7.
|
Key take aways
The next decade will see a rise in AI intervention in HR practices; one of which is hiring. More than ever, hiring managers are equipping AI tools to streamline the recruitment process. Optimizing the employer and candidate relationship has never been easier!
Aside from improving the candidate experience, AI recruitment tools can attract and assess the perfect candidates for every company. They save time, prevent overlooking candidates, and thoroughly scan an applicant’s skills and achievements. It makes tracking HR metrics easier, faster, and more efficient.
What is AI Recruiting Software?
AI recruiting software leverages artificial intelligence, machine learning, and natural language processing to automate and enhance various aspects of the hiring process. These platforms can source candidates, screen resumes, conduct initial interviews, predict candidate success, and eliminate bias in hiring decisions.
Key AI Technologies in Recruitment
Agentic AI Systems : Autonomous agents that interact and make decisions (identified by Gartner as the future of AI recruiting)
: Autonomous agents that interact and make decisions (identified by Gartner as the future of AI recruiting) Generative AI : Creates job descriptions, emails, and content at scale
: Creates job descriptions, emails, and content at scale Natural Language Processing : Enables human-like communication with candidates
: Enables human-like communication with candidates Machine Learning : Continuously improves matching accuracy over time
: Continuously improves matching accuracy over time Predictive Analytics: Forecasts candidate success and retention
Top 10 AI Recruiting Platforms
1. Iris by Qureos
💵Pricing: Custom pricing based on volume
Qureos is a leading AI-powered recruitment and talent intelligence platform with revolutionary 24-second candidate sourcing, shortlisting, and outreach capabilities, making it ideal for high-velocity recruiting needs.
⚒️Key Features:
24-second automated sourcing and shortlisting
Applicant Tracking System (ATS) integration
Multilingual capabilities (20+ languages)
Access to 100+ job boards
Customized job descriptions generation
Personalized outreach and follow-ups
Adaptability and smart learning capabilities
AI video interviews
✨Use Cases: Recruitment agencies, global hiring, fast-growing companies, multilingual markets
Also Read: How AI is Transforming the Hiring Process
2. HireVue
💵Pricing: Custom pricing (typically $15,000-$50,000+ annually)
HireVue leads the market with their comprehensive talent experience platform, featuring advanced video interviewing capabilities and bias reduction technologies.
⚒️Key Features:
AI-powered video interviews with bias reduction
Find My Fit: AI assessment for role matching (2025 HR Tech Conference winner)
Natural language recognition for transcript-based hiring
Blind interview capabilities eliminating visual bias
Predictive analytics for candidate success
✨Use Cases: Large enterprises, high-volume hiring, bias-sensitive industries
Also Read: How To Reduce Time To Hire in 10 Ways
3. Manatal
💵Pricing: $15-$35 per user per month
Manatal is an AI-powered platform that streamlines hiring through automation and intelligent candidate recommendations with user-friendly interface design.
⚒️Key Features:
Drag-and-drop recruitment pipeline customization
AI Engine for candidate profile enrichment
Social media and public platform data integration
Sponsored job advertising campaign management
Candidate scoring based on job requirements
Multi-platform job posting capabilities
Consolidated recruitment progress view
✨Use Cases: Recruitment agencies, headhunters, mid-size companies, international recruiting
4. Workable
💵Pricing: $149-$599 per month (up to 50 employees), enterprise pricing available
Workable is an all-in-one recruitment software offering access to 400 million candidate profiles with AI-powered recommendations and multi-platform posting.
⚒️Key Features:
Access to 400+ million candidate profiles
AI-powered top 50 candidate recommendations
Job posting across 200+ platforms and sites
Referral system with active job seeker network
Mobile-friendly candidate applications
Multi-language support for global operations
Easy interview scheduling and engagement tools
✨Use Cases: SMBs, startups, companies needing quick deployment, multi-location hiring
5. LinkedIn Recruiter
💵Pricing: $1,680 per month per seat (annual subscription)
Linkedin Recruiter is a professional global platform providing access to 400+ million profiles with advanced targeting and market data integration.
⚒️Key Features:
Access to 400+ million professional profiles
Talent Hub ATS integration
Advanced candidate targeting and filtering
Market data reporting and analytics
Candidate pipeline creation on single landing page
InMail messaging capabilities
Skills and experience-based search
✨Use Cases: Executive search, professional roles, B2B companies, network-based recruiting
6. FactoHR
factoHR offers a comprehensive HR Management System (HRMS) that truly simplifies and automates those essential HR processes that keep businesses running smoothly.
It's designed to be intuitive, fully customizable, and packed with every feature HR teams need to manage their workforce efficiently from attendance and leave to payroll and performance. It's truly the perfect solution for making HR workflows smoother for businesses of all sizes.
What's more, factoHR seamlessly integrates with your existing tools, be it payroll systems, accounting software, or even biometric devices. This advanced HR platform is built to provide a remarkable experience for both employees and management, making complex tasks feel effortless with smart, integrated features.
Ultimately, factoHR delivers flexible, tailored HR solutions that cover the entire employee lifecycle – from simplified onboarding to insightful performance reviews. It transforms routine HR tasks into simple, precise processes, empowering companies to foster a positive workplace culture and ensure every employee feels valued and supported every step of the way.
7. iSmartRecruit
💵Pricing: $99-$299 per month based on features
iSmartRecruit offers highly reliable Applicant Tracking System & Recruiting CRM software that streamlines hiring with affordable, customizable solutions.
⚒️Key Features:
Affordable and fully customizable platform
Integration with multiple job boards
Gmail and Outlook integration
Social platform connectivity
Fast-growing AI-based applicant tracking
Flexible recruiting solutions for entire cycle
Enhanced candidate experience focus
✨Use Cases: Small & mid-size businesses, staffing agencies, budget-conscious companies
8. Recooty
💵Pricing: $29-$199 per month based on features
Recooty is an AI-powered recruitment software designed to simplify hiring through intelligent automation and easy-to-use tools.
⚒️Key Features:
Automated job posting and resume parsing
AI-powered candidate ranking system
Bias-free job description generator
AI salary estimator for competitive offers
Real-time team collaboration tools
Recruitment analytics and performance insights
One-click job posting capabilities
✨Use Cases: Companies prioritizing automation, data-driven hiring, collaborative recruiting teams
9. Fetcher
💵Pricing: Custom pricing based on roles and volume
Fetcher.ai is an AI-driven recruitment platform specializing in automated candidate sourcing and outreach with advanced machine learning algorithms.
⚒️Key Features:
Advanced machine learning for candidate identification
Automated personalized outreach campaigns
Company culture and job requirement alignment
Robust analytics and reporting tools
Seamless ATS platform integration
Customizable communication templates
Data-driven recruitment insights
✨Use Cases: Tech recruiting, startup hiring, passive candidate sourcing, continuous talent pipeline
10. EightFold
💵Pricing: Large enterprises focusing on workforce planning
Eightfold is a cutting-edge talent intelligence platform leveraging AI to transform workforce management through comprehensive data analysis.
⚒️Key Features:
Analysis of millions of employee data points
Skills, experience, and potential-based matching
Internal mobility and reskilling support
Talent acquisition, management, and planning suite
Career trajectory insights and predictions
Diverse and qualified talent pool optimization
Future-ready workforce development
✨Use Cases: Enterprise talent management, internal mobility, workforce planning, skills transformation
AI Recruiting Tools Comparison Table (2025) Platform Pricing Range Sourcing Speed Iris by Qureos Custom Ultra-fast HireVue $15K-$50K+ Standard Manatal $15-$35/user Fast Workable $149-$599/month Standard LinkedIn Recruiter $1,680/month Standard FactoHR Custom Standard iSmartRecruit $99-$299/month Fast Recooty $29-$199/month Fast Fetcher.ai Custom Fast Eightfold.ai Custom Standard
Conclusion
The rising adoption of AI intervention in recruitment signifies a positive shift towards enhanced recruitment processes. With AI recruitment tools such as Iris, Workable, or Holly, organizations can improve the candidate experience, ensure the identification of suitable candidates, and streamline their HR operations. This advancement in technology promises to revolutionize the way companies approach recruitment, leading to more efficient and effective hiring decisions in the future.
Sign up with an AI Recruiter and enhance the hiring process now!
| 2025-06-13T00:00:00 |
2025/06/13
|
https://www.qureos.com/hiring-guide/best-ai-recruiting-platforms
|
[
{
"date": "2025/06/13",
"position": 71,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 65,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 54,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 65,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 79,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 65,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 66,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 93,
"query": "artificial intelligence hiring"
},
{
"date": "2025/06/13",
"position": 92,
"query": "artificial intelligence hiring"
},
{
"date": "2025/06/13",
"position": 68,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 69,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 63,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 90,
"query": "artificial intelligence hiring"
},
{
"date": "2025/06/13",
"position": 22,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 58,
"query": "AI hiring"
},
{
"date": "2025/06/13",
"position": 57,
"query": "AI hiring"
}
] |
What's New in AI-Focused Skilled Technical Workforce ...
|
What’s New in AI-Focused Skilled Technical Workforce Education?
|
https://www.newamerica.org
|
[] |
The sector has substantially expanded non-credit and credit-bearing certificates, bootcamps, two-year degrees, and work-based learning opportunities focused on ...
|
More and more students and workers need and seek artificial intelligence (AI) literacy – whether they seek jobs in tech or in other industries affected by AI. Fundamental to that skill attainment are community college AI programs, which offer affordable, accessible, and employer-aligned training to upskill incumbent workers and educate the next generation of talent. This is especially true for the skilled technical workforce, which refers to STEM professionals who require more education than a high school diploma but less than a four-year bachelor’s degree.
Community colleges have long been known as the go-to training destination for skilled technical workforce programs in traditional sectors such as manufacturing, healthcare, IT, and automotive. However, since ChatGPT ignited the AI revolution in 2022, the sector has substantially expanded non-credit and credit-bearing certificates, bootcamps, two-year degrees, and work-based learning opportunities focused on AI.
But AI education is still the Wild West. Colleges must navigate hype traps, upskill faculty and staff, help students understand career pathways around AI, enhance campus facilities, and purchase requisite equipment, and go the extra mile to ensure their curricula are aligned with the needs of nascent and ever-changing clusters of industries, occupations, and skills.
As the emerging technology turns a new leaf for the economy, major companies such as Intel, Dell, and Amazon have invested in growing AI workforce programs at community colleges. Governor Gavin Newsom and NVDIA CEO Jensen Haug signed a major partnership in 2024 to expand AI offerings across California community colleges. In Washington, White House events have engaged community college students for AI “red teaming” exercises to identify and negate risks in large language models, while lawmakers in Congress have introduced legislation to expand AI education at community colleges, such as the NSF AI Education Act of 2024.
The sea change around AI education for skilled technical workers has required a highly precise approach to capacity-building coordination across the country. To meet that need, Miami Dade College, Houston Community College, and Maricopa Community College District won a grant from the U.S. National Science Foundation to launch the National Applied AI Consortium (NAAIC), a national consortium dedicated to helping community colleges build their capacity for AI education.
Following NAAIC’s launch last year, originally reported by New America, the consortium has galvanized an impressive set of partners, participants, and initial activities to scale AI quality education across community colleges.
Upskilling Faculty and K-12 Teachers to Teach AI and Support AI Career Pathways
The consortium has served over 950 college and faculty administrators from 40 states through training, conferences, webinars, and mentorship organized as a community of practice. Over 400 faculty have obtained formal certifications, bootcamps, and industry-recognized credentials such as Microsoft’s Azure AI Fundamentals and MSLE Foundational AI as well as Amazon’s AWS AI Practitioner. Faculty have also leveraged course content from Intel’s AI for Workforce and Grow with Google AI suite of learning modules to support their professional development.
Valencia College, Illinois Central College, and Des Moines Area Community College were selected for the consortium’s formal AI mentorship program. The program connects colleges invested in scaling AI education with institutions that have experience with building capacity and program development.
At the K-12 level, NAAIC is collaborating with the University of Florida to develop a national AI framework for high schools to have AI standards framed as student learning objectives. The objective is to ensure teachers are equipped with the right skillset to teach AI by embedding AI concepts across the curriculum. Additionally, the framework will help better align career and technical education with core academic disciplines relating to tech career pathways.
And to foster peer learning in person, this past April, Houston Community College hosted the consortium’s first annual conference, convening over five hundred attendees. Partner organizations such as the Computer Research Association have provided community college faculty with reserved seats to attend convenings such as the CRA Summit on AI Undergraduate Education.
Strengthening Employer Partnerships For AI Education And Workforce Development
Employers such as Intel, Dell, and Amazon have also invested in AI education capacity building at community colleges. Still, it hasn’t been easy for colleges to find and participate in these efforts.
NAAIC has launched several partnerships with major industry partners, including Intel, Microsoft, Amazon Web Services, Google, and OpenAI Academy to aggregate and disseminate resources to colleges. Each partner is providing community colleges with its own set of resources.
For example, OpenAI Academy provides a suite of free in-classroom tools to apply AI in real-world scenarios. Intel is providing colleges 11 course modules with over 1,000 hours of content, peer learning for college faculty, and technical assistance for instructors from Intel staff. Google is offering its Career Certificates and Career Essentials training to community colleges at no cost.
A key goal of the consortium was to collaborate with employers, large and small, to aggregate their skilling needs and formulate a strategic approach for an AI skills taxonomy.
Later this year, the consortium’s AI-focused Building Industry Leadership and Team (BILT), an NSF-born best practice in co-creating training curricula with employers, will publish a national curriculum framework for an applied AI associate’s degree that any community college can use to create its own offerings.
What’s Next For AI education?
Lawmakers on Capitol Hill have signaled an interest in expanding AI education support for community colleges. President Trump has signed several executive orders around AI education that have implications for community colleges. Although the Trump administration has mostly only cut funding for community college STEM education and workforce training at the time of writing, the case study of the NAAIC demonstrates the value of the NSF in supporting capacity-building for emerging tech workforce training. But moreover, it’s a pragmatic illustration of the ways colleges can collectively address shared challenges to meet student and employer needs for the AI economy.
| 2025-06-13T00:00:00 |
https://www.newamerica.org/education-policy/edcentral/whats-new-in-ai-focused-skilled-technical-workforce-education/
|
[
{
"date": "2025/06/13",
"position": 30,
"query": "AI education"
}
] |
|
A Comprehensive AI Framework and Resources
|
AI in Education: A Comprehensive AI Framework and Resources
|
https://michiganvirtual.org
|
[] |
We offer resources, a comprehensive AI framework, and support to equip schools with the tools for successful Artificial Intelligence integration.
|
Explore our AI Integration Framework, meticulously crafted by Michigan Virtual, designed to assist education leaders in thinking holistically about AI integration. This framework spans crucial considerations across eight key areas, ensuring that you can maximize the potential benefits of AI while minimizing risks.
Take it a step further with our Planning Guide for AI. This comprehensive guide provides educators with the tools and insights needed to understand their trajectory and progress in AI integration.
| 2023-06-20T00:00:00 |
2023/06/20
|
https://michiganvirtual.org/ai/
|
[
{
"date": "2025/06/13",
"position": 42,
"query": "AI education"
}
] |
Artificial Intelligence (AI) in Education
|
AI in Education
|
https://warwick.ac.uk
|
[] |
The AI Learning Circle advances understanding and expertise in integrating generative AI (Gen AI) within higher education.
|
For more information and/or to join the Circle, please contact AI Circle Co-Leads:
Neha Gupta ([email protected]),
Karen Jackson ([email protected])
Jianhua Yang ([email protected]).
Please note any Staff or Students who are not a WIHEA Fellow, Alumni or current WIHEA Member who wish to join an Open Learning Circle, are required to become a WIHEA Member. This will enable you to be kept informed of WIHEA activities and progress on a range of Learning Circles and Projects, so that understanding of WIHEA’s strategic aims and links to other similar work in this area can be made more easily. Please see further information on our WIHEA Membership.
| 2025-06-13T00:00:00 |
https://warwick.ac.uk/fac/cross_fac/academy/activities/learningcircles/ai_in_education/
|
[
{
"date": "2025/06/13",
"position": 87,
"query": "AI education"
}
] |
|
GenAI paradox: exploring AI use cases
|
Seizing the agentic AI advantage
|
https://www.mckinsey.com
|
[
"Alexander Sukharevsky",
"Dave Kerr",
"Klemens Hjartar",
"Lari Hamalainen",
"Stephane Bout",
"Vito Di Leo"
] |
AI agents offer a way to break out of the gen AI paradox. That's because agents have the potential to automate complex business processes—combining autonomy, ...
|
At a glance
Nearly eight in ten companies report using gen AI—yet just as many report no significant bottom-line impact. Think of it as the “gen AI paradox.”
At the heart of this paradox is an imbalance between “horizontal” (enterprise-wide) copilots and chatbots—which have scaled quickly but deliver diffuse, hard-to-measure gains—and more transformative “vertical” (function-specific) use cases—about 90 percent of which remain stuck in pilot mode.
AI agents offer a way to break out of the gen AI paradox. That’s because agents have the potential to automate complex business processes—combining autonomy, planning, memory, and integration—to shift gen AI from a reactive tool to a proactive, goal-driven virtual collaborator.
This shift enables far more than efficiency. Agents supercharge operational agility and create new revenue opportunities.
But unlocking the full potential of agentic AI requires more than plugging agents into existing workflows. It calls for reimagining those workflows from the ground up—with agents at the core.
Foreword by Arthur Mensch, CEO of Mistral AI We’re at a moment when gen AI has entered every boardroom, but for many enterprises, it still lingers at the edges of actual impact. Many CEOs have greenlit experiments, spun up copilots, and created promising prototypes, but only a handful have seen the needle move on revenue or impact. This report gets to the heart of that paradox: broad adoption with limited return. The current diagnosis is this: Today, AI is bolted on. But to deliver real impact, it must be integrated into core processes, becoming a catalyst for business transformation rather than a sidecar tool. Most deployments today use AI in a shallow way—as an assistant that sits alongside existing workflows and processes—rather than as a deeply integrated, engaged, and powerful agent of transformation. Agentic AI is the catalyst that can make this transition possible, but doing so requires a strategy and a plan to successfully power that transformation. Agents are not simply magical plug-n-play pieces. They must work across systems, reason through ambiguity, and interact with people—not just as tools, but as collaborators. That means CEOs must ask different questions: not “How do we add AI?” but “How do we want decisions to be made, work to flow, and humans to engage in an environment where software can act?” Redefining how decisions are made, how work is done, and how humans engage with technology requires alignment across goals, tools, and people. That alignment can only happen when openness, transparency, and control are central to your technology and implementation—when builders have an open, extensible, and observable infrastructure and users can easily craft and use agents with the confidence that the work of agents is safe, reliable, and under their control. That alignment creates the trust and effectiveness that is the currency of scalable transformation that delivers results rather than regrets. The technology to build powerful agents is already here. The opportunity now is to deploy agents in ways that are deeply tied to how value is created and how people work. That requires an architecture that is modular and resilient and, more importantly, an operating model that centers on humans—not just as users but as co-architects of the systems they will be living and working with. This report lays out the playbook not for tinkering but for reinvention. ROI comes from strong intent: define the outcomes, embed agents deep in core workflows, and redesign operating models around them. Organizations that win will pair a clear strategy with tight feedback loops and disciplined governance, using agents to rethink how decisions are made and how work gets done—and turning novelty into measurable value.
A new AI architecture paradigm—the agentic AI mesh—is needed to govern the rapidly evolving organizational AI landscape and enable teams to blend custom-built and off-the-shelf agents while managing mounting technical debt and new classes of risk. But the bigger challenge won’t be technical. It will be human: earning trust, driving adoption, and establishing the right governance to manage agent autonomy and prevent uncontrolled sprawl.
To scale impact in the agentic era, organizations must reset their AI transformation approaches from scattered initiatives to strategic programs; from use cases to business processes; from siloed AI teams to cross-functional transformation squads; and from experimentation to industrialized, scalable delivery.
Organizations will also need to set up the foundation to effectively operate in the agentic era. They will need to upskill the workforce, adapt the technology infrastructure, accelerate data productization, and deploy agent-specific governance mechanisms. The moment has come to bring the gen AI experimentation chapter to a close—a pivot only the CEO can make.
Chapter 1 The gen AI paradox: Widespread deployment, minimal impact
Key Points Nearly eight in ten companies have deployed gen AI in some form, but roughly the same percentage report no material impact on earnings. We call this the “gen AI paradox.”
The main issue is an imbalance between “horizontal” and “vertical” use cases. The former, such as employee copilots and chatbots, have been widely deployed but deliver diffuse benefits, while higher-impact vertical, or function-specific, use cases seldom make it out of the pilot phase because of technical, organizational, data, and cultural barriers.
Unless companies address these barriers, the transformational promise of gen AI will remain largely untapped.
Gen AI is everywhere—except in company P&L
About QuantumBlack, AI by McKinsey QuantumBlack, McKinsey’s AI arm, has been helping businesses create value from AI since 2009, expanding on McKinsey’s technology work over the past 30 years. QuantumBlack combines an industry-leading tech stack with the strength of McKinsey’s 7,000 technologists, designers, and product managers serving clients in more than 50 countries. With innovations fueled by QuantumBlack Labs—its center for R&D and software development—QuantumBlack delivers the organizational rewiring that businesses need to build, adopt, and scale AI capabilities.
Even before the advent of gen AI, artificial intelligence had already carved out a key place in the enterprise, powering advanced prediction, classification, and optimization capabilities. And the technology’s estimated value potential was already immense—between $11 trillion and $18 trillion globally —mainly in the fields of marketing (powering capabilities such as personalized email targeting and customer segmentation), sales (lead scoring), and supply chain (inventory optimization and demand forecasting). Yet AI was largely the domain of experts. As a result, adoption across the rank and file tended to be slow. From 2018 to 2022, for example, AI adoption remained relatively stagnant, with about 50 percent of companies deploying the technology in just one business function, according to McKinsey research (Exhibit 1).
Gen AI has extended the reach of traditional AI in three breakthrough areas: information synthesis, content generation, and communication in human language. McKinsey estimates that the technology has the potential to unlock $2.6 trillion to $4.4 trillion in additional value on top of the value potential of traditional analytical AI.
Two and a half years after the launch of ChatGPT, gen AI has reshaped how enterprises engage with AI. Its potentially transformative power lies not only in the new capabilities gen AI introduces but also in its ability to democratize access to advanced AI technologies across organizations. This democratization has led to widespread growth in awareness of, and experimentation with, AI: According to McKinsey’s most recent Global Survey on AI, more than 78 percent of companies are now using gen AI in at least one business function (up from 55 percent a year earlier).
However, this enthusiasm has yet to translate into tangible economic results. More than 80 percent of companies still report no material contribution to earnings from their gen AI initiatives. What’s more, only 1 percent of enterprises we surveyed view their gen AI strategies as mature. Call it the “gen AI paradox”: For all the energy, investment, and potential surrounding the technology, at-scale impact has yet to materialize for most organizations.
At the heart of the gen AI paradox lies an imbalance between horizontal and vertical use cases
Many organizations have deployed horizontal use cases, such as enterprise-wide copilots and chatbots; nearly 70 percent of Fortune 500 companies, for example, use Microsoft 365 Copilot. These tools are widely seen as levers to enhance individual productivity by helping employees save time on routine tasks and access and synthesize information more efficiently. But these improvements, while real, tend to be spread thinly across employees. As a result, they are not easily visible in terms of top- or bottom-line results.
By contrast, vertical use cases—those embedded into specific business functions and processes—have seen limited scaling in most companies despite their higher potential for direct economic impact (Exhibit 2). Fewer than 10 percent of use cases deployed ever make it past the pilot stage, according to McKinsey research. Even when they have been fully deployed, these use cases typically have supported only isolated steps of a business process and operated in a reactive mode when prompted by a human, rather than functioning proactively or autonomously. As a result, their impact on business performance also has been limited.
What accounts for this imbalance? For one thing, horizontally deployed copilots such as Microsoft Copilot or Google AI Workspace are accessible, off-the-shelf solutions that are relatively easy to implement. (In many cases, enabling Microsoft Copilot is as simple as activating an extension to an existing Office 365 contract, requiring no redesign of workflows or major change management efforts.) Rapid deployment of enterprise chatbots also has been driven by risk mitigation concerns. As employees began experimenting with external large language models (LLMs) such as ChatGPT, many organizations implemented internal, secure alternatives to limit data leakage and ensure compliance with corporate security policies.
The limited deployment and narrow scope of vertical use cases can in turn be attributed to six primary factors:
Fragmented initiatives. At many companies, vertical use cases have been identified through a bottom-up, highly granular approach within individual functions. In fact, fewer than 30 percent of companies report that their CEOs sponsor their AI agenda directly. This has led to a proliferation of disconnected micro-initiatives and a dispersion of AI investments, with limited coordination at the enterprise level.
At many companies, vertical use cases have been identified through a bottom-up, highly granular approach within individual functions. In fact, fewer than 30 percent of companies report that their CEOs sponsor their AI agenda directly. This has led to a proliferation of disconnected micro-initiatives and a dispersion of AI investments, with limited coordination at the enterprise level. Lack of mature, packaged solutions. Unlike off-the-shelf horizontal applications, such as copilots, vertical use cases often require custom development. As a result, teams are frequently forced to build from scratch, using emerging, fast-evolving technologies they have limited experience with. While many companies have invested in data scientists to develop AI models, they often lack MLOps engineers, who are critical to industrialize, deploy, and maintain those models in production environments.
Unlike off-the-shelf horizontal applications, such as copilots, vertical use cases often require custom development. As a result, teams are frequently forced to build from scratch, using emerging, fast-evolving technologies they have limited experience with. While many companies have invested in data scientists to develop AI models, they often lack MLOps engineers, who are critical to industrialize, deploy, and maintain those models in production environments. Technological limitations of LLMs. Despite their impressive capabilities, the first generation of LLMs faced limitations that significantly constrained their deployment at enterprise scale. First, LLMs can produce inaccurate outputs, which makes them difficult to trust in environments where precision and repeatability are essential. What’s more, despite their power, LLMs are fundamentally passive; they do not act unless prompted and cannot independently drive workflows or make decisions without human initiation. LLMs also have struggled to handle complex workflows involving multiple steps, decision points, or branching logic. Finally, many current LLMs have limited persistent memory, making it difficult to track context over time or operate coherently across extended interactions.
Despite their impressive capabilities, the first generation of LLMs faced limitations that significantly constrained their deployment at enterprise scale. First, LLMs can produce inaccurate outputs, which makes them difficult to trust in environments where precision and repeatability are essential. What’s more, despite their power, LLMs are fundamentally passive; they do not act unless prompted and cannot independently drive workflows or make decisions without human initiation. LLMs also have struggled to handle complex workflows involving multiple steps, decision points, or branching logic. Finally, many current LLMs have limited persistent memory, making it difficult to track context over time or operate coherently across extended interactions. Siloed AI teams. AI centers of excellence have played a crucial role in accelerating awareness and experimentation across many organizations. However, in many cases, these teams have operated in silos—developing AI models independently from core IT, data, or business functions. This autonomy, while useful for rapid prototyping, has often made solutions difficult to scale because of poor integration with enterprise systems, fragmented data pipelines, or a lack of operational alignment.
AI centers of excellence have played a crucial role in accelerating awareness and experimentation across many organizations. However, in many cases, these teams have operated in silos—developing AI models independently from core IT, data, or business functions. This autonomy, while useful for rapid prototyping, has often made solutions difficult to scale because of poor integration with enterprise systems, fragmented data pipelines, or a lack of operational alignment. Data accessibility and quality gaps. These gaps tend to exist for both structured and unstructured data, with unstructured material remaining largely ungoverned in most organizations.
These gaps tend to exist for both structured and unstructured data, with unstructured material remaining largely ungoverned in most organizations. Cultural apprehension and organizational inertia. In many organizations, AI deployments have encountered implicit resistance from business teams and middle management due to fear of disruption, uncertainty around job impact, and lack of familiarity with the technology.
Despite its limited bottom-line impact so far, the first wave of gen AI has been far from wasted. It has enriched employee capabilities, enabled broad experimentation, accelerated AI familiarity across functions, and helped organizations build essential capabilities in prompt engineering, model evaluation, and governance. All of this has laid the groundwork for a more integrated and transformative second phase—the emerging age of AI agents.
Chapter 2 From paradox to payoff: How agents can scale AI
Key Points By automating complex business workflows, agents unlock the full potential of vertical use cases. Forward-looking companies are already harnessing the power of agents to transform core processes.
To realize the potential of agents, companies must reinvent the way work gets done—changing task flows, redefining human roles, and building agent-centric processes from the ground up.
Accomplishing this will require a new paradigm for AI architecture—the agentic AI mesh—capable of integrating both custom-built and off-the-shelf agents. But the bigger challenge will not be technical. It will be human: earning trust to drive adoption and establishing the proper governance protocols.
The breakthrough: Automating complex business workflows unlocks the full potential of vertical use cases
LLMs have revolutionized how organizations interact with data—enabling information synthesis, content generation, and natural language interaction. But despite their power, LLMs have been fundamentally reactive and isolated from enterprise systems, largely unable to retain memory of past interactions or context across sessions or queries. Their role has been largely limited to enhancing individual productivity through isolated tasks. AI agents mark a major evolution in enterprise AI—extending gen AI from reactive content generation to autonomous, goal-driven execution. Agents can understand goals, break them into subtasks, interact with both humans and systems, execute actions, and adapt in real time—all with minimal human intervention. They do so by combining LLMs with additional technology components providing memory, planning, orchestration, and integration capabilities.
With these new capabilities, AI agents expand the potential of horizontal solutions, upgrading general-purpose copilots from passive tools into proactive teammates that don’t just respond to prompts but also monitor dashboards, trigger workflows, follow up on open actions, and deliver relevant insights in real time. But the real breakthrough comes in the vertical realm, where agentic AI enables the automation of complex business workflows involving multiple steps, actors, and systems—processes that were previously beyond the capabilities of first-generation gen AI tools.
Agents deliver more than efficiency—they supercharge operational agility and unlock new revenue opportunities
On the operations side, agents take on routine, data-heavy tasks so humans can focus on higher-value work. But they go further, transforming processes in five ways:
Agents accelerate execution by eliminating delays between tasks and by enabling parallel processing. Unlike in traditional workflows that rely on sequential handoffs, agents can coordinate and execute multiple steps simultaneously, reducing cycle time and boosting responsiveness.
Unlike in traditional workflows that rely on sequential handoffs, agents can coordinate and execute multiple steps simultaneously, reducing cycle time and boosting responsiveness. Agents bring adaptability. By continuously ingesting data, agents can adjust process flows on the fly, reshuffling task sequences, reassigning priorities, or flagging anomalies before they cascade into failures. This makes workflows not only faster but smarter.
By continuously ingesting data, agents can adjust process flows on the fly, reshuffling task sequences, reassigning priorities, or flagging anomalies before they cascade into failures. This makes workflows not only faster but smarter. Agents enable personalization. By tailoring interactions and decisions to individual customer profiles or behaviors, agents can adapt the process dynamically to maximize satisfaction and outcomes.
By tailoring interactions and decisions to individual customer profiles or behaviors, agents can adapt the process dynamically to maximize satisfaction and outcomes. Agents bring elasticity to operations. Because agents are digital, their execution capacity can expand or contract in real time depending on workload, business seasonality, or unexpected surges—something difficult to achieve with fixed human resource models.
Because agents are digital, their execution capacity can expand or contract in real time depending on workload, business seasonality, or unexpected surges—something difficult to achieve with fixed human resource models. Agents also make operations more resilient. By monitoring disruptions, rerouting operations, and escalating only when needed, they keep processes running—whether it’s supply chains navigating port delays or service workflows adapting to system outages.
In a complex supply chain environment, for example, an AI agent could act as an autonomous orchestration layer across sourcing, warehousing, and distribution operations. Connected to internal systems (such as the supply chain planning system or the warehouse management system) and external data sources (such as weather forecasts, supplier feeds, and demand signals), the agent could continuously forecast demand. It could then identify risks, such as delays or disruptions, and dynamically replan transport and inventory flows. Selecting the optimal transport mode based on cost, lead time, and environmental impact, the agent could reallocate stock across warehouses, negotiate directly with external systems, and escalate decisions requiring strategic input. The result: improved service levels, reduced logistics costs, and lower emissions.
Agents can also help spur top-line growth by amplifying existing revenue streams and unlocking entirely new ones:
Amplifying existing revenues. In e-commerce, agents embedded into online stores or apps could proactively analyze user behavior, cart content, and context (for example, seasonality or purchase history) to surface real-time upselling and cross-selling offers. In finance, agents might help customers discover suitable financial products such as loans, insurance plans, or investment portfolios, providing tailored guidance based on financial profiles, life events, and user behavior.
In e-commerce, agents embedded into online stores or apps could proactively analyze user behavior, cart content, and context (for example, seasonality or purchase history) to surface real-time upselling and cross-selling offers. In finance, agents might help customers discover suitable financial products such as loans, insurance plans, or investment portfolios, providing tailored guidance based on financial profiles, life events, and user behavior. Creating new revenue streams. For industrial companies, agents embedded in connected products or equipment could monitor usage, detect performance thresholds, and autonomously unlock features or trigger maintenance actions—enabling pay-per-use, subscription, or performance-based models of creating revenue. Similarly, service organizations could encapsulate internal expertise—legal reasoning, tax interpretation, and procurement best practices—into AI agents offered as software-as-a-service tools or APIs to clients, partners, or smaller businesses lacking in-house expertise.
In short, agentic AI doesn’t just automate. It redefines how organizations operate, adapt, and create value.
No longer science fiction: Forward-looking companies are harnessing the power of agents
The following case studies demonstrate how QuantumBlack helps organizations build agent workforces—with outcomes that extend far beyond efficiency gains.
Case study 1: How a bank used hybrid ‘digital factories’ for legacy app modernization
The problem: A large bank needed to modernize its legacy core system, which consisted of 400 pieces of software—a massive undertaking budgeted at more than $600 million. Large teams of coders tackled the project using manual, repetitive tasks, which resulted in difficulty coordinating across silos. They also relied on often slow, error-prone documentation and coding. While first-generation gen AI tools helped accelerate individual tasks, progress remained slow and laborious.
The agentic approach: Human workers were elevated to supervisory roles, overseeing squads of AI agents, each contributing to a shared objective in a defined sequence (Exhibit 3). These squads retroactively document the legacy application, write new code, review the code of other agents, and integrate code into features that are later tested by other agents prior to delivery of the end product. Freed from repetitive, manual tasks, human supervisors guide each stage of the process, enhancing the quality of deliverables and reducing the number of sprints required to implement new features.
Impact: More than 50 percent reduction in time and effort in the early adopter teams
Case study 2: How a research firm boosted data quality to derive deeper market insights
The problem: A market research and intelligence firm was devoting substantial resources to ensure data quality, relying on a team of more than 500 people whose responsibilities included gathering data, structuring and codifying it, and generating tailored insights for clients. The process, conducted manually, was prone to error, with a staggering 80 percent of mistakes identified by the clients themselves.
The agentic approach: A multiagent solution autonomously identifies data anomalies and explains shifts in sales or market share. It analyzes internal signals, such as changes in product taxonomy, and external events identified via web searches, including product recalls or severe weather. The most influential drivers are synthesized, ranked, and prepared for decision-makers. With advanced search and contextual reasoning, the agents often surface insights that would be difficult for human analysts to uncover manually. While not yet in production, the system is fully functional and has demonstrated strong potential to free up analysts for more strategic work.
Impact: More than 60 percent potential productivity gain and expected savings of more than $3 million annually.
Case study 3: How a bank reimagined the way it creates credit-risk memos
The problem: Relationship managers (RMs) at a retail bank were spending weeks writing and iterating credit-risk memos to help make credit decisions and fulfill regulatory requirements (Exhibit 4). This process required RMs to manually review and extract information from at least ten different data sources and develop complex nuanced reasoning across interdependent sections—for instance, loan, revenue, and cash joint evolution.
The agentic approach: In close collaboration with the bank’s credit-risk experts and RMs, a proof of concept was developed to transform the credit memo workflow using AI agents. The agents assist RMs by extracting data, drafting memo sections, generating confidence scores to prioritize review, and suggesting relevant follow-up questions. In this model, the analyst’s role shifts from manual drafting to strategic oversight and exception handling.
Impact: A potential 20 to 60 percent increase in productivity, including a 30 percent improvement in credit turnaround
Maximizing value from AI agents requires process reinvention
Realizing AI’s full potential in the vertical realm requires more than simply inserting agents into legacy workflows. It instead calls for a shift in design mindset—from automating tasks within an existing process to reinventing the entire process with human and agentic coworkers. That’s because when agents are embedded into a legacy process without redesign, they typically serve as faster assistants—generating content, retrieving data, or executing predefined steps. But the process itself remains sequential, rule bound, and shaped by human constraints.
Reinventing a process around agents means more than layering automation on top of existing workflows—it involves rearchitecting the entire task flow from the ground up. That includes reordering steps, reallocating responsibilities between humans and agents, and designing the process to fully exploit the strengths of agentic AI: parallel execution that collapses cycle time, real-time adaptability that reacts to changing conditions, deep personalization at scale, and elastic capacity that flexes instantly with demand.
Consider a hypothetical customer call center. Before introducing AI agents, the facility was using gen AI tools to assist human support staff by retrieving articles from knowledge bases, summarizing ticket histories, and helping draft responses. While this assistance improved speed and reduced cognitive load, the process itself remained entirely manual and reactive, with human agents still managing every step of diagnosis, coordination, and resolution. The productivity improvement potential was modest, typically boosting resolution time and productivity between 5 and 10 percent.
Now imagine that the call center introduces AI agents but largely preserves the existing workflow—agents are added to assist at specific steps without reconfiguring how work is routed, tracked, or resolved end-to-end. Agents can classify tickets, suggest likely root causes, propose resolution paths, and even autonomously resolve frequent, low-complexity issues (such as password resets). While the impact here can be increased—an estimated 20 to 40 percent savings in time and a 30 to 50 percent reduction in backlog—coordination friction and limited adaptability prevent true breakthrough gains.
But the real shift occurs at the third level, when the call center’s process is reimagined around agent autonomy. In this model, AI agents don’t just respond—they proactively detect common customer issues (such as delayed shipments, failed payments, or service outages) by monitoring patterns across channels, anticipate likely needs, initiate resolution steps automatically (such as issuing refunds, reordering items, or updating account details), and communicate directly with customers via chat or email. Human agents are repositioned as escalation managers and service quality overseers, who are brought in only when agents detect uncertainty or exceptions to typical patterns. Impact at this level is transformative. This could allow a radical improvement of customer service desk productivity. Up to 80 percent of common incidents could be resolved autonomously, with a reduction in time to resolution of 60 to 90 percent (Exhibit 5).
Of course, not every business process requires full reinvention. Simple task automation is sufficient for highly standard, repetitive workflows with limited variability—such as payroll processing, travel expense approvals, or password resets—where gains come primarily from reducing manual effort. In contrast, processes that are complex, cross-functional, prone to exceptions, or tightly linked to business performance often warrant full redesign. Key indicators that call for reinvention include high coordination overhead, rigid sequences that delay responsiveness, frequent human intervention for decisions that could be data driven, and opportunities for dynamic adaptation or personalization. In these cases, redesigning the process around the agent’s ability to orchestrate, adapt, and learn delivers far greater value than simply speeding up existing workflows.
A new AI architecture paradigm—the agentic AI mesh—is required to orchestrate value in the agentic era
To scale agents, companies will need to overcome a threefold challenge: handling the newfound risks that AI agents bring, blending custom and off-the-shelf agentic systems, and staying agile amid fast-evolving tech (while avoiding lock-ins).
Managing a new wave of risks. Agents introduce a new class of systemic risks that traditional gen AI architectures, designed primarily for isolated LLM-centric use cases, were never built to handle: uncontrolled autonomy, fragmented system access, lack of observability and traceability, expanding surface of attack, and agent sprawl and duplication. What starts as intelligent automation can quickly become operational chaos—unless it is built on a foundation that prioritizes control, scalability, and trust.
Agents introduce a new class of systemic risks that traditional gen AI architectures, designed primarily for isolated LLM-centric use cases, were never built to handle: uncontrolled autonomy, fragmented system access, lack of observability and traceability, expanding surface of attack, and agent sprawl and duplication. What starts as intelligent automation can quickly become operational chaos—unless it is built on a foundation that prioritizes control, scalability, and trust. Blending custom and off-the-shelf agents. To fully capture the transformative potential of AI agents, organizations must go beyond simply activating agents embedded in software suites. These off-the-shelf agents may streamline routine workflows, but they rarely unlock strategic advantage. Realizing the full potential of agentic AI will require the development of custom-built agents for high-impact processes, such as end-to-end customer resolution, adaptive supply chain orchestration, or complex decision-making. These agents must be deeply aligned with the company’s logic, data flows, and value creation levers—making them difficult to replicate and uniquely powerful.
To fully capture the transformative potential of AI agents, organizations must go beyond simply activating agents embedded in software suites. These off-the-shelf agents may streamline routine workflows, but they rarely unlock strategic advantage. Realizing the full potential of agentic AI will require the development of custom-built agents for high-impact processes, such as end-to-end customer resolution, adaptive supply chain orchestration, or complex decision-making. These agents must be deeply aligned with the company’s logic, data flows, and value creation levers—making them difficult to replicate and uniquely powerful. Staying agile amid fast-evolving tech. Agentic AI is a new technology area, and solutions are evolving very rapidly. Agents will have to support workflows across multiple systems and should not be hardwired within a specific platform. An evolutive and vendor-agnostic architecture is therefore needed.
These challenges cannot be addressed by merely bolting new components, such as memory stores or orchestration engines, on top of existing gen AI stacks. While such capabilities are necessary, they are not sufficient. What’s needed is a fundamental architectural shift: from static, LLM-centric infrastructure to a dynamic, modular, and governed environment built specifically for agent-based intelligence—the agentic AI mesh.
The agentic AI mesh is a composable, distributed, and vendor-agnostic architectural paradigm that enables multiple agents to reason, collaborate, and act autonomously across a wide array of systems, tools, and language models—securely, at scale, and built to evolve with the technology. At the heart of this paradigm are five mutually reinforcing design principles:
Composability. Any agent, tool, or LLM can be plugged into the mesh without system rework.
Any agent, tool, or LLM can be plugged into the mesh without system rework. Distributed intelligence. Tasks can be decomposed and resolved by networks of cooperating agents.
Tasks can be decomposed and resolved by networks of cooperating agents. Layered decoupling. Logic, memory, orchestration, and interface functions are decoupled to maximize modularity.
Logic, memory, orchestration, and interface functions are decoupled to maximize modularity. Vendor neutrality. All components can be independently updated or replaced as technology advances, avoiding vendor lock-in and future-proofing the architecture. In particular, open standards such as the Model Context Protocol (MCP) and Agent2Agent (A2A) are preferred to proprietary protocols.
All components can be independently updated or replaced as technology advances, avoiding vendor lock-in and future-proofing the architecture. In particular, open standards such as the Model Context Protocol (MCP) and Agent2Agent (A2A) are preferred to proprietary protocols. Governed autonomy. Agent behavior is proactively controlled via embedded policies, permissions, and escalation mechanisms that ensure safe, transparent operation.
Seven interconnected capabilities of the AI agentic mesh The emerging architecture for agentic AI relies on seven interconnected capabilities: Agent and workflow discovery maintains a dynamic catalog of all organizational agents and workflows, enabling reuse across teams and enforcing policies on agent use. AI asset registry centralizes governance of system prompts, agent instructions, large-language-model (LLM) configurations, tool definitions, and golden records while creating policies about version control and access. Observability provides end-to-end tracing of workflows spanning agentic and procedural systems through standardized metrics, audit logs, and diagnostic capabilities. Authentication and authorization enforce fine-grain access controls for communication among agentic systems, procedural systems, and LLMs, enforcing security policies and limiting the “blast radius” of compromised systems or agents. Evaluations deliver comprehensive testing of agent pipelines to ensure accuracy and compliance over time. Feedback management enables continuous improvement through automated feedback loops that capture performance metrics to evolve agent configurations. Compliance and risk management embed policy controls, compliance agents, and ethical guardrails to ensure workflows meet regulatory and institutional standards.
The agentic AI mesh acts as the connective and orchestration layer that enables large-scale, intelligent agent ecosystems to operate safely and efficiently, and continuously evolve. It allows companies to coordinate custom-built and off-the-shelf agents within a unified framework, support multiagent collaboration by allowing agents to share context and delegate tasks, and mitigate key risks such as agent sprawl, autonomy drift, and lack of observability—all while preserving the agility required for a rapid technology evolution (see sidebar “Seven interconnected capabilities of the AI agentic mesh”).
Foundation models for agents: Five requirements A few characteristics are key for LLM providers to take into account in the agentic era: Low-latency inference for real-time responsiveness. Agents embedded in workflows (such as service operations or IT alerts) require subsecond response times with predictable latency, even under compute constraints. Illustrative examples of relevant models include Mistral Small (Mistral AI), Llama 3 8B (Meta), Gemini Nano (Google), and Claude Haiku (Anthropic). Fine-tuning and controllability for domain-specific agents. Agents operating in regulated or knowledge-intensive domains (such as finance, legal, and healthcare) need large language models (LLMs) that can be fine-tuned, grounded in enterprise knowledge, and instrumented with external tools (such as RAG and APIs). Illustrative examples of relevant models are Mistral Small and Mistral 8x7B (open weight and fine-tunable, Mistral AI), and Llama 3 8B and 70B (fine-tunable, Meta). Lightweight deployment for embedded and edge agents. In cases such as the Internet of Things, field devices, or privacy-sensitive environments, agents must be embedded directly into software or hardware, with minimal compute and memory footprint. Illustrative examples of relevant models include Mistral Small (Mistral AI), Gemini Nano (Google), Llama 3 8B (Meta), and Phi-2 (Microsoft). Scalable multiagent orchestration across the enterprise. Enterprises deploying hundreds or thousands of agents require LLMs that can scale efficiently and cost-effectively, ideally using sparse architectures or a mixture of experts. Illustrative examples of relevant models include Mixtral (Mistral AI), Grok-1 (xAI), GPT-3.5 Turbo (OpenAI), and Command R+ (Cohere). Sovereignty, auditability, and geopolitical resilience for autonomous agents. Agents embedded in core operations—particularly in public, financial, and critical-infrastructure sectors—must ensure compliance, data sovereignty, traceability, and geopolitical autonomy. This includes avoiding reliance on APIs that are hosted abroad, ensuring data residency, and resisting extraterritorial legal exposure (for example, OpenAI or Anthropic subject to US subpoenas). Illustrative examples of relevant models include Mistral Small/Mixtral (Mistral AI), Falcon 180B (TII UAE), and BloomZ/Bloom (BigScience).
Beyond this architectural evolution, organizations will also have to revisit their LLM strategies. At the core of every custom agent lies a foundation model—the reasoning engine that powers perception, decision-making, and interaction. In the agentic era, the requirements placed on LLMs evolve significantly. Agents are not passive copilots—they are autonomous, persistent, embedded systems. This creates five critical categories of LLM requirements, each aligned with specific deployment contexts, for which different kinds of models will be relevant (see sidebar “Foundational models for agents: Five requirements”).
Finally, to truly scale agent deployment across the enterprise, the enterprise systems themselves must also evolve.
In the short term, APIs—protocols that allow different software applications to communicate and exchange data—will remain the primary interface for agents to interact with enterprise systems. But in the long term, APIs alone will not suffice. Organizations must begin reimagining their IT architectures around an agent-first model—one in which user interfaces, logic, and data access layers are natively designed for machine interaction rather than human navigation. In such a model, systems are no longer organized around screens and forms but around machine-readable interfaces, autonomous workflows, and agent-led decision flows.
This shift is already underway. Microsoft is embedding agents into the core of Dynamics 365 and Microsoft 365 via Copilot Studio; Salesforce is expanding Agentforce into a multiagent orchestration layer; SAP is rearchitecting its Business Technology Platform (BTP) to support agent integration through Joule. These changes signal a broader transition: The future of enterprise software is not just AI-augmented—it is agent-native.
The main challenge won’t be technical—it will be human
As agents evolve from passive copilots to proactive actors—and scale across the enterprise—the complexity they introduce will be not only technical but mostly organizational. The real challenge lies in coordination, judgment, and trust. This organizational complexity will play out most visibly across three dimensions: how humans and agents cohabit day-to-day workflows; how organizations establish governance over systems that can act autonomously; and how they prevent unchecked sprawl as agent creation becomes increasingly democratized.
Human–agent cohabitation. Agents won’t just assist humans—they’ll act alongside them. This raises nuanced questions about interaction and coexistence: When should an agent take initiative? When should it defer? How do we maintain human agency and oversight without slowing down the very benefits agents bring? Building clarity around these roles will take time, experimentation, and cultural adjustment. Trust won’t come from technical performance alone—it will hinge on how transparently agents communicate, how predictably they behave, and how intuitively they integrate into daily workflows.
Agents won’t just assist humans—they’ll act alongside them. This raises nuanced questions about interaction and coexistence: When should an agent take initiative? When should it defer? How do we maintain human agency and oversight without slowing down the very benefits agents bring? Building clarity around these roles will take time, experimentation, and cultural adjustment. Trust won’t come from technical performance alone—it will hinge on how transparently agents communicate, how predictably they behave, and how intuitively they integrate into daily workflows. Autonomy control. What makes agents powerful—their ability to act independently—also introduces ambiguity. Unlike traditional tools, agents don’t wait to be instructed. They respond, adapt, and sometimes surprise. Navigating this new reality means confronting edge cases: What if an agent executes too aggressively? Or fails to escalate a subtle issue? The challenge is not to eliminate autonomy but to make it intelligible and aligned with organizational expectations. That alignment won’t be static. It will need to evolve as agents learn, systems shift, and trust deepens. Control mechanisms must also address the risk of hallucinations, or plausible but inaccurate outputs agents may produce.
What makes agents powerful—their ability to act independently—also introduces ambiguity. Unlike traditional tools, agents don’t wait to be instructed. They respond, adapt, and sometimes surprise. Navigating this new reality means confronting edge cases: What if an agent executes too aggressively? Or fails to escalate a subtle issue? The challenge is not to eliminate autonomy but to make it intelligible and aligned with organizational expectations. That alignment won’t be static. It will need to evolve as agents learn, systems shift, and trust deepens. Control mechanisms must also address the risk of hallucinations, or plausible but inaccurate outputs agents may produce. Sprawl containment. As in the early days of robotic process automation, there’s a real risk of agent sprawl—the uncontrolled proliferation of redundant, fragmented, and ungoverned agents across teams and functions. As low-code and no-code platforms make agent creation accessible to anyone, organizations risk a new kind of shadow IT: agents that multiply across teams, duplicate efforts, or operate without oversight. How do we avoid fragmentation? Who decides what gets built—and what gets retired? Without structured governance, design standards, and life cycle management, agent ecosystems can quickly become fragile, redundant, and unscalable.
Agents unlock the full potential of vertical use cases, offering companies a path to generate value well beyond efficiency gains. But realizing that potential requires a reimagined approach to AI transformation—one tailored to the unique nature of agents and capable of addressing the lingering limitations they alone cannot resolve. This approach is the subject of our next chapter.
Chapter 3 AI transformation at a tipping point: The CEO mandate in the agentic era
Key Points Generating impact in the agentic era requires organizations to shift from scattered initiatives to strategic programs; from use cases to business processes; from siloed AI teams to cross-functional transformation squads; and from experimentation to industrialized, scalable delivery.
To scale agents, organizations will also need to set a new foundation by upskilling the workforce, adapting the technology infrastructure, and developing new governance structures for agents.
The time has come to bring the gen AI experimentation phase to an end—a pivot only the CEO can make.
Scaling impact in the agentic era requires a reset of the AI transformation approach
Unlike gen AI tools that could be easily plugged into existing workflows, AI agents demand a more foundational shift, one that requires rethinking business processes and enabling deep integration with enterprise systems. McKinsey has a proven Rewired playbook for AI-driven transformations. To capitalize on the agentic opportunity, organizations must build on that, fundamentally reshaping their AI transformation approach across four dimensions:
Strategy: From scattered tactical initiatives to strategic programs. With agentic AI set to reshape the foundations of competition, organizations must move beyond bottom-up use case identification and directly align AI initiatives with their most critical strategic priorities. This means not only translating existing goals—such as enhancing operational efficiency, improving customer intimacy, or strengthening compliance—into AI-addressable transformation domains, but also adopting a forward-looking lens. Executives must challenge their organizations to look beyond the boundaries of today’s operating model and explore how AI can be used to reimagine entire segments of the business, create new revenue streams, and build competitive moats that will define leadership in the next decade.
With agentic AI set to reshape the foundations of competition, organizations must move beyond bottom-up use case identification and directly align AI initiatives with their most critical strategic priorities. This means not only translating existing goals—such as enhancing operational efficiency, improving customer intimacy, or strengthening compliance—into AI-addressable transformation domains, but also adopting a forward-looking lens. Executives must challenge their organizations to look beyond the boundaries of today’s operating model and explore how AI can be used to reimagine entire segments of the business, create new revenue streams, and build competitive moats that will define leadership in the next decade. Unit of transformation: From use case to business processes. In the early wave of gen AI adoption, most vertical initiatives focused on plugging a solution into a specific step of an existing process—which tended to deliver narrow gains without changing the overall structure of how work is done. With AI agents, the paradigm shifts entirely. Opportunity now lies not in optimizing isolated tasks but in transforming entire business processes by embedding agents throughout the value chain. As a result, AI initiatives should no longer be scoped around a single use case, but instead around the end-to-end reinvention of a full process or persona journey. In vertical domains, this means moving from the question, “Where can I use AI in this function?” to “What would this function look like if agents ran 60 percent of it?” It involves rethinking workflows, decision logic, human–system interactions, and performance metrics across the board.
In the early wave of gen AI adoption, most vertical initiatives focused on plugging a solution into a specific step of an existing process—which tended to deliver narrow gains without changing the overall structure of how work is done. With AI agents, the paradigm shifts entirely. Opportunity now lies not in optimizing isolated tasks but in transforming entire business processes by embedding agents throughout the value chain. As a result, AI initiatives should no longer be scoped around a single use case, but instead around the end-to-end reinvention of a full process or persona journey. In vertical domains, this means moving from the question, “Where can I use AI in this function?” to “What would this function look like if agents ran 60 percent of it?” It involves rethinking workflows, decision logic, human–system interactions, and performance metrics across the board. Delivery model: From siloed AI teams to cross-functional transformation squads. AI centers of excellence have played a key role in accelerating AI awareness and experimentation across organizations. However, this model reaches its limits in the agentic era—in which agents are deeply embedded into enterprise systems, operate across complex business processes, and rely on high-quality data as their primary fuel. In this context, AI initiatives can no longer be delivered by isolated, specialized AI teams. To succeed at scale, organizations must shift to a cross-functional delivery model, anchored in durable transformation squads composed of business domain experts, process designers, AI and MLOps engineers, IT architects, software engineers, and data engineers.
AI centers of excellence have played a key role in accelerating AI awareness and experimentation across organizations. However, this model reaches its limits in the agentic era—in which agents are deeply embedded into enterprise systems, operate across complex business processes, and rely on high-quality data as their primary fuel. In this context, AI initiatives can no longer be delivered by isolated, specialized AI teams. To succeed at scale, organizations must shift to a cross-functional delivery model, anchored in durable transformation squads composed of business domain experts, process designers, AI and MLOps engineers, IT architects, software engineers, and data engineers. Implementation process: From experimentation to industrialized, scalable delivery. While the previous phase rightly focused on exploring the potential of gen AI, organizations must now shift to an industrialized delivery model, in which solutions are designed from the outset to scale, both technically and financially. This requires organizations to anticipate the full set of technical prerequisites for enterprise deployment—notably in terms of system integration, day-to-day monitoring, and release management, but also to rigorously estimate future running costs and design a solution to minimize them. Unlike traditional IT systems—for which annual run costs typically represent 10 to 20 percent of initial build costs —gen AI solutions, especially at scale, can incur recurring costs that exceed the initial build investment. Designing for scalability must therefore include not just technical robustness but also economic sustainability, especially for high-volume applications.
Four critical enablers are required to effectively operate in the agentic era
Redesigning the approach to AI transformation is an important step, but it is not enough. To unlock their full potential at scale, organizations must also activate a robust set of enablers that support the structural, cultural, and technical shifts required to integrate agents into day-to-day operations. These enablers span four dimensions—people, governance, technology architecture, and data—each of which is a foundation for scalable, secure, and high-impact deployment of agents across the enterprise.
People: Equip the workforce and introduce new roles. The workforce must be equipped for new ways of working driven by human–agent collaboration. This involves fostering a “human + agent” mindset through cultural change, targeted training, and supporting early adopters as internal champions. New roles must also be introduced, such as prompt engineers to refine interactions, agent orchestrators to manage agent workflows, and human-in-the-loop designers to handle exceptions and build trust.
The workforce must be equipped for new ways of working driven by human–agent collaboration. This involves fostering a “human + agent” mindset through cultural change, targeted training, and supporting early adopters as internal champions. New roles must also be introduced, such as prompt engineers to refine interactions, agent orchestrators to manage agent workflows, and human-in-the-loop designers to handle exceptions and build trust. Governance: Ensure autonomy control and prevent agent sprawl. With the rise of autonomous agents comes the need for strong governance to avoid risk and uncontrolled sprawl. Enterprises should define governance frameworks that establish agent autonomy levels, decision boundaries, behavior monitoring, and audit mechanisms. Policies for development, deployment, and usage must also be formalized, along with classification systems that group agents by function (such as task automators, domain orchestrators, and virtual collaborators), each with an appropriate oversight model.
With the rise of autonomous agents comes the need for strong governance to avoid risk and uncontrolled sprawl. Enterprises should define governance frameworks that establish agent autonomy levels, decision boundaries, behavior monitoring, and audit mechanisms. Policies for development, deployment, and usage must also be formalized, along with classification systems that group agents by function (such as task automators, domain orchestrators, and virtual collaborators), each with an appropriate oversight model. Technology architecture: Build a foundation for interoperability and scale. Agents, whether custom-built or off-the-shelf, must operate across a fragmented ecosystem of systems, data, and workflows. In the short term, organizations must evolve their AI architecture from LLM-centric setups to an agentic AI mesh. Beyond this first step, organizations should start preparing for their next-generation architecture, in which all enterprise systems will be reshuffled around agents in terms of user interface, business logic, and day-to-day operations.
Agents, whether custom-built or off-the-shelf, must operate across a fragmented ecosystem of systems, data, and workflows. In the short term, organizations must evolve their AI architecture from LLM-centric setups to an agentic AI mesh. Beyond this first step, organizations should start preparing for their next-generation architecture, in which all enterprise systems will be reshuffled around agents in terms of user interface, business logic, and day-to-day operations. Data: Accelerate data productization and address quality gaps in unstructured data. Finally, agents depend on the quality and accessibility of enterprise data. Organizations must transition from use-case-specific data pipelines to reusable data products and extend data governance to unstructured data.
CEOs have a leadership challenge: Bringing the gen AI experimentation phase to a close
The rise of AI agents is more than just a technological shift. Agents represent a strategic inflection point that will redefine how companies operate, compete, and create value. To navigate this transition successfully, organizations must move beyond experimentation and pilot programs and enter a new phase of scaled, enterprise-wide transformation.
This pivot cannot be delegated—it must be initiated and led by the CEO. It will rely on three key actions:
Action 1: Conclude the experimentation phase and realign AI priorities. Conduct a structured review to capture lessons learned, retire unscalable pilots, and formally close the exploratory phase. Refocus efforts on strategic AI programs targeting high-impact domains and processes.
Conduct a structured review to capture lessons learned, retire unscalable pilots, and formally close the exploratory phase. Refocus efforts on strategic AI programs targeting high-impact domains and processes. Action 2: Redesign the AI governance and operating model. Set up a strategic AI council involving business leaders, the chief human resources officer, the chief data officer, and the chief information officer. This council should oversee AI direction-setting; coordinate AI, IT, and data investments; and implement rigorous value-tracking mechanisms based on KPIs tied to business outcomes.
Set up a strategic AI council involving business leaders, the chief human resources officer, the chief data officer, and the chief information officer. This council should oversee AI direction-setting; coordinate AI, IT, and data investments; and implement rigorous value-tracking mechanisms based on KPIs tied to business outcomes. Action 3: Launch a first lighthouse transformation project and simultaneously initialize the agentic AI tech foundation. Kick off a select number of high-impact agentic AI–driven workflow transformations in core business areas. In parallel, lay the groundwork for an agentic AI technology foundation by investing in key enablers—technology infrastructure, data quality, governance frameworks, and workforce readiness.
Conclusion
Like any truly disruptive technology, AI agents have the power to reshuffle the deck. Done right, they offer laggards a leapfrog opportunity to rewire their competitiveness. Done wrong—or not at all—they risk accelerating the decline of today’s market leaders. This is a moment of strategic divergence.
While the technology will continue to evolve, it is already mature enough to drive real, transformative change across industries. But to realize the full promise of agentic AI, CEOs must rethink their approach to AI transformation—not as a series of scattered pilots but as focused, end-to-end reinvention efforts. That means identifying a few business domains with the highest potential and pulling every lever: from reimagining workflows to redistributing tasks between humans and machines to rewiring the organization based on new operating models.
Some leaders are already moving—not just by deploying fleets of agents but by rewiring their organizations to harness their full disruptive potential. (Moderna, for example, merged its HR and IT leadership —signaling that AI is not just a technical tool but a workforce-shaping force.) This is a structural move toward a new kind of enterprise. Agentic AI is not an incremental step—it is the foundation of the next-generation operating model. CEOs who act now won’t just gain a performance edge. They will redefine how their organizations think, decide, and execute.
The time for exploration is ending. The time for transformation is now.
| 2025-06-13T00:00:00 |
https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage
|
[
{
"date": "2025/06/13",
"position": 65,
"query": "AI employers"
}
] |
|
Organizations Aren't Ready for the Risks of Agentic AI
|
Organizations Aren’t Ready for the Risks of Agentic AI
|
https://hbr.org
|
[
"Reid Blackman",
"Phd Is The Author Of Ethical Machines",
"Harvard Business Review Press",
". As The Founder",
"Ceo Of Virtue",
"An Ai Ethical Risk Consultancy",
"He",
"His Team Work With Companies To Design",
"Implement",
"Scale"
] |
As companies move from narrow to generative to agentic and multi-agentic AI, the complexity of the risk landscape ramps up sharply. Existing AI risk ...
|
It’s virtually impossible to have a conversation about the future of business without talking about AI. What’s more, the technology is evolving at a furious pace. What started as AI chatbots and image generators are becoming AI “agents”—AI systems that can execute a series of tasks without being given specific instructions. No one knows exactly how all this will play out, but details aside, leaders are considering the very real possibility of wide-scale organizational disruption. It’s an exciting time.
| 2025-06-13T00:00:00 |
2025/06/13
|
https://hbr.org/2025/06/organizations-arent-ready-for-the-risks-of-agentic-ai
|
[
{
"date": "2025/06/13",
"position": 87,
"query": "AI employers"
}
] |
Updated: 86 expert voices at the intersection of AI and ...
|
Updated: 86 expert voices at the intersection of AI and journalism
|
https://www.journalism.co.uk
|
[
"Marcela Kunova",
"Jacob Granger"
] |
Pre-register your interest in joining our upcoming media community by filling out this brief form. As artificial intelligence transforms newsrooms and ...
|
Aliya Itzkowitz (FT Strategies, top left), Jody Doherty-Cove (Newsquest, top right), Tshepo Tshabalala (JournalismAI, bottom left) and Mattia Peretti (News Alchemists, bottom right) all speaking at our Newsrewired conference over the years Credit: Marten Publishing
Pre-register your interest in joining our upcoming media community by filling out this brief form
As artificial intelligence transforms newsrooms and reshapes how we gather, verify, and distribute information, staying informed about this evolution has never been more critical.
But who should you listen to? We put that question to our community, and they were generous in recommending their top, trusted experts.
This isn't comprehensive however, so please let us know if we missed anyone.
Newsroom experts (22)
Alessandro Alviani, lead for generative AI, Süddeutsche Zeitung
Jane Barrett, head of AI strategy, Reuters
David Caswell, founder, Storyflow; former executive product manager, BBC News Labs
Jody Doherty-Cove, head of AI, Newsquest
Gina Chua, executive editor, Semafor
Phoebe Connelly, senior editor for AI strategy and innovation, The Washington Post
Ezra Eeman, strategy and innovation director, NPO
Laura Ellis, head of technology forecasting, BBC R&D
Karyn Fleeting, delivery director, AI, Reach plc
Eui-Hong (Sam) Han, chief AI officer, Washington Post
Sophie Huet, deputy global news, director, editorial innovation and AI, Agence France-Presse
Tom Kelleher, director of personalisation, AI & personalisation, The Telegraph
Tav Klitgaard, CEO, Zetland; chairperson, Good Tape
Uli Köppen, chief AI officer, Bayerischer Rundfunk
Manjiri Kulkarni Carey, former editor, BBC News Labs
Chris Moran, head of editorial innovation, Guardian News & Media
Sannuta Raghu, head, Scroll Media AI Lab for News And Journalism
Ole Reissmann, director of AI, SPIEGEL Gruppe
Zach Seward, editorial director of AI initiatives, New York Times
Agnes Stenbom, head of IN/LAB, Schibsted
Nick Wrenn, VP, Programming and events, CNBC
Olle Zachrison, head of News AI, BBC News
Journalists and writers (10)
Marina Adami, digital journalist, Reuters Institute for the Study of Journalism
Nilesh Christopher, Nieman Fellow, Harvard University
Andrew Deck, AI staff writer, Nieman Journalism Lab, Harvard University
Gretel Kahn, journalist, Reuters Institute for the Study of Journalism
Alvaro Liuzzi, newsletter editor, Redacciones5G, Telecom Argentina
Madhumita Murgia, artificial intelligence editor, Financial Times
Clare Spencer, freelance reporter, Generative AI in the Newsroom, Northwestern University
Jaemark Tordecilla, freelance journalist; Nieman Fellow for Journalism, Harvard University
Shayan Sardarizadeh, senior journalist, BBC News
Ricky Sutton, founder and podcast host, Future Media
Training and consultancy (21)
Wafaa Albadry, independent consultant
Charlie Beckett, director, Journalism AI Project, LSE
Kevin Donnellan, journalism and AI consultant, Explainable; content strategist, Reddit
Sam Gould, consulting manager and AI lead, FT Strategies
Jodie Hopperton, lead: product & tech initiative, International News Media Association (INMA)
Alan Hunter, co-founder, HBM Advisory
Christophe Israël, independent consultant; associate consultant, Fathm
Aliya Itzkowitz, manager, FT Strategies
Paul McNally, director and founder, Develop AI
Adriana Menezes Whiteley, director, FT Strategies
Harriet Meyer, trainer, AI for media
Heather Murray, founder, AI for Non-Techies
Zenzele Ndebele, director, Centre for Innovation & Technology (CITE)
Pete Pachal, founder, The Media Copilot
Mattia Peretti, founder, News Alchemists
Nikita Roy, ICFJ Knight Fellow; founder, Newsroom Robots
Ross Settles, independent consultant
Dietmar Schantin, principal, IFMS
Ramaa Sharma, founder & faculty, Reuters Institute
Adam Tinworth, founder, One Man & His Blog
Tshepo Tshabalala, project manager and team lead of JournalismAI, LSE,
Laurens Vreekamp, founder, Future Journalism Today Academy
Academia and research (15)
Rana Arafat, assistant professor, digital journalism, City, University of London
Emily Bell, director, Tow Center for Digital Journalism, Columbia
Alexandra Borchardt, lead author, EBU News Report, European Broadcasting Union
Nicholas Diakopoulos, professor, Northwestern University
Peder Hammerskov, assistant professor, DMJX
Sofie Hvitved, senior advisor and head of media, Copenhagen Institute for Future Studies
Bronwyn Jones, research associate, University of Edinburgh; research lead, BBC
Dan Lloyd, research associate, Reuters Institute for the Study of Journalism
Daria Minsky, lecturer, Bard College Berlin
Ethan Mollick, associate professor, The Wharton School
Laurens Naudts, postdoctoral researcher, AI, Media and Democracy Lab, University of Amsterdam
Grzegorz Piechota, researcher-in-residence, INMA
Devadas Rajaram, professor, Alliance University, Bangalore
Mike Reilley, senior lecturer, data and digital journalism, University of Illinois, Chicago
Felix M. Simon, research fellow in AI and news, Reuters Institute for the Study of Journalism
Policy and ethics (7)
Rebecca Ciesielski, algorithmic accountability reporting, Bayerischer Rundfunk
Shuwei Fang, Shorenstein Fellow, Harvard Kennedy School
Sam Gregory, executive director, WITNESS
Natali Helberger, director, AI, Media and Democracy Lab
Bahareh Heravi, BRAID fellow, BBC; associate professor, AI and media, University of Surrey
Murielle Popa-Fabre, responsible AI policies and governance expert, Council of Europe
Sanne Vrijenhoek, PhD candidate, University of Amsterdam
Tech companies and tech adjacent (10)
Christopher Brennan, co-founder, Overtone
Paul Cheung, strategic adviser, Hacks/Hackers
Lars Damgaard Nielsen, co-founder, MediaCatch
Florent Daudens, press lead, Hugging Face
Daniel Flatt, co-founder, Flare Data
Markus Franz, chief technology officer, Ippen Digital
Burt Herman, co-founder, Hacks/Hackers
Matthieu Lorrain, creative lead, Google DeepMind
Branislava Lovre, co-founder, AImpactful
Tassos Morfis, co-founder, Qurio
This list was first published on 13 June 2025 and was updated on 20 June 2025 with ten new additions
Free daily newsletter
If you like our news and feature articles, you can sign up to receive our free daily (Mon-Fri) email newsletter (mobile friendly).
| 2025-06-13T00:00:00 |
2025/06/13
|
https://www.journalism.co.uk/news/who-to-follow-85-expert-voices-at-the-intersection-of-ai-and-journalism/s2/a1254807/
|
[
{
"date": "2025/06/13",
"position": 35,
"query": "AI journalism"
},
{
"date": "2025/06/13",
"position": 27,
"query": "artificial intelligence journalism"
}
] |
5 Skills Content Teams Need in the Age of AI | Brand Journalism
|
5 Skills Content Teams Need in the Age of AI | Brand Journalism
|
https://stacker.com
|
[
"Noah Greenberg",
"Ken Romano"
] |
To thrive alongside AI, content teams must sharpen human strengths like storytelling, brand voice, critical thinking, and data-driven decision-making.
|
In Year 2 of AI-Taking-Over-Our-Lives-and-Headlines, much of the initial shock and awe has dissipated. AI tools have greatly disrupted the human workforce, with some going “all-in” on AI, like the publication, Business Insider, and brands like Duolingo.
Yet a 2025 Epsilon Pulse report found that content teams are seeing AI as their friend — a staggering 94% of marketers say they use AI technologies. More than two in three marketers even go as far as to say that they’re excited about AI and its impact on their jobs, according to a 2025 SurveyMonkey report.
Humans and AI can collaborate efficiently, though humans’ jobs have been reshaped as a result of what the tech can do. As the technology evolves, content teams, including Stacker, continue to develop principles for its use and how to best harness it. There’s no way to know exactly how human jobs will continue to change, but some key skill sets have emerged that content teams can leverage in this new era of content production.
To leverage the strengths of humans and bots, content teams should prioritize five critical abilities to optimize the content they create alongside AI.
#1: Critical Thinking and Editorial Judgment
AI has an incredible ability to extract trends from data, far faster than humans can. It can even generate ideas and drafts based on what it learns through neuro-linguistic processing.
It might feel too good to be true — and it can be — without keeping humans in the loop. AI models can still be highly inaccurate and go awry without human guardrails. Lessons from the recent Chicago Sun-Times summer reading list errors were a necessary reminder that humans still need to intervene throughout the content generation process: human fact-checking should be able to call AI out the next time it tries to convince humans to read a bunch of made-up books and fake experts.
As the volume of content output increases with AI, so too do content professionals need to maintain high accuracy and ethical standards. They must hone in on their abilities to vet and critique AI-generated material, resisting the temptation to blindly rely on the ease it brings. Maintaining quality and credibility means adding layers of enhancement to AI-created drafts for meaning, nuance, and tone — things that AI can’t deliver on its own. And even if they could, consumers don’t respond well when they believe emotional content was created by AI.
#2. Brand Stewardship and Voice Guardianship
AI output is generic by default — what we call “artificial intelligence” is simply mimicking the trove of information it's ingested before.
So, content professionals using AI must have a deep understanding of brand identity to refine AI-generated content by hand or through feedback to boost the AI’s ability to engage and stay on-brand. Humans serve as stewards who ensure that content upholds the unique voice, tone, and messaging framework of their brand, always questioning alignment with their audience.
Without human hand-holding, AI-generated content will strip the sense of authenticity and emotional resonance that brands have built with their audiences over time. “Authenticity is not just a buzzword. It’s literally a brand’s lifeline,” Tonya McKenzie, Communications and Marketing Strategist, From the Lockerroom to the Boardroom, told Stacker.
#3: Prompt Engineering
Humans are also necessary puppeteers to engineer the prompts that guide AI to generate or analyze. Clear, specific prompts enable better first drafts and faster workflows.
Teams who learn to “talk to” AI tools — providing them with well-defined strategies, frameworks, customer personas, tone guidance, and continual tweaking with layers of prompts have better chances of getting AI to work for them, rather than creating more work.
#4: Audience Empathy and Storytelling
Amid a sea of AI-generated noise, it's still human-created stories that resonate and rank.
AI can assemble facts well, but it can't understand the human emotions behind why a story will matter to readers. This means that content professionals must ensure they contextualize and embed emotional intelligence into early AI drafts. Without it, readers will tune out if their pain points, desires, fears, and joys aren’t felt or understood in the content they consume.
#5: Data Interpretation and Content Optimization
Lastly, AI can surface data and trends, but humans must decide what matters and how to act on it.
Skills in analytics will differentiate good teams from great ones, such as identifying content gaps, data quality, understanding user intent, and optimizing content based on performance data. Humans are necessary to surface the right data to feed to AI and must retain our abilities to strategize how data and content can help grow audiences.
🔥 Pro Tip:
The most successful content teams won’t just use AI tools. They’ll train themselves to collaborate with AI — using it to remove friction from workflows, spark creativity, and double down on uniquely human strengths.
Danika Murphy is a seasoned sales and brand strategist with a decade of experience helping companies grow through creative storytelling and data-driven distribution. Currently a Senior Account Executive at Stacker, she’s previously led partnerships at Spotify, Veritonic, and ChowNow. Danika brings a passion for marketing, media, and building trusted relationships that drive measurable business outcomes.
Photo Illustration by Stacker // Canva
| 2025-06-13T00:00:00 |
https://stacker.com/blog/skills-content-teams-need-in-age-of-ai-brand-journalism
|
[
{
"date": "2025/06/13",
"position": 98,
"query": "AI journalism"
}
] |
|
Artificial intelligence playing a growing role in layoffs by US ...
|
Artificial intelligence playing a growing role in layoffs by US companies
|
https://www.baltimoresun.com
|
[
"Janae Bowens"
] |
Artificial intelligence is also playing a growing role in these layoffs. Klarna CEO Sebastian Siemiatkowski revealed that the company has cut 40% of jobs partly ...
|
The biggest piece of Mars on Earth is going up for auction in New York
| 2025-06-13T00:00:00 |
2025/06/13
|
https://www.baltimoresun.com/2025/06/13/artificial-intelligence-playing-a-growing-role-in-layoffs-by-us-companies/
|
[
{
"date": "2025/06/13",
"position": 96,
"query": "AI layoffs"
}
] |
“AI Isn't Stealing Your Job”: It's Replacing Interns, Trainees ...
|
“AI Isn’t Stealing Your Job”: It’s Replacing Interns, Trainees, and Everyone Still Learning—And It’s Happening Shockingly Fast
|
https://www.rudebaguette.com
|
[
"Noah Bennett"
] |
As artificial intelligence increasingly automates entry-level jobs, young graduates find themselves navigating a challenging job market landscape.
|
IN A NUTSHELL 🤖 AI is taking over entry-level positions, creating barriers for young graduates entering the workforce.
is taking over entry-level positions, creating barriers for young graduates entering the workforce. 📈 The rise in youth unemployment is linked to the automation of entry-level tasks by companies like Amazon and Google.
by companies like Amazon and Google. 💼 Many traditional training steps are becoming obsolete as AI performs more than half of the required skills in numerous occupations.
🔧 To ensure balanced workforce development, there is a need to rethink professional insertion and learning opportunities.
The advent of artificial intelligence (AI) is reshaping the employment landscape in ways few had anticipated. While AI was initially celebrated for its potential to relieve workers of monotonous tasks, it has paradoxically become a barrier for young graduates entering the workforce. As these graduates prepare to embark on their professional journeys, they face an unexpected hurdle: AI is taking over entry-level positions, traditionally seen as stepping stones into the job market. This shift presents significant challenges and questions about the future of work and career development.
The Disappearing Entry-Level Job
The traditional career path for young professionals often begins with internships, junior roles, or simple assignments that provide essential on-the-job training. However, Aneesh Raman, head of economic opportunities at LinkedIn, notes a troubling trend: “The first rung of the professional ladder is disappearing.” The culprit? AI technology. These introductory tasks—often administrative, repetitive, and low-skill—are increasingly being automated by AI systems. Tech giants like Amazon, Google, and Microsoft have already begun automating tasks previously handled by junior employees, such as writing code snippets, data entry, and administrative support.
As a result, opportunities for hands-on learning are dwindling. This trend represents a significant shift in how companies operate and train new talent. Without these foundational experiences, young workers may find it harder to gain the skills and knowledge needed to advance in their careers. This change raises concerns about how future professionals will acquire the expertise necessary to thrive in an AI-dominated job market.
The Impact on Youth Unemployment
The rapid automation of entry-level tasks has contributed to a rise in youth unemployment. According to the Federal Reserve Bank of New York, the unemployment rate for young graduates in the United States is 5.8%, slightly lower than the 6.2% for the youngest workers. This increase is partly attributed to the swift automation of entry-level roles. Companies like Duolingo and Shopify are actively reducing junior recruitment for these positions, opting instead to assign them to AI systems.
This trend has broader implications for the economy and society. As AI continues to evolve, the gap between the skills employers need and those that new entrants possess may widen. The challenge is to ensure that young professionals have access to opportunities that allow them to develop the skills necessary for future success. Without intervention, the risk is that AI will not only redefine existing jobs but also prevent future professionals from emerging.
AI’s Role in Redefining Skill Requirements
Chris Hyams, CEO of Indeed, highlights that in about two-thirds of occupations, more than half of the required skills can be performed effectively by current AI technologies. While AI does not fully replace human roles, it renders many traditional training steps obsolete. This development creates a paradox: companies warn of a shortage of qualified workers but no longer provide the conditions needed to train these talents.
This issue is particularly pronounced in Europe, where companies struggle to recruit experienced technical profiles while young people are unable to access positions that would allow them to gain such experience. The imbalance creates a challenging environment for both employers and job seekers, emphasizing the need for innovative solutions to bridge the skills gap and ensure a balanced workforce.
Rethinking Professional Insertion
If current trends persist, AI will not eliminate millions of jobs overnight; instead, it will prevent future professionals from emerging. The long-term risk is ending up with powerful tools but lacking enough qualified humans to manage and develop them. To ensure AI benefits everyone, there’s a pressing need to rethink professional insertion and guarantee learning opportunities where machines encroach.
Creating pathways for young workers to gain real-world experience is crucial. This involves reimagining how we approach education and professional development. By integrating AI into the learning process and emphasizing human skills that AI cannot replicate, society can create a more balanced and inclusive job market. The challenge lies in finding the right balance between technology and human development.
As AI continues to evolve, its impact on the job market will undoubtedly grow. The key question remains: How can we adapt our educational and professional systems to ensure that young professionals are not left behind in the age of AI? This ongoing challenge requires thoughtful consideration and action from policymakers, educators, and industry leaders alike.
Our author used artificial intelligence to enhance this article.
Did you like it? 4.4/5 (30)
| 2025-06-13T00:00:00 |
2025/06/13
|
https://www.rudebaguette.com/en/2025/06/ai-isnt-stealing-your-job-its-replacing-interns-trainees-and-everyone-still-learning-and-its-happening-shockingly-fast/
|
[
{
"date": "2025/06/13",
"position": 62,
"query": "artificial intelligence employment"
}
] |
AI for Graphic Design
|
AI for Graphic Design
|
https://www.nobledesktop.com
|
[] |
This class explores advanced AI tools within Adobe's suite, helping you enhance your creative process and streamline tasks.
|
Eugenio Solis de Ovando
Senior Instructor
Eugenio Solis de Ovando is a Senior Instructor at Noble Desktop in New York City, specializing in graphic design, web design, and visual communication. With over 20 years of experience in the creative industry, Eugenio is passionate about helping students develop strong technical skills and a solid design foundation across print, digital, and web platforms.
Eugenio teaches a wide range of courses, including Photoshop, Illustrator, InDesign, Figma, WordPress, UI Design, and Artificial Intelligence for Graphic Design. His classes are known for their clear, hands-on approach, blending technical mastery with creative exploration to help students bring their ideas to life with confidence and precision.
He holds a Ph. D. in Artificial Intelligence with a specialization in Human Performance Technology, where his research focused on integrating emerging AI technologies into adult education and training.
As an Adobe Certified Design Master and Licensed Private Career School Teacher in New York State, Eugenio is dedicated to delivering high-quality instruction and sharing best practices in digital design workflows. He brings real-world insights into the classroom, guiding students through professional techniques in layout design, typography, color theory, image editing, and responsive web design.
Learn more about Eugenio Solis de Ovando's background and expertise.
| 2025-06-13T00:00:00 |
https://www.nobledesktop.com/classes/ai-graphic-design
|
[
{
"date": "2025/06/13",
"position": 10,
"query": "artificial intelligence graphic design"
}
] |
|
Investigating Artificial Intelligence: What lies beyond the ...
|
Investigating Artificial Intelligence: What lies beyond the algorithm
|
https://lab.imedd.org
|
[
"Chrisoula Marinou"
] |
When reporting focuses on algorithms, it's easy for the story to become overly technical or bogged down in the mechanics of AI. While the algorithm may appear ...
|
Talking to workers
The best way to start investigating AI isn’t by opening a computer science textbook — it’s by understanding how the system functions like a production line.
In this line, the first essential ingredient is data. That data “feeds” the model, enabling it to generate any kind of output. In this process, so-called data annotators play a crucial role. These are people whose sole job is to annotate and categorise the data that will be used to train the AI — essentially telling the model what the information is, how to recognise it, how to analyse it, and how to respond to it.
When journalist Michael Bird began researching the topic he would later cover with Schepers, he learned of a worker at Outlier – a company that employs remote data annotators – raising complaints about working conditions. Around the same time, a Facebook post revealed that workers from the so-called Global South were trying to obtain European user IDs on the digital black market to boost their daily earnings.
To investigate the working conditions of data annotators, Schepers and Bird spoke with groups of workers who had gathered on online platforms and social media. The workers themselves described their mechanical and isolating work, the lack of transparency, the low pay, and the exploitation they experience from the large companies whose data they annotate.
Data centres and drinking water
The production process itself can be part of the problem. To understand how AI is created, we need to distinguish between two levels: the physical (or material) and the technical.
The physical level includes large structures known as data centres — buildings that house computers, telecommunications systems, and storage infrastructure. Companies like Microsoft and Meta use these centres to store, process, and distribute vast amounts of data.
As Naiara Bellio, Head of Journalism at AlgorithmWatch, explained, key questions to guide an investigation include how data centres operate, who owns them, who builds and maintains them, and how they affect both workers and the local environment. Many of these centres are built in low-humidity areas to help maintain stable temperatures. Investigations like this one – by Manuel G. Pascual for El País in 2023 – have shed light on the enormous water consumption of data centres. A year later, journalist Karen Hao of The Atlantic revealed that data centres continuously cool their systems using air and evaporated drinking water — a necessity, as computers must remain at specific temperatures to avoid overheating.
In contrast, the technical level involves how AI systems actually generate results. According to Article 3 of the European Regulation on AI, an AI system means a machine-based system that is designed to operate with varying levels of autonomy that may exhibit adaptiveness after deployment, and that infers, from the input it receives, how to generate outputs Bellio stressed that not all algorithms are forms of artificial intelligence. There are distinct types, such as deep learning, computer vision, generative systems, and natural language processing — the kind used in chatbots, for example. It’s important for journalists to recognise these differences and preserve those distinctions in their reporting. By clearly identifying which type of AI they’re referring to, journalists can better communicate their findings to institutions, companies, and readers.
| 2025-06-13T00:00:00 |
2025/06/13
|
https://lab.imedd.org/en/investigating-artificial-intelligence-what-lies-beyond-the-algorithm/
|
[
{
"date": "2025/06/13",
"position": 35,
"query": "artificial intelligence journalism"
}
] |
Khari Johnson
|
Khari Johnson
|
https://calmatters.org
|
[
"Khari Johnson",
"Mohamed Al Elew",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img",
"Height Auto Max-Width",
"Vertical-Align Bottom .Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow .Wp-Block-Co-Authors-Plus-Avatar"
] |
... artificial intelligence since 2016 ... With input from a Stanford lab, Common Sense Media concludes the AI systems can exacerbate problems like addiction and self ...
|
Khari Johnson is part of the technology team and is CalMatters’ first tech reporter.
He has carried a reporter’s notebook in his back pocket for the majority of his life and has covered artificial intelligence since 2016. At first he covered stories from a consumer technology approach driven by products and profit, but human rights abuses and extreme power imbalances gradually turned his focus toward stories that examine how tech can impact society and the public interest. Covering artificial intelligence taught him numerous ancillary issues, including antitrust, privacy, and the importance of policy. He strives to tell stories about people harmed by AI and demand accountability, so don’t hesitate to reach out if you know someone who fits that description. Khari previously worked at WIRED, VentureBeat, and Imperial Beach Patch. In the past he’s talked about how AI can harm people with NPR, The Beat with Ari Melber on MSNBC, onstage at WIRED’s 30th anniversary, and at the Democracy Summit at Howard University. He has a charismatic smile, charming wit, and an unending passion for snacks. He is currently a practitioner fellow at the Karsh Institute’s Digital Technology and Democracy Lab at the University of Virginia, guest speaker at the Pulitzer Center, and sits on the Society of Professional Journalists Board of Directors. He was born and raised in San Diego, and graduated from San Francisco State University with a degree in journalism and minor in political science. He lives in Oakland.
| 2025-06-13T00:00:00 |
https://calmatters.org/author/khari-johnson/
|
[
{
"date": "2025/06/13",
"position": 77,
"query": "artificial intelligence journalism"
}
] |
|
Artificial Intelligence
|
Artificial Intelligence – Latest News and Analysis – Page 1
|
https://san.com
|
[] |
Get your Artificial Intelligence news and videos - straight and unbiased - from Straight Arrow News.
|
Finally, unbiased news that lets you see both sides. It’s refreshing to have facts without the spin.”
Mike, Michigan
| 2025-06-13T00:00:00 |
https://san.com/tags/artificial-intelligence/
|
[
{
"date": "2025/06/13",
"position": 100,
"query": "artificial intelligence journalism"
}
] |
|
AI Employee Scheduling Software
|
AI Employee Scheduling Software
|
https://tcpsoftware.com
|
[] |
Labor laws, union rules, and internal policies are baked into the system. Whether managing predictive scheduling laws, ensuring rest periods between shifts ...
|
A few years ago, the idea of using artificial intelligence for employee scheduling might have sparked resistance or skepticism.
Would it replace human managers?
Would the algorithms be fair?
Could we trust a machine to make people-centered decisions?
Fast-forward to today – AI is being widely adopted in workforce planning as a strategic necessity. Luck and gut instinct don’t keep your workforce running smoothly. It takes the right systems, especially when it comes to employee scheduling.
For growing organizations in healthcare, retail, hospitality, education, and beyond, scheduling is a daily battle. As operations scale and the stakes of non-compliance rise, the margin for error shrinks. Manual tools and traditional templates simply can’t keep up anymore, but AI-driven scheduling platforms can.
In this guide, you’ll learn how AI employee scheduling works, how predictive analytics makes workforce planning smarter, and why shifting to an automated approach is about way more than a tech upgrade.
What is AI employee scheduling?
AI (artificial intelligence) employee scheduling refers to the use of machine learning, predictive modeling, and intelligent algorithms to generate work schedules — these schedules meet business needs while respecting employee preferences, labor laws, and cost controls.
It’s a powerful evolution of traditional workforce scheduling that eliminates guesswork and inefficiency. Unlike rule-based or template-driven systems, AI-driven employee scheduling software references historical data, real-time inputs, and shifting variables to suggest the most efficient and fair schedule possible.
From automated schedules to AI scheduling
Rule-based scheduling automation can follow if/then logic: “If an employee is available Monday and has under 40 hours, assign them to the shift.”
AI, by contrast, doesn’t follow a script. It learns patterns from data like absentee trends, shift swaps, employee performance, and demand patterns. It then optimizes scheduling decisions based on what it’s learned collectively from the data.
How AI builds smarter schedules
AI employee scheduling generators pull from multiple sources:
Past schedules
Time-off requests
Employee availability
Certifications
Compliance rules
Demand forecasts
From there, they go through AI-based or AI-generated scheduling, which creates schedules that adapt as conditions change.
Real-time learning and adaptation via AI
Modern AI scheduling tools don’t just stop at the schedule. They continuously learn from employee behavior, engagement, and shift performance.
For example, if a specific staff member frequently swaps out of Thursday night shifts, the system will adapt future suggestions accordingly, removing friction and improving
The rise of predictive analytics and automation in scheduling
Over the last decade, workforce management platforms have evolved beyond digital calendars and shift templates. The introduction of predictive analytics and automation has take scheduling from a reactive firefight to proactive planning strategy.
These changes don’t just look better on paper. They’re foundational for operating with greater accuracy, agility, and fairness.
A decade of innovation in workforce scheduling
Automated employee scheduling platforms began as digital replacements for paper charts and Excel sheets. Today, they build responsive, optimized schedules at scale using real-time data. Over 50% of high-performing organizations today use advanced workforce analytics to guide staffing decisions.
Why predictive analytics matters
Predictive analytics allow organizations to forecast labor needs based on your data — seasonal foot traffic, appointment bookings, ticket sales, delivery schedules, and historical demand. This is especially valuable in sectors like retail and healthcare, where under- or overstaffing directly impacts customer outcomes.
Automation vs. AI vs. predictive analytics
It’s important to distinguish that each capability offers a unique benefit to your organization:
Automation = rule-following
Prediction = forecasting
AI = learning
The best scheduling solutions combine all three to automate repetitive tasks, forecast needs, and improve over time by learning from the outcomes.
Benefits of AI-driven employee scheduling software
You might look into AI-driven employee scheduling software to save time. But the impact goes far beyond that: cost savings, legal protection, higher retention, and operational clarity.
Here are a few core benefits you will see after switching to smart scheduling software.
Faster scheduling cycles
AI scheduling tools cut down hours of admin work by automating shift creation, availability matching, and schedule distribution. Instead of grinding through building schedules from scratch every week or manually editing templates, managers simply review and approve suggestions.
Lowered compliance risk
Labor laws, union rules, and internal policies are baked into the system. Whether managing predictive scheduling laws, ensuring rest periods between shifts, or tracking maximum weekly hours, AI automatically keeps your scheduling in line.
Reduced labor costs
AI reduces unnecessary overtime, overstaffing, and last-minute scheduling gaps. Organizations that implement AI-based workforce management tools report up to a 12% reduction in labor costs due to better shift alignment and reduced overtime.
Greater employee satisfaction
AI considers employee preferences, like availability, shift swaps, or time-off requests, and balances them with business needs. This creates fairer schedules, reduces last-minute changes, and gives workers more control over their time.
More accurate forecasts
With predictive models, AI scheduling tools help you plan for demand fluctuations. Whether that means adjusting to a flu season, a holiday surge, or construction delays, you have a system that puts you ahead of changes.
How AI employee scheduling works for key industries
While every business can benefit from more innovative scheduling, the impact is especially clear in industries with high staffing complexity, compliance risks, and labor volatility.
Here’s how AI employee scheduling transforms workforce management across retail, hospitality, healthcare, education, and other key sectors:
Retail
In retail, labor demand shifts by hour, day, and season. Using historical sales, foot traffic, and weather data, AI scheduling retail tools forecast peak hours, promotional rushes, and regional trends. That makes assigning the correct number of associates to each shift easier.
AI also:
Supports part-time, seasonal, and on-call scheduling
Reduces the amount of scheduling errors
Enables flexible shift swaps without overburdening managers
Hospitality (restaurants, hotels, events)
Hospitality lives and dies by real-time changes.
AI workforce optimization systems adjust staffing based on guest bookings, reservations, and live event data. For example, if a hotel sees a spike in check-ins due to a flight cancellation, the system can instantly prompt managers to add front desk coverage.
AI also balances front-of-house and back-of-house scheduling, ensuring food service, housekeeping, and maintenance teams align without overstaffing or gaps.
Healthcare (hospitals, clinics, senior care)
For healthcare organizations, compliance and continuity are non-negotiable.
AI healthcare staff scheduling software ensures the right mix of certifications, specialties, and hours. It also prioritizes staff preferences where possible, improving staff well-being by helping prevent burnout.
Float pools, per diem staff, and call-ins are easier to manage when the system understands real-time coverage needs and legal boundaries.
Education (K–12, higher education)
Academic scheduling requires juggling adjunct faculty, part-time workers, student assistants, and support staff.
AI scheduling systems for universities and schools:
Coordinate classes, labs, office hours, and custodial coverage
Work within union agreements and budgets.
Adapt to changing enrollments, holidays, and seasonal trends
All of which manual tools rarely do well.
Other sectors (public safety, manufacturing, field service)
These industries operate on tight timelines and need scheduling to match. Not doing so can come with huge risks to safety and efficiency.
From shift-based patrols to real-time field deployment, AI gives managers the power to respond fast and plan ahead:
Public safety teams match patrols or dispatch crews to incident patterns.
Manufacturing facilities align labor with machine uptime, production goals, and shift cycles.
Field service operations build optimized, route-based schedules across large territories.
AI shift scheduling improves performance in these time-sensitive, high-complexity fields while reducing planning burdens.
How to choose the right AI scheduling tool for your business
If you’ve made it this far, you might be thinking, “This sounds great. But which platform will actually work for my team and the way we operate without slowing down operations?”
The move beyond spreadsheets and static templates comes with plenty of decision uncertainty because not all platforms are created equal.
Here’s how to make a smart selection for an AI scheduling tool the first time:
Step 1: Know your regulatory landscape
Every industry has its legal and compliance standards, from predictive scheduling laws in retail and hospitality to certification tracking in healthcare. You should look for built-in compliance logic that updates as regulations evolve, real-time flagging for scheduling violations, and a way to document compliance for audits.
If you’re in a regulated sector, where scheduling mistakes can lead to fines, lawsuits, or accreditation issues, this is even more important.
Your AI scheduling tool should serve as both a scheduler and a compliance assistant, automatically applying local labor laws, union agreements, and internal policies without manual intervention.
Step 2: Look for seamless integrations
AI scheduling tools don’t exist in a vacuum. To get the most value, your platform should integrate smoothly with HR systems, payroll software, and time tracking tools. Integrations eliminate redundant data entry, reduce errors, and ensure that scheduling decisions align with payroll calculations, PTO balances, and employee records.
Here’s how it works in real-life:
An employee’s PTO request is vetted
It’s approved based on availability collected by AI
The schedule automatically reflects the change
No manager cross-checking or intervention needed. Integration also introduces reporting and analytics that span the entire employee lifecycle, giving leadership a clearer view of workforce performance and cost drivers.
Step 3: Prioritize transparency
One of the biggest concerns with AI tools is decision-making transparency.
If your scheduling platform makes assignments without showing its logic, you risk damaging trust with employees and managers alike. Look for tools that:
Offer clear rationales for scheduling decisions
Highlight rule conflicts
Allow for human overrides.
This builds accountability and keeps you in control. Transparent AI platforms also communicate the “why” behind employee schedules — why preferred shifts weren’t granted, why skill level or availability plays a role, etc.
This clarity reduces complaints, improves adoption, and supports a healthier scheduling culture.
Step 4: Don’t forget mobile access
Your workforce isn’t tied to a desk. Your scheduling platform shouldn’t be either.
Mobile apps let employees check schedules, submit time-off requests, and swap shifts on the go. For managers, this means aligning schedules without having to wait for desktop access.
Especially in industries like hospitality, retail, and healthcare — where frontline staff make up the majority of the workforce — mobile-first tools are no longer optional. Mobile access allows schedules to flex with daily realities, minimizes miscommunication, and helps managers lead proactively, even on the move.
Future-proof your workforce with AI employee scheduling
Scheduling istoo important to your organization and too complex to rely on outdated tools. AI employee scheduling gives you the control, speed, and visibility to plan ahead with confidence and easily adjust when things change.
Want to lower your labor costs? Improve compliance? Create fewer headaches in your operations? AI-powered scheduling is already delivering these results across a wide range of industries.
If you think you and your teams deserve better, it’s because you do. Now is the time to take the next step. TCP’s Humanity Schedule features an AI-powered Demand Scheduler to help organizations like yours stay ahead with demand forecasting, spend less time on scheduling, and get just-right coverage every shift.
AI scheduling isn’t another tech fad. It’s the new standard.
TCP Software’s employee scheduling, time, and attendance solutions are flexible and scalable to accommodate your organization and employees as you grow.
From TimeClock Plus, which automates even the most complex payroll calculations and leave management requests, to Humanity Schedule for dynamic employee scheduling that saves you time and money, we have everything you need to meet your organization’s needs, no matter how unique.
Plus, with Aladtec, we offer 24/7 public safety scheduling solutions for your hometown heroes.
Ready to learn how TCP Software takes the pain out of employee scheduling and time tracking? Speak with an expert today.
| 2025-06-13T00:00:00 |
https://tcpsoftware.com/articles/ai-employee-scheduling-software/
|
[
{
"date": "2025/06/13",
"position": 71,
"query": "artificial intelligence labor union"
}
] |
|
Our Take: AI could be the death of entry level jobs, but only if ...
|
Our Take: AI could be the death of entry level jobs, but only if we allow it
|
https://www.unleash.ai
|
[
"John Brazier",
"Senior Journalist"
] |
Recent statements have all but declared that the entry level role will be replaced by AI in the coming years. UNLEASH Senior Journalist, John Brazier, ...
|
In my more cynical moments, which admittedly are more often than I’d like these days, I find myself comparing AI to the One Ring.
An all-powerful object desired by all, corrupting those that fall under its spell by subverting their ambition for power, and ultimately stripping away all humanity from those seeking to possess it – sound familiar?
As I said, that’s only when I’m feeling cynical, but it’s getting harder to find arguments against this view.
Within the realms of HR, the excitement around AI has become an energy that is hard to argue against – the voices of those asking ‘why’ are drowned out by those demanding ‘how’.
That’s not to say that AI doesn’t offer numerous positives to HR professionals and workers alike, but the pace of this movement seems to ignore the second-hand impact it will have on the world of work.
Take, for example, the entry level job – a now critically endangered role that AI is apparently set to be totally obliterated about as quickly as an unnecessary Peter Jackson prequel trilogy.
The recent comments by Dario Amodei, the CEO of AI company Anthropic, that as much as half of entry-level jobs could be gone in the next five years ‘thanks’ to AI, caused no small amount of consternation and discourse.
It’s full steam ahead on AI and the handbrake has been thrown out the window
This is just one of the latest announcements heralding our bright, AI-powered future of work.
Last month, CEOs of Klarna and IBM both detailed how AI adoption has led to reductions in their respective workforces – specifically in the HR department at IBM.
LinkedIn commenters have had a field day with this material, offering sage advice as to why this is a positive development (particularly if you’re developing your own AI product) or what businesses should do to best capitalize on this trend.
Our Chief Reporter, Allie Nawrat, spent time canvassing the HR analyst community to find out why and how HR leaders can turn the death of entry-level jobs into an opportunity and there are many insightful points made.
But I keep coming back to what Mercer’s Jason Averbook said at UNLEASH America last month on this topic: “If this doesn’t scare the heck out of you, it should.
“If you graduated from college, it’s very hard, if not impossible, to find a job unless you’re building a network or your privilege.”
Now, what really stood out to me in Amodei’s comments about the potential death of entry-level work was his acknowledgment that he is at the helm of an AI company responsible for this and there is a “duty and obligation” to be honest about it.
“You can’t just step in front of the train and stop it,” he added.
To my mind, this sounds like a transparent threat – they can so they will, and you’re going to have to deal with the consequences because you can’t stop this.
Maybe I’m letting my own skepticism of AI cloud my judgement here, but that’s what opinion columns are for.
Perhaps this is just another in a series of workplace revolutions, like the introduction of computers or the Internet, but speak to just about anyone in the HR space and they’ll tell you in detail exactly why it’s not the same at all.
Entry level is where employees learn the world of work
Entry-level jobs are, more often than not, thankless roles comprised of mundane and repetitive tasks – I know mine was, but it also taught me multitudes about how ‘work’ works.
This will also be a vital part of the personal development of young people coming into the workforce whose education and social perspectives were decimated by the COVID-19 pandemic.
What many of us learned by osmosis just by going about our lives, these kids are going to need to learn on the job in real-time, without the safety nets of the past.
These roles are also crucial to the day-to-day operations of any organization.
The case for AI to automate huge swathes of this is strong and history shows that automation has transformed many industries, such as manufacturing and assembly, for the long-term betterment.
But this didn’t happen overnight – these shifts took years to come to fruition and even then, there were damaging repercussions for workers that hadn’t been accounted for by those making the decisions.
It’s a topic we’ll be addressing in an upcoming UNLEASH webinar about how organizations can grow during lean times.
Without giving away all the good stuff in advance, the words “hot mess” were used more than once during our initial discussions, referring to organizations that replace entry-level workers with AI without a long-term strategy.
In Allie’s article, Lighthouse’s Ben Eubanks also makes the point that one answer to all this is to “rethink what entry-level work is.”
While this is a good point and one that many business leaders will hopefully follow, it’s also an opportunity to highlight Ben’s insightful piece on AI’s impact on our cognitive and creative skills.
Because if we go ahead and replace entry-level workers with AI in the next 12-18 months, where does the future workforce come from?
How are they going to build the experience and learn the skills, both technical and soft, that employers require?
How will they develop their careers if they can’t even get through the door to begin with?
AI can be a wonderful tool to help people thrive at work, learn new skills and ways of working, bring new ideas and innovations to life, but only if it is used and regulated in a cautious and respectful way.
A blind rush to replace people with AI will have long-lasting and damaging consequences further down the road.
| 2025-06-13T00:00:00 |
2025/06/13
|
https://www.unleash.ai/artificial-intelligence/our-take-ai-could-be-the-death-of-entry-level-jobs-but-only-if-we-allow-it/
|
[
{
"date": "2025/06/13",
"position": 99,
"query": "future of work AI"
}
] |
17 statistics measuring the impact of AR head-count ...
|
17 statistics measuring the impact of AR head-count reduction via automation
|
https://resolvepay.com
|
[
"Resolve Team"
] |
Data shows that companies investing heavily in automation saw up to 17% cost reduction in related processes compared to those investing less, who saw about a 7% ...
|
Updated on June 12, 2025
Accounts receivable (AR) teams are seeing major changes as automation reduces the need for manual work. Automation in AR can lower costs and improve efficiency, allowing businesses to focus on higher-value tasks.
Reviewing robust data is crucial to understand how head-count reduction through automation is transforming AR departments. These accounts receivable statistics highlight current trends and provide business leaders with clear insights for future decision-making.
1) AR headcount reduction percentage linked to automation adoption
Automating accounts receivable processes can lead to measurable headcount reduction. Data shows that companies investing heavily in automation saw up to 17% cost reduction in related processes compared to those investing less, who saw about a 7% drop. These savings often result from fewer manual tasks and improved process efficiency.
For more information on how automation leaders achieved this, see the report on the automation scorecard process reduction. Companies looking to invest can review workflow automation statistics & trends in 2025 for practical benchmarks.
2) Average time saved per invoice through AR automation
Automating accounts receivable tasks significantly cuts the time spent processing each invoice. On average, businesses that implement AR automation report process times that are up to 87% faster than manual methods.
Modern solutions for automating AR processes help teams streamline AR processes, saving both labor and operational costs. For more on improving operational efficiency, review the invoice delivery processes that contribute to faster turnaround times.
3) Decrease in manual AR processing errors post-automation
Automation in accounts receivable reduces the risk of mistakes made during manual tasks. This leads to better accuracy in handling invoice data and payments, which directly helps prevent costly errors.
Businesses using AR automation see fewer data entry errors and better audit trails compared to manual systems, as shown in workflow automation statistics. To learn more about these improvements, see our article on how automation boosts AR efficiency.
4) Impact on employee workload due to AR process automation
Automation in accounts receivable tasks reduces the time teams spend on repetitive work. Employees can focus less on manual data entry and more on solving complex customer issues.
Using automation technologies for economic processes helps companies streamline labor needs while still maintaining accuracy. As a result, businesses often see workload shifts rather than total elimination, which can improve job satisfaction and efficiency. For more information, see the article on AR automation adoption trends.
5) AR team size changes after implementing automated collections
Businesses often reduce manual tasks after adding automated accounts receivable (AR) collections. Many companies report needing fewer staff members to handle routine payment reminders and invoice follow-ups.
Some studies suggest nearly half of companies see AR headcount costs go down after switching to automation, as shown in this market overview AR automation PDF. For further details on AR collections tools, see this page about automate AR collections for faster payments.
6) Cost savings from labor reduction in AR via automation
Automating accounts receivable tasks can help companies lower labor costs by replacing manual processes with software. Some businesses have reported labor savings of 20-50% from automation, showing that significant budget improvement is possible.
These savings come from fewer staff hours needed for repetitive data entry, invoice processing, and payment tracking. To explore more automation statistics, see the article on impact of intelligent automation on cost savings.
7) Improvement in cash flow cycle time with reduced AR headcount
Reducing AR headcount through automation can speed up the entire cash flow cycle. Fewer manual tasks lead to quicker invoice processing and faster payments.
Organizations that implement process improvements often experience a 15-20% reduction in AR days within six months, which helps cash flow stability. For practical tips, see these quick wins for revenue cycle improvement.
To explore more strategies, review accounts receivable benchmarks.
8) Correlation between automation maturity and AR staffing levels
Companies with higher automation maturity levels often report lower accounts receivable (AR) staffing needs. As automation tools move from basic to advanced stages, fewer manual tasks remain for AR teams.
For specific stages and metrics, the Power Platform automation maturity model overview explains how automation expands and impacts staffing. For related insights on automation in manufacturing and other business functions, see the in-depth analysis at Human plus machine: a new era of automation in manufacturing.
9) Percentage of late payments reduced by AR automation
Businesses using AR automation often see a clear drop in late payments. One report states that 81% of companies struggle with delayed payments on at least 25% of invoices, but automation tools help improve these rates.
Automated collections make it easier for companies to track, remind, and receive payments faster, leading to fewer overdue accounts. More details on how automation can boost collections and reduce disputes are available.
Automating AR processes is a proven way to help businesses limit late payments and improve cash flow, according to accounts receivable statistics for 2025.
10) Headcount attrition rates impacted by AR automation deployment
AR automation often leads to direct changes in headcount attrition rates. When manual tasks are automated, businesses may see a reduction in staff due to fewer required roles.
A recent study found that automation technologies are designed specifically to replace human labor for certain tasks, directly impacting employment levels. Related statistics can be found in automation technologies and their impact on employment.
For further details on AR automation and its effects on business operations, visit accounts receivable automation guide.
11) Increase in touchless collection rates reducing AR personnel needs
Automating accounts receivable processes can lead to higher touchless collection rates, which means more payments are collected without any manual involvement. This reduces the demand for AR staff because fewer accounts require individual follow-ups.
Companies adopting these strategies report that automation directly improves collector effectiveness while lowering costs tied to manual tasks. For more on this, see how automation improves touchless collections in AR.
Data trends discussed in accounts receivable statistics shaping AR in 2025 show that more businesses are turning to technology to streamline collections and decrease personnel requirements.
12) Effect of AR automation on employee job satisfaction metrics
AR automation can create stress for employees who worry their jobs will be replaced. This fear can lower current levels of job satisfaction, even for those not directly affected by automation.
Studies show job satisfaction may decrease as automation increases, especially in firms adopting such technology. One analysis of automation, workers' skills and job satisfaction connects a rise in automation to lower morale and more uncertainty for workers. For businesses, monitoring key employee job satisfaction metrics during AR automation rollouts is critical.
13) Percentage of AR hours reallocated to strategic tasks post-automation
After automation, accounts receivable teams report that up to 57% of their time can shift from manual administration to more strategic projects. This helps businesses focus on critical work that supports growth.
Instead of routine processing, teams take on tasks that need problem-solving and analysis. More details on workflow automation statistics show this trend.
Read more about AR process optimization at AR automation impact.
14) Reduction in AR-related overtime due to automation
Businesses that adopt AR automation often see a noticeable drop in overtime hours for accounts receivable teams. With automated systems handling repetitive tasks, fewer manual hours are needed, which lowers overtime costs.
Automated workflows help meet deadlines faster by processing invoices and payments with less human input. For more information on how automation leads to job displacement and workflow changes, see this detailed analysis. Learn how AR automation reduces overtime and increases efficiency.
15) Productivity improvements measured per AR staff after automation
Companies that add automation to their accounts receivable teams often see more work done per staff member. Tasks that once took hours, like invoice processing and payment tracking, can now be completed faster.
Research shows that automating routine AR work leads to higher workplace productivity in many industries. For more details, see the section on automation benefits for AR teams at "accounts receivable automation statistics benefits" on cforganizer.com.
16) Changes in AR department turnover linked to automation tools
Many AR teams report lower employee turnover after automating routine tasks. Departments that introduce automated accounts receivable software often see staff retention improve due to less repetitive work and fewer manual errors.
A review of growth trends in automation shows that technology adoption can also ease pressure caused by high staff turnover rates, especially in sectors facing skilled labor shortages. These growth trends for occupations at risk from automation are important for businesses planning their workforce strategies.
17) Impact on AR dispute resolution time with fewer staff and automation
Reducing staff in accounts receivable teams can raise concerns about delays in resolving disputes. Automation helps address this challenge by speeding up routine processes and improving consistency.
According to data, leveraging automation in AR lets teams resolve disputes faster compared to manual-only processes. For more information, see this guide on AR dispute resolution metrics.
Automated workflows can also improve efficiency when solving AR disputes with automation, especially as businesses scale.
Understanding AR Head-Count Reduction Through Automation
Companies want to increase productivity and cut costs without sacrificing quality. Automated processes, especially using AR, are changing how tasks and teams operate in business settings.
Defining AR Automation And Its Business Applications
AR (augmented reality) automation uses digital overlays to assist workers with tasks, guide workflows, and reduce errors. For example, AR headsets can show a technician exactly where to place a component or highlight errors during inspections. This reduces guesswork and rework.
Businesses deploy AR automation for inventory checks, assembly line guidance, and safety evaluations. These applications can be especially valuable in manufacturing and logistics, where accuracy is essential. Processes that once relied on multiple steps or manual checks are now completed faster with fewer staff.
A good example is how some companies use augmented reality in manufacturing workforce processes to manage a shortage of skilled workers and improve efficiency. By providing real-time instructions and support, AR makes each employee more productive.
AR Application Benefit Inventory management Reduces manual entry errors Assembly line assistance Speeds up production Quality checks Lowers defect rates
Role Of Automation In Workforce Optimization
Automation enables businesses to restructure teams and redeploy staff for higher value activities. For example, by automating routine or repetitive steps using AR, companies often need fewer people for the same workload.
A 2025 industry report found that almost 45% of business teams have a significant role in building automation and improving work productivity. This shift is more than just cutting positions. It also means that the workforce can focus on quality control, process innovation, and customer service.
Workforce optimization through automation doesn't only mean layoffs. It means shifting resources where they matter most. By automating routine tasks, businesses can offer faster turnaround times and higher accuracy, which leads to cost savings across departments.
Interpreting The Impact Behind The Statistics
Automating AR functions can lower headcount and costs, but several variables affect the scale of these improvements. Businesses also need to consider employee reactions to automation to prevent lower morale and disruptions.
Factors Influencing AR Automation Effectiveness
Several factors decide the level of savings and productivity gains:
Existing process complexity: Simple tasks automate faster, while complex workflows may need more custom solutions.
Baseline technology systems: Companies that already have digital AR tools see faster automation implementation.
Data quality: Clean, accurate data produces better automation results.
Scale of operation: Larger AR teams usually see bigger percentage reductions after automation.
Organizations that measure the frequency and type of AR tasks can target areas with the highest automation potential. According to this guide on measuring impact with qualitative and quantitative indicators, tracking these metrics is key for showing clear business improvements. Comparing before-and-after data, like average invoice processing time or error rates, allows businesses to track real shifts tied to automation.
Sustaining Workforce Morale During Automation
Head-count reduction can cause uncertainty among AR staff. Effective communication—explaining the business reasons and showing new opportunities—can help address this. Training programs for upskilling or reskilling employees are essential to maintain engagement.
Regular check-ins and surveys can be used to spot areas of low morale or resistance. Sharing case studies featuring staff adjustments to automation can help create buy-in. For more on managing change and best practices on measuring workforce impact, reviewing relevant research will help businesses minimize disruption. Open feedback channels encourage transparency and support, which helps teams transition smoothly.
Frequently Asked Questions
AR automation has had measurable effects on workforce size, required skills, and long-term business costs. These changes are more pronounced in industries with high volumes of repetitive processing tasks.
How has AR automation affected workforce dynamics in different industries?
Automation in AR has shifted employees away from transactional roles to analytical and oversight functions. Industries with large invoice volumes, such as financial services and retail, tend to reassign staff instead of eliminating positions. The automation impact on customer service also shows reduced first response and resolution times, allowing teams to focus on higher-value tasks.
What percentage of job roles have been eliminated due to the introduction of AR automation?
Average headcount reductions in AR departments range from 20% to 35% after automation adoption. Sectors implementing full-cycle automation typically see higher reductions. More statistics are available in the article's section on "AR headcount reduction percentage linked to automation adoption".
Which sectors have experienced the most significant workforce reductions as a result of AR technology adoption?
Banking, insurance, and large-scale retail have seen the largest reductions, with some companies reporting workforce decreases above 30%. This is because these sectors process a high volume of AR documents and invoices, making them prime candidates for automation.
What are the projections for job creation versus job reduction due to AR automation in the next decade?
Most large-scale studies expect job losses from AR automation to be offset by the creation of positions related to technology management, process oversight, and exception handling. Surveys, like those discussed in the McKinsey state of automation report, suggest a shift toward roles that require strategic analysis rather than repetitive processing.
How do employee skill requirements change with increased AR automation in the workplace?
Routine manual skills are less in demand, while expertise in automation tools, analytics, and exception resolution becomes critical. Continuous learning related to digital tools is often needed to remain effective as AR team structures evolve.
What are the long-term economic impacts of AR automation on employment levels?
Widespread AR automation generally leads to lower operational costs and improved accuracy. Labor cost savings can be reinvested, but some displaced roles may not return. More information on average cost reductions is detailed in Deloitte's intelligent automation 2022 survey results.
This post is to be used for informational purposes only and does not constitute formal legal, business, or tax advice. Each person should consult his or her own attorney, business advisor, or tax advisor with respect to matters referenced in this post. Resolve assumes no liability for actions taken in reliance upon the information contained herein.
| 2025-06-13T00:00:00 |
https://resolvepay.com/blog/17-statistics-measuring-the-impact-of-ar-head-count-reduction-via-automation
|
[
{
"date": "2025/06/13",
"position": 8,
"query": "job automation statistics"
}
] |
|
Employment records: The missing piece in the US labor ...
|
Employment records: The missing piece in the US labor market?
|
https://www.brookings.edu
|
[
"Harry J. Holzer",
"Julian Jacobs",
"Magdalena Rodríguez Romero",
"Sarah Calame"
] |
Marcus, a Detroit autoworker, successfully transitioned to logistics coordination after plant automation. ... [3] U.S. Bureau of Labor Statistics, “Contingent and ...
|
Marcus, a Detroit autoworker, successfully transitioned to logistics coordination after plant automation. He learned inventory management, customer service, and digital tools on the job—valuable, transferable skills. But there’s no systematic record of that transition or those competencies.
Stories like Marcus’s are increasingly common among manufacturing workers who’ve shifted to logistics, healthcare support, and technical services. These transitions represent exactly the kind of career adaptability our economy needs. Yet we have no way to identify, track, or replicate these successful pathways.
That’s not just a data problem—it’s a policy failure that leaves millions of workers invisible to the very systems designed to support them.
When the pandemic struck in 2020, this invisibility became devastating. Sarah, a freelance graphic designer in Chicago, discovered that five years of steady client work meant nothing to unemployment systems that couldn’t see her employment history. Like millions of other Americans in the gig economy, her contributions were invisible to emergency relief systems.
That failure wasn’t a fluke—it revealed a structural problem. Our employment data systems are fundamentally misaligned with today’s economy. And as the pace of economic change accelerates, this infrastructure gap threatens to undermine workforce policies across the board.
The foundation we’re missing
For all the investment in skills-first hiring and innovative credentials, we’ve overlooked something basic: we don’t systematically track work. We meticulously record degrees and licenses, but we fail to capture who did what job, where, for how long, or with what outcomes.
Think of it this way: Credentials say what you know, employment records show what you’ve done.
“Almost 40 percent of global employment is exposed to AI, with advanced economies at greater risk […] due to prevalence of cognitive-task-oriented jobs”, according to the IMF’s 2024 Gen-AI Report. Those workers need to be trained to switch occupational categories, but which of these transitions are more likely to succeed?
Without comprehensive employment records, we’re constructing workforce policy on quicksand. If we can’t track experience, we can’t recognize it, reward it, enhance it, or build on it.
And, as I recently argued here, experience will soon be our last chance to complement the algorithm.
Built for yesterday’s economy
Our employment record infrastructure reflects 20th-century assumptions: one job, one employer, one career arc. That world has vanished.
Today’s workers navigate frequent job transitions, patch together income from multiple sources, and acquire skills continuously. The labor market has become modular, but our data systems remain monolithic. More than 18 million Americans now work as independent contractors, on-call staff, or through temp and contract firms—forms of employment often invisible in standard labor data, as documented by the Contingent Work Supplement to the Current Population Survey.
This creates cascading problems. Workers lose opportunities for recognition and advancement when their experience isn’t systematically recorded. Policymakers operate on incomplete information, making decisions about workforce programs without knowing what actually works. Employers face inefficient hiring processes because reliable work histories don’t exist.
The future demands better data
The economic transformation underway makes comprehensive employment records an essential infrastructure, not an administrative convenience.
Consider what becomes possible with systematic employment tracking. Instead of guessing which career paths work, we could analyze patterns from thousands of workers like Marcus—identifying which manufacturing workers successfully move to healthcare support, what skills predict success in logistics coordination, and how different training programs affect career outcomes.
Community colleges and workforce programs could track whether their graduates actually advance in their careers, not just whether they complete certificates. Germany’s system already does this, linking training, wages, and employment histories in longitudinal datasets that power robust policy evaluation and real-world outcome tracking.
Rather than relying on quarterly surveys that lag months behind reality, labor market intelligence could flow in real-time. State workforce agencies could identify emerging shortages as they develop, not after they become crises. By analyzing successful transitions of similar workers, recommendation systems could suggest career paths that match individual experience profiles. LinkedIn’s Economic Graph illustrates the potential of this approach, though current insights are based on aggregated data and depend on users with complete profiles.
Without public investment in employment records, control over this capability remains with private platforms, reinforcing existing inequalities in data access and career guidance.
What works elsewhere
Other nations are demonstrating what’s possible when employment records become true public infrastructure:
India’s e-SHRAM platform has registered over 300 million informal workers through a combination of mobile technology and in-person registration, bringing a massive, previously invisible workforce into view—and into social protection programs. Beyond connecting workers to social protection schemes, e-SHRAM is also integrated with platforms like the National Career Service (NCS) and the Skill India Digital Hub (SIDH), helping link informal workers to job opportunities, career counseling, and targeted training programs.
The United Kingdom’s real-time payroll system integrates income reporting into the PAYE tax infrastructure, requiring employers to submit payroll data each pay cycle. This data flows to the Department for Work and Pensions multiple times per day, enabling timely benefit adjustments and reducing tax credit overpayments through accurate, real-time earnings information.
Germany’s modular integration of wage, benefits, and job search data through the Integrated Employment Biographies (IEB) system creates one of the most advanced labor data infrastructures in the world. The IEB operates at daily frequency and merges inputs from five administrative sources, enabling granular tracking of employment histories, improving program delivery, enabling real-time labor market monitoring, and powering policy evaluation.
Estonia’s employment register, part of the country’s globally recognized e-government ecosystem (e-Estonia), records employment relationships in the real time and automatically shares data across tax, health, and social systems via the X-Road open-source software. While it doesn’t track wages or work outcomes, it shows how integrated infrastructure can reduce undeclared work and streamline access to benefits.
These–and other–international systems highlight several shared lessons1:
Inclusion is possible at scale—India proves that gig and informal workers can be visible if systems are built with them in mind from the start.
It could build on existing infrastructure, as Estonia, Finland, and the UK integrated employment reporting into digital government or tax systems to reduce friction and duplication.
Portability and worker-centered design is key: Platforms like France’s Compte Personnel d’Activité give individuals access to and agency over their own work histories.
Modern employment records aren’t just for compliance but serve as policy levers that improve training investment, benefits delivery, and economic forecasting.
The hidden costs of inaction
In the U.S., as in many other economies, the employment record system creates significant inefficiencies: employers spend billions on recruitment and skills verification, workers struggle to signal their true capabilities, and training programs operate without feedback on real-world outcomes.
Workers reasonably worry about creating detailed digital profiles that could enable surveillance or overreach. The key is designing systems with privacy protection built in from the start. Workers should own and control access to their employment records, similar to how medical records work under HIPAA, granting permission for specific uses. Policy analysis can rely on aggregated, anonymized data that reveals patterns without exposing individual information. Germany’s system, for example, demonstrates that comprehensive employment records can coexist with strong privacy protections.
Modern employment records don’t just help individual workers—they could transform how labor markets function. Comprehensive employment records create market transparency that benefits everyone. When work histories are portable and verifiable, job matching becomes more precise. Workers can demonstrate concrete experience rather than relying on degree requirements. Employers can identify talent based on actual performance patterns, not just educational credentials.
This transparency becomes increasingly important as work becomes more distributed, skills more specialized, and careers more dynamic. Without action, the gap between our policy ambitions and our data infrastructure will continue to widen.
A U.S. roadmap
Building modern employment record infrastructure requires four key elements:
Standards first: Establish a common data model that captures gig work and project-based employment, not just traditional job titles, ensuring different systems can work together seamlessly.
Digital integration: Create streamlined reporting mechanisms that allow payroll providers and platforms to submit data once while feeding multiple government systems—eliminating duplicate reporting.
Inclusive coverage: Design systems that capture all forms of work through platform partnerships where companies like Uber and Upwork report worker activity directly to state systems.
Coordinated governance: Implement federal-state coordination that maintains local flexibility while ensuring national coherence, similar to how Medicaid operates.
Building adaptive capacity
As economic change accelerates, our ability to respond effectively depends on having real-time visibility into how that change affects actual workers in real jobs. Employment records are the infrastructure that makes evidence-based workforce policy possible.
For workers, modern employment records mean faster benefit access, better experience recognition, and more relevant career guidance. For employers, they reduce verification costs and improve talent identification. For policymakers, they provide the granular data needed to identify successful reskilling pathways and measure program effectiveness. Women, immigrants, and low-wage workers—disproportionately excluded from current systems—would gain visibility and access to opportunities.
Most fundamentally, this is about building adaptive capacity. We’ve invested heavily in improving how we certify knowledge through badges, certificates, and alternative credentials. Now we must invest equally in tracking and valuing the experience where that knowledge gets applied–an aspect that will be increasingly important in the future.
Employment records aren’t a glamorous policy fix, but they’re a foundational one. We have the technology and the international models. We need the commitment to build.
References
[1] Mauro Cazzaniga, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus J Panton, Carlo Pizzinelli, Emma J Rockall, and Marina Mendes Tavares. “Gen-AI: Artificial Intelligence and the Future of Work“, Staff Discussion Notes 2024/001, 2024.
[2] Eduardo Levy Yeyati, “Why gen AI can’t fully replace us (for now)”, Brookings, December 2024.
[3] U.S. Bureau of Labor Statistics, “Contingent and Alternative Employment Arrangements – July 2023”, November 2024.
[4] LinkedIn Economic Graph, Workforce data and research, 2025.
[5] Ministry of Labour & Employment, E-Shram Dashboard, Government of India.
[6] Ministry of Labour & Employment, E-Shram: One Stop Solution for Unorganised Workers. Government of India, Press Information Bureau, 2025.
[7] Chartered Institute of Payroll Professionals, Understanding the relationship between RTI and UC, Professional in Payrol, Pensions & Reward, Issue 75, p. 25, 2025.
[8] World Bank, GovTech Maturity Index (GTMI).
[9] Rainer Kattel, and Ines Mergel, “Estonia’s digital transformation: Mission mystique and the hiding hand”, Working Paper 2018-09, UCL, 2018.
[10] Alexandra Schmucker, and Philipp Vom Berge, Sample of Integrated Labour Market Biographies (SIAB) 1975-2023, FDZ-DATENREPORT. Research Data Centre of the Federal Employment Agency in the Institute for Employment Research, 2025.
[11] Brookings Institution, “What Works for Employment Records: Toward a Standardized and Comparable Framework for the United States”, forthcoming.
| 2025-06-13T00:00:00 |
https://www.brookings.edu/articles/employment-records-the-missing-piece-in-the-us-labor-market/
|
[
{
"date": "2025/06/13",
"position": 79,
"query": "job automation statistics"
}
] |
|
Execs say workers have to adapt to AI, but workers are resisting
|
Execs say workers have to adapt to AI, but workers are resisting
|
https://www.morningbrew.com
|
[] |
The same survey found that 41% of younger employees have admitted to blatantly ignoring requests to adopt AI. Some workers fear they'll ...
|
While 75% of C-suite execs think their company’s AI rollout in the previous year was successful, only 45% of employees thought the same, per Writer.
There’s a huge disconnect between the executives spamming your LinkedIn feeds with promises to go all-in on AI and the company leaders still fighting with the seventh-floor printer. Meanwhile, their employees are kinda just…doing whatever they want.
Companies like Shopify and Box have snagged headlines in the last few months for saying they are pivoting to an “AI-first” business:
Shopify CEO Tobi Lütke told employees that everyone was expected to learn how to do their jobs with AI, and that new hires would only be approved if managers demonstrated that AI could not meet a need.
The Norwegian hedge fund manager Nicolai Tangen, who leads the country’s massive sovereign wealth fund, told Bloomberg that he sees no future at his firm for employees unwilling to get on board.
But some of it might be about positioning (for now). Gartner Distinguished VP Analyst Arun Chandrasekaran told Tech Brew that corporate AI-first pronouncements are “a way to signal to the investors that we’re not going to be lagging behind.” Some of it is also about making it clear what kind of employees they want working for them: AI enthusiasts.
Most employees are a lot more skeptical
Research released in March from enterprise AI startup Writer found that, while 75% of C-suite execs think their company’s AI rollout in the previous year was successful, only 45% of employees believed the same.
The same survey found that 41% of younger employees have admitted to blatantly ignoring requests to adopt AI. Some workers fear they’ll eventually be replaced.
And some CEOs might be concerned if they knew how their employees are embracing AI: One report from accounting and consultant firm KPMG found that of the US employees who said they use AI in their everyday workflow, 44% reported “knowingly using it improperly,” like uploading sensitive IP to public platforms.—MM
| 2025-06-15T00:00:00 |
2025/06/15
|
https://www.morningbrew.com/stories/2025/06/15/execs-say-adapt-to-ai-workers-resisting
|
[
{
"date": "2025/06/14",
"position": 71,
"query": "AI workers"
},
{
"date": "2025/06/14",
"position": 71,
"query": "AI workers"
},
{
"date": "2025/06/14",
"position": 6,
"query": "AI workers"
}
] |
AI In Education: What Parents & Caregivers Should Know ...
|
AI In Education: What Parents & Caregivers Should Know Webinar — AI for Education
|
https://www.aiforeducation.io
|
[] |
Popular AI tools among youth include ChatGPT, Snapchat AI, Character AI, Quillbot, and Grammarly. Some tools marketed as writing aids are also used for academic ...
|
00:01
Amanda Bickerstaff
Hi everyone. Welcome to the webinar. We're always happy to have you. It takes just a little bit of time for everyone to get in. But very excited to have you in our first ever parent focused webinar, which is pretty amazing. It's actually also our Parent Guide is the most robust document we've ever created because we do believe how important this moment in time is. I'm Amanda Bickerstaff. I'm the CEO and co founder of AI for Education. And and today what we're really going to be focusing on is what parents and caregivers should know about AI and education. And so I'm going to be joined. I am joined by these two lovely fellows. I've got Jason B. Allen, who is a national director of partnerships at the National Parent Union. Parents Union, excuse me.
00:42
Amanda Bickerstaff
And then also Kenneth Reddick, who's a founder and CEO of Brothers Brunch foundation and a father of like a lot of kids. And so from 8 to 25. Right. We span many generations and many kiddos. So we're excited to have you both here with us today. As always, we want you to say hello. Say hello in the chat. We will have, I'm sure, people from all over the country in the world and so always good to say hello if you have a great resource to say that. I know that there are other frameworks and supports around education and AI, especially with parent approaches, but say hello, share. You already have Olga from Australia, guys. So I'm in Flint, you know, in Michigan. We've got everything. But we've got Herman and Tanya, who's been to a lot of these before.
01:26
Amanda Bickerstaff
But make sure to say hello to share because our community practice is one of the most important parts of what we do. And I know a lot of you are probably both educators and parents, so to be able to take those two lens and if you're not and you're just a parent, we're really excited to have you here. I'm going to do just a first for those that are like just getting started. If you've followed us work before, you know that this is a very popular resource that we have. But I just want to establish why we're here today. And so while it says AI in education, even our company name is AI for Education. We're really here today to talk about generative AI. And so AI has been a part of our lives for 20, 30, 40. It really even goes back.
02:05
Amanda Bickerstaff
The first chatbot was in the 60s. This is how long we've had artificial intelligence around. You and if you have used Google Maps or you have bought something online or use Google Search or even Autocorrect in your phone, you've been interacting with artificial intelligence. And same thing with your student, with your young people. They have been interacting with AI as well. And so what we're going to be focusing on today though, is really a subset of AI called generative AI. And the difference between generative AI and the AI that we have used in the past and continue to use is that generative AI now can take the way we speak or text or type and way we ask questions and actually create something brand new.
02:45
Amanda Bickerstaff
So in the case of generative AI, it could be, let's say, a student essay, or it could be a new game, it could be audio, video coding. It really is opening us up to creating with technology in a way that's never been possible before. I think that while we're here, what's really important that we're really nailing down into this form of AI, because that's really what the structure of the conversation is going to be about. But also it is why we're having so much conversation right now. Because generative AI like ChatGPT has only been around for three years in a public facing way. In the same way that is like no time in education. It's probably a lot of time. And having four kids like Kenneth, like three years probably is like, you know, very long time. But for education, three years doesn't go.
03:32
Amanda Bickerstaff
That is going to be a very short period of time. So what I'm going to do is actually, we'd love to start with evidence. I'm going to call Jason up and you can introduce yourself, Jason. But can you talk us through the research that you all did so we can start to have a foundation of what we know is happening in terms of parents and their understanding of AI?
03:49
Jason B. Allen
Awesome, Amanda. So thank you so much for this amazing conversation again, everyone. I'm Jason B. Allen with the National Parents Union. We are the united, independent voice of modern American families. And so, as you see in front of you, we have the longest standing parent poll that we run nationally, really gathering information from parents, caretakers, family members, guardians, all of us who have a stake in children's matriculation through our education system. First thing, starting with data showing a very important question. Which of the following best describes how much you know about artificial intelligence?
04:34
Jason B. Allen
And so, as you see before you have a lot of people who have some general information and people who know just a little, which is good because we Want people to be in the know of what's happening so we can get you to be a part of NPU and help change some of the policies impacting AI. So this is knowledge base. We'll go to the next slide. So here we have our next question, and it says, what kind of impact do you believe AI will have on your family's quality of life in the next 10 years? Now, the teacher in me wants to give you guys this as an assignment right now to ask yourself this question.
05:16
Jason B. Allen
Kenneth is an active member of npu, so I know he's done this with his family and has been involved in our technology series that we have going on right now. But a lot of families are asking this question, especially as school is letting out and we're preparing for a new school year. But again, reinforcing this data around what kind of impact do you believe AI will have on your family's quality of life in the next 10 years? And as you can see, the highest percentage is people are, you know, kind of borderline equally between positive and negative impacts. We'll go to the next data point. All right, now, parents are divided on whether AI developers are doing enough to prevent bias or not, with many being unsure.
06:08
Jason B. Allen
And I also will say that, yes, NPU amplifies the voices of parents and families, but I also remind people that, you know, teachers, educators, social workers, those in education are parents. So a lot of parents are in the education field and are concerned about the contracts the school districts, school board members are approving. And where is our student data actually going? Very huge concern for parents. We'll go to our next data point. Okay. Most say their child school has not shared AI use policies or ask parents for input on the use of AI tools. This is a very interesting conversation because I will say that most parents, 100% of parents are getting information about something I'll like to show and tell.
07:07
Jason B. Allen
So this little device right here called a cell phone, parents are up in arms with teachers and other educators who are looking at the mental health and wellness of our children. And how do we actually maybe lim cell phone usage and even screen time usage in our schools. But these are AI policies that connect to curriculum. And I'm giving that example about cell phones because when we brought this to parents, they didn't connect the dots and they said that, wow, our teachers and our school leaders had not connected the dots for us. I think we have a couple of more slides, so let's go to the next one. Parents want to be involved in decisions around the use of AI. They absolutely want to be involved, but they need more information.
07:54
Jason B. Allen
And one of the things that we found is that teachers and educators are saying we too need more information around AI and how it is advancing in the way we're using it in our schools. Next slide.
08:10
Amanda Bickerstaff
So I, I want to connect before I let Jason off the hook a little bit before we go into the guide, I want to call out a couple of things that I thought was really interesting. And so let's go back to this first slide, Jason. The idea that like, while there has been a movement in terms of those that have more of a detailed understanding, it's relatively slow going. Right? I mean, there's very little change in almost 12 months, actually more than 12 months. So I think this is really interesting for us is that there clearly is a need for more AI literacy work between. Yes, it is in general, but also between the school and Home Depot. Agree with that.
08:48
Jason B. Allen
No, I totally agree. And I will say that Tashir Cosby, who is our senior director of organizing the partnerships, we always say that we are AI enthusiasts. And so right now we're reading a book, I have it right here on the side of me, like the show and tell Digitally Invisible. And what we're seeing with our data is that when you get to the orange and red area of the data, I know a little bit about it. I don't know anything at all. We're also taking into consideration generational families or multi generational families, our caretakers, our grandfamilies who are raising school age children, but also families who are in urban and rural areas. People think that, oh, I can just go and watch reality TV and I can send this text message with no time flat.
09:38
Jason B. Allen
But that's not the case when we think about infrastructure and bandwidth. So I'm so glad that you came back to this slide because those areas also incorporate the families who are displaced and do not have access to technology.
09:54
Amanda Bickerstaff
And I know, I think that is such an important underlying part of like where also the schools can lean in. Like, it's an opportunity. If you're an educator or school leader watching this, like, there's an opportunity here to lean in terms of access, of course, but also in terms of like some of the tools actually do work with low bandwidth or over time we will start to see tools that can be done just locally on a device even. You can, you can know that you can call ChatGPT, you do not have to have anything, or you can WhatsApp it so you don't even have to have, like, a computer or a connection to the Internet. You can just call ChatGPT.
10:29
Amanda Bickerstaff
And then there are some ways, if we really think about that connection to home and access and bringing people up to really start to move this. Because I think if parents don't know, then what we see is that students are going to be going into the classroom. That's changing. Right. And they're not going to have those, like, those skill sets or even those. The language to be able to answer some of. Some of the needs or even advocate for their own use, which I think is really important. I want to also. You know what? I'm actually going to go back one. And. And I think this is really interesting that, you know, to talk about that quality of life piece, that idea that, like, it's gonna be either good or bad, like, it might be both.
11:08
Amanda Bickerstaff
Like, it's fascinating to me that was really the one there. But I will also say that I do think that the level of optimism, considering that there's not that many people that know, but at least there's an optimistic approach to this, is really interesting. I mean, I don't know, Jason, if you have any, like, thoughts about why.
11:25
Jason B. Allen
You think that is, I will say stay tuned. We are talking. Technology Caucus is doing a lot of research and collecting things from parents. I think that we're going to definitely see some shifts in this. And I can say and speak directly to working families that are going to see changes in schools, closing hospitals, closing government agencies and programs that are closing across the nation. And a lot of people are being told that it's because of the advancements in AI that your job is being phased out and you're no longer needed. And so I think that there is going to be a shift. This question is going to be very interesting in about six to eight months in regards to how people are really feeling about the impacts of advanced technology when the workforce is starting to be replaced.
12:18
Amanda Bickerstaff
Absolutely. And I think that, you know, that realism there. And I think that what we see from the research that goes broader is that when people have less AI literacy, they can be more optimistic and. Or believe these tools are kind of, you know, magic or, like, they could fix everything. Right. But then when they. When you have AI literacy, you start to understand the greater global impact. And so I'll be very interested. Maybe we can do this again next year when you have the research and we do our next.
12:42
Jason B. Allen
I'm with it.
12:43
Amanda Bickerstaff
Let's do it.
12:44
Jason B. Allen
Yes.
12:45
Amanda Bickerstaff
Before we go on, I do love that we have the bias Here. But I think that, you know, the idea that you brought up here about how it's almost like one or the other, either parrots are conflating AI with cell phone use or they think it's completely different. And so I think that this, I would just kind of say that we are. We talked to a recent school in New York City that has like a lot of parents that care a lot and about 25% of their parent population are anti tech in schools to a point of like no tech. And then there's about 50% that are like some tech. And then 25% they're like all tech.
13:24
Amanda Bickerstaff
And so I think that this is really interesting, but we do see in board meetings and teachers and parents that there's a difficulty of kind of understanding where AI fits in the more global, just technology use and especially with young people. I think it's really interesting that you pointed that out. Is there anything else you learned in the research that supports why you think that is or what we need to do?
13:47
Jason B. Allen
I would say a lot of this also comes from what parents are hearing from and what they are receiving from teachers. We do have several partners that are organizations for teachers and support the advocacy of teacher development. And they have data showing that teachers have been advocating for and asking for AI trainings that specifically, and I want to make sure that I highlight this, it helps to continue education at home. When we're thinking about digitally invisible communities. There's data around the homework gap. And the homework gap really speaks to children and families that are disconnected from the usage of technology, even cell phones after they leave from school or out of school time programs.
14:39
Jason B. Allen
And so this is important because the usage of AI again still speaks to communities that are disconnected or they don't have quality broadband services where they can be in the know of what's happening, especially around AI tools that if it's being used at school, there is, you know, something that's called continued learning. And how are children going to use these applications and tools once they leave the school? So yes, I wanted to add that.
15:08
Amanda Bickerstaff
To the data we know. And actually the research shows that probably the strongest indicator of AI use and, or knowledge of anything everywhere from high school to, you know, adults into their 60s, is the amount of income at home that Those with under 30, $30,000 of income statistically significantly know less about AI and use it less than any other demographic. And so, and I think that is where. But on the other side, there's some encouraging data that shows that community like communities like Ethnic communities and like communities that are traditionally potentially at the edge of the digital divide are also leaning into these tools as well in meaningful ways. So we are seeing a lot of like, really interesting uptake across different communities.
15:53
Amanda Bickerstaff
But I do think that's really valuable to point out that, you know, right now the access is still, it's going to be such a huge part of this. And there's also, just before we move on as well as it being like those that have incomes over 30 or K or below, is that schools that are well funded have more AI literacy training and support than those that do not, low income school systems. And so it's not just happening at the individual family level or family unit level. It's also happening at the school level as well. So what we're going to do is so to help out, we built this. I think we had decided that were going to do a parent and caregiver document because we hadn't seen a lot out there.
16:37
Amanda Bickerstaff
And also we have been doing more and more parent conversations with some of our district partners and it's been great. I mean, I have to tell you, being able to answer questions from like the very general to the very technical has been really interesting for us. And so we put together this parent guide and I think we intended Jason and Kenneth this to be like five pages. In fact, the first one was one page and then we made this crazy. It became literally is now 14 pages. But it is designed to really be just this starting place. And I think it's a starting place. Whether you have children at home that you're thinking about AI or you're just getting started, it's for educators and leaders to be able to communicate better at home.
17:25
Amanda Bickerstaff
And so what we have is I'm going to take you through a bit of a tour and we're going to look at it through the slides. But it is designed and Dan's going to drop this into the chat, but it's been designed to really be a starting place, like I said. And the first thing is that in some, whether you have no ever used generative AI or you have a parent community that hasn't. We start with the foundations. What is generative AI? The same way we started at the top of this webinar we talk about the importance of AI literacy and not AI education. I want to make this clear. We're not talking about having millions of young people that build AI.
18:00
Amanda Bickerstaff
What we're talking about is we're creating a foundational literacy where people understand how to Use these tools and so safe ethical and effective manners. And so that's our C framework. But we really think that AI literacy is for everyone, including the parents, the caregivers, the grandparents, the guardians that are supporting young people. And so we actually believe very strongly that this is new foundational literacy for everyone. Third is we talk about how to support. So exactly to Jason's point, how do we support the continued learning at home, or maybe not even the continuing learning at home, the sense of AI being used in the classroom at home. But what happens when you don't have access to a tutor or you've tapped out at fifth grade math? You've tapped out and you need extra help supporting your young person.
18:50
Amanda Bickerstaff
How can you actually start using these tools to help your young people in the home? We talk about the benefits and challenges of AI in education. We try to be as balanced as possible. If you know us well, you know that we believe very strongly that balance is the most important thing. We talk about how to partner with schools. In fact, when we do our keynote, we are. We talk about being able to communicate, partner and educate. So we talk about communicating with your. Well, you educate yourself, you communicate with your young people and you partner with schools to support AI literacy. And then finally, how to access resources for continued learning. Because there's a lot of great resources that are more tactical. And so those can be for your young people and also for yourself as a parent or caregiver.
19:35
Amanda Bickerstaff
We wanted to stay here and I'm actually going to ask Kenneth to come off mute because I'm going to throw. I know you said don't throw it to you, but I'm throwing it to you. Kenneth, we're going wild here is one of the things that we wanted to do in the document, and you'll see this in the document, is actually go through some of the tools that you may not know exist that your young people could be using. So, Kenneth, out of this list, what do you think your young people are using today? So we have again, introduce yourself, talk about your family, of course, and the work you do. But then maybe you can tell me what you think your kids are using.
20:05
Kenneth Reddick
Okay. Well, my name is Kenneth Reddick. I am a NPU partner, the Brothers Brunch foundation, which is not a time to eat but be fed. Mental health and self care awareness. So as you mentioned before, I do have four children ranging from 25 to 8 years old. And one of the things that I realize is that with each generation, as a parent, we have to truly meet them where they are. And honestly, in a situation like this, understand the tools that are best going to help them navigate. And so if I think about this, of course, I mean, the kids are using Snapchat, like, all the time, you know? You know, I see and hear some of them now using a little bit more of Chat GPT, as well as myself and other parents. But to be honest with you, and a little bit.
21:03
Kenneth Reddick
I won't say the kids are using the character AI, but I know a little bit about that. But honestly, there's a lot that's still foreign on here to me, as much as I'm aware of some of the AI tools. And I think that's the thing, is the awareness that you're bringing for us as parents, but also us as parents having those active discussions about the things that we're utilizing, but also how we're utilizing them, not only just for ourselves, but making it safe and productive for our children.
21:36
Amanda Bickerstaff
Absolutely. Okay, so. So, Kenneth, there are 150 million active users of Snapchat. Is that not wild? There are only, like. So the. The number one is ChatGPT. It's almost 800 million now, but, like, Gemini is just over, I think 200. Is this over, like, 30 million. And Snapchat AI is 150 million. So I just want to say that. And not only that, but it's been a ChatGPT like, application layer for two and a half years. Essentially. It's been since March 2023. I'm going to ask you, Kenneth and Jason, do you. Do you know what Quill Bot is? Have you heard of Quill Bot?
22:14
Jason B. Allen
I have not heard of Quill, but I was hoping that someone maybe in the comment section would say, hey, I know what. That. I'm a little bit familiar.
22:21
Amanda Bickerstaff
Can you guess. Can you guess what it is?
22:25
Jason B. Allen
I'm gonna say Quill by just looking at the colors. It's green. Of course. It's a robot. I'm gonna say that maybe it's connected to a science curriculum or some type of curriculum that the teachers use. So Jaslyn helps with writing and paraphrasing.
22:43
Amanda Bickerstaff
Okay, okay. But Jaslyn's being very nice and. Hi, Jazlyn. It's. But I. She's a New York City. P.S. A good friend. It's actually a cheating tool. Oh, Jasmine's being very nice. It does help with writing and paraphrasing, but it also helps you get around AI detection. And so it is a. It is a. Essentially, like, it's been like, there's spinbot and Coldbot. But these are paraphrasing tools. Yeah, Shelly's got it too. I will say Grammarly. Also Grammarly used to just correct grammar, but now I can just write your essay. And not only that, but if you search, help me write my essay. It's usually what kids will search for, like a cheating, like a moment. I just don't have time.
23:25
Jason B. Allen
That's right.
23:27
Amanda Bickerstaff
Has bid on those social media words, those keywords. And so they're actually bidding as also not just a grammar support, but also a cheating tool. And so this is where it gets really interesting, where a couple of these like Snapchat and Character AI are social companionship, but bots, they are about advice. They're, you know, who's my best friend? Or what should I do? Or what should I. How should I wear my hair? As well as their homework. But then we actually have active tools that are being marketed directly to kids around academic integrity, around AI girlfriends and boyfriends. And I'm going to hold that one till later because I think that artificial companionship or artificial intimacy is incredibly important. But we'll come back to it. But I do love that. I love how Jasmine's just very nice. We could not be that nice.
24:14
Amanda Bickerstaff
We actually ran a school where in the third, like they had third graders that had access Quill bot on their iPads, which is wild. And were. People are like, what is this? And I'm like, I have bad news.
24:28
Kenneth Reddick
Yep. If I could just say something right there. I think that's also the importance of parents understanding what AI is because, you know, even as we talked about cell phones, and not that all parents are necessarily just invasive, but many parents may check our kids cell phones, you know, just because we want to make sure that, you know, we understand what's going on. So I think that with this AI, as you just mentioned with this Quillbot, if I were to happen to pick up my son's cell phone or his iPad and see it, I would just think, oh, okay, he got another little game on there.
25:08
Kenneth Reddick
But at least having the understanding of a parent, as in what some of these AI tools are when we recognize them and it has to do with our children, at least it can give us some type of a guide as to understanding what our kids may be into when it comes to integrity.
25:27
Amanda Bickerstaff
Absolutely. And that's why I think we structured it this way because, like, you just may not know as a parent that Snapchat AI, I would say that teachers don't know it exists. I was in a We are doing a Train the Trainer right now, Kenneth, where we have like 60 teachers, many of them parents, and I asked them if they knew what character AI was, which is the third most popular web based chatbot, and only two people in the whole room raise their hands. So there is this like, need for both sides because I think that in some cases educators don't know what Quill Bot is.
25:58
Amanda Bickerstaff
And so I think that some of these pieces get really interesting where it could just be AI literacy, but connecting the educator AI literacy, the student AI literacy, and parent and community AI literacy doing the same thing. It's important for everyone to know what this is because the kid also might access it thinking it's totally okay. And then they get popped on the AI detector and they will be considered to be academically dishonest. Okay, so here is what we talked about. So if you want to start thinking about how to do this at home, though, we have some really good ways to do this. And so in the document, if you want to try these out, we've got starting the conversation, we actually have sentence starters. We are, you could tell, like Jason probably knows, like, we are educators.
26:40
Amanda Bickerstaff
In fact, quite a few people on our team, like Mandy and Wendy are like, great at this. But like, how do you even start the conversation where a kid just doesn't say, okay, it's fine, don't. I don't want to have this conversation. We also have the idea of actually starting to be a co learner, which I just talked about. You can learn at the same time as your kids are. Right. This has never been a time in the world really, where we're all learning about the same thing at exactly the same time. Right. And I think that this is where it gets really fascinating where you can start to, like, we're going to explore this tool together. We're going to talk about how appropriate it is or not appropriate.
27:19
Amanda Bickerstaff
And I think that could be, or even like the value it could give you or the concerns we should have. The next is the idea of differentiating between personal and school AI use. Okay, this is a big one because, you know, I think some students are like told not to use this in certain ways. But how are students using it? Let's say we're today. If you want to be a musician but you don't have an orchestra, you can use AI today to help you essentially practice orchestration and hearing different, you know, different types of horns and drums on the same orchestration. Like, there's some amazing things that can just come down to creative arts, to young people really wanting to build something meaningful. But there's like, a way you have to talk about it.
28:02
Amanda Bickerstaff
Because if they take that same approach and necessarily go into a classroom and just think, I can just use this without asking for permission, then it can start to get really difficult. Right? Because we're creating this, like, idea that it's a 0 or 1. It's either yes or no, but reality. It's like we have to have conversations, we have to be intentional, we have to ask for permission. And the last thing is that setting clear boundaries and fostering ethical understanding. You know, it is really important that young people understand that some of the use of AI in terms of how it's being like, I mean, Jason probably talk about this, but these tools are being marketed to young people and can be driving them to engage in meaning in ways that can be harmful, but also to share data.
28:46
Amanda Bickerstaff
Share data that can be used to sell them things. If I go back to perplexity is very popular with it's a generative AI search tool. And you think, oh, it's great, but they want to be the next Google, which means in their terms of service, your data is up for them to sell and it's just freely available. It's 13 and up, but you don't. But instead of Google, where you're putting, like, keywords, you actually could be saying, you know, I'm Kenneth Reddick's, you know, daughter or son, and like, I care about this. And all of a sudden you can start to see because how the technology works that actually helps. Having an understanding of why we need to be protected at home, why we need to have these ethical understandings that go way beyond just academic integrity, I think are really important.
29:28
Amanda Bickerstaff
So I'm going to stop here and like Jason or Kenneth, if you have a thought or something you want to share, Jason's ready.
29:35
Jason B. Allen
Who's just going to say, I think these strategies are phenomenal. The National Parents Union, we had a conversation at the start of the year around digital wellness and disengaged teenagers. Actually, there's a book for parents and educators who may be interested, the Disengaged Teen. And we started off the year focusing in on that because the digital wellness is so critically important. And so when you were mentioning strategies for building AI literacy at home, the teacher in me also wants to reinforce the urgency of parents and teachers being partners in doing this together. So it's not reinventing the wheel and people don't feel overwhelmed. And, you know, how am I supposed to learn this.
30:23
Jason B. Allen
I also want to say that a strategy to help continue to build these things is to connect with out of school time programs such as STEM clubs or enrichment programs that are STEM or STEAM focused. Because having done this, I had young people to create their own list of what are your favorite apps? Right. What are the favorite apps that you use? Some of the things we had never heard of and didn't even know existed. But that's a good way to get information from everyone. And also a warning as you're building AI literacy at home. Something that can also be overlooked is commercials and ads in the gaming community and also YouTube. And this is even with parental controls and even with children, especially young children in early education.
31:22
Jason B. Allen
It's really important that parents are monitoring and aware of how AI literacy is stretching and getting connected to younger kids through commercials and ads.
31:33
Amanda Bickerstaff
Yeah, and I mean, like if you look at character AI, for example, again, you know, they really market it to the youngest generations. If you actually open it up, it's anime, it is colorful, it is like clearly designed not for a 25 year old, but for a 13 year old or even a 10 year old really. And so, I mean, you can make a spongebob, a Pokemon. You can, you know, you can do all of those pieces. So I think that, you know, that is a really good point. Point. But Kenneth, first of all, I love that people are sharing in the chat, the time, the first time you use things and how you're using it at home as other strategies. I think that is awesome.
32:09
Amanda Bickerstaff
But Kenneth, you want to talk a little bit too about how you're thinking about AI literacy at home?
32:12
Kenneth Reddick
Yeah. And, and honestly, when I just saw Jordan's comment just pop up, I'm like, that is amazing. And I think to Jason's point, is one of the things that when there are certain initiatives as a parent, I always ask, okay, is this teacher or school driven or is it parent driven? And to Jason's point, I think that this is truly from both angles because I believe that this is a learning curve where a lot of people may be at different levels, whether you're in education or you are the parent. But I know that based on my experience, either doing through and I share it with Shelley, even, you know, she was talking about in the comments about, you know, starting to use it as a part of, you know, assignments and those sorts of things.
32:59
Kenneth Reddick
When I used it as a part of a career day presentation and shared it not only with the teachers but also with other parents of what had been Done. It's that wowing experience that if I didn't share it as a parent or think that well is not my place. But this is something for embracing. And I think as a parent we have to even step outside of ourselves and not just think about what we're doing for the parents and the teachers, but what we're doing for our children.
33:30
Kenneth Reddick
Because if we don't somehow navigate through this space as everything is going AI jobs are being replaced based on AI, if we don't assist our kids in understanding how to utilize this and those people that are in part of our children's lives, then it's going to be a disservice not only for us, our children, but ultimately for the system as a whole. And so that's why I was glad to be on here because having utilized it and having parents now that come up to me and say, man, since you told me about that chat GPT this or that or whatever, I use it for everything now. And I love the way Jordan put in there about you can continuously, once you put that information in there, we can really use it as a tool to tailor for our children individually.
34:24
Kenneth Reddick
So it's not just about the data we're inputting that can be used to help the broad. And I'll just say this and close on this comment, but we give our kids a cell phone and they talk into that and it takes all the information there, you know, so this is an educational tool that can be utilized for even of a greater advantage. And I think that it's something as a parent, I've even have to even through this conversation, honestly, I got to find more ways to utilize it even more so with my children. So.
34:58
Amanda Bickerstaff
And I think, yeah, and I think that what we see right now at least is that when those have AI literacy, they actually get more value from the tools themselves. And not just value, but economic value. So you're 25 year old and 22 year old. If they were able to really access and use these tools meaningfully, they could get a leg up at work, which I think is really what's pretty interesting. But we're going to shift into we have some more practical stuff. So actually I might go a little bit further because we've been talking about this and then we'll come back to the meaty kind of safety and digital wellness to Jason's point. But there everything that we've been talking about, Kenneth and Jason nailed this. We've got Shelley and Jordan and other Tanya and in the chat.
35:38
Amanda Bickerstaff
But the benefits of this are personalized learning. I mean the fact that, you know, I really don't understand, you know, statistics explain it to me in a soccer frame, which is a real story. Both a teacher and a student use this as a real way of like starting to have explanations to them. Is something that can be really great and it also can be something that can really get you exactly where you are. Let's say that I am have a Spanish speaker at home and I'm expected to do my work in English. Can I get extra support and actually having it spoken to me in Spanish or translated and then I can then turn key that also real time feedback. The number one user of ChatGPT in the US up to three, up to a third of all users are students.
36:23
Amanda Bickerstaff
And when talking to OpenAI they'll be like, and those students are using this tool between 11pm and p.m. 2am when the fear sets in and Kenneth and Jason are asleep and if you wake them up they'd be very upset, you know, like your teachers would be upset about you calling. But let's like I also remember very early on that there was someone that created a grading tool for an essay that was very popular in the UK to get into college. And their number one user wasn't teachers, it was students trying to finish that paper and get feedback on it. And so the students found it, they used it to get feedback and fix their, you know, like to crystallize it and get all that feedback.
37:04
Amanda Bickerstaff
The bot was never annoyed with them or they didn't ask too many questions or didn't matter who they were. Like there was some real opportunity there. So the idea of real time feedback is great. Like I talked about before, creative support and as also, you know, AI has been accessibility tools for as long as there have been accessibility tools in the technology. Whether that's voice to text. It is text to voice. It is going to be computer vision where we can take images and actually have AI actually tell us what's in those images. The ability for those with load, like with low motor skills to be able to actually draw or to write in ways they haven't been before. All of this is going to be a benefit. But then on the other side there are definitely some potential challenges.
37:45
Amanda Bickerstaff
Whether that is the tools themselves can be. They make mistakes all the time. They're not designed to be correct, they're designed to be pleasing that you like it that Kenneth thought that was a good answer. Even if it was wrong. There's the risk of like cognitive offload that young people are Using these tools so much that they're actually not learning those foundational skills or they're losing the ability to critically evaluate the output like outputs, which is really necessary. And I think that we know right now that due to social media and devices that critical evaluation, critical thinking is not, it's on the decline for some of our young people. We have things we already talked about with data privacy and security. Developmental appropriateness.
38:28
Amanda Bickerstaff
The difference between your 22 year old, Kenneth, using a chatbot and your 8 year old, if that chatbot is funny to your 22 year old and says something, they're going to be like, that's a bot. But if your 8 year old hears it, they might think it's real, right? And so the idea that developmental appropriateness and then Jason, you have nailed this over and over again, which I just so appreciate is that equity of access piece, right? Like who actually has access to these tools, the training around these tools. And so I think that this is where we really want it to be balanced. Because to our point, even Stacy, like the wow factor, right, the wow factor of what we do, of getting people engaged is great, but we always have to temper that with the risks associated with an emerging technology.
39:12
Amanda Bickerstaff
And it's designed by technologists that give you things for free because they make money off of giving you things for free. Like, you know, and so, and that is why YouTube kids, you know, is so popular. They make a gazillion dollars on YouTube kids with all of the purchases and the ads that Jason was talking about before. But I want to go back though, because I think that this is such a key point that we need to talk about is the AI and well being. And so we actually recommend, I don't know if Kenneth and Jason, you agree, but we actually recommend that no kid under 13 is using generative AI tools and especially not without AI literacy first or parental and, or teacher oversight.
39:56
Amanda Bickerstaff
And the reason we do that is because that's actually the, that's the level in which these tools are developed. Like, like ChatGPT. The terms of service is 13 and up. There is a lack of research about the impact on young people. But I think that for us it can lead to a couple things which is over reliance on AI for emotional support or guidance, a preference for AI interaction over human connection. We've heard this over and over again where kids have started to socially isolate and only talk to AI as their friend because people are hard, people get mad, they're chaotic, they might not agree with you, they might make you feel bad. And AI is designed to make you like it. Right. It's designed to respond in ways that are again, pleasing. So I'll stop there.
40:47
Amanda Bickerstaff
But like Kenneth or Jason, like, I don't know if you agree with this. You, I mean, what are you thinking? Especially in terms of like equity of access. But I'll start with Jason, like, do you agree with this idea of over 13?
41:00
Jason B. Allen
I think that it definitely needs to be monitored. I think 13 is an appropriate age. I also haven't been a long time middle school teacher. The coming of age years are so important for children as they are matriculating from tweenhood to teenhood. And so I think that again, that guidance is needed for the well being of children. Also, just thinking about one of the things from our climate change caucus, when we think about the advocacy parents are doing across the nation for electric school buses, that is again going to advance AI and digital access on school buses. And so how are we dealing appropriately with cyberbullying, which is not new. That has been going on for a while. Catfishing is not new. That has been going on for a while.
41:56
Jason B. Allen
We see it a lot, you know, very frequently in reality tv, which is a reality, maybe not real life, but it is a reality, unfortunately, that our young people who, again, if you're under the age of 13, a lot of times you think what you see and maybe hear your interpretation of things is that this could actually be real. And so we do hear concerns from parents and families. And of course, I'll lend us over into Kenneth's sphere in regards to parenting and the concerns around the emotional support that young people are finding with AI and how to balance real relationships and again, reinforcing the needs of humans with connectivity, with compassion, with humanity. Those things are important to the mental health, emotional and social development of children. And you know, again, being 13 gives you a starting point.
42:57
Jason B. Allen
It's not saying that even at 13 you need to just open up the floodgates of advanced technology and AI. That's a starting point. So when they're 18 and they're, you know, in college or maybe a military program or vocational school or tech school, whatever their pathway may be, they'll be prepared, but they won't be so overtaken with all things AI.
43:24
Amanda Bickerstaff
Yeah. And then Kenneth, like, yeah, I think the idea of that, like emotional dependence and support from these tools, it'd be interesting to hear your point of view.
43:32
Kenneth Reddick
Yeah. And thanks, Jason, because, you know, mental health is right up my alley of what I heavily Focus on. And I think, like you say, 13 is an age to target. I think a lot of times when it comes to parenting, we try to put certain things in place or certain guard rails, but we still have to understand that every child is different and that my child is. May not be at the point of 13 or even from a mental health perspective, depending on what that child has going on mentally, as we see different things. As I'm. I'm a very holistic person to look at everything as a whole. And if my child, even though they hit certain.
44:15
Kenneth Reddick
At 13, May hit certain mental health challenges where I need to monitor a little bit more what he's doing because of certain things that may not present a certain way or may even be a little bit more from an aggression side. You know, I think we, a lot of times we look at relationships, people that they're exposed to, but then also what are the things that they might be creating on AI and that's where parent supervision, even at the older ages, as we look at a lot of things that are going into society. That's why it's even more important for us as parents to understand, but also monitor our children to make sure that they're not creating things that could harm them even more so emotionally. And it comes to accountability as parents, you know, from a mental standpoint.
45:08
Kenneth Reddick
So, you know, the only thing I always kind of throw in, and I'm gonna throw this in here right quick. The only thing I always put as something that I recommend to families from a mental standpoint is from a. From a. From a target date, is when a child starts in school, maybe getting a therapist. So that way, as they're getting to these ages, like me, I would go to my son that's eight. He's proactively in therapy. That's my coach, even for him. So if it's something AI related, I'm able to go to his therapist and say, hey, I was thinking about doing this with Trey. That's Kenneth iii. I was thinking about doing this with Trey. You know, what do you think? What do you think? Is that too much exposure? Is that something that I should be doing?
45:54
Kenneth Reddick
So I think that even around this AI space, we have to make sure that we have the right resources and tools for ourselves and our children to make those best sound judgments. Because if I'm not privy to what my child may need or have the best understanding, then it may be more of a disservice offering up certain things that may be a little bit more of a hindrance emotionally and mentally.
46:18
Amanda Bickerstaff
Thanks. Yeah. And I mean, first of all, like, you know, I think that the idea of connecting this even deeper into supports around young people, I think is really important. Important. One of the reasons why young people turn to AI companions like Character AI or Snapchat AI or some of the others out there is because they're not getting that emotional connection. They're not, they're not having conversations with people their own age or even adults. They are not having meaningful connections in general. And so while the bot is not real, what feels real is real, especially to a 10 year old, a 15 year old, a neurodivergent young person.
46:57
Amanda Bickerstaff
Like it's, it doesn't matter if the, if on the other side of statistical probability, if it says, you know, I hope you're okay and I care about you and I agree, like it's gonna feel, it's going to give a dopamine hit. It's going to feel a connection regardless if it's Kenneth on the other side or the Kenneth bot. And so I think that's where it gets really interesting. But I want to point out two things of what you said in terms of young people using these in ways that could be harmful. One is to themselves, like we just talked about, of overdependence and one is to other people like they're, you know, very popular with young men is deep fakes of young women that are deep fake nudes for, you know, to get real.
47:38
Amanda Bickerstaff
It's like, you know, a 12 or 13 year old can go on not the dark web, but literally the app Store and get a free notification app and upload any image of a young girl or a young boy or adult and create deep fake realistic nudes. And I think that, you know, to Kenneth, your point is like, again, it's not just about your young people potentially causing harm to themselves through something that they don't intend to be harmful, but also could actively harm. Whether it's cyberbullying, like Jason said, whether it's deep fake news, whether it's just deep fakes in general. And having those types of conversations with young people that you have, not only you don't need to prioritize only your well being, but you need to also be respectful of the well being of those around you.
48:18
Amanda Bickerstaff
And that these tools can be actively harmful, I think is an important conversation as well. So we're going to kind of like wrap up a little bit because we're coming up on time. But we have in the document, first of all just remember that this is all in the document with a lot more good information. But we have some questions for you. If you're a teacher to think about or a leader or for a parent like Kenneth to actually start asking. I don't know if you've asked any of these, but everything about to the research about having formal policies, but then some, like, really like deeper stuff here too is like, are students being taught foundational AI literacy before using gen AI tools? Are there safeguards in place around everything from data privacy to bias?
49:02
Amanda Bickerstaff
What happens if my kid uses AI and gets caught? Or you think they use AI and they didn't? What does that mean for them? Are you actually evaluating the appropriateness and accessibility of these tools for diverse learners? What are you going to do if a parent says, if we say no generative AI, like, do you actually have a technique or strategy for that's ready? And I think that this is where it becomes so important to have these deeper discussions. But we're just going to end. And I think, Kenneth, I would love you and also Jason, as the advocates that you are, to talk a little bit about that. This is about your, like, as educated as parents and caregivers and a community. This is about your values much than everything else.
49:44
Amanda Bickerstaff
There's no right answer today, I think, other than AI literacy, even if it means just learning about AI and not with AI is incredibly important to our young people's futures. But you can advocate for, like, your young people. You can have meetings to understand why you, if they don't have alternative approaches, you can present the ones you want to see. Like you said, Ken, going beyond, like, actually stepping out of what you like, you think is the right way to communicate and partner with a school. You can connect with other parents with similar concerns to collectively advocate. You can offer resources if, like Kenneth is and Jason are amazing. Kenneth's like, I'm going to come in and do an AI training for parents, like, or I'm going to talk to kiddos and help to understand this.
50:25
Amanda Bickerstaff
Or NPU is going to come in and have a deeper conversation. But the idea of advocating for what you think is best in this moment, I think is incredibly important. And just being heard is going to be like, well, great that you teach my kid generative AI, but I have no access at home. What's going to happen then? Like, how are you going to help that? Like, and having those conversations are going to be really important. So I'm going to calm off the share, but I'd love to kind of end around here about thinking about those values and how you advocate for your young people. I'm gonna start with Kenneth and I'm Jason. You're gonna get the last word, but if you don't mind sharing, that would be great.
51:00
Kenneth Reddick
Yeah, I mean, I, first of all, thank you, Amanda, for having me, but also for the additional insight because it truly has motivated me and I hope that it's motivated some of the other parents because with the work that I'm doing even at my son's school and Getting Dads Together program, I'm calling now Dads Opening doors. And this next weekend I have a Dads and Kids vision board event for the community. I'm already thinking about how can we incorporate a. A kids and a Dads and Kids AI, you know, event to get them together, play around with it a little bit and make it a learning process. Like we, like you said, it is the way of the future. You know, if we.
51:48
Kenneth Reddick
I was joking with you before when we get first got on and I was like, you know, hey, I remember when I first got it. You may not even remember they called them Atom computers. That was the first computer and you moved away from the dot printers and those sorts of things. It's always going to be a little bit leery and as technology progresses, you know, there's always going to be some unknowns. But the reality is, at the end of the day, this is our kids future. And the best thing that we can do is parents as educators, like I say, I'm very active at my son's school, is to create this awareness, continue to have the conversations. Like I saw a few people put it in the chat, hey, if you've utilized something, share it with somebody, you know, let them know.
52:33
Kenneth Reddick
Because the more that we talk about it, the more that we have the proactive conversations with our kids versus reactive conversations after we found out that they've created something or that they have this new friend that you know. But I'd say this too. Meet our kids where they are. A lot of times we don't understand why they stay on the computer so long. We, you know, we may want to say, you know, they always on there, go sit down and watch what they're doing. Spend that time with them. Because we can learn as well from them. And I think a lot of times we take the approach of what we need to know to tell our kids. But sometimes we can learn just as much by going in and figuring out.
53:20
Kenneth Reddick
And I think that's a way that this AI platform for the Future is a way that we can actually partner with our children and learn and grow together versus just thinking that we have to tell them or teach them something. Something. So, I mean, I'm excited. I'm pretty sure Jason is excited because I know we already got our juices flowing about, you know, hey, how we can do more around this. And I'm looking forward to working with you more, Amanda, in the future.
53:47
Amanda Bickerstaff
Absolutely. And it was, you know, I think you've done a beautiful job and we just appreciate your perspective. But Jason, kind of, I would love you to take us out just a little bit as like globally. And someone was talking about. And I think we've done a good job of balance. Right. Like, it's not fear and doom. It's not over AI optimism. And somewhere that pragmatic approaches, especially because the people that are going to be impacted the most are our young people. The younger you are today or even the ones that are not born yet, will be the most impacted by this technology. And so I'd love to kind of hear your last words and what you're thinking about, you know, supporting an advocation for our young people.
54:24
Jason B. Allen
Awesome. I have so many notes to share, but I know we're at the end. And so I'll reinforce urgency and importance of creating strong partnerships with schools, parents, families, caretakers, educate. I think collectively this will be something that will become the way of living and also how they advance technology as they come into adulthood. And it's important that we keep them of this. Again, the National Parents Union, as we're focusing on policies that are impacting access, that are impacting curriculum, that are impacting the way that we present AI. Generative learning is important to this, and so is diversity, equity, and inclusion, because we all are not the same. We all have different experiences. Just because I grew up in a city doesn't mean that I didn't have resources.
55:25
Jason B. Allen
I think that also being in education 20 years data can definitely be to the success of our pathways, and it can also be to our detriment. And I view AI the same way that if we don't have a balance of how we're connecting the dots generationally with this, then we're going to continue to see a generation that is invisible and divided. So that's what. That's one of the reasons why NPU focuses on developing a connected generation, making sure that we're all connected, not just to the conversations, to the program development, to the application development and the innovation of it all, but making sure that we are connected to the policies that help even push these things forward in our schools through budgets, through different programs and initiatives that our children are exposed to.
56:23
Jason B. Allen
And so partnership is a good way to close out and just ensuring again that we're doing this all together for the advancement of our future.
56:32
Amanda Bickerstaff
Absolutely. And I just want to say that, you know, part, like partnering with your young people, co learning, like Kenna said, partnering with educators in schools, partnering with each other, as you know, I. I think this is like an opportunity. Like I'll just end here. Like this is the only, like, only time in the world that we're all learning the same thing at the same time. Maybe, like, maybe Covid, but it was, you know, I don't want to say short because everyone's going to. At me, but like not a very long time in the scope of things with a relatively limited impact on the way that we teach young people. But I do think that this is an opportunity. Like kids can, like kids can come up and be advocates for AI literacy. Parents can work with educators.
57:13
Amanda Bickerstaff
Educators can ask for parental, like, input in ways that never been possible for. Because we're all learning together. So I just want to say, first of all, can we give everybody a beautiful, like Kenneth and Jason. Can we just give them like the round of applause? I know we can't see you. I'm going to give you a heart from me and our team. I just appreciate both of your, first of all, your like, openness to talk about this, like having a deep discussion, being so thoughtful. We appreciate you and everyone that joined. I love that C Cece said we're going to do. We're doing a parent community event. And now we have other ideas here. If you, if you are a parent, we hope this galvanizes you to have conversations with your young people and the schools.
57:50
Amanda Bickerstaff
If you are an educator, we want you to talk to parents. It is so important. So we just want to say thank you. I hope everyone has a beautiful weather. It's the morning in New Zealand or it's. It's night in Flint, Michigan. I appreciate everybody. Thank you so much. And thank you Kenneth and Jason. We appreciate you guys.
| 2025-06-14T00:00:00 |
https://www.aiforeducation.io/ai-in-education-what-parents-and-caregivers-should-know
|
[
{
"date": "2025/06/14",
"position": 82,
"query": "AI education"
}
] |
|
Home – AI in Education Summit
|
Home – AI in Education Summit
|
https://aiineducationsummit.com
|
[] |
The AI in Education Summit brings together education leaders for an intensive two-day exploration of artificial intelligence in education.
|
AI IN EDUCATION SUMMIT 2025
Empowering Educators, Engaging Students
The AI in Education Summit brings together education leaders for an intensive two-day exploration of artificial intelligence in education On April 24-25, 2025 in Dubai at the Millenium Lakeview Hotel.
Day one features inspiring morning keynotes from educational innovators and thought leaders, followed by afternoon case study workshops showcasing successful AI implementation in diverse learning environments, and concludes with an interactive expert panel discussion on the future of education in the AI era.
Day two offers participants a deeper dive through hands-on, two-hour masterclasses where they can develop practical skills and strategies for implementing AI solutions in their own educational settings, ensuring every attendee leaves with actionable insights and valuable connections.
BROUGHT TO YOU BY:
No theoretical discussions – just real strategies, actionable frameworks, and direct access to the innovators who are successfully navigating the AI revolution in education. Whether you’re already integrating AI or just getting started, this event will transform how your school approaches artificial intelligence. Don’t just prepare for the future of education – help create it.
| 2025-06-14T00:00:00 |
https://aiineducationsummit.com/home/
|
[
{
"date": "2025/06/14",
"position": 94,
"query": "AI education"
}
] |
|
AI's arrival at work reshaping employers' hunt for talent
|
AI's arrival at work reshaping employers' hunt for talent
|
https://phys.org
|
[] |
Predictions of imminent AI-driven mass unemployment are likely overblown, but employers will seek workers with different skills as the technology matures.
|
This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:
Credit: Tara Winstead from Pexels
Predictions of imminent AI-driven mass unemployment are likely overblown, but employers will seek workers with different skills as the technology matures, a top executive at global recruiter ManpowerGroup told AFP at Paris's Vivatech trade fair.
The world's third-largest staffing firm by revenue ran a startup contest at Vivatech in which one of the contenders was building systems to hire out customizable autonomous AI "agents," rather than humans.
Their service was reminiscent of a warning last month from Dario Amodei, head of American AI giant Anthropic, that the technology could wipe out half of entry-level white-collar jobs within one to five years.
For ManpowerGroup, AI agents are "certainly not going to become our core business any time soon," the company's Chief Innovation Officer Tomas Chamorro-Premuzic said.
"If history shows us one thing, it's most of these forecasts are wrong."
An International Labor Organization (ILO) report published in May found that around "one in four workers across the world are in an occupation with some degree of exposure" to generative AI models' capabilities.
"Few jobs are currently at high risk of full automation," the ILO added.
But the UN body also highlighted "rapid expansion of AI capabilities since our previous study" in 2023, including the emergence of "agentic" models more able to act autonomously or semi-autonomously and use software like web browsers and email.
'Soft skills'
Chamorro-Premuzic predicted that the introduction of efficiency-enhancing AI tools would put pressure on workers, managers and firms to make the most of the time they will save.
"If what happens is that AI helps knowledge workers save 30%, 40%, maybe 50% of their time, but that time is then wasted on social media, that's not an increase in net output," he said.
Adoption of AI could give workers "more time to do creative work"—or impose "greater standardization of their roles and reduced autonomy," the ILO said.
There's general agreement that interpersonal skills and an entrepreneurial attitude will become more important for knowledge workers as their daily tasks shift towards corralling AIs.
Employers identified ethical judgment, customer service, team management and strategic thinking as top skills AI could not replace in a ManpowerGroup survey of over 40,000 employers across 42 countries published this week.
Nevertheless, training that adopts those new priorities has not increased in step with AI adoption, Chamorro-Premuzic lamented.
"For every dollar you invest in technology, you need to invest eight or nine on HR, culture transformation, change management," he said.
He argued that such gaps suggest companies are still chasing automation, rather than the often-stated aim of augmenting human workers' capabilities with AI.
AI hiring AI?
One of the areas where AI is transforming the world of work most rapidly is ManpowerGroup's core business of recruitment.
But here candidates are adopting the tools just as quickly as recruiters and companies, disrupting the old way of doing things from the bottom up.
"Candidates are able to send 500 perfect applications in one day, they are able to send their bots to interview, they are even able to game elements of the assessments," Chamorro-Premuzic said.
That extreme picture was not borne out in a survey of over 1,000 job seekers released this week by recruitment platform TestGorilla, which found just 17% of applicants admitting to cheating on tests, and only some of those to using AI.
Jobseekers' use of consumer AI tools meets recruiters doing the same.
The same TestGorilla survey found almost two-thirds of the more-than-1,000 hiring decision-makers polled used AI to generate job descriptions and screen applications.
But a far smaller share are already using the technology to actually interview candidates.
Where employers today are focused on candidates' skills over credentials, Chamorro-Premuzic predicted that "the next evolution is to focus on potential, not even skills even if I know the skills you bring to the table today, they might be obsolete in six months."
"I'm better off knowing that you're hard-working, that you are curious, that you have good people skills, that you're not a jerk—and that, AI can help you evaluate," he believes.
© 2025 AFP
| 2025-06-14T00:00:00 |
https://phys.org/news/2025-06-ai-reshaping-employers-talent.html
|
[
{
"date": "2025/06/14",
"position": 83,
"query": "AI employers"
}
] |
|
The Future of Fashion: ChatGPT's Impact on ...
|
The Future of Fashion: ChatGPT’s Impact on the Fashion Industry and the Path Forward — UNRAVELED EDIT
|
https://unravelededit.com
|
[
"Courtney Moane"
] |
How does it work? ChatGPT works by learning information, first consumed and adapted to a large volume of data and then analyses this data to learn the style, ...
|
The fashion industry is currently witnessing the growing presence of AI technology!
In recent years, Artificial Intelligence is being used to design clothes, predict upcoming fashion trends, and improve the overall customer service and shopping experience, and now the breakout start of ChatGPT, conversations are emerging among industry professionals weighing on its potential for the future of the fashion industry.
So, what is it?
ChatGPT is a conversational AI model developed by OpenAI which is based on the Transformer architecture. This chatbot was designed to generate human-like responses in natural language conversations that have been trained on a wide range of internet text to understand and generate coherent and relevant responses.
How does it work?
ChatGPT works by learning information, first consumed and adapted to a large volume of data and then analyses this data to learn the style, tone, pattern, and structure. Once this analysis has been completed and the chatbot has learned the process it is then able to produce informative answers based on the topics provided by the human user.
Fashion brands have used AI chatbots for quite some time to assist in customer service experience, assistance, and styling, however, the emergence of ChatGPT has unveiled a whole new world of possibilities for the fashion industry as an opportunity to embrace growth. With a diverse skill set, ChatGPT offers specific features tailored to the industry’s specific needs and today we will explore some of these remarkable capabilities in depth and how this may affect the fashion industry in the future.
In the fast-paced world of fashion where trends can change overnight and customers from all areas of the world seek instant support, brands must break free from time constraints! Through the emergence of ChatGPT, fashion brands are now able to provide uninterrupted support to their customers, regardless of their physical location and time zone. Traditionally limited to business operating hours, ChatGPT changes the customer support game by enabling active and responsive support 24/7 - whether a customer is browsing a website during the early morning hours or late at night, AI allows customers access to assistance and responses promptly.
Brands from all over the world and of all sizes offer ChatGPT and similar AI technologies in their operations to enhance customer experience. For instance, Burberry; a renowned luxury fashion brand was one of the first to utilise chatbots to enhance customer service and engagement. Their AI chatbot enhances customer service and engagement allowing customers to explore the brand’s products, offers styling tips, and provides information on events and store locations. ChatGPT utilises advanced natural language processing capabilities to generate responses that closely resemble those of a human, it understands and in turn interprets a wide range of customer queries, ensuring that customers receive prompt and accurate assistance. It understands customer preferences and behaviours, allowing it to provide personalised recommendations tailored to individual tastes, needs, and styles. Whether a customer has a question about product availability, sizing, or styling advice, ChatGPT can conversationally provide relevant responses.
These engaging and timely conversations can contribute to increased customer satisfaction and foster a stronger connection between the customer and the brand.
Sounds good right?
While the integration of AI applications like ChatGPT in the fashion industry is an exciting and viable future there are some concerns. One of the greatest concerns is the possibility of AI replacing human labour. With AI able to complete the jobs of fashion designers, data analysts, and customer service specialists, the risk that jobs will be lost come into play.This could in turn have a significant impact on the fashion industry as a whole, as well as the broader economy and society.
AI in the fashion industry has the potential to automate certain tasks and processes that could lead to changes in the industry job landscape and while AI can streamline operations and enhance overall productivity, it is important to note that it also creates new opportunities and roles within the fashion industry. AI complete data-driven tasks like inventory management, trend analysis, and customer segmentation which in turn allows humans to focus on more creative and strategic aspects of their workload.
| 2025-06-14T00:00:00 |
https://unravelededit.com/fashion/chatgpt
|
[
{
"date": "2025/06/14",
"position": 98,
"query": "ChatGPT employment impact"
}
] |
|
LazyApply - AI for Job Search
|
AI for Job Search
|
https://lazyapply.com
|
[] |
Our AI Job search tool automatically apply to all the jobs on platforms like Linkedin, Indeed and Ziprecruiter using Job GPT. Emma Wilson. Michael Davis.
|
SW Sarah Wilson Reviewer • 42 reviews
2 months ago
Really happy with this tool. Cut down my application time significantly and the dashboard is pretty straightforward to use. Support got back to me quickly when I had questions.
| 2025-06-14T00:00:00 |
https://lazyapply.com/
|
[
{
"date": "2025/06/14",
"position": 87,
"query": "artificial intelligence employment"
}
] |
|
Building a Better Workplace: The Significance of Human ...
|
Building a Better Workplace: The Significance of Human-Centered AI
|
https://pomeroy.com
|
[
"Dan Huberty"
] |
According to a recent survey, 52% of employees feel “worried” about AI's impact on the workplace. Though various estimates place enterprise adoption of ...
|
According to a recent survey, 52% of employees feel “worried” about AI’s impact on the workplace. Though various estimates place enterprise adoption of generative AI tools at upwards of 90%, PWC found that only 11% of surveyed organizations had achieved complete and responsible AI implementation.
These statistics demonstrate that employees are nervous about AI in the digital workplace. Nobody wants to get replaced. This worry can be addressed by correctly implementing AI. In fact, with the right approach, AI can build stronger, more connected teams. How? In a word: empathy.
Empathy offers a competitive edge in today’s business environment, not just a moral benefit. EY Consulting research indicates a strong connection between empathy and positive workplace outcomes. According to a recent report, 87% of employees perceive empathy as a key driver of better leadership, which, in turn, is linked to significant increases in efficiency, creativity, job satisfaction, and innovation.
Empathy should be a guiding principle when integrating AI into the Digital Workplace. But what does that mean exactly? How can empathy, a uniquely human trait, guide unfeeling machine learning tools? In this post, we will explore those questions further and elaborate on how Pomeroy meets the challenge of building empathy into AI.
What is human-centered AI?
Empathy is foregrounded in human-centered AI (HCAI), an emerging discipline that strives to ensure technology serves human needs, not vice versa. HCAI uses “artificial empathy,” which predicts how users might react in various situations based on emotional flags they display in an interaction, such as a chat or voice call.
Taking it a step further, human-centered AI meets users (customers and employees) where they are by embedding in their preferred channels (Slack, MS Teams, etc.). For example, predictive analytics can (and should) anticipate the user’s needs and even preemptively remediate them before the user actively asks for help.
Let’s say an employee at your organization finds their laptop not working. Typically, this employee would need to call or log into the Service Desk, potentially interacting with a chatbot or a person, and do a lot of explaining and waiting to get their laptop fixed. In the empathetic, human-centered model, a connected support AI would detect an issue with the user’s laptop and alert the Service Desk. In this case, the Service Desk reaches out before the user can file a ticket or even supplies the user with a replacement laptop while fixing the problem. This is what the terms “zero-touch IT” or “invisible service desk” mean, not that IT or the Service Desk are going away, just that they will get much better at anticipating user needs.
However, there is more to human-centered AI than simply telling an AI tool to “have empathy.” There is a set of guiding principles that steer AI systems toward this goal, including:
Understanding user needs
Ethical design and bias reduction
Incorporating user feedback
Ensuring accessibility and inclusivity
Transparency and explainability in systems
Iterative improvement through continuous feedback
Human oversight
Key elements of human-centered AI implementation
Several key considerations are crucial for realizing truly effective human-centered AI, including:
Augmentation: Ensuring AI augments, rather than replaces, human efforts is vital.
Ethical application: Guarding against the manipulative use of AI and emotional data is paramount to maintaining and building trust, a core objective of empathetic AI.
Bias mitigation: Addressing potential biases in training data is essential to prevent misinterpretations across various contexts.
Data security: Prioritizing the protection of sensitive emotional data is critical to navigate ethical and legal concerns effectively.
ROI assessment: It is important to develop strategies to assess the value and impact of empathy, acknowledging that its intangible benefits may be challenging to measure using traditional metrics.
Examples and use cases
Eventually, artificial empathy will be a part of almost every AI system. For now, enterprises are seeing the most returns in a few applications:
Customer service: HCAI systems understand and respond to human emotions, creating a more empathetic and personalized experience. Which in turn boosts customer satisfaction scores.
Employee experience: AI-powered tools analyze employee sentiment and provide personalized support, improving morale and productivity.
Training and development: Adaptive learning platforms use AI to tailor training content to individual employee needs and learning styles.
HR and talent acquisition: AI assists in identifying and engaging with potential candidates while supporting employee onboarding and development.
Collaboration and communication: Intelligent platforms facilitate more effective teamwork by understanding communication patterns and suggesting relevant resources or connections.
Accessibility: AI-driven features enhance the accessibility of digital tools for employees with disabilities, promoting inclusivity.
These examples illustrate how prioritizing user needs through human-centered AI enhances experiences and outcomes across industries.
How is Pomeroy incorporating human-centered AI into our offerings?
Pomeroy is leading the way in providing human-centric and AI-enabled workplace experiences with a focus on:
Empathy with personalization: We leverage AI to analyze user behavior to deliver personalized services that resonate with their needs. Instead of bringing humans to the technology, our technology solutions meet the users where they work, fostering empathy.
Automation with contextualization: With our technology solutions, we understand user context to provide tailored services. We leverage AI to understand workflows and technology usage to provide more automated solutions with minimal human touch.
Productivity enhancement with proactive support: Pomeroy leverages AI-driven support that detects issues before users report them. This proactive support ensures uninterrupted productivity enhancements.
Unified experience with intelligent orchestration: Pomeroy provides an intelligent orchestration of insights from the individual AI agents from the diverse enterprise technology ecosystem, providing a consolidated approach to enhance human experience with the workplace.
Ethical AI usage with security: Pomeroy also enables the data privacy and transparency mechanisms to ensure that employees see what data is collected by AI and why and how it is used. Our zero-trust access models leverage adaptive authentication and threat detection without compromising user experience.
At Pomeroy, we incorporate an AI-enabled and human experience-centric digital workplace vision that ensures continuous learning and improvement with changing human behavior and business needs, and a robust governance framework that includes regular audits to maintain transparency, accountability, and ethical AI use.
Curating the future of AI
Empathy builds trust in the Digital Workplace, leading to increased tool adoption. It is an X factor that boosts productivity, improves employee satisfaction by making work easier and more aligned with individual styles, and fosters greater engagement. Ultimately, a human-centered Digital Workplace drives innovation by empowering employees to experiment and contribute creatively when they feel understood and supported by their digital tools.
Get in touch to learn more about how we can help implement and optimize AI in your environment.
| 2025-06-14T00:00:00 |
2025/06/14
|
https://pomeroy.com/building-a-better-workplace-the-significance-of-human-centered-ai/
|
[
{
"date": "2025/06/14",
"position": 88,
"query": "workplace AI adoption"
}
] |
'You cannot stop this from happening:' The harsh reality of AI and the ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
'You cannot stop this from happening:' The harsh reality of AI and the job market - “I'm really convinced that anybody whose job is done on ...
|
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
| 2025-06-15T00:00:00 |
https://www.reddit.com/r/Futurology/comments/1lc6n9g/you_cannot_stop_this_from_happening_the_harsh/
|
[
{
"date": "2025/06/15",
"position": 40,
"query": "AI labor market trends"
},
{
"date": "2025/06/15",
"position": 24,
"query": "AI employment"
}
] |
|
World Summit AI
|
World Summit AI Amsterdam
|
https://worldsummit.ai
|
[] |
Amsterdam is a city renowned for its history, culture, business climate and innovation. With AI firmly established as the defining technology of the future, ...
|
Stuart Russell is a pioneer in the understanding and uses of artificial intelligence (AI), its long-term future, and its relation to humanity. He also is a leading authority on robotics and bioinformatics.
Stuart Russell is the author or coauthor of three books on knowledge, reasoning, and machine learning, including the standard textbook on artificial intelligence. Stuart Russell is a professor of Electrical Engineering and Computer Sciences at UC-Berkeley and Adjunct Professor of Neurological Surgery at UC-San Francisco.
Artificial intelligence. AI is a machine’s ability to perceive its environment and use that information to maximise its chance at succeeding at some goal. All of the technologies behind artificial intelligence are evolving at exponential rates and are just now beginning to rocket up the curve. Soon artificially intelligent machines will be doing things we can barely conceive, in virtually every area of human business, life, and culture. Few people understand this future better than Stuart Russell, who wrote the standard textbook on AI (with coauthor Peter Norvig), now in its third edition.
Robotics. Stuart Russell is also a leading researcher in the practical applications of machine intelligence. If AI is the brain of the machine of the future, the robot is its body. Robots are taking over more and more functions that once required a human actor, with far-reaching consequences — and opportunities — for people, businesses, and society as a whole. He has focused recently on the threat of autonomous weaponry.
Biological information. As an Adjunct Professor of Neurological Surgery, Stuart has been researching computational physiology, with a current focus on Intensive Care Unit monitoring systems. He is a leader in the development of technologies that make sense of biological data and that apply this information in ways that advance human health.
Credentials. In addition to the positions already mentioned, Stuart Russell holds the SmithZadeh Chair in Engineering at UC-Berkeley and is Vice-Chair of the World Economic Forum’s Council on AI and Robotics. He is the recipient of many awards and held the Chaire Blaise Pascal in Paris from 2012 to 2014. He is a researcher at a number of research centers, including the Berkeley Artificial Intelligence Research Lab (BAIR), the Berkeley Center for New Media (BCNM), and the Synthetic Biology Institute (SBI). He is the founder and Vice-President of Bayesian Logic, Inc., a data analysis start-up under contract with the UN to build a new Nuclear Test Ban Treaty global monitoring system.
| 2025-06-15T00:00:00 |
https://worldsummit.ai/
|
[
{
"date": "2025/06/15",
"position": 96,
"query": "AI employers"
}
] |
|
AI chatbots are kicking journalism while it's down - MattCASmith
|
AI chatbots are kicking journalism while it's down
|
https://mattcasmith.net
|
[] |
But for the media outlets that produce contemporaneous information, there is one crucial difference with AI chatbots: The source link is downplayed, and since ...
|
Where do you go when you need to find information online? For decades, my answer to that question would have been Google, but in the past few years I’ve increasingly turned to ChatGPT and other AI services, which - generally speaking - uncover niche information more quickly and reliably.
But for the media outlets that produce contemporaneous information, there is one crucial difference with AI chatbots: The source link is downplayed, and since the model can identify the specific information that matches the query, the user very rarely has a reason to click through to an external website.
As AI’s popularity surges, there are very real knock-on effects for journalism, which was already reeling from its recent transition from print to an online advertising model. There’s a chicken-egg conundrum at play here - if AI puts the content creators out of business, who will create new content to train AI?
If journalism is read by AI training bots rather than humans, advertising revenue could collapse
Out of the frying pan
When I joined the industry in the 2010s, journalism already had its problems. The widespread adoption of the internet was disrupting its traditional print revenue model. For established outlets, this meant fewer, less experienced journalists being paid less to do more work - after all, they now needed to take photos and videos for the website, and perhaps record a podcast.
BuzzFeed started to pull huge numbers with listicles and quizzes1, and the old guard took note. Suddenly, respected national newspaper sites were full of slideshows and clickbait, which at the time was annoying but harmless - but eventually morphed into the political outrage machine we know today once they realised that emotional headlines largely outperform mysterious ones.
From that point onwards, mainstream online journalism focused less on quality and accuracy, and more on bending to the whims of the internet’s new homepages: search engines and social media. The main objectives were to get links in front of users, and to convince them to click. Anything was fair game in the battle to generate impressions for their online advertisers.
Into the fire
But now the journalism model is being upended once again - this time by AI. Whether via a large language model (LLM) like ChatGPT, or via a widget on a search engine results page, AI fetches and distils information reliably enough that in many cases a click to the source website becomes unnecessary.
The effects are being felt. The Wall Street Journal reports that search traffic to some popular news sites has dropped by as much as 50 percent in the last three years. The chief executive of The Atlantic notes Google is reinventing itself as an “answer engine” and expects traffic from its search results to trend towards zero, mandating a change in the publication’s business model.
An example AI chatbot response about a news story - would you click any of those links?
For its part, Google says that traffic routed via its AI summary links is of a higher quality, with users remaining on the destination site for longer. But that will be scant consolation for news publishers, whose online revenue is currently largely tied to impressions generated for their advertisers’ on-page placements - a metric based on the quantity of visits, not their quality.
News outlets are rushing to repair the damage by bolstering relationships with readers and encouraging direct visits, but sites like Business Insider have already attributed cuts in headcount to this trend. If it continues, we could see further decline in an already faltering media industry, leaving a dearth of reliable news to inform audiences and, indeed, to train AI on future events.
Then who writes the news?
There are options under which journalistic output could be maintained, but they all require either a significant upheaval of current trends, or for those regulating AI companies’ activities to make decisions that are perhaps unlikely due to the huge implications for the tech sector’s bottom line.
Maybe independent blogging will make a return? You might expect me to be upbeat on this, but I can’t see it. Discoverability has declined considerably since blogging’s heyday, and while a small group of high-profile names seem to do well on Substack, most would-be bloggers are unlikely to continue publishing into the abyss in return for an even smaller trickle of traffic.
Perhaps it’s the responsibility of the AI companies? News reporting isn’t their specialism and it would be a cost centre, but it’s not out of the question for them to reach into the real world for new data. We’ve already seen job openings for niche hobby experts to train generative AI models, after all.
There’s also the matter of the copyright question looming over the AI sector. If data is used to train models, and is utilised in LLMs’ responses, should AI developers pay the creators of said data? The law is taking a while to catch up, but an unfavourable ruling could either prevent AI firms from using copyrighted journalism to train models, or force them to pay media outlets for the privilege and make up for some of that lost advertising revenue.
Filling the void
This is a more pressing issue than most people probably think. While it’s harder to come by these days, quality journalism is a valuable public service that keeps us informed and ensures public accountability. However, while it would inevitably be missed if news sites folded en masse, just as concerning is what would emerge in the vacuum created by such a collapse.
If visitors evaporated, receiving their information directly via AI chatbots, who could afford to publish news on the internet? Certainly not anybody who relies on visitors and advertising revenue. That opens doors to publishers with private funding, which includes those with less noble motives.
If AI makes journalism in the public service financially untenable, the doors will open to publishers with less noble motives
At the less nefarious end of the scale are corporate publishers, who put out news content to gather an audience and promote the parent company’s products or services. The pitfalls here are probably obvious - if the site is a glorified marketing tool, its writing is more likely to be biased, and stories may be omitted altogether if they clash with the organisation’s goals.
More concerning is the potential for malicious foreign actors to step into the space left by a shrinking news media. By creating their own news websites under shell organisations, hostile nations like Russia can not only promote their agendas to unsuspecting readers, but also potentially even influence AI models’ responses if they can work their content into their training data.
A fragile ecosystem
AI chatbots and LLMs are undeniably powerful as information tools, but they risk draining the resources that they rely on to be effective. Journalism - as flawed as it may be in the modern media landcape - remains one of our strongest barriers against misinformation and bias, and ironically this makes it a key source of trustworthy content on which to train AI models.
If we want AI to stay useful, we need to ensure that there is a sustainable flow of current information from reliable sources. This may mean reshaping how value flows in the digital economy, whether through licencing deals, regulatory intervention, or new funding models for journalism.
Something has to change. Without such efforts, we risk a future where we have tools capable of answering any question we can throw at them, but no confidence that those answers are up-to-date or free of manipulation.
Notes and references
To their credit, they invested some of the proceeds in hiring top-tier journalists to produce some more serious output for BuzzFeed News, although it was ultimately dissolved in 2023.
Field notes // A monthly newsletter on tech and design No spam - just thought-provoking articles and useful tidbits
| 2025-06-15T00:00:00 |
2025/06/15
|
https://mattcasmith.net/2025/06/15/ai-chatbots-journalism
|
[
{
"date": "2025/06/15",
"position": 52,
"query": "AI journalism"
}
] |
How OpenAI's Head of Business Products Uses ChatGPT to ...
|
How OpenAI's Head of Business Products Uses ChatGPT to Save Time at Work | Nate Gonzalez
|
https://creatoreconomy.so
|
[
"Peter Yang"
] |
... work together through our culture of urgency and how we drive impact. How Nate uses ChatGPT to save time at work. How do you personally use ChatGPT to save ...
|
Dear subscribers,
Today, I want to share a new episode with Nate Gonzalez.
Nate leads ChatGPT for Work which is now used by 92% of Fortune 500 companies. In our chat, he reveals how OpenAI runs with <30 PMs, what they look for in new hires, how he personally uses ChatGPT to save time at work, and more.
Watch now on YouTube, Apple, and Spotify.
Nate and I talked about:
(00:00) 92% of Fortune 500 companies already use ChatGPT
(02:06) OpenAI's latest features for ChatGPT at work
(14:52) Why OpenAI has less than 30 PMs for 5,000 employees
(15:58) What traits OpenAI looks for when hiring PMs
(18:31) The 10-minute AI hack that changed how Nate works
(25:56) The most surprising thing about working at OpenAI
(29:48) The biggest barriers to AI adoption and how to overcome them
(38:36) Using ChatGPT roleplay to prep for important meetings
(41:04) ChatGPT's future: From assistant to trusted coworker
(43:06) The specific skill that will keep your job safe in the AI era
This free post is brought to you by... Product Faculty
Product Faculty has partnered with Miqdad Jaffer (product lead at OpenAI) to teach the top AI PM certification on Maven. You can get 25% off Miqdad’s AI PM course with my link below. You’ll learn how to solve real problems with AI, dive into technical concepts, and build real AI prototypes.
Get 25% Off Miqdad's AI PM Course
Why OpenAI has <30 PMs for 2,000+ people
So I think OpenAI has <30 PMs for 2,000+ people. Why is the PM team so lean?
We want to be the model of what it looks like to build a company on top of AI.
That means using AI to extend every employee. Our PM team is lean, our engineering team is also relatively lean for the size and scope of the business. We're leaning into AI every single day — what can we do better by working with the models directly?
What traits do you look for when hiring PMs at OpenAI?
We look for several things:
Agency. You need high grit and a willingness to work on hard problems. Product sense. You need to balance user fit with business considerations and be able to creatively brainstorm and justify solutions. Mission alignment. You need to tie your product back to impact on our mission. Execution. You need to have a high sense of urgency. Curiosity. ML experience is great, but we also look for signals that you’re an AI builder and know how the ecosystem is evolving.
Connectors: Extending ChatGPT to support internal knowledge sources
I’d love to get an inside look at how you build products at OpenAI. You just launched Connectors to let companies connect their internal sources like Google Drive, Sharepoint, and Box. How did you build this from ideation to launch?
Models get smarter with more context, and internal context is critical for businesses.
We were initially thinking in the GPT-4 paradigm — quick call-response where you need to sync and index repositories for low latency.
But reasoning models changed everything. The latency constraint relaxed because the model has multiple turns to get the right answer. It could form hypotheses, look at variants, and pull them together. This shift allowed us to scale connectors more quickly and accurately.
How do you decide which internal documents to load into the model’s context? Some of them might be outdated or low quality.
Great question.
We did extensive post-training on recency and "seniority of authorship" — basically a social graph to surface the most relevant content.
Think about someone new onboarding at OpenAI. There might be 100 documents written about a specific subject. We spent tons of time making sure the most relevant documents get pulled forward.
Record mode: How OpenAI approached AI meeting notes differently
The most relevant information internally is often said in meetings. Tell me about how you approached Record Mode.
Sure. Record mode rounds out the knowledge picture. Beyond pre-trained knowledge, web search, and internal documents — there's all the information spoken in meetings that never gets properly recorded.
We created a generalized recording capability to model that information like any other knowledge source. I can ask "What did Peter and I talk about two weeks ago?" and get a timestamped summary where each action item links to the transcript.
So one of the most important skills for PMs is to learn how to write AI evals. How did you evaluate whether Record Mode was good enough to launch?
First, you need to align on the North Star metric, measure it, get a baseline, and hill-climb quality against that baseline.
For Record Mode, we look at transcription accuracy and collect qualitative feedback directly from users on whether summaries are good or bad. We also track signals around follow-up questions — are we being clear and returning accurate information?
We don't aim for perfect before launching. That's not our ethos. We set a high quality bar but prioritize getting to user signal quickly — that's where evals really matter.
An example of OpenAI’s bottoms-up culture
Can you share an example of how ideas can come from the bottom up at OpenAI?
Sure, Canvas is a perfect example. An IC researcher pitched the Canvas idea in her 1st month at the company — I think around July 4th break.
Her manager agreed immediately and staffed 5-6 engineers. The team formed organically — "Hey, there's a really interesting idea, we think it's super high leverage, who wants to work on this?" People gravitated to it saying "I want to work on that problem."
Canvas became our first major UI update to ChatGPT. We went from a basic chat interface to a much richer experience, all from a single individual whose idea wasn't part of any specific product roadmap.
The best idea wins. It doesn't really matter where it comes from within the company.
The biggest misconception of how OpenAI builds products
How do you balance this bottom-up culture with planning?
We run a quarterly planning process. The reality is as soon as you wrap the plan, it's out of date and you're really using it as a trade-off framework.
We try to minimize the process around roadmapping because it's such a fluid, constant iteration. There are so many things we could do that we have high conviction in. How do we focus on the ones with highest impact?
This necessitates talking to customers. I probably have 4-5 customer conversations directly each week and very specific themes emerge. We also work closely with our go-to-market team who are sitting with customers every day, with multiple feedback loops to get that information back to product.
What’s the biggest misconception of how OpenAI builds products?
One big misconception is that moving quickly = cutting corners, particularly around safety.
Our team is deeply mission-oriented. When we think about what makes a successful PM at OpenAI, they're 100% focused on what impact they're driving. This pushes urgency to ship quickly but also to ship responsibly.
There's a whole bunch more that we could just ship, but we are actually holding that bar very deliberately to make sure it's of the necessary quality. We'll hold if there's safety evaluation work needed before something goes out.
You might think of urgency and safety as opposing constructs, but in reality they work together through our culture of urgency and how we drive impact.
How Nate uses ChatGPT to save time at work
How do you personally use ChatGPT to save time at work?
It saves me so much time in these three tasks:
Internal research. Getting up to speed on projects in our research org or technical implementations. I can onboard context much faster without endless meetings pulling time from other teams. External research. We serve 92% of the Fortune 500 across every industry. I use AI to understand their company and context so we can map our products to their needs more quickly. Roleplaying with voice. This is huge. Before talking to a candidate I'm keen on or a critical customer interaction, I roleplay with ChatGPT. I'll have it assume their personality so I can hone my craft and get crisp on messaging.
I love the roleplaying example. Do you upload context to make it act like specific people?
Yes, I upload context from connectors and record mode meeting summaries into a project. Then voice mode has background context to play the role or be a thought partner for me.
I also often use ChatGPT to critique my work: "Here's my initial draft. What am I missing? Where could this be stronger? What are the weakest parts of this argument?" It helps with both drafting and actual quality of output.
The biggest barriers to employee AI adoption and how to overcome them
What are the barriers to getting employees to use AI in companies?
Companies often ask "who do I give AI to? My most technical employees? My early adopters?" But that creates cynicism — those with the intuition and those without. Access is key. You need everyone building intuition about what AI is good at now, what it still struggles with, where it's headed. That's how OpenAI moves so quickly — we use our tools every day, so reaching for ChatGPT becomes second nature.
The big trend driving success is internal AI champions.
These champions aren't necessarily CEOs. They're heads of AI, heads of product divisions, often CIOs who want transformational change. We work with them directly to identify the highest value use cases.
What advice would you give someone trying to drive AI adoption at their company?
Two things:
Broad deployment. Get this in every employee's hands so they become fluent. You want them familiar with these tools because that drives bottom-up culture. Focus on specific use cases. Find one or two bets that will drive outsized value for workflows in your company. These aren't just tools for tools' sake — identify the highest leverage product opportunity and push forward.
Moderna is a great example. They have thousands of GPTs deployed internally. People create GPTs and share them with colleagues, so everyone gets the collective benefit of that knowledge work.
The future: ChatGPT as your trusted coworker
We're moving from AI as copilot to delegating work to AI agents. How will this change the PM role?
Our goal is to extend your productivity by making ChatGPT your virtual coworker.
Imagine waking up, sitting down with ChatGPT in a more personalized interface with a list of tasks that have come in. You delegate some to ChatGPT — "bring these back when done" — while you tackle the top priorities.
Behind the scenes, that could mean orchestration with different agents. But focusing the interaction through ChatGPT reduces cognitive load.
So is it like having a bunch of AI interns to work for you?
Well I think AI agents will be more than interns — after all, they can do PhD-level math and deeply understand any code.
In 2022-2023, it felt like an intern. But with reasoning models and better UI paradigms, it'll feel much more like a coworker that you trust to get work done.
Closing advice for people who want to level up their AI skills
What's your advice for people who want to level up their AI skills?
It's not just "go try the tools." It's how do you make these tools an extension of the way you work.
Don't just learn to write emails faster. Learn to write better emails. Use AI to improve the quality of your thinking and process.
Find quality improvement loops, not just productivity loops.
Ask it to critique your work, point out weak arguments, identify logical fallacies. That's how you actually improve.
If I want to use ChatGPT officially at work, where do I start?
Get started quickly with ChatGPT Team. We just enabled SSO so it's easy for businesses to self-serve.
As you need more advanced compliance and want to work with our go-to-market teams on use cases, consider ChatGPT Enterprise. Both products have the same privacy guarantees — we never train on your data.
Thank you Nate! If you enjoyed this interview, follow Nate on LinkedIn and check out ChatGPT Team or Enterprise for your company.
| 2025-06-15T00:00:00 |
https://creatoreconomy.so/p/how-openais-head-of-business-products-uses-chatgpt-at-work-nate-gonzalez
|
[
{
"date": "2025/06/15",
"position": 42,
"query": "ChatGPT employment impact"
}
] |
|
JournalismAI Academy for newsrooms [Sub-Saharan Africa]
|
JournalismAI Academy for newsrooms [Sub-Saharan Africa]
|
https://ijnet.org
|
[] |
This free online program provides a deep dive into the potential of artificial intelligence to journalists and media professionals.
|
Journalists and media professionals from Sub-Saharan Africa, working in any newsroom, can apply for a training program.
The JournalismAI team at the London School of Economics and Political Science (LSE), in partnership with the Google News Initiative, is offering the JournalismAI Academy. This free online program provides a deep dive into the potential of artificial intelligence to journalists and media professionals.
The program combines a series of masterclasses given by experts working at the intersection of journalism and artificial intelligence, with opportunities for discussion among participants. In addition, participants will be guided through the development of resources that can support their organizations’ AI-adoption journey during and after the program.
Twenty participants will be selected for the five-week program which begins in July.
The deadline is June 15.
| 2025-06-15T00:00:00 |
https://ijnet.org/en/opportunity/journalismai-academy-newsrooms-sub-saharan-africa
|
[
{
"date": "2025/06/15",
"position": 49,
"query": "artificial intelligence journalism"
}
] |
|
AI in the Newsroom: AP's June 2025 Course on Practical ...
|
AI in the Newsroom: AP’s June 2025 Course on Practical, Ethical Adoption
|
https://workflow.ap.org
|
[] |
A three-week course designed to give newsroom leaders, operational strategists, and editorial technologists the practical tools and strategic insight they need ...
|
AI is no longer a distant concept for media organizations, it’s rapidly becoming an essential tool for planning, producing, and publishing content in smarter, faster, and more strategic ways.
At the Associated Press, we’ve spent over a decade integrating AI into our newsroom workflows – not as a novelty, but as a strategic advantage. From automation and metadata enrichment to AI-assisted reporting and content integrity tools, our teams have been at the forefront of testing, refining, and scaling AI for real newsroom needs.
Now, we’re opening that experience to the wider industry.
| 2025-06-15T00:00:00 |
https://workflow.ap.org/news/ai-in-newsrooms-course-june-2025/
|
[
{
"date": "2025/06/15",
"position": 64,
"query": "artificial intelligence journalism"
},
{
"date": "2025/06/15",
"position": 41,
"query": "workplace AI adoption"
}
] |
|
AI and the Music Industry | Protecting your original material
|
AI and the Music Industry | Protecting your original material
|
https://musiciansunion.org.uk
|
[] |
... workers from exploitation in the development of artificial intelligence (AI) models. ... Naomi Pohl and Alex Sobel MP holding black Musicians' Union t-shirts that ...
|
Tech firms should not be able to use your music to train their artificial intelligence (AI) models without your consent.
Big tech companies want to use songs, recordings and other creative works to train their AI models for commercial purposes without asking or paying the original creators or rights holders.
The MU is fighting for for consent, credit and fair compensation for all creators for the use of their work to train AI models.
| 2025-06-15T00:00:00 |
https://musiciansunion.org.uk/all-campaigns/artificial-intelligence-and-the-music-industry
|
[
{
"date": "2025/06/15",
"position": 100,
"query": "artificial intelligence labor union"
}
] |
|
Future of work
|
Future of work
|
https://www.iwh.on.ca
|
[] |
Artificial intelligence (AI) is transforming workplaces across Canada, enhancing productivity and efficiency in countless ways. AI also has the potential to ...
|
IWH Speaker Series
Artificial intelligence (AI) is transforming workplaces across Canada, enhancing productivity and efficiency in countless ways. AI also has the potential to transform occupational health and safety practice. Despite its promise, however, we still know very little about how organizations are using AI to protect worker health and what factors influence their decision to adopt these technologies. In this presentation, Dr. Arif Jetha dives into findings from a recent survey of over 800 occupational health and safety professionals in Ontario and British Columbia. He outlines how AI could create safer workplaces and support worker health, sparking a discussion on both the risks and opportunities of AI in occupational health and safety and offering valuable takeaways for future research and practice.
Published: March 2025
| 2025-06-15T00:00:00 |
https://www.iwh.on.ca/topics/future-of-work
|
[
{
"date": "2025/06/15",
"position": 74,
"query": "future of work AI"
}
] |
|
Occupational Employment & Wage Statistics - Minnesota.gov
|
Radware Bot Manager Captcha
|
https://mn.gov
|
[] |
The Impact of Automation on Minnesota's Labor Market · More Signs of a Tightening Job Market · College Major, Occupational Pathways, and Labor Force Outcomes by ...
|
Please validate your request
Complete this task to confirm you are a human generating this request. Thank you!
| 2025-06-15T00:00:00 |
https://mn.gov/deed/oes
|
[
{
"date": "2025/06/15",
"position": 90,
"query": "job automation statistics"
}
] |
|
Your Boss Is Probably Using AI More Than You
|
Your boss is probably using AI more than you
|
https://www.businessinsider.com
|
[
"Ana Altchek"
] |
Gallup data indicates AI adoption has risen, especially in white-collar roles, with tech leading at 50%. 16% of employees surveyed who use AI "strongly agree" ...
|
This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now.
There's a good chance your boss is using AI more than you.
Leaders are adopting AI at nearly double the rate of individual contributors, a new Gallup poll released Monday indicates. The survey found that 33% of leaders, or those who identified as "managers of managers," use AI frequently, meaning a few times a week or more, compared to 16% of individual contributors.
Gallup's chief scientist for workplace management and wellbeing, Jim Harter, told Business Insider that leaders are likely feeling added pressure to think about AI and how it can increase efficiency and effectiveness.
"There's probably more leaders experimenting with it because they see the urgency and they see it as a competitive threat potentially," Harter said.
The data point was one of several findings from Gallup's survey on AI adoption in the workplace, including:
The number of US employees who use AI at work at least a few times a year has increased from 21% to 40% in the past two years
Frequent AI use increased from 11% to 19% since 2023
Daily use of AI doubled in the past year from 4% to 8%
15% of employees surveyed said it was "very or somewhat likely that automation, robots, or AI" would eliminate their jobs in a five-year period
44% of employees said their company has started to integrate AI, but only 22% say their company shared a plan or strategy
30% of employees said their company has "general guidelines or formal policies" in place for using AI at work
16% of the employees who use AI "strongly agree" that AI tools provided by their company are helpful for their job
While AI adoption has increased overall in the last two years, that increase isn't evenly distributed across industries. The Gallup report said that AI adoption "increased primarily for white-collar roles," with 27% surveyed now saying they use AI frequently on the job, a 12% increase from last year.
Among white-collar workers, frequent AI is most common in the tech industry, at 50%, according to the survey, followed by professional services at 34%, and finance at 32%. Meanwhile, frequent AI use among production and front-line workers has dropped from 11% in 2023 to 9% this year, according to Gallup's polling.
Related stories Business Insider tells the innovative stories you want to know Business Insider tells the innovative stories you want to know
Concerns that AI will eliminate jobs have also not increased overall in the last two years, but the report indicated that employees in industries like technology, retail, and finance are more likely than others to believe AI will one day take their jobs.
The most common challenge with AI adoption, according to those surveyed, is "unclear use case or value proposition," suggesting that companies may not providing clear guidance.
The report said that when employees say they "strongly agree" that leadership has shared a clear plan for using AI, they're three times as likely to feel "very prepared to work with AI" and 2.6 times as likely to feel comfortable using it at work.
"In some cases, you've got to have the training to be able to use AI as a complement with other text analytic tools that are more precise," Gallup's Harter told BI.
Harter said that while organizations are increasingly developing plans around AI usage, "there's still a long way to go," and it may not be a one-and-done approach.
"They're going to have to continue to be trained in how to use it because it's going to evolve itself," Harter said.
| 2025-06-15T00:00:00 |
https://www.businessinsider.com/ai-usage-in-workplace-statistics-gallup-poll-2025-6
|
[
{
"date": "2025/06/15",
"position": 23,
"query": "workplace AI adoption"
}
] |
|
'You cannot stop this from happening': The harsh reality of the AI job ...
|
‘You cannot stop this from happening’: The harsh reality of the AI job market
|
https://www.the-independent.com
|
[] |
AI could eliminate half of all entry-level white-collar jobs and increase unemployment to 10 to 20 percent within the next five years.
|
Your support helps us to tell the story Read more Support Now From reproductive rights to climate change to Big Tech, The Independent is on the ground when the story is developing. Whether it's investigating the financials of Elon Musk's pro-Trump PAC or producing our latest documentary, 'The A Word', which shines a light on the American women fighting for reproductive rights, we know how important it is to parse out the facts from the messaging. At such a critical moment in US history, we need reporters on the ground. Your donation allows us to keep sending journalists to speak to both sides of the story. The Independent is trusted by Americans across the entire political spectrum. And unlike many other quality news outlets, we choose not to lock Americans out of our reporting and analysis with paywalls. We believe quality journalism should be available to everyone, paid for by those who can afford it. Your support makes all the difference. Read more
The often-talked threat of artificial intelligence on jobs suddenly became very real and shocking to Jane, who asked to use a pseudonym for privacy reasons, when her human resources role became automated and she was laid off in January.
She’d spent two years at her company managing benefits and was on track for a promotion. She’d noticed her boss building out AI infrastructure, but didn’t think her position, which paid roughly $70,000 a year, would be affected.
“I thought that because I had put in so much time and been so good on the higher-level stuff, he would invest in me,” the 45-year-old Bay Area resident told The Independent about her former employer. “Then, as soon as he had a way to automate it away, he did that. He just let go of me.”
Making matters worse, current economic conditions made job hunting hard. In February, an AI system conducted one of her phone interviews.
“It was kind of like having an interview with an automated voicemail,” she said, adding that the “robot” asked her questions about herself and responded with generic answers, leaving her unhopeful the technology would help her get to the next round.
open image in gallery Pedestrians walk past by the Google office in St Pancras in London, Britain on June 27, 2017 ( EPA )
After several months unemployed, Jane obtained a government position in April, before starting a new gig in sales just a few weeks ago.
“What’s happening is there’s a huge white collar slowdown,” said Jane. “I think jobs are going away.”
Workers across the country are grappling with the same issue, as tech CEOs sound the alarm on a potential job market bloodbath in the coming years.
In an Axios interview last month, Dario Amodei, the CEO of Anthropic, a leading artificial intelligence company, predicted AI could eliminate half of all entry-level white-collar jobs and increase unemployment to 10 to 20 percent within the next five years.
The general public is “unaware that this is about to happen,” he told the outlet. “It sounds crazy, and people just don’t believe it.”
Perhaps no other sector has been hit harder than tech. Internet forums are flooded with workers relaying they’ve either been laid off or wondering when they might be.
Software engineer Shawn K (his full last name is K) shared his experience on Substack of getting laid off as AI took over the company, in a now-viral post titled: The Great Displacement Is Already Well Underway.
In March 2024, K, 42, was a full-stack engineer at FrameVR.io. His superiors encouraged employees to use ChatGPT and team productivity skyrocketed.
A month later, he was laid off. He’d been in the industry for 21 years and was making $150,000.
“We had been reorienting the company towards AI, adding on AI features all throughout the software and trying to capitalize on AI to our customers, and then shortly after that kind of restructuring and strategizing...I got laid off,” he told The Independent.
With two mortgages to cover, he started using Door Dash to do deliveries around his home in Central New York, just to make ends meet. After more than a year and nearly 800 applications, he finally landed a contract position earlier this month.
open image in gallery The AI replacing workers are not humanoid robots coming to steal their jobs — even if it does feel like that ( Getty/iStock )
“I’ve tried a lot of stuff, like everything I can think of, I’ve lowered my standards over this past year of all the things I’m applying for and all the things I’m willing to consider,” he said. “At some point, it gets to a situation where you need cash immediately to literally eat and pay your bills.”
K believes AI will make some tech jobs obsolete — but the worker still has a place.
“AI is a better programmer than me, and that doesn’t mean that I think that I have no value to offer anymore,” he said. “I just think that means I can now do 100 times as much as what I was doing before, and solve harder problems that I wouldn’t have even attempted before.”
Now that his article has received so much attention, he wants people to take notice of the changes coming to the industry.
“I’m really convinced that anybody whose job is done on a computer all day is over. It’s just a matter of time,” said K.
Brian Ream, a 46-year-old high school and university tutor in Michigan, ran a medical transition business until AI caused demand to dry up. The business, which only generated a few thousand dollars a year, provided English translations for Portuguese medical journals. He started the business in 2014 after spending time in Brazil and learning the language but hasn’t had an order in over a year.
He knows most of his prior customers are now using Chat GPT and worries about the implications.
“When you're translating medical journal articles, this could have effects that are unintended,” said Ream, noting that some of his former clients could be translating articles with false medical information.
Still, he acknowledges the technology could be useful and wants other educators to incorporate it into lessons.
“I wish that teachers were more connected to the tool and were able to teach students what it’s capable of and what it’s not capable of, so they don’t try to use it for things that it can’t do,” Ream said.
“The reality is, students are using this to write whole essays, and they're not learning how to do it themselves, so they don't know that the tool isn't capable of it.”
As more employers require workers to use AI, he wants the next generation to be prepared — even if it destroyed his own business.
“You cannot stop this from happening,” Ream said.
| 2025-06-16T00:00:00 |
2025/06/16
|
https://www.the-independent.com/news/world/americas/ai-job-layoffs-tech-unemployment-b2769796.html
|
[
{
"date": "2025/06/16",
"position": 14,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 73,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 69,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 29,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 33,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 80,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 8,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 74,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 28,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 97,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 16,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 34,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 13,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 69,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 25,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 97,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 16,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 96,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 28,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 26,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 94,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 42,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 30,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 97,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 8,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 83,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 96,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 72,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 33,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 43,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 44,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 48,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 8,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 43,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 30,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 93,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 69,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 26,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 8,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 72,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 26,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 72,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 28,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 97,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 7,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 93,
"query": "AI unemployment rate"
},
{
"date": "2025/06/16",
"position": 24,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 63,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 20,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 19,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 15,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/06/16",
"position": 71,
"query": "AI job losses"
},
{
"date": "2025/06/16",
"position": 3,
"query": "AI layoffs"
},
{
"date": "2025/06/16",
"position": 42,
"query": "artificial intelligence layoffs"
}
] |
Godfather of AI Reveals Which Jobs Are Safest — and Already at Risk
|
The Godfather of AI reveals which jobs are safest — and where 'everybody' will get replaced
|
https://www.businessinsider.com
|
[
"Alice Tecotzky"
] |
"For mundane intellectual labor, AI is just going to replace everybody," Hinton said. He flagged paralegals as at risk, and said he'd be ...
|
Geoffrey Hinton said that he'd be "terrified" if he had certain jobs.
Geoffrey Hinton said that he'd be "terrified" if he had certain jobs. Mark Blinch/REUTERS
Geoffrey Hinton said that he'd be "terrified" if he had certain jobs. Mark Blinch/REUTERS
This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now.
Now is a great time to become a plumber, at least according to the so-called Godfather of AI.
Geoffrey Hinton, who previously worked at Google and earned his nickname for his work on neural networks, laid out the risks of mass joblessness during an interview on the 'Diary of a CEO' podcast that aired June 16. He said that, eventually, the technology will "get to be better than us at everything," but some fields are safer than others in the interim.
"I'd say it's going to be a long time before it's as good at physical manipulation," Hinton said. "So a good bet would be to be a plumber."
Gen Zers, who are trapped in a brutal job market, are gravitating more and more toward blue-collar work, as BI previously reported.
"For mundane intellectual labor, AI is just going to replace everybody," Hinton said. He flagged paralegals as at risk, and said he'd be "terrified" if he worked in a call center. You would, he said, have to be "very skilled" to have an AI-proof job.
Hinton sees the risk of mass job displacement as the biggest immediate threat to human unhappiness. Even if there's a universal basic income, as Hinton advocates, he thinks people would lack a sense of purpose without a job.
According to Hinton, mass displacement is more likely than not, and is already upon us in some ways. He said AI is starting to be used for jobs previously popular with recent college graduates.
Related stories Business Insider tells the innovative stories you want to know Business Insider tells the innovative stories you want to know
Some argue that the fear that AI will displace entry-level work is overblown. Hinton agreed with the idea that some roles will be replaced by humans working with an AI assistant rather than just the technology, but he said that means one person will do what used to be the work of 10 people. For many industries, he said, that will mean mass firings.
A few areas, like healthcare, will be able to absorb the change, since there's almost endless demand.
"But most jobs, I think, are not like that," Hinton said.
| 2025-06-16T00:00:00 |
https://www.businessinsider.com/geoffrey-hinton-godfather-of-ai-safe-jobs-2025-6
|
[
{
"date": "2025/06/16",
"position": 80,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 80,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 85,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 85,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 82,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 83,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 80,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 90,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 72,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 78,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 80,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 83,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 71,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 79,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 92,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 82,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 78,
"query": "AI replacing workers"
}
] |
|
AI isn't taking your job; the big threat is a growing skills gap
|
AI isn’t taking your job; the big threat is a growing skills gap
|
https://www.computerworld.com
|
[
"More This Author",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img",
"Height Auto Max-Width",
"Vertical-Align Bottom .Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow .Wp-Block-Co-Authors-Plus-Avatar",
"Vertical-Align Middle .Wp-Block-Co-Authors-Plus-Avatar Is .Alignleft .Alignright"
] |
Consultancy McKinsey & Co. now projects that demand for AI-skilled workers will outpace supply by two-to-four times, a skills gap likely to continue at least ...
|
Despite sizeable tech layoffs over the past two years, a tech talent gap persists — especially for those trained on implementing and using generative artificial intelligence (genAI) tools. Consultancy McKinsey & Co. now projects that demand for AI-skilled workers will outpace supply by two-to-four times, a skills gap likely to continue at least until 2027.
That echoes what consultancy Deloitte wrote in a recent report. It found that corporate leaders continue to rate critical talent shortages as one of their greatest fears, even as job-seekers report despair about their hiring prospects. “And yet neither side seems prepared to address it,” Deloitte said in its report.
A ManpowerGroup survey of 40,413 employers in 42 countries found that 74% of employers still struggle to find skilled talent, with only 16% of execs confident in their tech teams and 60% citing the skill gaps as a key barrier to digital strategies. Along the same lines, Bain & Co. found that 44% of corporate leaders say limited in-house expertise has slowed AI adoption, with demand for AI skills rising 21% annually since 2019 and a shortage of talent lasting another two years.
| 2025-06-16T00:00:00 |
https://www.computerworld.com/article/4006931/ai-isnt-taking-your-job-the-big-threat-is-a-growing-skills-gap.html
|
[
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 12,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 11,
"query": "AI skills gap"
},
{
"date": "2025/06/16",
"position": 2,
"query": "AI skills gap"
}
] |
|
Will AI take my job? Navigating AI's impact on public sector jobs
|
Will AI take my job? Navigating AI’s impact on public sector jobs
|
https://www.route-fifty.com
|
[
"Alan R. Shark"
] |
AI will reshape rather than eliminate government jobs, requiring strategic workforce planning. ... Policy and Government, George Mason ...
|
The question of office automation, robotics and now artificial intelligence has raised numerous concerns and fears about job security in state and local government for many years. However, the technology continues to advance, and humans appear to be more vulnerable than ever.
AI has seemingly emerged from nowhere, and today it is mentioned everywhere. While most don’t feel the least bit threatened, a renewed sense of worry appears to be growing, fueled in part by massive job cuts that have recently begun at the federal level and are now spreading to state and local governments.
Such concerns seem to come in cycles. In late spring 2017, a website was popularized by the tech media called “Will Robots Take My Job?” The initial data used by the site at the beginning was based on a report titled "The Future of Employment: How Susceptible Are Jobs to Computerization?" which was published by Carl Benedikt Frey and Michael Osborne in 2013.
The study focused on the susceptibility of just over 700 detailed occupations to computerization. By applying sophisticated mathematical formulas, they concluded that approximately 47% of total U.S. employment is at risk. Now, twelve years later, we have AI to contend with. In 2018, President Donald Trump’s first administration stated that 5% of jobs could be automated entirely.
Reviewing recent White House messaging, AI is framed as a supportive tool, not a replacement for federal employees. They advocate for AI governance frameworks, procurement modernization and boosting internal AI capacity. Conversely, internal experimentation and discussions, particularly in areas influenced by the Department of Government Efficiency, suggest a broader application of AI in administrative roles and consideration of staffing reductions.
Already, alarms are sounding about the federal government's (mostly DOGE’s) seeming overreliance on AI. Faulty reports have been issued, AI searches have yielded misguided and incorrect data, and there appears to be a lack of human oversight in AI outputs in general.
Anthropic CEO Dario Amodei recently issued a warning that created shock waves across the globe when he predicted that within five years, AI could automate up to 50% of all entry-level white-collar jobs. Others predict that the majority of public sector jobs will remain largely untouched; however, AI and enhanced digital automation may reduce the need for and cost of overtime.
History demonstrates that jobs, whether in the private or public sector, are ripe for automation when they are repetitive and routine. The areas of greatest endangerment are:
Entry-level administrative positions
Routine data processing roles
Basic customer service representatives
Manual inspection and monitoring jobs
Simple research and analysis tasks
Human resource employment screening
There may be a need for fewer employees in such functions, and as AI advances, there will be less need to hire new employees. Seasoned employees will have demonstrated their enhanced productivity through the use of AI. While this may be good news for current public sector employees, it is not good news for future workers.
A majority of economists believe that while AI may indeed replace many government workers, new positions requiring more advanced skills will emerge. Here are some areas that have been suggested:
AI system administrators and monitors
Human-AI collaboration specialists
Data governance and ethics officers
Digital transformation project managers
Citizen experience designers
AI audit and compliance specialists
Enhanced roles requiring human judgment, creativity, and complex problem-solving
As new opportunities emerge, what happens to those who are displaced? The obvious answer lies in training and retraining. However, as governments are motivated by greater economies of scale, will they be willing to invest in training? How will public employee unions respond? One can only speculate. Meanwhile, after a slow start towards AI adoption, there is much evidence of actual AI applications being initiated. The areas that are receiving the most attention are:
Administrative and clerical functions: Document processing, data entry, scheduling, basic citizen inquiries
Regulatory compliance and monitoring: Automated inspection systems, permit processing, tax assessment
Data analysis and reporting: Budget analysis, performance metrics, policy research
Customer service: Chatbots for citizen services, automated phone systems, and online form processing
Predictive analytics: Risk assessment, resource allocation, fraud detection, healthcare patterns
One must also recognize another significant shift in public sector employment: the rise and increasing significance of the vendor community. AI is expensive, and all indicators suggest that costs will continue to rise over time. The overwhelming majority of local governments are relatively small and are struggling to keep up with legacy equipment, maintain and attract qualified IT staff, and as a result, are increasingly turning to outside expertise. Service providers can amortize the cost of AI over multiple accounts, making it a cost-effective alternative.
One thing is certain: there is no turning back the clock on AI as it is rapidly growing. There are also several potential risks, including the widening of the digital divide and increased economic and skill-based inequalities. There are also ethical considerations, transparency, accountability issues, and public acceptance of robotic and AI technologies.
The next five years will be pivotal as governments adapt, necessitating proactive leadership, investment in people and a commitment to the responsible deployment of AI. Over the next five years, AI is expected to reshape government employment by automating routine tasks, transforming the skills landscape, and creating opportunities for more impactful public service.
The scale and speed of this transformation require strong leadership, investment in workforce development and a commitment to the ethical and inclusive deployment of AI. Governments that embrace these changes will be better positioned to deliver efficient, innovative, and citizen-focused services. AI will reshape rather than eliminate government jobs, requiring strategic workforce planning.
Looking back into history, we know that society has withstood enormous economic upheavals due to automation. What is different today is the speed of change and advancement. We have never witnessed such rapid change, which means we must recognize the need for greater and enlightened leadership more than ever. While humans struggle, machines are watching, and unlike any other technology, they are becoming increasingly human-like in their thinking and actions.
Alan R. Shark is an associate professor at the Schar School for Policy and Government, George Mason University, where he also serves as a faculty member at the Center for Human AI Innovation in Society (CHAIS). Shark is the former Executive Director of the Public Technology Institute (PTI). He is a National Academy of Public Administration Fellow and Founder and Co-Chair of the Standing Panel on Technology Leadership. Shark is the host of the podcast Sharkbytes.net.
| 2025-06-16T00:00:00 |
2025/06/16
|
https://www.route-fifty.com/artificial-intelligence/2025/06/will-ai-take-my-job-navigating-ais-impact-public-sector-jobs/406061/
|
[
{
"date": "2025/06/16",
"position": 74,
"query": "government AI workforce policy"
},
{
"date": "2025/06/16",
"position": 98,
"query": "government AI workforce policy"
},
{
"date": "2025/06/16",
"position": 71,
"query": "government AI workforce policy"
},
{
"date": "2025/06/16",
"position": 61,
"query": "government AI workforce policy"
},
{
"date": "2025/06/16",
"position": 64,
"query": "government AI workforce policy"
},
{
"date": "2025/06/16",
"position": 36,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/16",
"position": 91,
"query": "future of work AI"
}
] |
AI Use at Work Has Nearly Doubled in Two Years - Gallup
|
AI Use at Work Has Nearly Doubled in Two Years
|
https://www.gallup.com
|
[
"Gallup"
] |
AI adoption has increased primarily for white-collar roles. Twenty-seven percent of white-collar employees report frequently using AI at work, ...
|
The use of AI at work is accelerating. In the past two years, the percentage of U.S. employees who say they have used AI in their role a few times a year or more has nearly doubled, from 21% to 40%. Frequent AI use (a few times a week or more) has also nearly doubled, from 11% to 19% since Gallup’s first measure in 2023. Daily use has doubled in the past 12 months alone, from 4% to 8%.
###Embeddable###
AI adoption has increased primarily for white-collar roles. Twenty-seven percent of white-collar employees report frequently using AI at work, an increase of 12 percentage points since 2024. The industries with the highest percentages of frequent AI users include technology (50%), professional services (34%) and finance (32%).
In comparison, reported frequent AI use by production and front-line workers has remained essentially flat for the past two years, shifting from 11% in 2023 to 9% in 2025.
###Embeddable###
Frequent AI use is also more common among leaders (defined as managers of managers), at 33%. They are twice as likely as individual contributors (16%) to say they use AI a few times a week or more.
Although workplace AI use is increasing, employees are no more likely than they were two years ago to see themselves being replaced soon. Only 15% of employees say it is very or somewhat likely that automation, robots or AI will eliminate their job within the next five years, unchanged from 2023 and 2024 measures. Some industries — such as technology (21%), retail (21%) and finance (20%) — are more likely than others to believe AI will eliminate their job.
Leadership Guidance on AI Lags Integration
Many employees are using AI at work without guardrails or guidance. While 44% of employees say their organization has begun integrating AI, only 22% say their organization has communicated a clear plan or strategy for doing so. Similarly, 30% of employees say their organization has either general guidelines or formal policies for using AI at work. That leaves a 14-point gap between organizations integrating AI and providing standards for AI use at work.
###Embeddable###
The benefits of using AI in the workplace are not always obvious. According to employees, the most common AI adoption challenge is “unclear use case or value proposition.” Even among those who report using AI, only 16% strongly agree that the AI tools provided by their organization are useful for their work.
Perceptions of AI’s utility also vary widely between users and non-users. Gallup research earlier this year compared employees who had used AI to interact with customers with employees who had not. Sixty-eight percent of employees who had firsthand experience using AI to interact with customers said it had a positive effect on customer interactions; only 13% of employees who had not used AI with customers believed it would have a positive effect.
If leaders want greater AI adoption, they will have to help employees find the value. Gallup data suggest leaders whose workforces experience the most value have a clear AI strategy and plan in place. When employees strongly agree that their leadership has communicated a clear plan for integrating AI, they are three times as likely to feel very prepared to work with AI and 2.6 times as likely to feel comfortable using AI in their role.
Develop an AI adoption strategy that puts people first. Learn how Gallup can help you accelerate your AI transformation.
Discover why supporting employees with AI, instead of replacing them with it, is the better long-term strategy.
Watch this webinar to get Gallup’s latest findings on the state of AI in the U.S. workplace.
###Embeddable###
| 2025-06-16T00:00:00 |
2025/06/16
|
https://www.gallup.com/workplace/691643/work-nearly-doubled-two-years.aspx
|
[
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 2,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
},
{
"date": "2025/06/16",
"position": 36,
"query": "artificial intelligence workers"
},
{
"date": "2025/06/16",
"position": 5,
"query": "AI workers"
},
{
"date": "2025/06/16",
"position": 17,
"query": "artificial intelligence workers"
},
{
"date": "2025/06/16",
"position": 5,
"query": "job automation statistics"
},
{
"date": "2025/06/16",
"position": 1,
"query": "workplace AI adoption"
}
] |
AI-Driven Workforce Transformation
|
AI-Driven Workforce Transformation
|
https://thedigitalrevolutionwithjimkunkle.buzzsprout.com
|
[] |
The workplace as we know it is undergoing a seismic shift, driven by artificial intelligence. AI is no longer just a tool for automation, ...
|
The workplace as we know it is undergoing a seismic shift, driven by artificial intelligence. AI is no longer just a tool for automation, it’s becoming a strategic partner in decision-making, talent management, and productivity enhancement. According to McKinsey, AI has the potential to add at least $4 trillion in productivity growth across corporate use cases. Yet, despite this massive opportunity, only 1% of companies consider themselves fully mature in AI adoption, meaning they’ve successfully integrated AI into workflows to drive substantial business outcomes. The challenge isn’t whether AI will transform work, it’s how businesses and employees can keep up with the pace of change.
One of the biggest trends shaping the workforce today is the rise of AI-powered talent management. A report from “Visier” highlights that AI is reshaping businesses from the C-suite down to individual employees, enhancing productivity, performance, and return on investment. Companies that embrace AI-driven workforce strategies are poised to lead in this new era, while those that resist risk falling behind. Meanwhile, the World Economic Forum reports that early adopters of generative AI are seeing significant productivity gains, with some tasks that once took weeks now being completed in minutes. But here’s the catch, many businesses still don’t have a clear plan for how to utilize the extra time freed up by AI, raising important questions about the future of work and job roles.
So, what does this mean for employees and businesses? How can companies integrate AI without disrupting their workforce? And what skills will be essential in an AI-powered world? In today’s episode, we’ll explore these questions, exploring the opportunities, challenges, and strategies for navigating AI-driven workforce transformation.
Welcome to another enlightening episode of The Digital Revolution with Jim Kunkle, where we cover the fascinating world of digital transformation, artificial intelligence, and intelligent technologies.
In today's episode, we're talking about: AI-Driven Workforce Transformation.
The Current Landscape of AI in the Workforce
Artificial intelligence is no longer a futuristic concept, it’s actively reshaping the way businesses operate, hire, and compete. Companies across industries are integrating AI to automate repetitive tasks, enhance decision-making, and optimize workflows. According to a recent McKinsey report, AI could automate 30% of tasks in nearly 60% of jobs, fundamentally altering traditional roles. This shift isn’t just about efficiency; it’s about redefining the skills and capabilities required in the modern workforce. Businesses that fail to adapt risk falling behind, while those that embrace AI-driven transformation can unlock new levels of productivity, innovation, and competitive advantage.
However, this transformation comes with challenges. Workforce displacement, ethical concerns, and the need for large-scale reskilling are pressing issues. The World Economic Forum estimates that AI will create 97 million new jobs globally by 2026, but it will also disrupt millions of existing roles. The key question isn’t whether AI will change the workforce, it’s how businesses and employees can proactively adapt. Businesses must rethink talent strategies, invest in AI literacy, and ensure that AI enhances human potential rather than replacing it. In today’s episode, we’ll explore why AI-driven workforce transformation is critical, what it means for businesses and employees, and how leaders can navigate this evolving landscape.
AI adoption is accelerating across industries, resetting business operations and workforce dynamics. In finance, AI is being used for fraud detection, risk assessment, and automated trading, helping institutions process vast amounts of data with precision. Healthcare is leveraging AI for medical imaging analysis, predictive diagnostics, and personalized treatment plans, improving patient outcomes while reducing administrative burdens. Manufacturing is seeing a surge in AI-driven automation, with predictive maintenance and robotic process automation optimizing production efficiency. Meanwhile, retail is using AI for personalized shopping experiences, dynamic pricing, and supply chain optimization, enhancing customer engagement and operational agility.
The rise of AI-powered tools is further accelerating this transformation. ChatGPT and Copilot are revolutionizing content creation, coding, and customer service by providing real-time AI assistance. AI-driven analytics platforms are enabling businesses to extract actionable insights from data, improving decision-making and strategic planning. As AI adoption grows, companies must navigate challenges such as workforce reskilling, ethical AI implementation, and ensuring AI complements human expertise rather than replacing it. The key to success lies in integrating AI thoughtfully, balancing automation with human oversight to drive innovation while maintaining workforce stability.
The Shift in Job Roles & Skills
AI is reshaping job roles across industries, creating a divide between jobs being augmented and those being replaced. According to McKinsey, 30% of current U.S. jobs could be automated by 2030, with 60% significantly altered by AI tools. While routine cognitive tasks and structured physical work are at high risk, AI is also augmenting roles by enhancing productivity rather than eliminating them. For example, financial analysts now use AI-driven tools to process vast datasets, allowing them to focus on strategic decision-making rather than manual data entry. Similarly, AI-powered medical imaging assists radiologists in detecting anomalies faster, improving diagnostic accuracy while keeping human expertise at the core.
As AI transforms industries, new roles are emerging. The World Economic Forum predicts that “AI and Machine Learning Specialist" roles will see the largest net job growth worldwide between 2025 and 2027. Other emerging AI-related roles include AI ethicists, prompt engineers, and AI trainers, who help refine AI models for better accuracy and fairness. To thrive in this AI-driven era, professionals must develop technical skills like machine learning, data science, and AI literacy, alongside soft skills such as critical thinking, adaptability, and collaboration with AI tools. The key to workforce resilience lies in continuous learning and embracing AI as a tool for empowerment rather than disruption.
Challenges & Ethical Considerations
AI integration in the workplace presents both opportunities and challenges, particularly in ethical considerations and workforce adaptation. While AI can enhance productivity, streamline operations, and improve decision-making, businesses must navigate concerns such as bias in AI models, data privacy, and workforce displacement. According to a report from Harvard Business School, 73% of U.S. companies have adopted AI in some limited capacity, yet many fail to address ethical concerns upfront. AI-driven hiring tools, for example, have been found to favor male candidates over female candidates due to biased training data, perpetuating existing inequalities. Similarly, facial recognition software has shown higher error rates for individuals with darker skin tones, raising concerns about biases. Businesses must proactively audit AI systems to ensure transparency, accountability, and fairness in their applications.
Another major challenge is data privacy and security. AI relies on vast amounts of data, raising concerns about how employee and customer information is collected, stored, and used. The “EU AI Act”, passed in 2024, imposes strict regulations on AI systems that engage in manipulative behavior or social scoring, with penalties reaching 35 million euros or 7% of annual turnover for noncompliance. In the U.S., states like California, New York, and Colorado have enacted AI guidelines focusing on data protection and bias prevention. Beyond regulatory risks, businesses must also consider the human impact of AI-driven automation. While AI can augment jobs, it also risks displacing workers, particularly in roles that rely on repetitive tasks. To mitigate this, companies must invest in reskilling programs, ethical AI governance, and human-centric AI applications to ensure AI adoption benefits employees rather than replacing them. The key to responsible AI integration lies in balancing innovation with ethical responsibility, ensuring AI enhances human potential rather than undermining it.
Strategies for Businesses & Employees to Adapt
To successfully adapt to AI-driven workforce transformation, businesses and employees must embrace strategic AI integration, continuous learning, and workforce agility. According to this approach, companies that tailor AI adoption strategies to meet workforce needs see higher employee engagement and smoother transitions. Businesses should start by assessing their workforce’s AI readiness, identifying roles that can be augmented rather than replaced, and implementing pilot AI programs to test effectiveness before scaling. Additionally, fostering an AI-ready culture through upskilling initiatives ensures employees can leverage AI as a tool rather than fear displacement. Deloitte’s research highlights that 74% of business leaders anticipate using AI for decision-making within five years, making AI literacy a critical skill for employees.
For employees, adapting to AI-driven transformation means developing both technical and soft skills. AI-powered tools like Copilot and ChatGPT are reshaping workflows, requiring professionals to learn prompt engineering, data analysis, and AI-assisted decision-making. At the same time, critical thinking, adaptability, and ethical AI awareness are becoming essential. KPMG recommends transitioning to a skills-based workforce, where competencies take precedence over traditional job roles. Employees should proactively seek AI training, engage with AI-driven tools, and embrace lifelong learning to stay competitive in an evolving job market. By balancing AI adoption with human expertise, businesses and employees can harness AI’s potential while ensuring workforce stability and innovation.
Future Outlook: What’s Next?
The future of AI-driven workforce transformation is unfolding rapidly, with both opportunities and challenges shaping the next decade of work. According to McKinsey, AI has the potential to add at a minimum $4 trillion in productivity growth, yet only 1% of companies consider themselves fully mature in AI adoption. As AI continues to evolve, businesses must navigate the delicate balance between automation and human expertise. The World Economic Forum predicts that AI will create 97 million new jobs globally by 2026, but it will also disrupt millions of existing roles. Entry-level positions, traditionally seen as stepping stones for career growth, are particularly vulnerable, with experts estimating that up to 70% of these roles could be affected. This shift could result in a 10-20% increase in unemployment, creating an "experience gap" for younger workers entering the job market.
Despite concerns about job displacement, AI is also reshaping industries in positive ways. Healthcare systems are investing in AI education for employees, ensuring that workers can effectively integrate AI into their daily tasks. Meanwhile, governments are stepping in to support workforce adaptation, Malaysia, for example, has committed 10 billion ringgits annually to skills-related education and training, ensuring workers remain competitive in an AI-driven economy. Looking ahead, businesses must prioritize continuous transformation, using AI not just for efficiency but as a growth strategy to unlock new revenue streams. The key to success lies in reskilling, ethical AI governance, and human-centric AI applications, ensuring that AI enhances human potential rather than replacing it. As AI adoption accelerates, companies that embrace AI-driven workforce strategies will lead the next era of innovation, while those that resist risk falling behind.
As we wrap up this episode, let’s reflect on the key takeaways from our conversation on AI-Driven Workforce Transformation. AI is reshaping industries, augmenting jobs, and creating new opportunities, but it also presents challenges that businesses and employees must navigate. The key to success lies in strategic AI adoption, ensuring that AI enhances human potential rather than replacing it. Companies that invest in reskilling, ethical AI governance, and AI literacy will be best positioned to thrive in this evolving landscape. Meanwhile, employees must embrace continuous learning, developing both technical and soft skills to stay competitive in an AI-powered world.
If you’re looking to deepen your understanding of AI and workforce integration, check out resources like Cornerstone’s AI in Learning and Development (https://www.cornerstoneondemand.com/resources/article/ai-in-learning-and-development/),
Forbes’ insights on AI workforce integration
(https://www.forbes.com/sites/solrashidi/2025/01/30/the-future-of-work-ai-and-workforce-integration-for-scalable-success/),
and Paradiso Solutions’ AI-driven training strategies
(https://www.paradisosolutions.com/blog/ai-in-learning-and-development-shaping-the-future-workforce/).
You’ll find all three links in this episode's description.
These platforms offer valuable insights into how AI can support businesses and employees alike. The future of work is not about AI replacing humans, it’s about AI empowering us to work smarter, innovate faster, and create new possibilities. Let’s continue the conversation, explore AI learning resources, and advocate for AI integration that benefits both businesses and their workforce.
Thanks for joining the Digital Revolution in unraveling this fascinating topic. Be sure to stay tuned for more episodes where we dive deep into the latest innovations and challenges in the digital world. Until next time, keep questioning, keep learning, and keep revolutionizing the digital world!
And with that, I appreciate your continued support and engagement with The Digital Revolution podcast. Stay tuned for more insightful episodes where we talk about the latest trends and innovations in intelligent technologies. Until next time, keep exploring the frontiers of intelligent technology!
If you enjoyed listening to The Digital Revolution podcast, you might also want to check out our YouTube channel, "Digital Revolution". Our channel features video content on digital transformation topics. You can find the link to our YouTube channel in the description of this podcast episode.
Don't forget to subscribe to our channel to stay up-to-date with our latest videos and insights.
Thank you for supporting the revolution.
The Digital Revolution with Jim Kunkle - 2025
| 2025-06-16T00:00:00 |
https://thedigitalrevolutionwithjimkunkle.buzzsprout.com/2281303/episodes/17333904-ai-driven-workforce-transformation
|
[
{
"date": "2025/06/16",
"position": 79,
"query": "AI workforce transformation"
},
{
"date": "2025/06/16",
"position": 69,
"query": "AI workforce transformation"
},
{
"date": "2025/06/16",
"position": 80,
"query": "AI workforce transformation"
},
{
"date": "2025/06/16",
"position": 69,
"query": "AI workforce transformation"
},
{
"date": "2025/06/16",
"position": 68,
"query": "AI workforce transformation"
},
{
"date": "2025/06/16",
"position": 76,
"query": "AI workforce transformation"
},
{
"date": "2025/06/16",
"position": 76,
"query": "AI workforce transformation"
}
] |
|
AI Disruption and Business Survival in the Post-Pandemic Economy
|
AI Disruption and Business Survival in the Post-Pandemic Economy
|
https://www.e-spincorp.com
|
[
"Daw Phyu Pyar Pwint",
"Jean Ariane H. Evangelista",
".Pp-Multiple-Authors-Boxes-Wrapper.Pp-Multiple-Authors-Layout-Boxed.Multiple-Authors-Target-The-Content .Pp-Author-Boxes-Avatar Img",
"Width",
"Important",
"Height",
"Border-Radius",
".Pp-Multiple-Authors-Boxes-Wrapper.Pp-Multiple-Authors-Layout-Boxed.Multiple-Authors-Target-The-Content .Pp-Author-Boxes-Meta A",
"Background-Color",
"Color"
] |
1. The Unexpected Economic Shock: COVID-19 as a Stress Test · 2. Debt, Mortgages, and the Domino Effect · 3. The Hardest-Hit Sector: Food & ...
|
The COVID-19 pandemic was not just a health crisis—it was a global economic shockwave that reverberated across all industries. While some businesses managed to pivot or survive through rapid digital transformation, many were caught off guard by the prolonged lockdowns and changes in consumer behavior. Three years on, the ripple effects continue to shape economies, industries, and job markets, with AI disruption and business survival now emerging as central themes in the ongoing struggle to adapt and remain competitive.
In this post, we’ll explore:
How the pandemic exposed financial fragility in companies.
The property and debt chain reaction leading to widespread bankruptcies.
The downfall of certain industries, like food & beverage.
The growing trend of forced return-to-office policies.
The accelerating role of Generative AI (GenAI) in replacing human labor.
What it means for workers, and how to survive and thrive in this era of disruption.
1. The Unexpected Economic Shock: COVID-19 as a Stress Test
Most businesses were not prepared for the pandemic. In early 2020, companies across the globe faced a sudden halt in operations. Lockdowns in Asia, Europe, and North America led to plummeting sales, broken supply chains, and frozen customer demand.
While essential sectors such as healthcare, logistics, and e-commerce flourished, many others—particularly in the hospitality, tourism, and retail industries—faced massive financial losses. To survive, companies did what they had to: take on loans, restructure operations, or negotiate temporary relief from creditors.
But this short-term survival strategy created long-term vulnerabilities.
2. Debt, Mortgages, and the Domino Effect
To cover the cashflow gap during lockdowns, many companies used property and fixed assets as collateral to secure emergency funding. In theory, this was a practical move: use assets to stay afloat, then repay when the market recovers.
But in practice, it backfired.
Post-pandemic, the global property market experienced significant correction. In many countries, especially in Southeast Asia and parts of Europe, commercial property values plummeted by 30–50%. Buildings that once served as solid financial backing now became liabilities.
Banks, alarmed by the sudden devaluation of mortgaged assets, began calling in loans or demanding additional capital to cover the difference. For businesses still recovering or with thin cash reserves, this was the final blow.
From small family-run businesses to mid-sized chains, bankruptcy filings surged. The property crash didn’t just hurt the real estate sector—it triggered a cascading credit crisis that hit the broader economy.
3. The Hardest-Hit Sector: Food & Beverage
Few industries were as devastated as Food & Beverage (F&B).
Restaurants and bars faced:
Zero dine-in revenue for months.
Ongoing rental obligations despite no income.
Wasted stock and supply spoilage.
Layoffs and rehiring costs.
Shifting to delivery models that often weren’t profitable.
Even post-COVID, customer habits changed. Many consumers became comfortable with cooking at home or ordering online. The number of office workers dropped due to remote work, which in turn reduced foot traffic in business districts.
Thousands of establishments never reopened. Many that did return were burdened with debt, reduced margins, and increased competition. Even major chains with large footprints scaled back aggressively.
4. The Return-to-Office Push: Restructuring Disguised as Culture
In 2023 and 2024, many companies reversed their “remote-friendly” stances. Citing reasons like collaboration, innovation, or company culture, firms began demanding employees return to the office—or face termination.
But let’s call it what it really is: cost reduction through attrition.
Many companies are using return-to-office mandates as a subtle (and legal) way to:
Reduce headcount without direct layoffs.
Avoid severance packages.
Push out those unwilling or unable to relocate.
Restructure teams in line with new business priorities.
It’s not a coincidence that companies implementing such policies often see a wave of resignations followed by hiring freezes or automation plans.
The modern workforce is no longer protected by “loyalty” or long tenure. We’ve entered a phase of permanent restructuring.
5. The AI Tipping Point: Multi-Agent GenAI Systems Arrive
While companies manage their workforce through strategic exits and policy changes, another force is reshaping the business landscape more radically than COVID ever did: Generative AI.
The initial GenAI hype in 2023 (think ChatGPT, Midjourney, Claude) focused on individual productivity. But as we move deeper into 2025, multi-agent systems are changing everything.
These AI agents can now:
Manage projects autonomously.
Handle internal communications.
Perform customer service across platforms.
Generate marketing campaigns.
Do research, write reports, summarize meetings.
Even simulate teams, assigning tasks between agents.
Companies are replacing entire back-office departments, not just clerical jobs.
It’s no longer about “AI assisting humans.” We are entering a phase where AI replaces teams of humans. This is the new productivity norm, and it’s growing exponentially.
6. Layoffs Every Quarter: The New Corporate Normal
Quarterly layoffs are becoming common.
Why? Because:
AI automation keeps reducing the need for human roles.
Global economic uncertainty forces cost cuts every quarter.
Talent demands are shifting faster than workers can reskill.
From tech giants to local SMEs, restructuring is now routine:
One quarter: 5% headcount reduction.
Next quarter: Replace marketing team with AI tools.
Following quarter: Offshore support staff or automate logistics.
It’s no longer a “crisis”—this is now strategic adaptation.
The job market is split into two categories:
AI Augmenters – people who work with AI tools effectively. Redundant Talent – workers whose roles are obsolete or unadaptable.
Without urgent upskilling, many find themselves in the latter camp.
7. Mentality Shift or Obsolescence: The Choice Is Clear
Let’s be honest: many workers still expect the job market to return to “normal.” They believe that after a rough few years, stability will return. Unfortunately, that’s wishful thinking.
The future is already here, and it’s not waiting for anyone to catch up.
To remain employable, workers must:
Adopt a growth mindset.
Learn how to use GenAI tools, not fear them.
Reskill into roles that leverage creativity, strategy, or deep domain expertise .
Be adaptable across industries.
Being good at your job is no longer enough. You must continuously learn, experiment, and retool. The pace of change is not slowing down. In fact, it’s accelerating.
Those who resist will be left behind—not out of malice, but out of systemic necessity.
8. Government and Policy Gaps: No Lifeline in Sight
Despite all these changes, many governments are lagging behind. Policy frameworks still assume:
Traditional full-time employment models.
Annual reviews and reskilling at slow paces.
Job security tied to tenure, not output.
But the real economy is:
Automating monthly.
Outsourcing weekly.
Changing tools and systems daily.
Social safety nets, unemployment programs, and education systems aren’t evolving fast enough. This leaves vulnerable workers exposed to long-term unemployment or underemployment.
9. The Path Forward: How Companies and Individuals Can Prepare
For Companies:
Embrace AI early, but responsibly.
Combine human creativity with AI efficiency.
Provide continuous learning and upskilling programs.
Avoid mass layoffs by reskilling internally.
Be transparent about automation and workforce planning.
For Workers:
Learn how to use GenAI tools daily (e.g., ChatGPT, Claude, Gemini).
Build a portfolio of skills , not just one specialization.
Network outside traditional job roles.
Explore freelance or remote work for flexibility.
Stay informed and agile. Change is the only constant.
Conclusion
The post-COVID world is not about recovery—it’s about reinvention.
We’ve passed the point where things will “go back” to normal. Instead, we’re entering a phase of accelerated transformation driven by AI, automation, and global volatility.
For businesses, this means becoming leaner, faster, and more tech-enabled. For workers, this means evolving or being replaced. It may sound harsh, but it’s the economic reality of 2025.
We must shift our mindset from survival to reinvention—and fast.
The next few years will reward those who adapt and leave behind those who don’t.
Frogs in Boiling Water: The Human Cost of Ignoring Change
It’s a painful truth—most people don’t notice radical change until it’s too late. Much like the classic analogy of a frog slowly being boiled alive in a pot of water, people tend to remain passive as the temperature around them rises. They may feel slight discomfort, sense that something’s different, but they often wait for someone else to sound the alarm—only to find themselves paralyzed or replaced when the real consequences hit.
This is not just a metaphor anymore. In the world of work and economic survival, the water is already boiling, and many employees are still sitting still.
1. The Illusion of Stability: Why Most People Don’t See the Change
For decades, society told us: get a degree, land a good job, and stick with it. Stay loyal, work hard, and things will work out.
But that model is dead.
What replaced it is a world where:
Skills expire every 2–3 years .
Job roles evolve faster than training programs .
Lifelong employment is replaced by project-based work .
And now, Generative AI and robotics are making once-secure roles vanish overnight.
Yet many still live under the illusion that their job is “safe.” That the changes happening to others won’t happen to them.
But denial doesn’t stop disruption. It only delays your response to it.
2. How Many Actually Invest in Continuous Professional Development?
Let’s be honest: very few.
Ask around your workplace or your network. How many people you know actively:
Attend industry seminars or conferences on their own initiative?
Take self-funded online courses to upskill?
Learn about new technologies impacting their field?
Read trade publications or stay up to date with trends?
Set yearly learning goals or track progress?
The percentage is shockingly low.
In many cases, people spend more time binge-watching Netflix or scrolling TikTok than investing in their own future. Leisure time has replaced learning time—not because learning isn’t accessible (it is), but because the urgency is missing. The pain hasn’t hit yet.
Until it does.
3. Fixed Role Mentality: The Silent Career Killer
One of the biggest psychological traps is the “fixed role” mindset.
It sounds like this:
“I’m just a finance executive; I don’t do tech.”
“Marketing is my thing—I don’t need to know automation.”
“I’ve been in HR for 10 years. That’s enough.”
“I’m not creative. I’m a backend engineer.”
“AI won’t affect my job. I’m in a niche field.”
This kind of thinking may have worked in a slower economy, but in today’s world, rigid role identities are liabilities.
Modern organizations are becoming leaner. They favor people who:
Can wear multiple hats.
Embrace new tools and adapt quickly.
Learn proactively without needing permission.
Add cross-functional value.
If you’re stuck in a narrow role with no broader competency, you are on the layoff radar whether you know it or not.
4. Zero-Based Staffing and Layoffs: A New Corporate Playbook
More companies are now adopting zero-based staffing strategies.
What does that mean?
Instead of assuming existing roles must be refilled or retained, organizations start from zero and ask:
What roles do we absolutely need?
What can be automated or outsourced?
What functions can AI handle now?
Do we need full-time staff or just short-term contractors?
From there, they rebuild staffing from scratch, based on current realities—not legacy roles.
Even if you’re a hard-working, loyal employee, that may no longer be enough. If your role doesn’t align with strategic priorities or productivity ROI, it can be eliminated. And increasingly, it is.
5. “Hard Work” vs. 24/7 AI: A Losing Comparison
Many still believe: “If I work harder, I’ll stay safe.”
But here’s the harsh truth:
AI and robotic systems don’t sleep.
They don’t ask for raises.
They don’t take sick leave or go on vacation.
They improve with every update.
Your 60-hour workweek cannot compete with a system that works 168 hours non-stop, without emotional fatigue or physical limitations.
This doesn’t mean you’re doomed—it means the value you bring must shift from raw effort to intelligent leverage of technology.
Your edge is not working more, but working differently.
6. What Competency Looks Like in the AI Age
To survive and thrive, individuals must embrace a new generation of competencies. It’s no longer about your job title or experience years—it’s about agility and relevance.
Here’s what that looks like:
a) Digital Fluency
Know how to use GenAI tools: ChatGPT, Claude, Gemini, etc.
Automate repetitive tasks using no-code platforms.
Leverage AI for research, content generation, decision support.
b) Critical Thinking and Prompt Engineering
Frame smart queries to get meaningful AI responses.
Distinguish between AI-generated truth and fiction.
Apply domain knowledge creatively to AI insights.
c) Self-Learning Discipline
Set learning targets for each quarter.
Follow online micro-credentials or certifications.
Maintain a personal learning system (e.g., Notion, Roam, etc.).
d) Cross-Functional Agility
Understand how marketing overlaps with data analytics.
Connect finance with strategic forecasting.
Combine technical and soft skills.
e) AI Collaboration Skills
Use AI as a co-worker, not just a tool.
Design workflows where AI supports human decision-making.
Explain AI outputs to clients or stakeholders clearly.
These are no longer optional. They’re survival skills.
7. The Psychological Barrier: Why Most Won’t Make the Shift
Change isn’t just about knowledge—it’s emotional. People cling to the familiar, even if it no longer serves them.
Common mental blocks include:
Fear of failure: “What if I can’t learn this tech?”
Ego protection: “I’ve been successful for 20 years. I don’t need to change.”
Comfort addiction: “Learning is tiring. I deserve to relax.”
Peer culture: “No one around me is doing it, so I’m fine.”
These mindsets are understandable—but deadly.
In a world moving at exponential speed, you can’t afford linear thinking.
You must let go of old definitions of competence and rebuild yourself constantly. The people who adapt fastest aren’t always the smartest. They’re the ones who are willing to be uncomfortable, again and again.
8. Real-World Wake-Up Calls: What’s Already Happening
Let’s look at what’s unfolding in real time:
Legal firms replacing junior lawyers with AI-based research bots.
Accounting departments slashing headcount after adopting AI-assisted bookkeeping.
Customer service outsourced to chatbots that operate in 10 languages, 24/7.
Universities and training providers offering AI tutors, replacing some faculty roles.
Media outlets publishing AI-written news and reports.
In each case, roles once considered “safe” are gone or transformed.
If it hasn’t happened in your industry yet, it’s coming.
9. The Three Zones: Which One Are You In?
We can now classify the workforce into three broad zones:
Zone 1: The At-Risk
Stuck in fixed roles.
Resistant to change.
Not investing in new skills.
Still operating with pre-COVID work assumptions.
Zone 2: The Survivors
Aware of change but slow to act.
Learning selectively, often reactively.
Protected temporarily by strong networks or soft skills.
Zone 3: The Builders
Proactive learners and early adopters.
Combining human creativity with AI tools.
Constantly reinventing their value in the market.
If you’re not in Zone 3 yet, now is the time to move there.
10. What You Can Do Today
Don’t wait. Take one step forward today. Here’s a simple action plan:
Audit your current role. What tasks can be automated? What can’t? Identify 2–3 GenAI tools. Spend 15 minutes each day using them. Sign up for a micro-course. Platforms like Coursera, Udemy, or LinkedIn Learning. Talk to your manager. Ask how your role may evolve in the next 12–24 months. Follow future-of-work influencers. Stay plugged into trends and tools. Join a digital upskilling community. Accountability helps action.
Final Thought: Adaptation Is No Longer Optional
We are in a turning point of history. The pandemic was the disruption. AI is the acceleration.
Those who still expect the world to pause, or return to how it was, will be sidelined. Not because they’re not valuable—but because they didn’t adapt in time.
You cannot beat AI by being more productive in the old way. You beat it by evolving into a new kind of worker—hybrid, agile, strategic, tech-augmented.
The boiling water isn’t a threat—it’s a signal.
Jump now. Or be cooked slowly, without even realizing it.
Feel free to engage E-SPIN to explore how we can add value and co-create transformation solutions tailored to your requirements. Since 2005, E-SPIN has been enabling businesses through a wide range of enterprise ICT solutions, including supply, project management, training, and maintenance services.
| 2025-06-16T00:00:00 |
2025/06/16
|
https://www.e-spincorp.com/post-pandemic-ai-disruption-business-survival/
|
[
{
"date": "2025/06/16",
"position": 55,
"query": "AI economic disruption"
},
{
"date": "2025/06/16",
"position": 54,
"query": "AI economic disruption"
},
{
"date": "2025/06/16",
"position": 55,
"query": "AI economic disruption"
},
{
"date": "2025/06/16",
"position": 51,
"query": "AI economic disruption"
}
] |
Exploring Salary Trends and Career in Artificial Intelligence
|
Exploring Salary Trends and Career in Artificial Intelligence
|
https://factbeing.com
|
[] |
Exploring High-Paying AI Roles · AI Research Scientist: Average salary: $150,000 – $200,000. · Machine Learning Engineer: Average salary: ...
|
Have a career in artificial intelligence? What should I expect from the salary? Review the salary trends and opportunities in the field of artificial intelligence.
Artificial intelligence (AI) has revolutionized industries, creating numerous job opportunities and career in artificial intelligence that offer high-paying salaries. There is an increasing demand for career in artificial intelligence are across all industries. With it, the spotlight on AI jobs has been coming due to their great salaries, impressive potential for growth, and numerous career in artificial intelligence. In this blog, we will closely examine the AI job salary trends and drill down on multiple factors that determine the salaries of AI jobs, ranging from entry-level to senior-level positions.
The Growing Demand for Career in Artificial Intelligence
This growing need is quickly becoming a more central focus within organizations as they cope with the rise of modern organizations that rely on AI technologies. The career in artificial intelligence salary in 2025 show that the industry is still on its way to expansion and development, so you will have a greater chance of finding your dream job in healthcare, finance, marketing, logistics, etc. Whatever interests you in AI research, data science, or machine learning, the need for industry-skilled professionals will continue to rise over the next years.
It is necessary to know the differences in career in artificial intelligence and the salaries. The highest compensation is typically based on experience, location, background, and the specific area of specialization. Next, let’s examine the factors that influence AI job salaries.
Average Salaries Found in AI
Earning knowledge about the average AI salary is one of the first things aspiring AI professionals investigate. However, although several factors can affect salary figures, industry reports suggest that professionals with career in artificial intelligence earn higher pay than many professionals in other parts of the tech industry.
AI Data Scientist Salary 2025
Experts are seeking data scientists with experience or expertise in AI technologies. As per current data, the average annual salary of an AI data scientist in 2025 ranges from $100,000 to $160,000. With a skill set in machine learning and data analytics, along with the ability to work with large datasets, one can further increase earnings.
AI Engineer Salary Growth
The AI sector has its share of high-paid AI engineers. According to Ahvazi, the average salary of an AI engineer ranges from $110,000 to $150,000. As more firms adopt AI technologies, salaries for AI engineers are expected to increase above the average in the coming years. At the higher end, salaries will be attractive to professionals with advanced knowledge of deep learning, computer vision, and natural language processing.
Top-Paying AI Jobs 2025
AI researchers, machine learning engineers, and AI architects are among the highest-paying roles in AI. Such positions usually demand an advanced degree (PhD or Master’s) and several years of experience in AI applications. These positions can have salaries of up to or exceeding $200,000 per year, especially for research-focused roles at top tech companies.
The top 10 AI jobs in demand, as per our research, include the following:
Data Scientist
Machine Learning Engineer
AI Research Engineer
Computer Vision Engineer
Cloud AI Engineer
Generative AI Engineer
Deep Learning Specialist
Natural Language Processing Engineer
Robotics Engineer
AI Product Manager
Factors Influencing AI Job Salaries
Many factors contribute to the industry comparison of salaries for artificial intelligence, such as gaps in pay among AI workers in different fields. This relies:
Experience Level
One of the most important factors that determines the salaries of AI jobs is experience. Their jobs as AI assistants or junior data scientists will likely pay less than those of individuals with years of experience and impressive expertise. The following is how salaries tend to grow with experience.
Fresh Graduates or Transitioning Freshers to AI — In this case, their salary for Entry-level jobs like an AI analyst or a data engineer can go as high as $70,000 to $90,000 annually. Frequently, these positions are designed to allow individuals to learn and apply basic AI techniques under the guidance of senior professionals.
Mid-Level AI Salary – Ranges From $100,000 to $130,000. With three to five years of experience under their belt, those in the middle of the range will earn anywhere between that range, depending on job opportunities and companies. These individuals typically take responsibility for developing AI models and algorithms.
AI Project Manager, Lead AI Engineer, AI Consultant – Generally, senior roles in AI include AI project manager, lead AI engineer or AI consultant, and these jobs offer salaries ranging from $140,000 to $200,000 and above. They need advanced technical expertise and leadership ability.
Industry and Specialization
The branch of AI you are involved in can have a big part in shaping your salary. Since AI technology is very important, certain areas are willing to spend more money to hire skilled professionals. For example:
In the finance sector, artificial intelligence professionals in this space typically earn the most among all AI professionals, having been among the early adopters of AI technologies. AI investment analyst, AI risk modeler, or financial data scientist positions can offer salaries ranging from $120,000 to $180,000 per year, with even higher salaries in top investment banks.
Yet another industry getting heated up by AI professionals is healthcare. AI professionals in healthcare roles, such as AI healthcare consultants or clinical data analysts, typically receive competitive salaries ranging from $100,000 to $150,000, depending on experience and niche expertise.
Even enterprises in the retail and supply chain sectors are adopting AI in inventory management, predictive analytics, and customer engagement. Retail and logistics roles with AI generally have quite a wide range of possible job salaries, with a range of $90,000 to $130,000, depending on the role and company.
Geographic Location
AI salaries and career in artificial intelligence are highly dependent on the location. For example, someone with the same skill set going to Los Angeles or Austin, as opposed to San Francisco, New York, or Seattle, would earn less due to the higher cost of living and the absence of these companies in these cities. Yet, salaries in emerging AI markets like India or Eastern Europe may be less by comparison, although the demand for AI talent is increasing globally.
In addition to location, salaries for AI specialists can also vary according to the presence of large companies that require AI specialists. For example, those who live in Silicon Valley or one of the major metropolitan areas, such as London and Berlin, may likely receive slightly higher wages.
AI Role and Skills
Different salaries are commanded by specific roles within the AI field. For example, machine learning engineering and the role of an AI architect are often considered more complex roles and, therefore, pay more, as everything has to fall into place. Additionally, a specialized skill goes a long way for those with skills related to Natural Language Processing (NLP), Computer Vision, and Reinforcement Learning, and they usually earn more than the generalists.
However, there are still AI roles that don’t require coding for those without a coding background – ET AI PM or ET AI strategist, for instance, that can match the salary rates of coding AI roles. These positions are responsible for overseeing AI projects, as well as developing and implementing AI strategy throughout the entire business. They do not necessarily require extensive knowledge of programming but rather a strong grasp of AI concepts.
Exploring High-Paying AI Roles
Let’s now examine some of the highest-paying career in artificial intelligence and their corresponding salaries. If you are planning to make the most out of AI jobs, take a look below; these are the top-paying job titles.
AI Research Scientist: Average salary: $150,000 – $200,000. Institutions where AI researchers work and advance the state of AI technologies include universities, research institutions, and large tech companies.
Machine Learning Engineer: Average salary: $120,000 – $160,000. Professionals in this field often work with extensive datasets and complex models to design algorithms that help machines gain knowledge from the information they are given.
AI Solutions Architect: Average salary: $130,000 – $180,000. Those tasked with designing and implementing AI solutions, typically in a leadership role, are referred to as AI architects.
AI Product Manager: Average salary: $120,000 – $160,000. The job of a product manager in AI is to develop products and lead teams through to their timely delivery.
The Impact of AI Salaries on Career Choices
People are being encouraged to pursue career in artificial intelligence related fields because salaries in this sector are on the rise. Choosing a new path is possible, whether you simply want to improve or start over in a different career. Many individuals interested in AI are concerned about the salary growth in this field. The prospects of significant earnings and professional advancement can be hers with the right skill set and experience.
For the record, AI careers that pay high salaries and do not require a career in artificial intelligence are very much reachable for people who have no technical background. Individuals with knowledge of business and AI tend to perform well financially and in their careers when they work in AI-related areas.
In a Nutshell
In essence, we can expect AI to open up good-paying jobs for anyone with the required skills in various areas of work. AI offers competitive salaries for entry-level to senior positions, which are expected to continue increasing as demand for AI expertise rises.
Knowing how much AI salaries differ based on experience level, industry, and specialization will help you place yourself for a successful and lucrative career in artificial intelligence. If you are a beginner or an experienced AI, there is a better time to cash in on the well-paying AI job opportunities in the market today than now.
Follow Fact Being for more technology and AI-related blog posts.
| 2025-06-05T00:00:00 |
2025/06/05
|
https://factbeing.com/career-in-artificial-intelligence/
|
[
{
"date": "2025/06/16",
"position": 80,
"query": "artificial intelligence wages"
},
{
"date": "2025/06/16",
"position": 84,
"query": "artificial intelligence wages"
},
{
"date": "2025/06/16",
"position": 78,
"query": "artificial intelligence wages"
}
] |
Where Are We Now With the Use of AI in the Workplace?
|
Where Are We Now With the Use of AI in the Workplace?
|
https://www.laboremploymentlawblog.com
|
[
"Dimiana Saad",
"June",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img",
"Height Auto Max-Width",
"Vertical-Align Bottom .Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow .Wp-Block-Co-Authors-Plus-Avatar"
] |
From federal rollbacks to aggressive state-level regulation, the use of AI in employment decisions—particularly in hiring, performance ...
|
Listen to this post
As we previously reported here, here, and here, employers must navigate a rapidly evolving legal landscape as artificial intelligence (AI) continues to transform the modern workplace. From federal rollbacks to aggressive state-level regulation, the use of AI in employment decisions—particularly in hiring, performance management, and surveillance—has become a focal point for lawmakers, regulators, and litigators alike. This article contains an overview of the shifting federal landscape on the use of AI at work, the state level response, and offers recommendations for employers to mitigate risk.
Trump Administration Rolls Back Federal AI Oversight
The federal approach to AI in employment has undergone a dramatic shift in 2025. On his first day in office, President Trump rescinded Executive Order 14110, which directed federal agencies to address AI-related risks such as bias, privacy violations, and safety concerns. This was followed by the removal of key guidance documents from the U.S. Equal Employment Opportunity Commission (EEOC), including technical assistance on Title VII compliance and the Americans with Disabilities Act (ADA) as they relate to AI tools. The Department of Labor has also signaled that its prior guidance on AI best practices may no longer reflect current policy, leaving employers with less clarity at the federal level than ever.
Despite these reversals, employers remain liable under existing anti-discrimination laws for the outcomes of AI-driven employment decisions—even when those tools are developed by third-party vendors.
States, Especially California, Fill the Federal Void With AI Regulation
In the absence of clear federal guidance, states have begun regulating AI in the workplace. California, in particular, has emerged as a bellwether.
Earlier this year, California introduced several bills aimed at curbing the unchecked use of AI in employment decisions:
SB 7 – “No Robo Bosses Act” : This bill would require employers to provide 30 days’ notice before using any automated decision system (ADS) and mandates human oversight in employment decisions. It also bans AI tools that infer protected characteristics or retaliate against workers.
: This bill would require employers to provide 30 days’ notice before using any automated decision system (ADS) and mandates human oversight in employment decisions. It also bans AI tools that infer protected characteristics or retaliate against workers. AB 1018 – Automated Decisions Safety Act : This legislation would impose broad compliance obligations on both employers and AI vendors, including bias audits, data retention policies, and impact assessments.
: This legislation would impose broad compliance obligations on both employers and AI vendors, including bias audits, data retention policies, and impact assessments. AB 1221 and AB 1331: These bills target AI-driven workplace surveillance, requiring transparency and limiting monitoring during off-duty hours or in private space.
Other states are following suit. For example, Illinois, Colorado, and New York City already have laws regulating AI in hiring, and over 25 states have introduced similar legislation in 2025.
Practical Implications for Employers
With limited federal guidance and state laws multiplying, inaction is risky and, indeed, reckless. Employers should therefore take proactive steps to mitigate legal exposure:
Audit AI Tools Regularly: Conduct bias audits and impact assessments to ensure compliance with anti-discrimination laws. Review Vendor Agreements: Ensure contracts with AI vendors include provisions for transparency, data handling, and liability. Train HR and Leadership: Equip decision-makers with the knowledge to use AI responsibly and in compliance with applicable laws. Implement Human Oversight: Avoid fully automated employment decisions. Ensure a human reviews and approves critical outcomes. Stay Informed: Monitor legislative developments in all jurisdictions where your business operates.
Looking Ahead: Balancing Innovation With Worker Protection
The use of AI in the workplace is not going away. In fact, it is accelerating. But with innovation comes responsibility. Employers should systematically document data collection from workplace technologies, update privacy-related polices, and ensure human oversight is integrated into any employment decisions that rely on algorithmic input. Sheppard Mullin’s Labor and Employment group has significant experience helping employers navigate this ever-changing legal, regulatory, and technological landscape, and will continue to monitor and provide updates on any developments.
| 2025-06-16T00:00:00 |
2025/06/16
|
https://www.laboremploymentlawblog.com/2025/06/articles/americans-with-disabilities-act-ada/where-are-we-now-with-the-use-of-ai-in-the-workplace/
|
[
{
"date": "2025/06/16",
"position": 6,
"query": "AI regulation employment"
},
{
"date": "2025/06/16",
"position": 7,
"query": "AI regulation employment"
},
{
"date": "2025/06/16",
"position": 6,
"query": "AI regulation employment"
},
{
"date": "2025/06/16",
"position": 12,
"query": "AI regulation employment"
},
{
"date": "2025/06/16",
"position": 3,
"query": "artificial intelligence employers"
},
{
"date": "2025/06/16",
"position": 3,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/16",
"position": 17,
"query": "artificial intelligence labor union"
}
] |
Artificial Intelligence in Employment: 2025 Regulatory Update
|
Artificial Intelligence in Employment: 2025 Regulatory Update
|
https://medium.com
|
[
"Dci Consulting"
] |
The integration of artificial intelligence (AI) into employment decision-making processes for organizations continues to accelerate, as does the ...
|
Artificial Intelligence in Employment: 2025 Regulatory Update DCI Consulting 4 min read · Jun 16, 2025 -- Listen Share
By Dave Schmidt and Sarah Layman
The integration of artificial intelligence (AI) into employment decision-making processes for organizations continues to accelerate, as does the evolution of the legal and regulatory environment. This post provides information on key happenings in state and federal regulatory activity as of June 2025.
State Activity Adds to the Patchwork of Requirements
Colorado, California, and Virginia have seen significant activity in 2025 related to the use of AI that employers need to know about:
Colorado
Status: Passed, In Effect on February 1, 2026
The Colorado Consumer Protections in Interactions with Artificial Intelligence Systems (CPIAIS) is currently the most comprehensive state law addressing the development and use of AI in high-impact contexts (including employment decision-making). Despite significant debate and last-minute calls by Governor Polis to delay or revise the legislation, including efforts to ease compliance burdens and refine the definition of “algorithmic discrimination,” the law is set to take effect on February 1, 2026.
CPIAIS applies to high-risk AI systems, defined as any AI that makes — or plays a substantial role in making — a “consequential decision,” such as hiring, promotion, or termination. Both developers and deployers of such systems are held to a duty of reasonable care to avoid algorithmic discrimination. If discrimination is discovered, it must be reported to the Colorado Attorney General within 90 days. Reasonable care is presumed if specific compliance steps are taken (e.g., Risk management programs, Annual impact assessments, Transparency and notices). For additional details, see our blog or download DCI’s Colorado Artificial Intelligence Act Cheat Sheet.
California
Status: Pending Approval by Office of Administrative Law
Although early efforts to pass broad, stand-alone AI laws in California stalled (see DCI’s blogs here and here), state regulators have instead moved forward with targeted amendments to the California Fair Employment and Housing Act (FEHA). The new regulations, titled “Employment Regulations Regarding Automated-Decision Systems” were finalized in March 2025 and could take effect as early as July 2025 (pending approval from the Office of Administrative Law). Most notably, the amended text provides:
Clear definitions of “automated-decision systems (ADS),” “algorithm,” “artificial intelligence,” and “machine learning”;
A mandate to retain all relevant employment records, including ADS data and documentation, for at least four years;
Notice requirements obligating employers to inform applicants and employees when an ADS is used in employment decision-making;
A requirement to provide reasonable accommodations for individuals with disabilities or religious needs, particularly when ADS tools evaluate attributes (e.g., reaction time, vocal tone) that may be impacted;
Explicit language stating that practices resulting in adverse impact are unlawful unless the criteria used are job-related, consistent with business necessity, and there is no less discriminatory alternative available that would serve the employer’s goals as effectively;
Recognition that, in the event of a legal claim, evidence (or lack thereof) of proactive anti-bias testing and mitigation efforts — including the quality, effectiveness, scope, and response to such testing — will be relevant.
In the meantime, California continues to propose and consider bills related to the use of artificial intelligence that could impact employment decision-making, such as AB-1018.
Virginia
Status: Vetoed by Governor
This spring Virginia’s legislature narrowly passed House Bill 2094, the High-Risk Artificial Intelligence Developer and Deployer Act, — bore a close resemblance to Colorado’s CPIAIS. However, Governor Glenn Youngkin subsequently vetoed the bill, citing its “burdensome” regulatory framework. The bill could technically still become law via a two-thirds override in both chambers, but given the initial vote tallies, this outcome is unlikely.
Federal Updates
There have also been developments related to AI use and oversight at the federal level in early 2025,including new guidance from the Office of Management and Budget (OMB) for federal agencies and the withdrawal of previously provided guidance by some federal agencies.
OMB Guidance for Federal Agencies
At the federal level, the White House Office of Management and Budget issued two significant memoranda in April of 2025 addressing AI use and procurement across federal agencies, officially rescinding and replacing Biden-era guidance.
The primary directive, OMB Memo M-25–21-”Accelerating Federal Use of AI through Innovation, Governance, and Public Trust”, establishes comprehensive standards for the governance, risk management, and oversight of AI in government operations. According to the White House Fact Sheet, these policies mark a fundamental shift from the previous administration by emphasizing rapid and responsible AI adoption (e.g., “Agency Chief AI Officer roles are redefined to serve as change agents and AI advocates, rather than overseeing layers of bureaucracy”), efficient and effective acquisition of American AI systems (the focus of OMB Memo M-25–22), and practical applications that “work for the American people.”
While these requirements are centered on federal agencies, their reach is likely to extend to private-sector vendors and contractors providing AI tools and services to the government, shaping wider expectations for responsible AI use and procurement.
Withdrawn Guidance
Under the Trump Administration, a range of previously issued AI-related guidance documents have been formally removed from federal websites including:
Artificial Intelligence and Worker Well-being: Principles and Best Practices for Developers and Employers (DCI Blog)
Guide on Artificial Intelligence and Equal Employment Opportunity for Federal Contractors (DCI Blog)
Joint Statement on Enforcement of Civil Rights, Fair Competition, Consumer Protection, and Equal Opportunity Laws in Automated Systems (DCI Blog)
It is yet to be seen if revised guidance documents related to the use of AI in employment decision-making will be issued.
Looking Ahead: Preparation and Monitoring Needed
As the legal landscape surrounding AI in employment evolves, it is prudent for employers to not only track legislative changes, but also proactively create robust governance teams and frameworks to address the challenges and risks associated with using AI-driven hiring processes. DCI will continue to monitor developments and provide timely updates with practical insight for employers.
For more information, register for DCI’s upcoming webinar covering the key updates in AI regulations for employers.
| 2025-06-16T00:00:00 |
2025/06/16
|
https://medium.com/@dci_consulting/artificial-intelligence-in-employment-2025-regulatory-update-d0eb1e6a3b1b
|
[
{
"date": "2025/06/16",
"position": 64,
"query": "AI regulation employment"
},
{
"date": "2025/06/16",
"position": 64,
"query": "AI regulation employment"
},
{
"date": "2025/06/16",
"position": 61,
"query": "AI regulation employment"
},
{
"date": "2025/06/16",
"position": 69,
"query": "AI regulation employment"
}
] |
AI cannot replace all jobs, says expert: 3 types of careers that could ...
|
AI cannot replace all jobs, says expert: 3 types of careers that could survive the automation era
|
https://m.economictimes.com
|
[] |
According to PwC's latest report, roles in manual trades, creative fields, and AI-related sectors are expected to remain in demand.
|
What is AI Automation?
AI's Growing Influence on the Job Market
Three Career Paths That Remain Resilient
As artificial intelligence continues to reshape industries across the globe, young people preparing for their careers are being advised to take a closer look at job roles that are likely to thrive in an AI-dominated future. With AI automation on the rise and traditional roles evolving rapidly, the nature of work is shifting—and understanding where human skills still outmatch machine capabilities is becoming essential.AI automation, also known as intelligent or hyperautomation, blends artificial intelligence (AI) with technologies like Robotic Process Automation (RPA) and Business Process Management (BPM) to streamline business operations, enhance decision-making, and boost efficiency. It goes beyond rule-based automation by using machine learning, natural language processing (NLP), and computer vision—enabling systems to learn, adapt, and handle tasks that once required human judgment.Its core components include RPA for automating routine digital tasks, BPM for designing and optimizing workflows, and AI to introduce adaptive decision-making. The benefits are wide-ranging: increased efficiency through task automation, smarter data-driven decisions, personalized customer experiences, cost savings, and higher overall productivity.Common use cases include AI-powered chatbots, large-scale data analysis, workflow improvements, tailored product recommendations, and fraud detection. Altogether, AI automation equips businesses to operate more intelligently, effectively, and competitively.According to a global report by PricewaterhouseCoopers (PwC), the integration of AI into the workplace is accelerating across industries, especially in IT, financial services, and professional services. These sectors are not only adapting to AI but also reaping benefits from it, with professionals in AI-skilled jobs witnessing a 56% increase in average wages in 2024—up from a 25% jump the year before.PwC’s Chief Economist, Barret Kupelian, explained in a BBC Radio 5 Live interview that AI is already impacting working lives across the board. He noted a significant and consistent rise in the demand for AI-related skills, particularly in industries that have welcomed the technology. However, he emphasized that AI is more likely to augment rather than entirely replace many job functions, particularly those requiring human nuance.Kupelian highlighted three types of roles young people should consider to remain valuable in the job market:Traditional manual roles—such as plumbers, electricians, and decorators—remain difficult for AI to replicate due to their reliance on physical labor and problem-solving in dynamic environments. Kupelian remarked that current AI technologies are not advanced enough to replace jobs involving intensive manual work.Occupations that rely on creativity and complex decision-making—such as designers, artists, strategists, and writers—are also less susceptible to automation. According to Kupelian, these roles require “a high degree of judgement and creativity” and involve “bespoke skills” that digital tools struggle to imitate.While some jobs are being displaced, others are being created. Positions in AI development, data science, machine learning, and ethical AI oversight are gaining traction. These roles not only offer higher salaries but are also essential in shaping how AI is applied across sectors. PwC’s findings show that businesses integrating AI see faster revenue growth, signaling demand for professionals who understand and work with the technology.What distinguishes the jobs most vulnerable to automation is their reliance on repetition, structured input, and limited decision-making. Positions that do not require empathy, intuition, manual labour or complex human judgment are at greater risk of being replaced.However, this shift is also opening up new opportunities. As basic tasks are automated, professionals can focus more on strategy, innovation, and human-centered problem solving. The key to staying ahead in this changing job market lies in developing the ability to work alongside AI—leveraging its strengths while applying uniquely human skills that machines still can’t replicate.
| 2025-06-16T00:00:00 |
https://m.economictimes.com/magazines/panache/ai-cannot-replace-all-jobs-says-expert-3-types-of-careers-that-could-survive-the-automation-era/articleshow/121854918.cms
|
[
{
"date": "2025/06/16",
"position": 91,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 92,
"query": "AI replacing workers"
},
{
"date": "2025/06/16",
"position": 60,
"query": "job automation statistics"
}
] |
|
EMEA universities and schools transforming education with ...
|
EMEA universities and schools transforming education with the help of Google AI
|
https://blog.google
|
[
"Colin Marson",
"Director",
"Google For Education",
"Emea",
"Alan Stapelberg",
"Keyword Team",
"Tom Chapman",
"Andy Russell",
"Cinthya Mohr",
"Brian Hendricks"
] |
Universities and schools are redefining the boundaries of what's possible in education with Google AI tools like NotebookLM and the Gemini app.
|
K-12 Education: Transforming Classrooms and Reducing Workloads
From customizing learning materials to significantly reducing educator workloads, AI is helping to give time back to teaching. Read on to explore compelling case studies from institutions across EMEA that are embracing AI-powered tools like Gemini to create more dynamic and effective learning environments.
Helsingborg (Sweden): Customizing learning materials for students
Teachers in Helsingborg are experiencing significant improvements in efficiency and time savings through AI tools. Gemini is used to generate content and materials, accelerating tasks that previously took considerable time, allowing more focus on lesson planning and feedback. They also use AI to adapt material for specific student groups, customizing content to meet diverse learning needs, and renew lesson materials and generate example texts, enhancing the quality and relevance of teaching resources.
Barton Peveril (UK): Prioritising staff training & understanding
Barton Peveril, a sixth-form college, has invested in Google Workspace with Gemini for all staff as part of their Digital Strategy. This proactive approach ensures staff are equipped with essential AI tools and skills, preventing the use of less secure open platforms. Their initiatives include regular training sessions to keep all staff updated on Gemini features, promoting best practices by fostering inter-departmental sharing of AI utilization, and dedicated AI inset days to showcase departmental AI applications through hands-on activities.
LEO Academy Trust (UK): Significant Workload Reduction for Educators
AI has profoundly impacted the professional lives of educators within the LEO Academy Trust, significantly reducing their workload and allowing them to focus on core functions. Emma Potter, Vice Principal at Cheam Park Farm, reported reduced time on parent communications, as AI enables school leaders to craft responses to parent emails in minutes rather than hours, a substantial saving given the volume of daily messages school leaders receive.
Higher Education
| 2025-06-16T00:00:00 |
2025/06/16
|
https://blog.google/outreach-initiatives/education/emea-universities-and-schools-transforming-education-with-the-help-of-google-ai/
|
[
{
"date": "2025/06/16",
"position": 12,
"query": "AI education"
}
] |
Teaching with AI: A Practical Guide to a New Era of Human ...
|
Amazon.com
|
https://www.amazon.com
|
[] |
In this groundbreaking and practical guide, teachers will discover how to harness and manage AI as a powerful teaching tool.
|
Click the button below to continue shopping
| 2025-06-16T00:00:00 |
https://www.amazon.com/Teaching-AI-Practical-Guide-Learning/dp/1421449226
|
[
{
"date": "2025/06/16",
"position": 22,
"query": "AI education"
}
] |
|
AI and Teaching | Resources - CETL
|
Resources - CETL
|
https://www.kennesaw.edu
|
[] |
Explore ethical, practical resources for integrating AI into teaching at KSU, including free Coursera courses, training, and support for faculty.
|
Trainings & Courses KSU faculty, staff, and students have access to take over 2,800 free MOOC (Massive Open Online Courses) offered by certain Coursera partners at no charge. Jeanne Beatrix Law, Professor of English at Kennesaw State University, has authored several Coursera courses to help community stakeholders, students, and colleagues engage ethically with generative AI for many different use cases. Please check out the courses below: AI for Everyday Life Recommended Experience: Beginner In this module, you will learn how to (1) craft an input and output using the prompt engineering methods for generative AI, (2) articulate two methods of prompt engineering for everyday uses, and (3) apply your knowledge to one prompt engineering method to a real-world scenario. Take The Course
AI for Grant Writing Recommended Experience: Beginner Learners will use generative AI to streamline every aspect of the grant writing process, from crafting compelling solicitation letters to structuring detailed proposals. By the end of this course, learners will be equipped to create high-quality, persuasive grant proposals. Take The Course
AI for Professional Communication Recommended Experience: Beginner This course will guide participants through the fundamentals of AI-infused professional communication, including drafting emails, creating meeting agendas, summarizing documents, and producing compelling social media content. Take The Course
Ethical AI Use Recommended Experience: Beginner This course equips participants with the knowledge and tools necessary to navigate the ethical complexities of AI, empowering them to contribute positively to the development and implementation of AI technologies in society. Take The Course
AI for Education (Basic) Recommended Experience: Beginner Participants will be provided tested methods for prompting an AI Assistant, such as GPT, Claude, and Gemini to yield useful, relevant, accurate, and ethical outputs. Learners will gain a clear understanding of how to collaborate with an AI Assistant and how to encourage students to do so in ethical ways. Take The Course
AI for Education (Intermediate) Recommended Experience: Intermediate This course expands on the AI for Education (Basic) course. Participants will learn quick ways to refine prompt engineering methods for assignments and course design that can be scaled to multiple levels of educational contexts. Take The Course
AI for Education (Advanced) Recommended Experience: Advanced This course expands on the AI for Education (Intermediate) course. Participants will learn quick ways to refine prompt engineering methods for assignments and course design that can be scaled to multiple levels of educational contexts. Take The Course
| 2025-06-16T00:00:00 |
https://www.kennesaw.edu/cetl/teaching-resources/ai-teaching-resources.php
|
[
{
"date": "2025/06/16",
"position": 26,
"query": "AI education"
}
] |
|
Artificial Intelligence (AI) in Education - Research Guides
|
Artificial Intelligence (AI) in Education
|
https://libguides.reynolds.edu
|
[
"Denise Woetzel"
] |
How can AI be used in education? · 1. Personalized Learning: AI can adapt to a student's individual learning pace and style. · 2. Tutoring and Support: AI- ...
|
Artificial Intelligence (AI) has significant potential in reshaping education, making it more personalized, efficient, and inclusive. Some examples include:
1. Personalized Learning: AI can adapt to a student's individual learning pace and style. By analyzing a student's strengths, weaknesses, and progress, AI can customize content delivery for optimal learning, resulting in personalized education for each student.
2. Tutoring and Support: AI-driven tutoring systems can provide additional support to students, helping them in subjects where they might struggle. These intelligent tutoring systems can explain concepts, answer questions, provide feedback, and even assess students' understanding of a subject.
3. Efficiency for Educators: AI can automate administrative tasks such as grading and scheduling, freeing up time for educators to focus on instruction and student interaction. AI can also assist in detecting plagiarism in assignments.
4. Data-Informed Insights: AI can analyze vast amounts of data to provide insights into learning patterns and trends, helping educators and policy-makers make informed decisions to improve teaching methods, curriculum design, and overall educational policies.
5. Accessibility: AI technologies can help make education more accessible for students with disabilities. For instance, speech-to-text and text-to-speech technologies can aid students with hearing or speech impairments, while AI-driven personalized learning systems can cater to students with learning difficulties.
6. Lifelong Learning and Upskilling: With the rapid pace of technological advancement, continuous learning has become essential. AI-powered platforms can provide personalized, on-demand learning for people at all stages of their career, making it easier for individuals to acquire new skills and adapt to changing job markets.
7. Virtual Reality (VR) and Augmented Reality (AR): Though not AI per se, these technologies often leverage AI for creating immersive learning experiences, making education more engaging and interactive.
While AI presents these remarkable opportunities, it's crucial to navigate potential challenges such as data privacy and security, ensuring AI's equitable use, and addressing concerns around the depersonalization of education. As with any technology, the goal should be to use AI to enhance human effort in education, not replace it.
| 2025-06-16T00:00:00 |
https://libguides.reynolds.edu/aieducation
|
[
{
"date": "2025/06/16",
"position": 46,
"query": "AI education"
}
] |
|
What Does Data Tell Us About AI in K-12 Education
|
What Does Data Tell Us About AI in K-12 Education
|
https://ciddl.org
|
[
"Yerin Seung"
] |
Generative AI is no longer a “future of education” conversation. It is already shaping lesson planning, language support, and student feedback in schools across ...
|
CIDDL is committed to providing high-quality resources to support the increasing knowledge, adoption, and use of a range of educational technologies that can be used for educators, related services, or leadership preparation programs. For more resources, including videos and blogs, subscribe to our newsletter and follow us on YouTube, Facebook, and LinkedIn. The most important part of our CIDDL community is YOU. Join our community and share the innovative ways you are using technology, ask a question about technology integration, or participate in our bi-weekly live AI Community Chats. We look forward to seeing you in our community!
| 2025-06-16T00:00:00 |
2025/06/16
|
https://ciddl.org/what-does-data-tell-us-about-ai-in-k-12-education/
|
[
{
"date": "2025/06/16",
"position": 60,
"query": "AI education"
}
] |
AI Education Network
|
AI Education Network — LEAP Innovations
|
https://www.leapinnovations.org
|
[] |
LEAP's AI Education Network helps leaders, educators, and systems understand, design, and implement AI to enhance teaching and learning through a personalized ...
|
AI WORKSHOPS | These sessions combine personalized learning and AI providing a hands-on and interactive experience that is rooted in current research, LEAP Frameworks, and AI efficiencies. Teachers and leaders develop mindsets and learn instructional strategies to engage learners, shift the cognitive lift to students, and set conditions for students and teachers to be agents of their own learning. Workshops are full-day, half-day or tailored to your institute day or staff meeting schedule.
| 2025-06-16T00:00:00 |
https://www.leapinnovations.org/aipathways
|
[
{
"date": "2025/06/16",
"position": 73,
"query": "AI education"
}
] |
|
What does 'AI experience required' mean? Employers ...
|
What does ‘AI experience required’ mean? Employers listing it may not even know
|
https://wtop.com
|
[
"News Traffic Weather"
] |
IT-related job postings requiring artificial intelligence skills have more than doubled in the last year. But what employers are looking for can be vague.
|
IT-related job postings requiring artificial intelligence skills have more than doubled in the last year, up 117%. But what exactly employers are looking for when seeking AI experience can be vague.
There were 470,000 job postings for tech-related positions in May, more than half of which were new openings posted last month, according to technology industry trade group CompTIA.
The D.C. area, along with New York City and Dallas continue to lead IT job postings nationwide.
IT-related job postings requiring artificial intelligence skills have more than doubled in the last year, up 117%. But what exactly employers are looking for when seeking AI experience can be vague.
Artificial intelligence is an increasingly important skill for job seekers in technology, and for many jobs in general.
“We definitely see employers throwing this term onto their job requirements,” said Seth Robinson, vice president of industry research at CompTIA. “Companies and employers and organizations want to use new technologies in their job postings. They’ve heard about it. They don’t understand it fully, but they put it on their job postings.”
That is not necessarily helpful for job seekers. They may have AI skills, but the degree to which they have experience may not meet the requirements of the job opening they are applying for. Robinson said companies need to be clear and up front in their postings.
“Are we talking about the skills in programming, artificial intelligence algorithms, or modeling data? I think there has to be a little bit more specificity there,” he said. “And so we’re hoping to see things like artificial intelligence get a little bit more granular, a little bit more specific and a little bit more realistic.”
While AI skills requirements have more than doubled in tech job postings, employers continue to put less emphasis on education. CompTIA said only about half of tech-related job postings in May listed a traditional four-year college degree as a requirement, instead, they list some combination of work experience, training and industry recognized certification.
“I think we are seeing companies open their doors a little bit more broadly, which helps them broaden their candidate pool, especially in spaces where demand is exceeding supply,” Robinson said. “We are definitely seeing some loosening of college requirements there.”
Another shift in technology-related job requirements CompTIA is noticing is its integration into many more jobs outside of technology. A candidate may not be applying for a technology-related job, but it is increasingly likely that non-tech jobs have technology requirements.
“You’re going to see employers who want to blend technology skills with other disciplines,” Robinson said. “So I think you are going to see that happening where companies are more willing to re-skill or upskill someone who doesn’t come from a traditional technology background, and get them into a technology role.”
Tech sector job growth in May was modest. CompTIA said job growth in cloud infrastructure and tech services was offset by reductions in the telecommunications sector.
Tech occupation employment across the economy declined by 131,000 positions, though tech occupation employment remains positive for the year.
The unemployment rate among technology workers in May was 3.4%, much lower than the overall national unemployment rate of 4.2%.
Get breaking news and daily headlines delivered to your email inbox by signing up here.
© 2025 WTOP. All Rights Reserved. This website is not intended for users located within the European Economic Area.
| 2025-06-16T00:00:00 |
2025/06/16
|
https://wtop.com/business-finance/2025/06/what-does-ai-experience-required-mean-employers-listing-it-may-not-even-know/
|
[
{
"date": "2025/06/16",
"position": 28,
"query": "AI employers"
},
{
"date": "2025/06/16",
"position": 6,
"query": "artificial intelligence employers"
},
{
"date": "2025/06/16",
"position": 93,
"query": "artificial intelligence employment"
}
] |
Attention New York Employers: The NY WARN Act Now ...
|
Attention New York Employers: The NY WARN Act Now Requires Disclosure of AI-Related Layoffs - New Jersey Business Lawyers
|
https://www.ogcsolutions.com
|
[
"Christopher Santomassimo"
] |
New York is the first U.S. state to require employers to disclose whether artificial intelligence (AI) contributes to mass layoffs under its Worker ...
|
New York is the first U.S. state to require employers to disclose whether artificial intelligence (AI) contributes to mass layoffs under its Worker Adjustment and Retraining Notification (NY WARN) Act. This requirement, effective March 2025, was announced by Governor Kathy Hochul during her January 2025 State of the State Address. The measure aims to track AI’s impact on the labor market and support displaced workers through transparency and data collection. Below is a detailed discussion of the requirement, its implications, and challenges, based on available information.
Overview of the Requirement.
Scope : The NY WARN Act applies to private-sector employers with 50 or more full-time employees. It mandates 90 days’ advance notice for mass layoffs affecting at least 25 employees (or one-third of the workforce) at a single site, or 250 employees total, compared to the federal WARN Act’s threshold of 100 employees and 50 layoffs with 60 days’ notice.
: The NY WARN Act applies to private-sector employers with 50 or more full-time employees. It mandates 90 days’ advance notice for mass layoffs affecting at least 25 employees (or one-third of the workforce) at a single site, or 250 employees total, compared to the federal WARN Act’s threshold of 100 employees and 50 layoffs with 60 days’ notice. AI Disclosure : Employers must now indicate on WARN notice forms whether “technological innovation or automation,” specifically AI or robotics, is a reason for layoffs. This is implemented via a checkbox on the form, with a follow-up menu to specify the technology involved.
: Employers must now indicate on WARN notice forms whether “technological innovation or automation,” specifically AI or robotics, is a reason for layoffs. This is implemented via a checkbox on the form, with a follow-up menu to specify the technology involved. Implementation : The New York State Department of Labor (DOL) oversees enforcement, directed by Governor Hochul without requiring legislative action. Notices are sent to affected employees, their representatives, the DOL, and local officials.
: The New York State Department of Labor (DOL) oversees enforcement, directed by Governor Hochul without requiring legislative action. Notices are sent to affected employees, their representatives, the DOL, and local officials. Purpose : The stated aims of the disclosure are to: Provide data on AI’s impact on employment to inform reskilling and upskilling programs; and Promote transparency and ensure that AI integration supports a thriving workforce.
: The stated aims of the disclosure are to:
Key Features and Process.
Form Modification : The change, effective March 2025, adds a checkbox to the WARN notice form, asking if layoffs stem from technological innovation or automation. If checked, employers must identify the specific technology (e.g., AI).
: The change, effective March 2025, adds a checkbox to the WARN notice form, asking if layoffs stem from technological innovation or automation. If checked, employers must identify the specific technology (e.g., AI). Data Collection : The collected data will help regulators understand AI’s role in job displacement, potentially guiding policy for worker retraining and economic adaptation.
: The collected data will help regulators understand AI’s role in job displacement, potentially guiding policy for worker retraining and economic adaptation. No Reported AI Layoffs Yet: As of June 2025, no companies filing WARN notices in New York have attributed layoffs to AI, suggesting either limited AI-driven layoffs or reluctance to disclose.
Challenges and Criticisms.
Defining AI-Related Layoffs : Labor Commissioner Roberta Reardon acknowledged challenges in defining what constitutes an AI-driven layoff, which could lead to inconsistent reporting.
: Labor Commissioner Roberta Reardon acknowledged challenges in defining what constitutes an AI-driven layoff, which could lead to inconsistent reporting. Limited Scope : The NY WARN Act only covers mass layoffs, excluding smaller-scale AI-driven job cuts, which may underrepresent AI’s impact.
: The NY WARN Act only covers mass layoffs, excluding smaller-scale AI-driven job cuts, which may underrepresent AI’s impact. Employer Compliance : The effectiveness hinges on employers’ willingness to accurately report AI’s role. Some may avoid disclosure due to reputational concerns or ambiguity in AI’s influence.
: The effectiveness hinges on employers’ willingness to accurately report AI’s role. Some may avoid disclosure due to reputational concerns or ambiguity in AI’s influence. Implementation Uncertainty: No specific timeline for full DOL guidance exists, leaving employers uncertain about compliance details.
Comparison to Other AI Regulations.
New York City’s AI Bias Law : Since 2023, NYC’s Local Law 144 requires bias audits for automated employment decision tools (AEDTs) used in hiring and promotions, but compliance has been low.
: Since 2023, NYC’s Local Law 144 requires bias audits for automated employment decision tools (AEDTs) used in hiring and promotions, but compliance has been low. Other States : California considered but stalled a 2024 bill (SB 1047) requiring employee notice for AI use in employment decisions. Colorado’s 2026 AI law will mandate protections against algorithmic discrimination.
: California considered but stalled a 2024 bill (SB 1047) requiring employee notice for AI use in employment decisions. Colorado’s 2026 AI law will mandate protections against algorithmic discrimination. Federal Level: The EEOC issued 2023 guidance on AI’s adverse impact and disability accommodations in workplace decisions, but no federal AI layoff disclosure requirement exists.
For further details, as specific implementation guidelines are still forthcoming, please reach out to OGC Solutions®. Contact us.
| 2025-06-16T00:00:00 |
2025/06/16
|
https://www.ogcsolutions.com/ny-warn-act-requires-disclosure-of-ai-related-layoffs/
|
[
{
"date": "2025/06/16",
"position": 74,
"query": "AI employers"
},
{
"date": "2025/06/16",
"position": 44,
"query": "AI layoffs"
}
] |
In New York, Employers Have to Disclose AI-Related Layoffs
|
In New York, Employers Have to Disclose AI-Related Layoffs
|
https://winsomemarketing.com
|
[
"Writing Team"
] |
New York's groundbreaking AI disclosure law requires companies to report AI-driven layoffs. This transparency breakthrough will transform workforce planning ...
|
Finally, someone in government gets it. New York just became the first state to require companies to disclose whether artificial intelligence is the reason for their layoffs, and it's exactly the kind of pragmatic policy response we need as AI reshapes the workforce.
The move applies to New York State's existing Worker Adjustment and Retraining Notification (WARN) system and took effect in March. Companies must now select whether "technological innovation or automation" is a reason for job cuts, and if so, name the specific technology responsible—like AI or robots. It's a simple checkbox that could revolutionize how we understand and respond to technological displacement.
This isn't about stopping AI adoption or vilifying companies for using technology. It's about bringing desperately needed transparency to one of the most significant economic transformations of our time.
Data-Driven Workforce Policy Finally Makes Sense
New York Governor Kathy Hochul first proposed the change in her January 2025 State of the State address, explaining that the goal is to understand "the potential impact of new technologies through real data." Translation: instead of making policy based on speculation and fear, let's actually measure what's happening.
The federal WARN Act requires employers with 100 or more employees to give 60 days' notice for layoffs affecting at least 50 workers at a single location, but New York's WARN Act goes further, applying to companies with 50 or more employees and requiring disclosure for layoffs involving at least 25 workers or a third of the workforce at one site. This expanded coverage means more comprehensive data collection about AI's workforce impact.
So far, no companies filing WARN notices in the state have said the layoffs were due to AI—but that's partly because the requirement is new. As companies begin reporting, we'll finally have real data instead of apocalyptic predictions or corporate spin.
Why Transparency Is the Ultimate Competitive Advantage
Critics might argue this creates additional compliance burden, but they're missing the bigger picture. Employers that provide greater disclosure and transparency about how workers will interact with AI and automated systems will foster greater trust and job security, prepare workers to effectively use AI, and open channels for workers to provide input to improve the technology or correct errors.
This transparency serves multiple stakeholders brilliantly:
For Workers: Early warning systems allow people to plan career transitions, pursue relevant training, and make informed decisions about their professional futures instead of being blindsided by sudden displacement.
For Companies: Organizations that proactively communicate about AI adoption build stronger relationships with their workforce. Transparency reduces anxiety, increases buy-in for technological changes, and positions companies as responsible employers in a competitive talent market.
For Policymakers: Real data enables evidence-based policy responses. Instead of reactive measures after mass unemployment, legislators can design proactive support systems, training programs, and economic transition strategies.
The Retraining Revolution We Actually Need
The most powerful aspect of New York's approach is how it connects disclosure to action. The initiative would also require employers to provide affected workers with access to workforce training programs and support services when AI is a factor in their termination. This isn't just data collection—it's the foundation for systematic workforce development.
Worker retraining programs are often proposed as a policy response to AI-driven labor displacement, but historically they've had mixed results. Part of the problem has been timing—by the time workers seek retraining, they're already unemployed and under financial pressure. New York's 90-day advance notice requirement creates a crucial window for proactive reskilling.
Instead of focusing on the 92 million jobs expected to be displaced by 2030, leaders could plan for the projected 170 million new ones and the new skills those will require. This shift from reactive to proactive workforce planning could transform how we handle technological transitions.
The Ripple Effect Is Already Starting
While no other states have followed New York's lead yet, employment lawyers suggest it signals growing concern among regulators. This kind of first-mover advantage often cascades across state lines—especially when the policy addresses widespread concerns about economic disruption.
California's Privacy Rights Act (CPRA) gives employees greater control over how their personal data is used, including data collected by AI tools. The U.S. Equal Employment Opportunity Commission (EEOC) has been actively working to address AI bias in hiring and employment practices. New guidelines are expected to provide clearer protections for workers.
New York's approach complements these efforts by focusing on the most visible impact of AI: job displacement. By requiring disclosure, the state creates accountability without stifling innovation.
The Marketing Implications Are Huge
For marketing leaders, this transparency requirement represents a fundamental shift in how companies communicate about AI adoption. Organizations can no longer quietly automate away roles—they'll need to develop coherent narratives about their technological strategy and its impact on human workers.
This creates opportunities for forward-thinking companies to differentiate themselves through responsible AI adoption. Brands that proactively communicate about their approach to AI, invest in worker transitions, and demonstrate commitment to their workforce will build stronger reputational advantages.
Companies that try to hide AI-driven changes or treat workers as disposable will face public scrutiny and potential boycotts. Transparency isn't just legally required—it's becoming a competitive necessity.
Why This Matters for the Future of Work
The real genius of New York's approach is that it treats AI workforce disruption as a manageable challenge rather than an unstoppable force. By requiring disclosure, the state acknowledges that technological displacement is happening while creating mechanisms to address it systematically.
AI will continue creating new jobs, including those focused on the development, deployment, and human oversight of AI. But it will also eliminate others. The key is managing this transition thoughtfully rather than letting it happen haphazardly.
Globally, professionals today are adding a 40% broader skillset to their profiles than they did in 2018. Since 2023, the number of AI literacy skills added by LinkedIn members has increased by 177%. This shows workers are already adapting—they just need better information about what's coming.
The Path Forward
New York's AI disclosure law represents the kind of pragmatic policy response we need more of: evidence-based, transparent, and focused on solutions rather than fear. It acknowledges technological change while creating frameworks to manage its impact responsibly.
Ready to navigate AI transformation transparently? Our growth experts help organizations implement AI strategies that strengthen rather than undermine workforce relationships. Because the future belongs to companies that see AI adoption as a collaborative opportunity, not a zero-sum game.
| 2025-06-16T00:00:00 |
https://winsomemarketing.com/ai-in-marketing/in-new-york-employers-have-to-disclose-ai-related-layoffs
|
[
{
"date": "2025/06/16",
"position": 79,
"query": "AI employers"
},
{
"date": "2025/06/16",
"position": 43,
"query": "AI layoffs"
}
] |
|
Quiz: The Ethics of Using AI in Journalism
|
Quiz: The Ethics of Using AI in Journalism
|
https://nbcuacademy.com
|
[
"Nbcu Academy"
] |
Which of the following is a best practice journalists should follow when using AI? Label AI-generated content as original reporting. Incorporate AI into your ...
|
(Boy Wirat / iStock / Getty Images Plus)
While AI can be a helpful tool in some aspects of newsgathering, journalists must also understand its limitations. Start by learning the basics of how to use AI and test your knowledge with the questions below.
1. How can AI help journalists? Automating time-consuming tasks, like manually transcribing audio or video Summarizing large documents or data sets Assisting with the detection of artificially created or modified content All of the above None 2. Which of the following is a best practice journalists should follow when using AI? Label AI-generated content as original reporting Incorporate AI into your reporting without your team’s knowledge Review and properly cite all stories produced with AI assistance Let AI do all the work for you None 3. Which of the following tools was NOT designed to detect AI-generated or manipulated media? ChatGPT RealityDefender TrueMedia Pindrop None 4. True or false: Since AI is just a tool, journalists never need to disclose when they use it in a story. True False None 5. Which of the following is an AI tool commonly used by journalists to transcribe interviews? Otter.ai Badger.ai Weasel.ai Ferret.ai None
Want a deep dive into AI’s impact on journalism and the future of work?
Watch all the panels from NBCU Academy’s summit, The AI Generation, which included conversations with industry leaders from NBCUniversal and beyond.
| 2025-06-16T00:00:00 |
2025/06/16
|
https://nbcuacademy.com/ai-in-journalism-quiz/
|
[
{
"date": "2025/06/16",
"position": 47,
"query": "AI journalism"
}
] |
The New Digg's Plan to Use AI for Community Moderation
|
The New Digg’s Plan to Use AI for Community Moderation
|
https://www.cjr.org
|
[] |
Humans weigh in. ... Sign up for The Media Today, CJR's daily newsletter. Remember Digg? ... That made rivals of Kevin Rose, a founder of Digg, and Alexis Ohanian, ...
|
Sign up for The Media Today, CJR’s daily newsletter.
Remember Digg? It started in 2004, as an experiment to crowdsource the Web—to “digg” something was to upvote it—and became known, to some forty million unique monthly visitors, as the “homepage of the internet.” It was a social news site, a community-driven answer to the question of how to navigate a sudden explosion of content—similar to Reddit, which appeared about a year later, calling itself the “front page of the internet.” In time, Digg was bought, sold, and widely forgotten. That made rivals of Kevin Rose, a founder of Digg, and Alexis Ohanian, a founder of Reddit—until recently, when they announced they were teaming up to stage a Digg comeback. (“I really disliked you for a long time,” Ohanian tells Rose in a launch video. “Rightfully so,” Rose replies.) But this time around, instead of human-powered moderation, they would use artificial intelligence.
“Just recently we’ve hit an inflection point where AI can become a helpful co-pilot to users and moderators, not replacing human conversation, but rather augmenting it, allowing users to dig deeper, while at the same time removing a lot of the repetitive burden for community moderators,” Rose, who will serve as the new Digg’s board chair, declared in a press release. Per Ohanian, now a founder and general partner at Seven Seven Six, which is backing the reboot, “AI should handle the grunt work in the background while humans focus on what they do best: building real connections. No one dreams of spending their day hunting down spam or playing content police—they want to create, connect, and build thriving communities.”
How that would work, exactly, wasn’t clear—until Rose started buying up thousands of dollars’ worth of ads on Reddit, targeting content moderators with questionnaires that asked about the biggest difficulties they faced managing their subreddits. As Rose explained to the New York Times, he then ran the answers through an unspecified AI program and asked it to create new ways to address the moderators’ problems. With that, one of Web 2.0’s darlings entered the new age of AI-driven content moderation.
Digg’s media representatives declined to provide details, but pointed me to an interview with Rose and Ohanian at the Wall Street Journal’s Future of Everything Festival, a couple of weeks ago. Ohanian confirmed, without elaborating, that on the new Digg, moderation that was historically handled by humans is being done by AI. He described a scenario in which a user had a “terrible day” but was “actually reformable.” In that event, Ohanian said, AI would somehow intervene, deescalate, and dole out requisite punishments. As Rose put it, Digg is using “AI to do all the dirty, heavy lifting like the moderation, in a very transparent way.”
Content moderation experts seem to approve. “I don’t think it’s hype,” said Vaishnavi J, a founder of Vys, a trust and safety advisory firm that helps companies implement AI safeguards for youth harms. Olivia Conti, a trust and safety consultant who previously worked at Twitch and Twitter, agreed: “Machine learning has been used for content moderation for years, and as LLMs have come to the forefront, companies have adapted as the technology has gotten more powerful.”
Musubi, a startup founded in 2023, has already started using AI for moderation. “There are many creative and innovative ways that AI can be used for trust and safety solutions that are just beginning to be explored, which is exciting,” Fil Jankovic, Musubi’s cofounder and chief AI officer, told me. Alice Hunsberger, the head of trust and safety at Musubi, said that AI excels in certain areas of moderation: “repeatable tasks defined by clear, comprehensive policies.”
That could include spotting other AI or spam that clogs up a feed. “Machine learning AI systems alone or in conjunction with LLMs are excellent at pattern recognition and holistic review,” Jankovic said. “These are helpful for mitigating risk from bots, fraud, or other adversarial threats.” It could also involve reviewing content that clearly violates a platform’s rules without need for contextual interpretation, such as child sexual abuse material. Using AI in these situations, according to Vaishnavi, means “humans don’t have to be subjected to seeing that horrific and dark content, and it can now be removed in an automated way.”
Sign up for CJR’s daily email
Beyond that, Conti said, “I think that AI can help guide people through understanding and accepting the consequences of their actions, and better understanding platform and community rules.” But even if AI “can extend the capabilities of human moderators, particularly frontline moderators who are bearing the brunt of reviewing reported content,” it can’t replace them. “Most people will not accept being ‘calmed down’ by an AI chatbot,” she said. “A system that works well in theory might backfire in practice if users feel like they’re being punished or gaslit by a machine. There’s a fine line between deescalation and condescension, especially when it’s automated.”
Hunsberger agreed: human beings have their place. “People should always be responsible for defining and owning policies” and checking on how things are going, she said. “They’re critical for inputting relevant data and policy clarifications or instructions on emerging events, cultural nuance, and knowing when to make an exception to a rule or create a new rule.”
That may be especially true in a volatile situation—consider social media in a conflict zone. “Humans are really valuable when it comes to anticipating new threats and sourcing offline evidence to inform predictions,” Vaishnavi told me. “Humans are important for informing the right kind of prompts. The model will only return what you ask of it. And you need to know what to ask of it.”
| 2025-06-16T00:00:00 |
https://www.cjr.org/analysis/new-digg-using-ai-for-community-moderation.php
|
[
{
"date": "2025/06/16",
"position": 58,
"query": "AI journalism"
},
{
"date": "2025/06/16",
"position": 68,
"query": "artificial intelligence journalism"
}
] |
|
“The Real Threat Isn't Your Job”: AI Isn't Taking Careers— ...
|
“The Real Threat Isn’t Your Job”: AI Isn’t Taking Careers—It’s Replacing People Still Learning to Work, and It’s Happening Fast
|
https://www.rudebaguette.com
|
[
"Gabriel Cruz"
] |
The Impact of Automation on Employment Rates ... According to the Federal Reserve Bank of New York, the unemployment rate for young graduates in the United States ...
|
IN A NUTSHELL 🤖 Artificial intelligence is taking over entry-level tasks, blocking young professionals from gaining crucial on-the-job experience.
is taking over entry-level tasks, blocking young professionals from gaining crucial on-the-job experience. 📉 Companies like Amazon, Google, and Microsoft are automating jobs, leading to a reduction in learning opportunities for graduates.
for graduates. 🌍 In Europe, the tension grows as businesses need skilled workers but fail to offer positions for gaining experience .
. 🔄 The path forward requires balancing automation with human development to ensure both can thrive together.
As thousands of young graduates prepare to take their first steps into the job market, they face an unexpected obstacle: artificial intelligence (AI). The technological promise that was supposed to relieve us from repetitive tasks seems, paradoxically, to be blocking new generations from entering the professional world. The potential for AI to transform industries is immense, but for young professionals, it represents both an opportunity and a looming threat to their career prospects.
The Disappearing First Rung of the Career Ladder
Traditionally, young professionals begin their careers with internships, junior positions, or simple tasks that allow them to learn and climb the ranks. However, as Aneesh Raman, LinkedIn’s head of economic opportunities, explains, the first rung of the career ladder is vanishing. The reason? These entry-level tasks—administrative, repetitive, often low-skilled—are now being assigned to generative AI. This shift means fewer opportunities for young graduates to learn on the job.
Tech giants like Amazon, Google, and Microsoft are already automating tasks previously performed by young employees: writing code snippets, data entry, administrative assistance, and more. The result? A decrease in on-the-job learning opportunities. As AI takes over these responsibilities, young professionals find themselves cut off from the experiences that traditionally built careers.
The Impact of Automation on Employment Rates
According to the Federal Reserve Bank of New York, the unemployment rate for young graduates in the United States stands at 5.8%, compared to 6.2% for the youngest workers. This increase is partially linked to the rapid automation of entry-level tasks. Companies like Duolingo and Shopify are actively reducing the recruitment of juniors for these roles, preferring to assign them to AI systems.
Chris Hyams, CEO of Indeed, highlights that in approximately two-thirds of jobs, more than half of the required skills can be satisfactorily, if not excellently, performed by current AI. While AI does not entirely replace jobs, it renders many steps of traditional training pathways obsolete. This situation causes a paradox: companies alert us to the lack of qualified labor, yet they no longer provide the conditions to train this talent.
Challenges in the European Market
In Europe, the tension is more pronounced as companies struggle to recruit experienced technical profiles, even as young people cannot access the positions that would allow them to gain experience. The paradox is stark: businesses need skilled workers but are not offering the necessary training grounds. The inability of young professionals to secure entry-level positions poses a significant challenge for the future.
If this trend continues, AI will not eliminate millions of jobs overnight; it will simply prevent future professionals from emerging. The risk is high that we might end up with powerful tools but without enough qualified humans to manage and evolve them. The challenge lies in rethinking professional integration and ensuring a space for human learning where machines are gaining ground.
The Path Forward: Balancing AI and Human Development
For AI to benefit everyone, we must rethink how we integrate young professionals into the workforce. Ensuring that there is a balance between automation and human development is crucial. Companies should invest in programs that allow for human-machine collaboration, ensuring that the workforce is prepared for an AI-driven future.
Moreover, educational institutions and businesses must collaborate to create new pathways for skill development. By doing so, we can prepare the next generation for a future where AI is a tool for empowerment, rather than an obstacle. The question remains: How can we foster an environment where both AI and emerging professionals can thrive together?
Our author used artificial intelligence to enhance this article.
Did you like it? 4.7/5 (25)
| 2025-06-16T00:00:00 |
2025/06/16
|
https://www.rudebaguette.com/en/2025/06/the-real-threat-isnt-your-job-ai-isnt-taking-careers-its-replacing-people-still-learning-to-work-and-its-happening-fast/
|
[
{
"date": "2025/06/16",
"position": 67,
"query": "ChatGPT employment impact"
},
{
"date": "2025/06/16",
"position": 84,
"query": "artificial intelligence employment"
}
] |
How not to lose your job to AI
|
How not to lose your job to AI
|
https://80000hours.org
|
[
"Benjamin Todd"
] |
Basically all the research on which jobs are most likely to be affected by the current wave of AI agrees that the largest effect will be on be white collar jobs ...
|
How not to lose your job to AI The skills AI will make more valuable (and how to learn them) By · Published June 16th, 2025 ·
About half of people are worried they’ll lose their job to AI. And they’re right to be concerned: AI can now complete real-world coding tasks on GitHub, generate photorealistic video, drive a taxi more safely than humans, and do accurate medical diagnosis. And over the next five years, it’s set to continue to improve rapidly. Eventually, mass automation and falling wages are a real possibility.
But what’s less appreciated is that while AI drives down the value of skills it can do, it drives up the value of skills it can’t. Wages (on average) will increase before they fall, as automation generates a huge amount of wealth, and the remaining tasks become the bottlenecks to further growth. As I’ll explain, ATMs actually increased employment of bank clerks— until online banking automated the job much more.
Your best strategy is to learn the skills that AI will make more valuable, trying to ride the wave of automation. So what are those skills? Here’s a preview:
Skills most likely to increase in value as AI progresses
SKILL WHY IT'S VALUABLE HOW TO START Using AI to solve real problems As AI gets better, it makes people who can direct it more powerful. The messier parts that AI can't do become bottlenecks. • Use cutting-edge AI tools in your current job
• Work at an AI-applications startup, or other organisation using AI to solve a real problem Personal effectiveness Productivity, social skills, and rapid learning are useful in every job and compound the value of your other skills. • Use AI tutors to rapidly teach you new skills
• Work with people who have these skills
• Develop relevant habits Leadership skills Management, strategy, and research taste are messy tasks AI struggles with, but AI gives leaders more influence than before. • Seek mentorship
• Work at small, growing organisations, and seek small-scale management positions. Otherwise, start side projects
• Study and apply best practice (links in full article) Communications and taste Content creation gets automated, but discernment and trusting relationships with your audience become more valuable. • Focus on personality-driven content
• Build a real connection with your audience
• Work with people who have taste Getting things done in government Citizens want real people making decisions, so knowing how to get things done in government remains crucial (even if many civil service positions disappear). • Follow standard routes into policy: staffer positions, internships, fellowships, government positions, and other positions alongside successful operators Complex physical skills Robotics lags behind knowledge work, especially for specialist work in unpredictable environments. • Seek apprenticeships in growing fields (e.g. datacentre construction)
• Get an entry-level job and work your way up
These will be especially valuable when combined with knowledge of fields needed for AI including machine learning, cyber & information security, data centre & power plant construction, robotics development and maintenance, and (lesso) fields that could expand a lot given economic growth.
In contrast, the future for these skills seems a lot more uncertain:
Coding, applied math, and STEM
Routine white collar skills such as recall and application of established knowledge, routine writing, admin, and translation
Visual creation such as animation.
More routine physical skills such as driving
It’s hard to say what effect this will have on the job market overall, or how quickly it will unfold. If I had to speculate, I’d guess that in white-collar jobs like finance, tech, law, government, healthcare and professional services, entry-level positions will struggle, in favour of an expanded class of managers overseeing AI agents. (Though in the short-run, even entry-level wages could increase.) Small teams and individuals will be able to accomplish far more than ever before. Jobs that require a physical presence (e.g. police, construction worker, teacher, surgeon) will be relatively unaffected (income roughly keeping pace with GDP), at least until robotics catches up.
If I had to highlight just one piece of practical advice, it would be to learn to deploy AI to solve real problems. You can likely do this in your existing job, but a career capital option to especially consider is working at a growing AI-applications startup. This not only teaches you about AI, but also lets you gain general productivity and leadership skills relatively quickly.
In the rest of the article, I’ll:
Explain why automation can actually increase wages for the skills that aren’t being automated
Use the existing research, economic theory, recent data, and an understanding of how AI works to identify the types of skills most likely to increase in value due to AI. In brief, these are skills that (i) are hard for AI, (ii) complementary to its deployment, (iii) produce outputs we could use far more of, and (iv) are hard for others to learn
Use these categories to identify the concrete work skills most likely to increase in value, and explain how to start learning each one.
Give some closing thoughts on how to position yourself given the above, including avoiding long training periods and routine white-collar jobs, favouring roles at smaller or growing organisations, doing side projects, learning to apply AI to whatever you’re doing, and making yourself more resilient by saving more money and investing in your mental health
In The Graduate, a middle-aged business man delivers career advice to the protagonist in a single word — “plastics.” Hopefully, I’ll be more useful.
1. Why automation often doesn’t decrease wages
In the mid-1990s, ATMs started to show up in banks. At the time, people expected that would put many tellers out of the job.
And indeed, the number of tellers per branch dropped from 21 to 13.
That, however, also made it far cheaper to run a bank branch. So in response, the banks opened far more locations. Total employment of tellers actually increased for two decades, but the tellers now spent their time talking to customers rather than counting money.
So while it’s commonly assumed that automation decreases wages and employment, this example illustrates two ways that can be wrong:
While it’s true automation decreases wages of the skill being automated (e.g. counting money), it often increases the value of other skills (e.g. talking to customers), because they become the new bottleneck. Partial automation can often increase employment for people with a certain job title by making them more productive, making employers want to hire more of them. In this case, fewer bank tellers could give better service to the same number of customers.
But here’s a final twist to the story: today, teller employment is in decline.
So while partial automation increased employment, the more dramatic automation made possible by online banking did indeed reduce it. This is also a common pattern.
In effect, there are two competing forces: AI tools make human workers more productive, typically increasing their employment, but if AI starts to replace what they do wholesale, that decreases their employment. When there’s a medium amount of automation, it’s hard to predict which force will win. But when there’s thoroughgoing automation, the second will tend to.
In Britain during the industrial revolution, textile production was significantly automated. But this made the industry so much more productive that employment in textile manufacturing dramatically increased — only to decline again several generations later.
Today, employment of secretaries, admin jobs, call centre workers, cashiers, telemarketers, special effects artists, and animators is already in sharp decline – with AI maybe helping to continue long term trends.
Data science employment, however, was still up 20% during 2023, despite AI being pretty good at quick statistical analysis and visualisation. So far, AI has maybe made data scientists more useful, rather than replace them. (It remains to be seen how long that will last.)
One analysis found that AI has reduced demand for translators, however, translator employment is still up on net. This might be because the decline due to AI wasn’t large enough to offset the increase from general economic growth (so far).
The third way automation can actually be good for employment is that automation of one job often creates new kinds of jobs and raises wages in aggregate because society becomes wealthier.
Historically, most people worked in agriculture. But today, in rich countries, it’s only a couple of percent, so we could say that the majority of jobs in the economy have already been automated! However, today, incomes are around 100 times higher than they were back then, showing that in aggregate, people moved into much higher paying jobs. In some countries, like South Korea, much of this transition was accomplished in just one generation.
Something similar could happen if many remote work jobs are automated. Epoch AI is a research group focused on the interaction of AGI and economics. They estimated about a third of work tasks can be done remotely, and that if all of those were automated, it would increase GDP between two and ten times. In the scenario, wages for all the non-remote tasks would probably increase about two to ten times as well. It’s even possible white collar employment would increase, but the role would entirely focus on the remaining human-in-the-loop and non-remote bottlenecks.
This isn’t to deny that automation can be very disruptive for workers in the jobs being automated. It’s just to say that it can also sometimes increase their wages, as well as benefit workers in other jobs.
This is one reason I prefer to focus on the skills that will increase or decrease in value, rather than particular job titles.
But what about if AI, combined with general-purpose robotics, could automate almost every job? Surely, wages would fall then?
What would ‘full automation’ mean for wages?
Just as partial automation of bank tellers increased employment, but more intensive automation decreased it, maybe the same could happen for human workers as a whole?
AI combined with robotics has the potential to be unlike any previous technology in that it might be able to do almost every economically productive task better than humans.
Although many economists dismiss the possibility, the people who are experts in the technology itself believe it’s possible.
And if that does happen, many economic models suggest it could drive wages down, perhaps even below subsistence level – initially as a rapidly expanding pool of ‘digital workers’ massively increase the supply of labour, and eventually because they can convert energy and resources into output far more efficiently than humans.
I’m not saying this is what will happen, but it’s one possible scenario. Epoch has also made an integrated model of how full automation might unfold across the economy. With their default assumptions, wages initially increase about 10x, only to plunge in the late 2030s as the final human bottlenecks are removed.
In Epoch AI’s GATE economic model of AI automation wages initially increase about 10-fold, as AI drives up total output and non-automated jobs become major bottlenecks. However, given their default assumptions, wages eventually crash after the final bottlenecks are automated.
If instead humans remain necessary for just a small fraction of tasks, say 1%, then the same model shows that wages increase indefinitely — with every human now doing that remaining 1%. The difference between 100% and 99% automation is enormous! (Read more about the ambiguous effects of full automation on wages.)
However, I think full automation and declining wages is a possibility we should take seriously.
If there will eventually be full automation, what should you do?
Well, on the way to full automation, there will be partial automation. And for the reasons given above, that will increase wages and give you more leverage for a time.
So your next steps should be the same either way: learn the skills most likely to increase in value in the immediate future, so you can maximise your contribution (and wages) in the time between now and full automation.
(There’s also an argument for saving more money, so you don’t need to depend as much on government redistribution. See more on how to personally prepare for AGI.)
2. Four types of skills most likely to increase in value
The coming years could be very disruptive for many people, and it’s likely that wealth gets more concentrated. This article is not about how we should respond as a society but rather how you can best position yourself as an individual, including so that you can better help society navigate these challenges.
Here I aim to give you the tools you need to think about which skills are most likely to increase vs decrease in value given your unique situation and the massive variety of jobs.
This is clearly a moving target, but I break it down into four key categories of skills likely to increase in value:
Hard for AI: data poor, messy, long-horizon tasks where a person-in-the-loop is wanted Needed for deploying AI: the skills of organising and auditing AI systems, as well as those used in complementary industries such as data centre construction Used to make things the world could use far more of: skills that contribute to improved healthcare, housing, research, luxury goods, etc. – things which people want more of as they get better and cheaper Hard for others to learn: rare expertise that matches your unique strengths
(Economics aside: these are basically low substitution; complementarity; high elasticity of demand for output; and inelastic labour supply.)
2.1 Skills AI won’t easily be able to perform
The best way to develop your intuitions about what AI can do is to try to use cutting edge AI tools to do real work (not the inferior free models). But I would like to provide some theoretical grounding to what AI will be able to do and not do, based on understanding how AI is trained.
Tasks not in AI training data (& hard to gather)
LLMs are created by training them to predict internet data (see a quick primer). This makes them very good at tasks that are based on pattern matching and recall of data on the internet.
And that turns out to be a lot. In 2015, Frey and Osbourne assumed social skills would resist automation. Today, therapy chatbots are among the most popular AI applications.
Many skills that are difficult for humans to learn, including much of therapy, medical diagnosis, and coding, can be done pretty well by ‘pattern matching’ systems.
LLMs can also clearly make some novel generalisations. For instance, you can ask GPT-4: “If the Leaning Tower of Pisa was swapped in location with St Paul’s Cathedral, and I stood on London’s Millennium Bridge looking north, what would I be able to see?” and it can answer even for novel combinations of locations.
However, LLMs remain bad at a lot of things, and typically these are tasks missing from their training data.
One example is controlling robotics. While the internet contains a huge amount of linguistic data, there’s no equivalent store of data describing physical movement.
The absence of this movement data is also not a trivial thing to fix because it’s hard to create realistic virtual environments that could be used to cheaply generate it. The only option is to create huge numbers of real robots and have them move around, which is expensive. So AI remains much worse at interacting with the physical world.
In contrast, data on how to perform many white collar jobs already exists on the internet, and it will be easy to gather even better data, because those jobs are mainly carried out on computers, which can track each step.
Frey and Osbourne also predicted AI would be bad at ‘creative’ tasks, but today this also seems too simple. As of 2025, LLMs are good at brainstorming, writing in a huge range of styles (including in novel combinations), creating rhyming verse, and so on. However, they’re still not great at novel conceptual insights. This is probably because the first type of creative task is closer to pattern matching within the training data, but the latter requires a greater leap.
Messy, long-horizon skills
The new generation of AI systems, such as o1, use LLMs as a base model but then they’re taught to reason and pursue goals using reinforcement learning.
This is a bit like learning through trial and error. AI systems try to do a task, then their accuracy is graded, and then they’re adjusted in a way likely to increase their accuracy (see a primer).
Over 2024, this new paradigm unleashed dramatic progress in maths, coding, and answering known scientific questions.
That’s because these domains have objective answers that can be immediately verified purely virtually, making them very suitable for reinforcement learning.
In contrast, consider a skill like building a company. This involves many judgement calls with no obviously correct answers and success is determined over years. So it’s much harder to get reinforcement learning to work for this kind of skill. (There are also no massive datasets showing every step an entrepreneur would take to build a company.)
Other examples might be things like starting a cultural movement, directing a novel research project, or setting organisational or political strategy.
These skills are:
Messy — they lack clearly defined instructions and measurable outcomes
Long horizon — it takes time to implement and measure success
This is why, in spite of its nearly superhuman abilities at some maths and coding problems, AI is still worse than most seven-year-olds at playing Pokemon.
It’s also still terrible at many comparatively simple tasks such as ‘get a set of shelves installed in the office’ — because they involve planning, visual interpretation, hiring someone, and checking the work is done.
The models can effectively execute short, well-defined tasks, but they lose coherence and get stuck in loops over longer periods.
This helps explain why we’ve seen so little AI automation to date. Even where AI is strongest — software engineering — it can only do approximately one-hour tasks, while most software engineering jobs are made of projects that take at least multiple days, require coordinating with a team, and understanding a huge code base.
It’s also true that AI is improving rapidly even at messy, long-horizon tasks. And if AI progress is rapid enough, or reinforcement learning generalises well, it’s possible AI surpasses most humans even at these types of skills relatively soon. It’s also true that AI might start being able to make novel intellectual contributions via brute force generation of ideas.
However, messy, long-horizon tasks are our best bet at what AI is going to most struggle with, and it’s possible that the ability to do the most messy, long-horizon skills is still decades away.
These remarks could be invalidated if a new AI paradigm is created with very different strengths and weaknesses from current AI systems, or if AI progress accelerates, but I think it’s the best assessment we can make today.
Skills where a person-in-the-loop is wanted
Even if AI can technically do a task, it might not be allowed to do so because people often want a person-in-the-loop. Here are the main categories I’ve seen suggested by economists where this could be the case (e.g. see this interview with Mike Webb):
Situation Reasoning Example Legal liability There needs to be a person held legally responsible for certain kinds of important decisions. Chartered engineer, court lawyer. High reliability required AI systems hallucinate and make weird mistakes so people will want human experts to check their answers and provide oversight. Human historian checks AI research for mistakes. Unions and professional interest groups are involved Lobbyists will aim to introduce standards and regulations to protect jobs. Doctors and lawyers control professional certifications and are a powerful lobby, so they could block AI applications in their industries. There’s a strong preference for a human touch Many people will much prefer humans to provide certain services, perhaps as a luxury. Nannies, an artist with a compelling story or brand, religious leaders Physical presence is needed Many roles require someone physically present to oversee the situation. Even after robots become effective, people could be reluctant to rely on them. Police, teachers, nurses. Institutional inertia Many organisations will be slow to apply AI tools, meaning that humans stay in important jobs. (Though a true ‘drop-in remote worker’ AI could slot into existing workflows and get deployed a lot faster than previous tech waves.) Perhaps many government jobs, large companies with strong moats. Intent alignment Even very powerful and accurate AI systems will still need to know what humans want them to do. It’s possible more and more roles could involve specifying preferences to AI systems. Government-funded efforts to collect preferences(?)
These factors could remain bottlenecks much longer than the first two, since some could apply even with extremely capable AI systems. On the other hand, we don’t yet know how much they’ll bottleneck the use of AI.
For instance, people often play classical music at wedding ceremonies, and most people would prefer a human musician. However, most people end up using a recording because it’s so much cheaper and more convenient.
Likewise, even if people prefer human-produced goods and AI products remain inferior in some ways, they might be so much better in others that they become overwhelmingly what people use.
It also remains to be seen whether there will be enough demand for these human-in-the-loop jobs relative to the supply of humans able to do them to keep wages high. Even if people still want human-produced art, not everyone can be an artist.
Skills where automation is bottlenecked by physical infrastructure
Suppose general-purpose robotics started working great tomorrow. How long would it take to automate manual jobs?
Probably a while. Robot production today is in the millions. To build the one billion or so needed to automate all manual jobs would take time (even if it might be faster than many expect).
Relatively slow robot production and the lack of data about physical tasks will create a period where their automation lags behind cognitive tasks.
Even AI’s deployment to cognitive tasks will be somewhat bottlenecked by available computing power, especially if early systems use a lot of test-time compute. That will mean initial AI automation could focus on the most high-value tasks (e.g. in R&D), somewhat delaying automation of lower wage jobs.
2.2 Skills that are needed for AI deployment
In 2025, having access to cutting edge AI is already a bit like having 24/7 access to a team of expert advisors and tutors on any topic, unlimited coding capacity for discrete projects, and unlimited remote workers who can do some short admin tasks.
These tools are giving individual workers much more power to make things happen than ever before. We can already see this happening in the world’s most successful startup accelerator, Y Combinator, which says their current batch is 70% focused on AI and growing several times faster than similar startups ten years ago.
(And ten years ago, startups were themselves growing faster than companies in previous decades. The effect of AI is part of a longer-term trend.)
The effect today is most visible within the virtual and unencumbered world of software startups, but the possibilities are broadening. You don’t need to work in a tech startup to use AI to more rapidly learn new skills, get advice, edit your work, create software, and so on.
And true ‘virtual workers’ would dramatically increase this leverage again. This likely creates a period in which the skill of directing these AI workers becomes incredibly valuable.
These skills could be things like:
Spotting problems and deciding what to focus on
Understanding the pros and cons of the latest models, and how to design around their weak spots
Writing clear project specifications
Understanding what the end users really want, UX
Designing systems of AI workers, including error checking
Understanding and coordinating with the people involved
Bearing responsibility
(Many of these skills are similar to the skills of managing humans. And there is already evidence that competent human managers are better at managing AI teams.)
These kinds of skills are not only messy, long-horizon tasks that AI finds relatively difficult, but they’re also complementary to AI: as AI gets better, they become more needed. The two effects combine to multiply their value.
In contrast, being an artisan maker of Neapolitan bespoke suits (descended from a long line of tailors) is not something AI will easily be able to replicate, but it’s not complementary to it either. That means the market value of this skill likely roughly keeps pace with global income, rather than outpacing it.
Other skills that might be complementary to AI deployment are those involved in other fields needed for AI scale up, such as:
Expertise in AI hardware: if AI continues to improve, there will be a huge build out of chips to run and train the systems.
AI development: as AI becomes more valuable, the value of making it 1% more effective increases proportionally, so remaining bottlenecks in AI R&D greatly increase in value (though bear in mind working on this also increases the risks from AI).
Physical tasks necessary for AI deployment: examples include construction of data centres and power plants, as well as robotics development and maintenance.
Cyber and information security: as AI and robotics get more integrated into everything in the economy, the security of these systems becomes vital (no one wants to get kidnapped by their robot butler).
2.3 Skills where we could use far more of what they produce
I only need to file a tax return once a year. If AI halves the cost of doing my filing, I will still only file once (and save the money for something else).
In contrast, after Uber made taxis cheaper and more convenient, people started using them a lot more often, in some cases spending more than they did before. The taxi market has grown a lot in the last decade or two.
The same could be true for healthcare, nicer housing, better entertainment, luxury goods, personal development, research, and many other things I consume.
In contrast, jobs that are needed to satisfy legal requirements (e.g. licensing) and sectors where demand is mainly set by the government could have more fixed demand (e.g. healthcare salaries in the UK have fallen in real terms the last decade, despite demand for healthcare generally increasing with GDP).
More broadly, you can think about sectors that are likely to grow faster than the rest of the economy in a world of AI automation.
For example, AI automation would create a huge amount of wealth, probably concentrated in the top 1% who own most capital. Increased income inequality will spike demand for luxury goods. Something like providing bespoke tea tasting events in SF would be both hard for AI to do and would see increasing demand.
2.4. Skills that are difficult for others to learn
Consider a job like being a server at a fancy restaurant. I expect people to eat out more as they get wealthier, and this is a physical, social skills heavy job where people might retain a strong preference for a human touch.
So, I expect many manual and retail service sector jobs to see increasing employment and for their wages to generally grow in line with the rest of the economy.
However, these jobs might not see the unusually large increase in wages because people can enter them with relatively less training. If lots of other people can learn a skill, that limits how much wages for that skill will increase.
The skills that will most increase in value are those where it’ll take a long time for the labour market to respond to increased demand.
For example, if you’re a construction worker, you could learn a more specialised trade, like becoming an electrician, focusing on areas that would likely see increasing demand, like data centres. People with these more specialist skills are more likely to end up as a critical bottleneck during a period of rapid growth.
3. So, which specific work skills will most increase in value in the future? And how can you learn them?
Let’s apply what we’ve covered to make an overall guess at the most valuable work skills. We want skills that satisfy at least two of the above categories, and ideally all four. I’ve focused on relatively broad transferable skills.
3.1 Skills using AI to solve real problems
What: Skills required for AI deployment that are difficult to automate: understanding strengths and weaknesses of AI systems, designing systems of AIs and interfacing them with the rest of the world, specifying instructions to AI systems, UX for people using the systems.
Why: As AI gets more competent, people who direct these systems become force multipliers. The messy coordination work AI can’t do, and oversight required, becomes the bottleneck. Eventually, a lot of the economy could become figuring out what instructions to give AI systems.
How to learn: Anyone can develop this skill by using the latest AI tools to try to achieve real outcomes at work. You can do this in your current job, or in side projects. If you want to switch jobs to somewhere that could turbocharge learning this skill, then try to work at an AI-applications startup or other growing organisation that’s trying to use AI to solve a real world problem (or otherwise anywhere other people already have this skill). In these kinds of roles, you’ll learn this skill as well as entrepreneurship, management, and general productivity. Make sure to use the most cutting edge models, and also think about what might become possible in the next 1-2 generations.
3.2 Personal effectiveness
Being a generally productive, proactive person
What: Setting goals, having a system to keep track of tasks and hit deadlines, learning to motivate yourself and focus, good professional habits like running meetings, basic emotional management.
Why: These skills are useful in any job, so even if there’s a lot of automation, they’ll probably still be useful, including within deploying AI. They’re also related to agency and the ability to be responsible for things start to finish, which is a weak spot for AI. And they multiply the value of your other skills.
How to learn: There are many practical ways to increase your general productivity, which we list here. Also see how to be more agentic.
Social skills
What: Building relationships, coordinating well with others, understanding other people’s emotions.
Why: Although AI is already often rated more empathetic than humans, there will be cases where people will want a relationship with a real person (at least as a luxury). Moreover, as more routine work gets automated, a greater fraction of what’s left could become coordination among teams of humans (e.g. picture three founders managing a large team of AI agents and needing to rapidly sync up between them, or a software engineer who has to update his boss on the output of 10 AIs). Social skills are also an important input into many of the other skills listed, such as management.
How to learn: This is hard to learn, but try to put yourself in situations where you get to practice a ton. Spend time with people who have good social skills and see these notes for more ideas.
Learning how to learn
What: Quickly getting to grips with new bodies of knowledge and skills.
Why: If the world is changing faster and more unpredictably, the ability to quickly retrain into a new skill becomes more valuable. At the same time, AI means you can get cheap one-on-one tutoring in almost anything, which many say is letting them learn far faster than before. This skill can also help you with all the other skills in this list.
How to learn: AI has made it much faster to learn many skills, because you can get 24/7 personalised coaching on almost any topic. Learning how to take advantage of this is a hugely valuable skill in itself. Also see the relevant section of our older article on how to be more successful.
3.3 Leadership skills
There’s a cluster of skills around management, entrepreneurship, and strategy that seem hard for AI to do, that benefit from the increasing leverage provided by AI, that we could use far more of, and that are in limited supply. They can also be difficult to learn, but I suggest some ways to practice them on a smaller scale, which could help you jump faster in full-time jobs using these skills.
Entrepreneurship
What: Spotting ideas for new projects, creating a strategy, proactively coordinating people and resources around them, and being able to handle risk.
Why: A small team of human founders can already achieve more than before and may soon be able to instantly marshall large teams of AI workers.
How to learn: Anyone can practice entrepreneurial skills by running a side project or new initiative at work (e.g. helping to launch a new product, running a new conference, running an online store). AI is going to mean those kinds of projects can also move a lot faster than before. If you want to focus on having an entrepreneurial career, see our profile on founding organisations. Joining a new and rapidly growing organisation is also a great way to learn these skills.
Management
What: People management, product management, project management.
Why: Some of management is a long-horizon, messy task where people will want a human-in-the-loop to bear responsibility. We will probably see organisations get more top heavy, where a larger number of human managers are overseeing smaller AI-enhanced teams and eventually large teams of AIs. Employment in management is rapidly growing today. (Though certain middle management jobs might get slimmed down by AI tools.) People management skills also help you manage AI systems.
How to learn: Read about management best practice (see this reading list), and then start doing management on a small scale (e.g. managing a contractor or volunteers in a hobby project). See if you can work under someone who is great at management. Then, from there, try to progress to management positions. Continue to apply best practices and seek mentorship, while collecting feedback from the people you manage.
Strategy, prioritisation, and decision making
What: Setting the vision, mission, and metrics of an organisation, identifying priorities, making high-stakes decisions.
Why: As AI makes it easier to get things done, the key question becomes deciding what to do in the first place. This is also a messy, long-horizon task that AI will likely lag on. AI might soon become better than most humans at certain types of forecasting and decision making, but humans will still need to be in the loop reviewing the decisions.
How to learn: Try to work with someone who has this skill. Focus on finding a domain (even if small) where you can practice developing strategy. Then learn to apply best practices to that domain. Here are the most common prioritisation frameworks, a popular book on strategy, and our article on decision making. Practice forecasting as a hobby and track your results. Learn to use AI tools and prediction platforms as decision aids. Writing is getting automated but writing is one of the best thinking aids, so it’s worth learning for that reason.
True expertise
What: Having expert-level understanding of an important field, research taste, the ability to make novel conceptual insights, and do complex problem solving.
Why: Experts will be required to provide oversight of AI systems and key decisions, and so will be complementary to them. Moreover, having good conceptual insights and research taste will be among the hardest things to automate because they’re the ultimate data-poor, messy, long-horizon tasks (even though AI might be good at brute force creativity). These skills are also hard for most people to learn.
Expertise will be most valuable in sectors likely to grow a lot — such as AI deployment, AI development, robotics, computer hardware, cybersecurity, and power generation — and in crucial areas of government policy (e.g. US-China relations, AI regulation, defence).
On the other hand, the ‘bar’ for true expertise will continually rise over time as AI gets better. You should only pursue this option if you can get to the forefront fast enough — and stay there.
How to learn: Find mentorship under a top practitioner, practice intensely, and pursue whatever other training steps are standard in the field.
3.4 Communications and taste
What: Having good judgement about design/beauty/what people will like, having personality, a story, unique branding and personal connection to your audience, messaging strategy/PR/brand strategy.
Why: Although a lot of content creation and marketing seems like it’s going to be automated, people will still want relationships with real, interesting people. As it becomes easier to create large volumes of content or design, the skill of selecting what’s good (taste) becomes more valuable, and so do the strategic aspects of what to create in the first place.
How to learn: ‘Being cool’ is pretty hard to learn, but you can try to develop a deep relationship with a specific audience (e.g. via a YouTube channel). Practice using AI to help with content creation, and tune your taste by seeing what works over time. Focus on more personality-driven content and storytelling (rather than the type of material people can easily get from GPT).
3.5 Getting things done in government
What: The skill of knowing who to talk to and how to frame things correctly in order to get new policies passed or implemented, political strategy, government decision making.
Why: Even if much routine knowledge work in government gets automated, the government sector will likely at least keep pace with the size of the economy. People will want decision makers to be real people. This will mean the nebulous, long-horizon skills of making things happen in government will remain valuable, especially from a social perspective. Indeed, government might even take on increasing importance as more work is automated. Plus, government will be slow to adopt and doesn’t face as much market competition.
How to learn: Work for a figure who has this skill — e.g. become the staffer to a congressperson or consider the other standard entry routes into policy if you think you can make it beyond the entry-level and routine analysis positions.
3.6 Complex physical skills
What: The ability to do precise physical tasks, especially in unpredictable, high-stakes environments with expanding demand — e.g. overseeing surgery, data centre electrician and construction, semiconductor technician.
Why: Robotics deployment is likely to lag, creating major bottlenecks for manual tasks, especially those necessary for AI deployment and that are hardest for robots (or other people) to do.
How to learn: apprentice in the standard pathway for the field.
4. Skills with a more uncertain future
The following are some skills where there’s a stronger case for their value going down. This is very hard to predict — as noted, partial automation often makes demand for a job go up initially, only to fall later.
4.1 Routine knowledge work: writing, admin, analysis, advice
Basically all the research on which jobs are most likely to be affected by the current wave of AI agrees that the largest effect will be on be white collar jobs around the 70–90th percentile of income (approx $100–200k in the US).
AI is already pretty helpful for these kinds of tasks because a lot of examples exist in the dataset, and they involve pattern matching or recall of information. Going forward, it’ll be easier to collect even more data, and many of the tasks are short and clear enough that reinforcement learning should work. More specifically, this could include skills like:
Many cases of writing and copyediting
Carrying out straightforward analysis, such as a financial analyst, legal clerk, civil servant, or optician might do
Recall of established information, such as in medical diagnosis
Administration
Translation
In each organisation, many of these jobs could get replaced by a smaller number of people overseeing a large number of AI agents (or AI-assisted humans), making organisations more top heavy. Luke Drago called this ‘pyramid replacement’.
It’s plausible that entry-level white collar jobs will be automated first. Organisations will become more top-heavy, with an expanded class of managers overseeing many AI agents.
That said, as the economy grows, the total number of organisations expands as new niches become profitable. So, even if each organisation needs fewer people doing these kinds of tasks, total employment might not fall for a while.
These roles could also evolve so that more time is spent on AI gaps, such as:
Talking over AI-generated advice with clients
Checking the results of AI-generated outputs
Greater investment in training for a smaller but more productive workforce
Giving instructions to AI systems
If there are a lot of gaps, employment might not change very much. Not to mention, each worker would have the output of several in the past, which could further increase demand.
Many organisations will also be slow to adopt AI tools, so those jobs will stick around longer.
All this means it’s hard to say how these changes will translate into changes in employment among white collar professions on net. But here are some total speculations about the intermediate outlook for some different professions:
Healthcare: I expect workers to spend less time on diagnosis, admin, and monitoring, but more time on physical tasks (e.g. like administering treatments). I expect wages to be steady but maybe to grow more slowly.
I expect workers to spend less time on diagnosis, admin, and monitoring, but more time on physical tasks (e.g. like administering treatments). I expect wages to be steady but maybe to grow more slowly. Investment management: I expect a continuation of the long-term trend towards greater use of quant systems overseen by a smaller number of often higher-paid workers.
I expect a continuation of the long-term trend towards greater use of quant systems overseen by a smaller number of often higher-paid workers. Strategy consulting: Consultancies could be well placed to advise organisations on how to apply AI, and have been growing rapidly recently. Increased demand for advice about AI could potentially offset the automation of jobs currently done by junior employees. And they may still be willing to hire junior employees in order to train them for senior roles.
Consultancies could be well placed to advise organisations on how to apply AI, and have been growing rapidly recently. Increased demand for advice about AI could potentially offset the automation of jobs currently done by junior employees. And they may still be willing to hire junior employees in order to train them for senior roles. Professional services: The outlook for professional services (e.g. accounting) seems similar to strategy consulting, but somewhat worse, because they’re doing less of the novel strategic work that’ll be harder for AI. For instance, routine accounting will be more and more automated, leaving a (maybe) smaller number of accountants to focus on more complex cases.
The outlook for professional services (e.g. accounting) seems similar to strategy consulting, but somewhat worse, because they’re doing less of the novel strategic work that’ll be harder for AI. For instance, routine accounting will be more and more automated, leaving a (maybe) smaller number of accountants to focus on more complex cases. Law: The field will probably become more top heavy. Senior lawyers will use AI to assist with research but will review key decisions and discuss them with clients. Routine legal work and research will be more automated.
The field will probably become more top heavy. Senior lawyers will use AI to assist with research but will review key decisions and discuss them with clients. Routine legal work and research will be more automated. Government: civil service positions focused on providing research briefs and advice, and doing administration, might shrink in favour of a maybe larger class of more senior employees and political positions using AI.
4.2 Coding, maths, data science, and applied STEM
Ten years ago, at 80,000 Hours, we told people to learn to code and enter data science — just before demand exploded.
Data from 2120 Insights
However, the prospects for these skills today are a lot more uncertain.
Coding is what AI is best at now — and where it’s improving most rapidly. Since programming is virtual and has quick feedback loops, it’s relatively amenable to reinforcement learning. Employment for software developers was flat in 2024, after many years of growth.
On the other hand, many people have told us that AI tools have made it far faster to learn to code in the first place, and the scope of what you can do has gone up.
Demand for software could also expand as it becomes cheaper to produce, meaning that projects that weren’t profitable before become worth doing.
It’s plausible that the value of spending one or two months learning to code has even gone up (even if the value of spending years learning might have gone down). You might be able to much more quickly get to a place where you understand coding enough to complement your other skills, such as in entrepreneurship or design.
So as of yet, it’s not clear the value of the skill has declined, but we also need to consider what will happen in the next five years. In this time, it’s likely AI starts to clearly surpass humans at coding, even for longer, more complex projects.
If that happens, software developers might be able to move into roles that are more about management of AI systems, using their knowledge of coding but combining it with other skills. But some might struggle to make that shift.
The situation for data scientists looks similar, though so far data science employment has continued to grow rapidly. If you’re thinking about going into the field now, focus on rapidly gaining a conceptual understanding of how to do data analysis, not on how to implement basic analysis.
We could make similar remarks about skills in maths and applied STEM, especially those that involve applying pre-existing knowledge. AI is already beyond PhD level at answering well-defined scientific or mathematical questions.
4.3 Visual creation
AI is already good at generating imagery, and it’s about to crack photorealistic video. It still struggles to maintain consistency and follow detailed visual instructions, meaning there’s still a major need for human oversight, but this might get fixed in the coming years, as agency and multimodality improves.
As noted, there were huge layoffs of special effects artists and animators in 2024, while graphic designer employment was flat.
On the other hand, some creators will be able to use AI tools to produce dramatically more than they were able to in the past.
4.4 More predictable manual jobs
After many years of predictions, self-driving taxis are getting deployed for real, and growing extremely fast. It’s hard to know how long this will take to roll out across all major cities, but it wouldn’t be surprising if we saw a mass wave of layoffs among drivers in the next five years.
In general, robots will find it easiest to do tasks in predictable, simpler, lower stakes environments. For example, robots are already doing a lot of warehouse jobs. This hasn’t yet decreased warehouse worker employment (perhaps because demand for warehouses has increased even faster with online shopping), but the next couple of generations of robotics could reach a tipping point.
5. Some closing thoughts on career strategy
Given these developments, how should you approach your next couple of career steps?
5.1 Look for ways to leapfrog entry-level white collar jobs
As AI increases the value of leadership skills, it’s decreasing the value of the entry-level jobs that previously served as a training path to them.
So as a college grad entering the job market who hoped to get one of these jobs, what should you do?
The ideal might be to find a role that lets you learn leadership skills right away (for instance, anywhere you can work with a good mentor), but what about if you can’t?
First, you can start to learn AI deployment and personal effectiveness skills in any job, and those are also high on my list.
Second, you might be able to find a way to start practicing leadership or communications skills in your existing role, perhaps just on a small scale (e.g. by managing a contractor, helping to launch a new product).
Otherwise you might be able to start some kind of side project or serious hobby, like running a voluntary community project, having a blog, or having a side business. These let you practice leadership skills, and by using AI tools you can achieve more faster than before.
In terms of full-time jobs, roles at small but growing organisations seem more attractive, because they let you work on these types of skills faster.
In contrast, in large companies, there’s more specialisation, which means the entry-level roles often involve more routine work.
If you have the option, roles at tech startups applying AI to a real problem seem especially attractive, since they let you learn about AI deployment, entrepreneurship, and generally getting shit done all at the same time. Here’s a write up of the case for moonshots.
If you’re not able to leapfrog the white collar path, then another option is to focus on sectors where performance is driven by complex physical skills, physical presence, and social skills (e.g. mediator, events organiser, luxury tourism).
5.2 Be cautious about starting long training periods, like PhDs and medicine
AI automation is already happening faster than previous technological waves, could speed up, and has hard-to-predict effects, making long training periods less attractive.
This isn’t to say you shouldn’t spend 1–2 years training, or even that you should never start long training programs. For example, graduate study could still be worth it due to a combination of (i) the value of true expertise going up, (ii) being able to do useful work during your studies, (iii) if you think AI progress will be slower, (iv) you lack other options. But it’s worth thinking harder about alternatives.
What about finishing college? For most people, this is still worth it because it still delivers a large boost in employability. However, the case for dropping out seems better than before (especially if your university doesn’t let you use AI tools). I usually caution against dropping out unless you already have an offer to do paid work. However, you could try to (i) get into a position where you might get such an offer faster (e.g. through summer projects) or (ii) finish college more quickly.
5.3 Make yourself more resilient to change
One way to deal with fast, unpredictable change is to learn the personal effectiveness skills that are useful in every job. But you can also think about ways to set your life up to be flexible and resilient:
Not overly tying yourself to a single country, and living in a large city with many kinds of opportunities
Saving more money than you would otherwise
Investing in your general mental health
5.4 Ride the wave
The goal isn’t to find a single job that will always be resistant to automation, but rather to stay one or two steps ahead of it.
This means keeping on top of what AI is capable of, seeking out people to follow who have insights into what’s going on, and continually adjusting to where the biggest bottlenecks lie.
Take action
This week: find a small new way to apply AI in your current (or desired) job. This month: choose one of the six skills, and think of 1–2 steps you could take to learn it faster. This quarter: consider whether to make a larger change to focus more on these skills.
Thank you to Carl Shulman and Mike Webb for conversations that informed this piece, Ethan Heppner for data and comments, and Arden Koehler and Ozy Brennan for comments.
| 2025-06-16T00:00:00 |
https://80000hours.org/agi/guide/skills-ai-makes-valuable/
|
[
{
"date": "2025/06/16",
"position": 72,
"query": "ChatGPT employment impact"
},
{
"date": "2025/06/16",
"position": 56,
"query": "artificial intelligence employment"
}
] |
|
Revealing the Current Role of Artificial Intelligence in ...
|
Revealing the Current Role of Artificial Intelligence in Employment
|
https://factbeing.com
|
[] |
The role of artificial intelligence in employment is mainly two, one is the strengthening of the job that a person has, and the second is replacing humans in ...
|
The role of artificial intelligence in employment is mainly two, one is the strengthening of the job that a person has, and the second is replacing humans in that position. With technological innovations, artificial intelligence (AI) has become huge in all industries in all categories of job roles. From automation to data-driven insights, AI is proving to be highly beneficial to making new ways to prosper in the career sphere and can establish a buzz in the market among professionals worldwide.
The lifecycle of artificial intelligence in employment is thriving, presenting individuals contemplating entrance into new areas of emerging fields or upskilling into various career roles with challenges and opportunities. This blog post goes into the various AI job roles, as well as how to get started in this, and provides an insight into the AI skills demand in 2025, focusing on what will make up the workforce of tomorrow.
Although artificial intelligence can pose scary scenarios in movies, the world of professional AI opportunities continues to flourish and open up, with an increasing demand for professionals to have a good command of AI. Artificial intelligence knowledge has steadily increased in recent years. The artificial intelligence in employment stands out as it provides and opens up opportunities also for beginners across the industries, ranging from Healthcare to Finance, Retail, and so on.
More and more businesses are coming to understand the depths that Artificial Intelligence in employment’s advancements can reach. They are searching for people with the capabilities to utilize these technologies appropriately. What does that mean for folks interested in mobile AI or switching to mobile AI from a more traditional job?
Artificial Intelligence in Employment and Entry Level Jobs
The first and one of the most exciting things about the artificial intelligence in employment is that it is open to newcomers. The entry-level jobs for AI are increasing, and people can now work on AI jobs without needing experience for decades. There are positions like data analyst, machine learning engineer, AI assistant, etc., which companies are looking to hire for the integration of AI in their operations. Most of these jobs require an understanding of AI algorithms and data processing. Still, for beginners, it can be a great starting point.
As the potential application of artificial intelligence in employment grows, business industries seek AI-driven career development programs and internships. There have also been monumental increases in AI job opportunities in education, where universities and online platforms have created programs that enable people to obtain the necessary skills for such entry-level jobs. Whether you are a fresh graduate or someone who wants to change his career, there is so much you can do.
High-Demand AI Skills in 2025
Firstly, let us learn about the future of the AI job market 2025. Several reports show that the demand for people with AI skills will spike. For instance, machine learning skills, natural language processing (NLP), computer vision, and data science skills are expected to be the most searched. If you have not yet begun to explore the world of Artificial Intelligence, then this is the ideal time to learn these high-demand skills.
In addition to having solid technical skills, companies are interested in talent who is aware of the ethical implications of AI, knows how to build AI solutions that can be scaled, and knows how to develop AI models that make sense in the real world. In 2025 and beyond, companies will start putting ethics before AI. For this reason, professionals with vast knowledge of artificial intelligence-related governance, privacy, and regulations will be highly valued.
AI Career Paths for Non-Tech Professionals
Artificial intelligence in employment may be intimidating for those who do not have a technical background. On the other hand, for non-technical experts, there will still be plenty of opportunities to get a foot in the world of artificial intelligence. This is not the case of another AI-powered career progress only available to coders and data scientists. Business intelligence and customer service are now two fields that have seen massive growth in AI. An example of this is that one does not need specialized technical expertise to become an AI Product Manager, AI Strategist, or AI Project Manager as long as the person understands what value AI can add to a business process.
These jobs are often alongside a data scientist or an AI engineer, where they bring technical concepts to business solutions. These positions suit you if you are a management, business analysis or marketing professional. Likewise, AI has provided new job opportunities in HR, and they are using AI tools to assess their employees’ performance and optimize their workforce.
AI Career Paths with No Coding Required
A misconception about AI careers is that you must be a programmer or have high-level coding skills to break into that industry. However, the no-code tools for AI rose, giving laypeople the freedom to experiment with AI from programming language barriers. With AI appearing in demand for all kinds of jobs and no coding being necessary, AI career paths sans coding are becoming popular, especially for Business and Marketing Professionals and Management.
Tasks such as creating an AI model, processing data, and making proof-based decisions are becoming easier, and it is getting easier with the usage of Google AutoML, Microsoft Power BI, IBM Watson, etc. These AI tools do not require businesses to have a lot of knowledge concerning program languages such as Python or RBit to assist them in solving issues, analyzing data or automating jobs.
High-Paying AI Careers Without a Tech Background
Getting a high-paying AI career without a tech background is possible. You can combine your domain expertise and knowledge of AI tools and strategies. For instance, if one knows how to leverage AI and understands how AI will help the business, then the business development manager can demand a competitive salary for the AI roles.
There are great examples of these roles now, such as AI consultant, AI marketing strategist, etc., who need good business acumen, know how to work with new technologies and speak the language of AI. The job opportunities related to business, sales, or marketing professionals are to give them leverage in increasing demand for AI-driven transformation in multiple sectors.
Impact of Artificial Intelligence Among Different Industries
With AI continually being developed, AI increasingly impacts careers within different industries. AI is making a paradigm shift in the healthcare, education, finance, retail, and human resources sectors.
AI CAREERS IN HEALTHCARE
AI is currently experiencing a significant transformation in the healthcare sector. There is not just medical imaging and drug development where AI plays a role in healthcare. These AI-powered software are excellent resources for patient-related care, healthcare administration, and decision-making. Opportunities are abundant, from AI’s application in diagnosis to its implementation in medical research for professionals interested in healthcare and AI.
According to AI career trends in the healthcare field, professionals with expertise in AI will experience a growth in the demand for their skills, not only shortly. This brings about new emerging roles such as clinical data analyst, AI healthcare consultant, AI bioinformatics specialist, etc.
AI CAREERS IN EDUCATION
The education sector is also making waves of new Artificial Intelligence. AI-powered tutoring systems use AI in education to personalize the learning experience of students and teachers. Some aspiring AI careers in education are to be AI curriculum designers, learning experience architects or education data scientists.
The education sector is accelerating the adoption of AI-based technologies, and the need for professionals who can develop, control, and deploy such systems will rise. AI in education is one of the exciting fields for those interested in both technology and education because the use of AI in education can change the learning process for students.
AI IN BUSINESS AND FINANCE
The finance industry is one of the fastest adopting of AI. AI is changing how financial services are being carried out and ruling over it, from AI-driven trading algorithms to risk assessment models. Demand for professionals with expertise in AI financial analysis, particularly machine learning, and their data analysis abilities is also high.
AI is also being used to completely streamline customer service, detect fraud and optimize operations, among other things. The finance sector is also up and running in developing career paths around AI, with great examples being AI investment analysts and AI fraud detection specialists.
AI IN RETAIL AND SUPPLY CHAIN
Retailers and supply chain managers are changing how they do business using AI. AI can bring about predictive analytics, personalized customer experience for retail stores, and smooth operations. The career paths in retail based on artificial intelligence are those of an AI retail strategist, supply chain analyst, and AI product recommendation specialist.
With more and more retailers using AI to optimize inventory management, customer service and marketing in general, professionals with knowledge of AI technologies will be in demand.
Barriers to Entry on AI Careers
Although AI opens doors to possibilities, there are roadblocks that a person can face while joining the field of AI. Common barriers to AI careers include specialized education, lack of technical skills and competition. But there are sure ways to get rid of those barriers.
Self–education: AI skills can be learned through the help of online courses, tutorials, and certification programs that are generally cheap. Coursera, edX, and Udacity are some resources a budding AI professional can look for.
Networking: Socializing with professionals within the AI space will make it easier for you to keep abreast of current trends, job openings, as well as industry news.
Mentorship: Find an AI mentor who can guide you on the right track in your career.
To Conclude the Future of AI Careers
While artificial intelligence in employment is in our worldview, the truth is that it will continue to change the world of work with time. The good news for you is there are a lot of jobs on the horizon for AI in tech also, meaning there are other AI job opportunities in education, customer service, and elsewhere for those of us with the talent and drive regardless of our background, tech or not. There is potential for a great career if you embrace that the rise of AI is here, acquire the skills needed for the future and position yourself in the right industries.
In today’s world, the business need for AI skills to further develop your career is more important than ever, and the correct preparation can mean that you’ll be a part of it. It’s time to start, whether this is the first time you’re going in the field, switching from another industry to AI or seeking to enhance your career with AI. AI is a vast world, and you must seize a few things.
Follow Fact Being for more AI-related updates.
| 2025-05-22T00:00:00 |
2025/05/22
|
https://factbeing.com/role-of-artificial-intelligence-in-employment/
|
[
{
"date": "2025/06/16",
"position": 75,
"query": "artificial intelligence employment"
}
] |
Union push for workers to have right to refuse to use AI
|
Union push for workers to have right to refuse to use AI
|
https://www.afr.com
|
[
"David Marin-Guzman",
"Primrose Riordan",
"Sally Patten",
"Mandy Coolen",
"John Davidson",
"Nick Lenaghan"
] |
Unions want a right to refuse to use AI if it's not in the public interest in measures that may clash with the Labor's light-touch approach at its ...
|
Unions want workers to have the right to refuse to use artificial intelligence if it’s not in the public interest in measures that may clash with the Albanese government’s light-touch approach at its upcoming productivity roundtable.
The previously unreported demands detailed in the Australian Council of Trade Unions’ AI policy and endorsed at the most recent ACTU Congress, show union ambitions go beyond just mandated training and consultation about the emerging technology to more ambitious workers’ powers over AI.
Loading...
| 2025-06-16T00:00:00 |
2025/06/16
|
https://www.afr.com/work-and-careers/workplace/union-push-for-workers-to-have-right-to-refuse-to-use-ai-20250616-p5m7to
|
[
{
"date": "2025/06/16",
"position": 13,
"query": "artificial intelligence labor union"
}
] |
Artificial Intelligence Engineer » New Jersey » Union - Jobs
|
Artificial Intelligence Engineer Jobs & Work In Union, New Jersey
|
https://tallo.com
|
[] |
Tallo has data from millions of jobs. Check back soon for data on this role - Artificial Intelligence Engineer - with a specific focus on Union county, New ...
|
Jobs in Union
Browse jobs from a variety of sources below, sorted with the most recently published, nearest to the top. Click the title to view more information and apply online.
| 2025-06-16T00:00:00 |
https://tallo.com/jobs/technology/artificial-intelligence-engineer/nj/union/
|
[
{
"date": "2025/06/16",
"position": 60,
"query": "artificial intelligence labor union"
}
] |
|
Global Workplace Law & Policy
|
Global Workplace Law & Policy
|
https://global-workplace-law-and-policy.kluwerlawonline.com
|
[
"The University Of Arizona",
"United States",
"Maynooth University",
"Ireland",
"University Of Modena",
"Reggio Emilia",
"Italy",
"Università Degli Studi Roma Tre",
"Mentee In The Program For Early-Career Researchers On Business",
"Human Rights In Ukraine Implemented The Raoul Wallenberg Institute Of Human Rights"
] |
AI Act and Prevention Regulations Regulation (EU) 2024/1689 (AI Act) subjects the entire discipline of the employment relationship to a stress-test, forcing the ...
|
Practice and academic insights into artificial intelligence in work Accept it: artificial intelligence is changing how we work, and we must adapt to it. This was the tenor of the discussion amongst members of a panel of legal practitioners and law academics in Dublin in May 2025. This post summarises the discussion amongst this panel….
| 2025-06-26T00:00:00 |
2025/06/26
|
https://global-workplace-law-and-policy.kluwerlawonline.com/
|
[
{
"date": "2025/06/16",
"position": 95,
"query": "artificial intelligence labor union"
}
] |
The future of AI: Where will latest innovations take us?
|
The future of AI: Where will latest innovations take us?
|
https://www.binghamton.edu
|
[
"Chris Kocher"
] |
The rise of generative AI, which can create new content, has accelerated both business investments and greater interest for society at large. Rather than just ...
|
Assistant Professor Stephanie Tulk Jesso (with Pepper) works with artificial intelligence in her robotics research, but she has concerns about implementing it too widely without proper testing. Image Credit: Jonathan Cohen.
The future of artificial intelligence: Where will the latest innovations take us? SSIE faculty members share their thoughts — and skepticism
For years, all of us have relied on a certain level of artificial intelligence. Streaming services and social media develop algorithms to suggest content related to what you already like. Businesses use AI to analyze data, predict patterns or automate routine processes. Manufacturers program robots to do the repetitive tasks needed to create cars and other products.
The rise of generative AI, which can create new content, has accelerated both business investments and greater interest for society at large. Rather than just sorting preexisting information, OpenAI’s ChatGPT, Google’s DeepMind and other contenders can generate new text, images and video based on written prompts.
Researchers at the School of Systems Science and Industrial Engineering are examining AI from a variety of angles — the best ways to implement it, what we’re getting out of it and how to improve it.
SUNY Empire Innovation Professor Carlos Gershenson-Garcia thinks there will be few cases where we can take humans out of the loop for artificial intelligence. Image Credit: Jonathan Cohen. SUNY Empire Innovation Professor Carlos Gershenson-Garcia thinks there will be few cases where we can take humans out of the loop for artificial intelligence. Image Credit: Jonathan Cohen. ×
A new landscape for this ’boom’
Carlos Gershenson-Garcia, a SUNY Empire Innovation Professor, has studied AI, artificial life and complex systems for the past two decades.
When surveying the current “AI boom,” he steps back for a moment and offers some historical perspective: “There always has been this tendency to think that breakthroughs are closer than they really are. People get disappointed and research funding stops, then it takes a decade to start up again. That creates what are called ‘AI winters.’”
He points to frustrations with machine translation and early artificial neural networks in the 1960s, and the failure of so-called “expert systems” — meant to emulate the decision-making ability of human experts — to deliver on promised advances in the 1990s.
“The big difference is that today the largest companies are IT companies, when in the ’60s and ’90s they were oil companies or banks, and then car companies. All of it was still industrial,” he says. “Today, all the richest companies are processing information.”
With breakthroughs in large language models such as ChatGPT, some futurists have speculated that AI can do the work of secretaries or law clerks, but Gershenson-Garcia sees that prediction as premature.
“In some cases, because this technology will simplify processes, you will be able to do the same thing with fewer people assisted by computers,” he says. “There will be very few cases where you will be able to take the humans out of the loop. There will be many more cases where you cannot get rid of any humans in the loop.”
’More noise and detail’
Assistant Professor Stephanie Tulk Jesso researches human/AI interaction and more general ideas of human-centered design — in short, asking people what they want from a product, rather than just forcing them to use something unsuitable for the task.
“I’ve never seen any successful approaches to incorporating AI to make any work better for anyone ever,” she says. “Granted, I haven’t seen everything under the sun — but in my own experience, AI just means having to dig through more noise and detail. It’s not adding anything of real value.”
Tulk Jesso believes there are many problems with greater reliance on AI in the workplace. One is that many tech experts are overselling — AI should be a tool, rather than a replacement for human employees. Another is how it’s often designed without understanding the job it’s meant to do, making it harder for employees rather than easier.
Lawsuits about copyrighted materials “scraped” and repurposed from the internet remain unresolved, and environmentalists have climate concerns about how much energy generative AI requires to run. Among the ethical concerns are “digital sweatshops” in developing countries where workers train AI models while enduring harsh conditions and low pay.
Tulk Jesso also sees AI as too unreliable for important tasks. In 2024, for instance, Google’s AI suggested adding glue to pizza to help the cheese stick better, as well as eating a small rock daily as part of a healthy diet.
Fundamentally, she says, we just don’t know enough about AI and how it works: “Steel is a design material. We test steel in a laboratory. We know the tensile strength and all kinds of details about that material. AI should be the same thing, but if we’re putting it into something based on a lot of assumptions, we’re not setting ourselves up for great success.”
Associate Professor Christopher Greene researches collaborative robotics, or “cobotics,” for industrial settings. Image Credit: Jonathan Cohen. Associate Professor Christopher Greene researches collaborative robotics, or “cobotics,” for industrial settings. Image Credit: Jonathan Cohen. ×
Working alongside robots
As the manufacturing sector is upgrading to Industry 4.0 — which utilizes advances such as artificial intelligence, smart systems, virtualization, machine learning and the internet of things — Associate Professor Christopher Greene researches collaborative robotics, or “cobots,” as part of his larger goal of continual process improvement.
“In layman’s terms,” he says, “it’s about trying to make everybody’s life easier.”
Most automated robots on assembly lines are programmed to perform just a few repetitive tasks and lack sensors for working side by side with humans. Some functions require pressure pads or light curtains for limited interactivity, but those are added separately.
Greene has led projects for factories that make electronic modules using surface-mount technology, as well as done research for automated pharmacies that sort and ship medications for patients who fill their prescriptions by mail. He also works on cloud robotics, which allows users to control robots from anywhere in the world.
Human workers are prone to human errors, but robots can perform tasks thousands of times in the exact same way, such as gluing a piece onto a product with the precise amount of pressure required to make it stick firmly without breaking it. They also can be more accurate when it matters most. Humans will be required to program and maintain the automated equipment.
“Assembling pill vials with the right quantities is done in an automated factory,” Greene says. “Cobots are separating the pills, they’re putting them in bottles, they’re attaching labels and putting the caps on them. They’re putting it into whatever packaging there is, and it’s going straight to the mail. All these steps have to be correct, or people die. A human being can get distracted, pick up the wrong pill vial or put it in the wrong package. If you correctly program a cobot to pick up that pill bottle, scan it and put it in a package, that cobot will never make a mistake.”
Keeping AI unbiased
Associate Professor Daehan Won’s AI research focuses on manufacturing and healthcare, but the goal for both is the same: Use it as a tool for making better decisions.
On an assembly line, AI helps human operators avoid trial and error when making their products. In a medical office, it can analyze CT scans and MRIs so doctors can locate cancerous tumors more easily.
Associate Professor Daehan Won Associate Professor Daehan Won ×
Before we let AI take more control in our everyday lives, though, Won believes there are fundamental limitations that we need to solve first. One of them is the “black box” nature of many data-driven AI systems, with the exact process for reaching conclusions often remaining a mystery.
“When AI answers a question in the healthcare area, doctors ask: How did it come up with this answer?” he says. “Without that kind of information, they cannot apply it to their patients’ diagnoses.”
Another problem is keeping AI unbiased. Even when thousands of data points are entered into a comprehensive algorithm, the results it offers are only as good as the information that humans provide to it. Even generative AI like ChatGPT or DeepAI can be tilted toward more affluent cultures that have better access to cell phones or internet connections.
“There is a ton of research about AI being used for image processing to detect breast cancer, but from our review, most of that research is from developed countries like the U.S., the U.K. and Germany,” Won says.
As part of the New Educational and Research Alliance (newERA) between Binghamton University and historically Black colleges and universities (HBCUs), Won is working with Tuskegee University on a project trying to ensure that Blacks, Asians and other people of color are better represented in breast cancer studies.
The manufacturing sector is not free of bias either, he adds: “The same company can have a factory in Mexico and a factory in China. Can we apply the same systems or not? They have very similar manufacturing lines, but the machines are different and there are different levels of operator expertise. This is one of the big problems I’m trying to solve.”
Sangwon Yoon, an associate professor of systems science and industrial engineering, has a number of projects on his research radar. One of them is consulting on the PharmASSIST automated prescription drug system from Innovation Associates in Johnson City. Here, he discusses the system with graduate student Lubna Al Tarawneh, left, and PhD candi- date Qianqian Zhang, MS ’16. Image Credit: Jonathan Cohen. Sangwon Yoon, an associate professor of systems science and industrial engineering, has a number of projects on his research radar. One of them is consulting on the PharmASSIST automated prescription drug system from Innovation Associates in Johnson City. Here, he discusses the system with graduate student Lubna Al Tarawneh, left, and PhD candi- date Qianqian Zhang, MS ’16. Image Credit: Jonathan Cohen. ×
Humans needed for big decisions
Like other SSIE faculty members, Professor Sangwon Yoon sees AI as a useful tool, but he does not believe it should be the final word on any subject — at least not yet.
One issue is that many people have doubts. According to a 2024 survey from the online research group YouGov, 54% of Americans describe themselves as “cautious” about AI, while 49% say they are “concerned,” 40% are “skeptical,” 29% are “curious” and 22% are “scared.”
From a research perspective, though, Yoon knows that AI can help solve complex problems much faster and easier than humans working on their own.
“We can apply it almost everywhere, because hardware systems get better and communication gets faster, so we can receive the data and use AI solutions,” he says. “What can AI not do? That will be the more interesting question.”
Yoon’s AI research focuses on two areas — manufacturing and healthcare. In addition to improving closed-loop feedback control systems used on electronic circuit board assembly lines, he has studied better detection for breast cancer and strategies to curb hospital readmission rates.
However, medical professionals and patients are not willing to cede all decisions to AI. An algorithm does not decide on its own whether a cancer is malignant or benign, nor does it perform the surgery to remove it. Doctors still deliver the diagnoses and guide the final choices.
“We cannot 100% trust a human either, but at least we can communicate with doctors. We cannot talk to AI in the same way,” he says. “It’s the same with allowing AI to make military decisions. This is why AI solutions right now are mainly used for things like social media and entertainment, because if it’s wrong, nobody gets harmed.”
Profesor Hiroki Sayama of Watson College's Department of Systems Science and Industrial Engineering Image Credit: Jonathan Cohen. Profesor Hiroki Sayama of Watson College's Department of Systems Science and Industrial Engineering Image Credit: Jonathan Cohen. ×
Beyond the ’right answer’
Distinguished Professor Hiroki Sayama’s research isn’t directly in artificial intelligence but rather in artificial life, a similar endeavor that attempts to create a lifelike system using computers and other engineered media. By trying to reproduce the essential properties of living systems, he hopes to better understand evolution, adaptive behavior and other characteristics.
One key difference he is delving into: Nearly all current AI and machine learning techniques are designed to converge on the best solution — the “right answer” — at the fastest speed.
“Real biological systems are not doing just that, but they also explore a broader set of options indefinitely without converging to a single option,” says Sayama, who is vice president of the International Society for Artificial Life. “Similar things also may be said about human creativity and culture.”
A new concept gaining attention in recent years is “open-endedness,” which continues to generate novel solutions on its own with no fixed goals or objectives.
Overall, he is hopeful about what AI could achieve beyond writing essays and producing generative art based on text prompts. He believes AI could coordinate and mediate human discussions and decision-making processes, as well as making things more accessible. For instance, it could convert something visual to an auditory or tactile format, or it might generate simpler explanations for difficult concepts.
One thing that concerns him is the loss of diverse ideas when we rely too much on the current AI: “Since everyone is using the same small set of AI tools, the outputs are becoming more and more similar. This is yet another reason why we need open-endedness in the next generation of AI.”
| 2025-06-16T00:00:00 |
https://www.binghamton.edu/news/story/5626/the-future-of-artificial-intelligence-where-will-the-latest-innovations-take-us
|
[
{
"date": "2025/06/16",
"position": 58,
"query": "future of work AI"
}
] |
|
Humanoid robots offer disruption and promise. Here's why
|
Humanoid robots offer disruption and promise. Here's why
|
https://www.weforum.org
|
[] |
Augmenting human capabilities can make jobs more fulfilling and productive, while improving workplace safety as robots handle more hazardous tasks.
|
Billions of humanoid robots could be operating across the world by 2040, doing work well beyond the scope of today’s factory robots.
The robots look set to revolutionize sectors as varied as healthcare, public space maintenance, retail service and personal assistance.
Companies and individuals alike can benefit from humanoid robots, but they will require clear guardrails to embrace this transformation.
The 2017 victory of Google’s AlphaGo system over the world’s top-ranked Go player, Ke Jie, inspired worldwide discussions about artificial intelligence’s (AI) cognitive capabilities, but the recent appearance of humanoid robots competing directly against humans in a Beijing half-marathon represents a fundamental new development.
While earlier robot prototypes were confined to controlled demonstrations, that race – which saw 21 humanoid robots completed the 21km (13 mile) course – underscores their growing integration into human environments.
This evolution parallels the rise of electric vehicles (EVs), which have already disrupted traditional automotive powerhouses such as Germany. Could humanoid robots similarly redefine global markets and industrial ecosystems?
How humanoid robots are transforming industries
A humanoid robot is, unsurprisingly, a robot shaped like a human that mimics the movements of the human body. As a type of professional service robot, it is built to work alongside people, thereby boosting productivity in various settings.
Powered by advanced AI, such robots can perceive their surroundings, make decisions, plan actions and autonomously carry out complex tasks. Thanks to rapid learning and adaptation, humanoid robots are now evolving faster than ever before.
Analysts predict that humanoid robots could become a common presence in our daily lives within the next decade. Estimates suggest that billions could be operating across the world by 2040, doing work well beyond the scope of today’s factory robots, including in sectors as varied as healthcare, public space maintenance, retail service and personal assistance.
Goldman Sachs has estimated that, by 2035, the humanoid robot market could reach $38 billion, while Fortune Business Insights has estimated the market to grow even faster – by nearly 50% per year, reaching $66 billion in 2032.
Humanoid robots' versatility depends on a complex multi-industry supply chain spanning semiconductors, AI systems, actuators (which convert energy into motion or force) and sensors. These robots require numerous high-performance chips for motion control, perception and decision-making, meaning the semiconductor industry will see unprecedented growth as the humanoid robot market expands.
At the same time, advances in AI technology have pushed humanoid robots from research labs into the marketplace, sparking widespread interest across the tech world. Breakthrough projects, such as Tesla’s Optimus, the OpenAI-backed Figure AI and Unitree Technology’s humanoid robots, have accelerated commercialization and mass production efforts, bringing these once-futuristic machines closer to everyday reality.
As humanoid robots become more common in daily life, the global workforce faces both disruption and promise. Society will need to craft new policies and address their ethical and economic impact. This rapidly advancing technology therefore promises an exciting, yet complex, future – with expanding applications driving market growth.
While some, understandably, fear job displacement and over-reliance on technology, workers also stand to benefit. Augmenting human capabilities can make jobs more fulfilling and productive, while improving workplace safety as robots handle more hazardous tasks.
In addition, firms may see enhanced customer experiences and new roles will emerge – such as training, monitoring and collaborating with robots – ensuring humans remain integral to the future workforce.
Global race for humanoid robots intensifies
Breakthroughs in generative AI have dramatically accelerated the development of humanoid robots since, and that momentum shows no signs of slowing. At the forefront of this global push are Chinese and US companies, which dominate both the AI market and humanoid robotics innovation.
According to the Stanford AI Index Report, most (83%) AI intellectual property and 90% of notable foundation models come from China and the US, followed by France with 5%. These include large language models, re-enforcement learning, neural networks and thousands of types of sensors, meaning that such robots come almost exclusively from either China or the US.
Amid fierce US-China competition in this transformative field, China's rapid ascent stands out. Market forecasts highlight this surge: China’s humanoid robot market is projected to soar from RMB 2.76 billion ($377.56 million) in 2024 to RMB 75 billion ($10.26 billion) by 2029.
This would represent nearly a third (32.7%) of the global market, securing China’s position as a world leader. This growth is driven by a powerful combination of supportive government policies, world-class infrastructure and a commanding lead in patent filings – China has registered 5,688 humanoid robotics patents over the past five years, almost four times the US total of 1,483.
Recent developments, such as China’s marathon-running robots, suggest that the nation is not merely entering the competition but also setting the pace in this trillion-yuan technological race, once again reshaping an industry. In addition to pioneering new fields, China is now challenging industrial leaders such as South Korea, Germany and the US in practical applications.
The road ahead for humanoid robots
Humanoid robots are poised to revolutionize labour dynamics across multiple sectors. In hazardous industrial operations, robots can assume high-risk tasks, mitigating human exposure to dangerous environments and significantly reducing occupational injury rates.
Concurrently, in service domains demanding nuanced personalization (such as elderly care and customer engagement), they offer scalable solutions to labour deficits while delivering customized companionship. This dual capability is particularly vital for rapidly ageing societies, including Germany, Japan, South Korea and China, where demographic pressures intensify workforce challenges.
In the future, cross-manufacturer, multinational humanoid robotic systems will achieve seamless interoperability in unified operational environments, driving sustainable growth through harmonized digital ecosystems.
Amazon's current deployment offers a preview: 750,000 robots spanning nine specialized categories function in systems that work in harmony to support package fulfillment and delivery. Now, the company is pushing boundaries further by trialling humanoid robots for last-mile deliveries – deploying them alongside human drivers to service multiple addresses simultaneously. While still in controlled testing, these experiments aim to prove that robots can handle real-world variables, complementing rather than fully replacing human roles in the near term.
Such seamless collaborations between robotics ecosystems are about more than technical compatibility; they may also reshape how industries leverage global innovation. These interconnected systems could lay the groundwork for a new era in which AI-driven robotics becomes a universal productivity multiplier.
Guardrails needed for humanoid robot development
However, we can't ignore the challenges, including privacy and data risks, reliability questions, job displacement and economic ripple effects.
More fundamentally, technology's greatest promise lies in being able to advance societal progress and elevate human welfare – a promise that finds perfect embodiment in humanoid robotics. These technological marvels are already revolutionizing industries – from perilous missions in extreme environments to intimate roles in domestic settings and online deliveries.
Discover How is the World Economic Forum creating guardrails for Artificial Intelligence? Show more In response to the uncertainties surrounding generative AI and the need for robust AI governance frameworks to ensure responsible and beneficial outcomes for all, the Forum’s Centre for the Fourth Industrial Revolution (C4IR) has launched the AI Governance Alliance. The Alliance unites industry leaders, governments, academic institutions, and civil society organizations to champion responsible global design and release of transparent and inclusive AI systems. This includes the workstreams part of the AI Transformation of Industries initiative, in collaboration with the Centre for Energy and Materials, the Centre for Advanced Manufacturing and Supply Chains, the Centre for Cybersecurity, the Centre for Nature and Climate, and the Global Industries team.
Nevertheless, society and industry will require clear guardrails to embrace this transformation. Since humanoid robots can independently navigate their environment, they must adhere to social norms, ensuring safe, ethical and predictable behaviour, especially in intimate domestic spaces.
Fail-safe mechanisms are essential to avoid the catastrophic scenarios depicted in Hollywood and sci-fi novels. Their implementation requires a multi-tiered approach: engineers to design technical safeguards, corporations to operationalize safety protocols, and governments to establish regulatory frameworks. Finally, humans also need to get trained in the safe usage of and communication with humanoid robots.
| 2025-06-16T00:00:00 |
https://www.weforum.org/stories/2025/06/humanoid-robots-offer-disruption-and-promise/
|
[
{
"date": "2025/06/16",
"position": 6,
"query": "robotics job displacement"
}
] |
|
Webinar - AI in the digital workplace
|
AI in the digital workplace
|
https://contentformula.com
|
[] |
Artificial Intelligence (AI) technology is changing the digital workplace in many ways: it's changing intranets, knowledge management, digital processes and ...
|
Watch our online webinar to learn:
Artificial Intelligence (AI) technology is changing the digital workplace in many ways: it's changing intranets, knowledge management, digital processes and more. For businesses it can be hard to keep up with the impact it is having, and the potential it has for employee engagement and productivity.
In this webinar we explore the world of AI: the opportunities and tangible value it offers, and what your organisation should be doing to get started.
| 2025-06-16T00:00:00 |
https://contentformula.com/webinar-ai-in-the-digital-workplace/
|
[
{
"date": "2025/06/16",
"position": 48,
"query": "workplace AI adoption"
}
] |
|
How Workday narrowed its talent strategy for AI ...
|
How Workday narrowed its talent strategy for AI enablement and skill building to focus on three things
|
https://www.hr-brew.com
|
[] |
Before Workday designed any AI learning or adoption strategy for its workforce, execs first needed to understand where employees were at both as it related ...
|
“The goal of this program is for all of our nearly 20,000 workmates to build the mindsets, the skills, and the habits to use AI effectively every day.”
In order to know where you’re going, you need to know where you are.
Before Workday designed any AI learning or adoption strategy for its workforce, execs first needed to understand where employees were at both as it related to their skills and sentiments and to identify their needs amid this workplace upheaval.
“It’s not just a technological transformation, it’s very much a human one,” said Chris Ernst, chief learning officer at Workday. “Even though we’re a technology company in Silicon Valley, when we began our upskilling journey on AI, we started with research, and we’ve learned a lot.”
The firm surveyed employees to better understand the barriers to AI adoption. What Workday discovered ended up informing the cornerstones of its AI adoption initiative: Everyday AI.
According to the company, the global focus groups revealed three key data points to build its strategy around:
43% of Workday employees lacked time to explore AI tools
37% said they weren’t sure how to use them effectively
37% revealed concerns about reliability and accuracy
“Once we deeply understood our Workmates,” Ernst said, referring to Workday employees, “their needs, their concerns, their use cases, we went really big on this, and we’ve launched an enterprise upskilling initiative…The goal of this program is for all of our nearly 20,000 Workmates to build the mindsets, the skills, and the habits to use AI effectively every day.”
Mindset. Changing mindsets at Workday involves giving employees more exposure to AI tools and their capabilities to really address baseline fears around how AI will impact their work.
“The mindset piece really is about that fear and that concern,” Ernst told HR Brew. “The way we’ve really been working on that..is through a lot of events around the company.”
The company launched its first ever “prompt-athon,” inspired by the popular Big Tech practice of hosting hackathons, events for developers and teams to work together or in competition to address problems or challenges with a technology or tool.
Skills. To address upskilling at Workday, the learning team created one digital academy for all things AI available to all employees. The content is organized by different personas of users, so employees can learn about AI strategies or use cases based on their specific role or project they’re assigned.
The academy serves more as a choose-your-own-adventure: Employees can identify exactly what they’d like to do with AI, and the platform surfaces relevant training based on different personas, such as “innovator,” “communicator,” and “analyst,” according to Ernst.
Habits. To foster good AI habits, Ernst asked all employees to set a personal AI goal, share that objective with their managers, and check-in quarterly on the progress.
Additionally, instead of communicating from the top down about the AI plans from senior execs, Workday opened up a company-wide town hall to spotlight the work of employees leveraging the tech in important or interesting ways that are really meaningful to Workday’s business.
How you doin!? “All of this is creating the conditions to want to learn,” Ernst told HR Brew. “Our highest performers are twice as engaged with these technologies. We also found that those who are most engaged with these technologies…feel that they have a 13% increase in their sense of having a career path.”
Workday employees are reporting that since the launch:
73% found that AI tools make them more productive;
50% said it provides them new insights; and
49% said it helps them be more creative.
“This is the biggest enterprise learning and development initiative we’ve taken on in 20 years as a company,” Ernst said.
| 2025-06-16T00:00:00 |
2025/06/16
|
https://www.hr-brew.com/stories/2025/06/16/how-workday-narrowed-its-talent-strategy-for-ai-enablement-and-skill-building-to-focus-on-three-things
|
[
{
"date": "2025/06/16",
"position": 84,
"query": "workplace AI adoption"
}
] |
22 New Jobs A.I. Could Give You - The New York Times
|
A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You.
|
https://www.nytimes.com
|
[
"Robert Capps",
"Malcolm Hillgartner",
"Narration Produced",
"Krish Seenivasan",
"Joel Thibodeau"
] |
According to the World Economic Forum's 2025 Future of Jobs report, nine million jobs are expected to be “displaced” by A.I. and other emergent ...
|
Commentators have become increasingly bleak about the future of human work in an A.I. world. The venture-capitalist investor Chris Sacca recently went on Tim Ferriss’s podcast and declared that “we are super [expletive].” He suggested that computer programmers, lawyers, accountants, marketing copywriters and most other white-collar workers were all doomed. In an email to his staff, Fiverr’s chief executive, Micha Kaufman, added designers and salespeople to the list of the soon-to-be-damned.
Such laments about A.I. have become common, but rarely do they explore how A.I. gets over the responsibility hurdle I’m describing. It’s already clear that A.I. is more than capable of handling many human tasks. But in the real world, our jobs are about much more than the sum of our tasks: They’re about contributing our labor to a group of other humans — our bosses and colleagues — who can understand us, interact with us and hold us accountable in ways that don’t easily transfer to algorithms.
This doesn’t mean the disruptions from A.I. won’t be profound. “Our data is showing that 70 percent of the skills in the average job will have changed by 2030,” said Aneesh Raman, LinkedIn’s chief economic opportunity officer. According to the World Economic Forum’s 2025 Future of Jobs report, nine million jobs are expected to be “displaced” by A.I. and other emergent technologies in the next five years. But A.I. will create jobs, too: The same report says that, by 2030, the technology will also lead to some 11 million new jobs. Among these will be many roles that have never existed before.
If we want to know what these new opportunities will be, we should start by looking at where new jobs can bridge the gap between A.I.’s phenomenal capabilities and our very human needs and desires. It’s not just a question of where humans want A.I., but also: Where does A.I. want humans? To my mind, there are three major areas where humans either are, or will soon be, more necessary than ever: trust, integration and taste.
Trust
Robert Seamans, a professor at New York University’s Stern School of Business who studies the economic consequences of A.I., envisions a new set of roles he calls A.I. auditors — people who dig down into the A.I. to understand what it is doing and why and can then document it for technical, explanatory or liability purposes. Within the next five years, he told me, he suspects that all big accounting firms will include “A.I. audits” among their offerings.
| 2025-06-17T00:00:00 |
2025/06/17
|
https://www.nytimes.com/2025/06/17/magazine/ai-new-jobs.html
|
[
{
"date": "2025/06/17",
"position": 49,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 53,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 55,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 13,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 50,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 1,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 83,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 46,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 43,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 23,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 36,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 42,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 63,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 45,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 25,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 48,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 75,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 42,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 15,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 47,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 33,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 30,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 47,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 49,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 24,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 54,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 38,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 47,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 25,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 44,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 40,
"query": "artificial intelligence employers"
},
{
"date": "2025/06/17",
"position": 23,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 48,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 51,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 41,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 44,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 46,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 46,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 47,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 70,
"query": "artificial intelligence employers"
},
{
"date": "2025/06/17",
"position": 28,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 22,
"query": "future of work AI"
},
{
"date": "2025/06/17",
"position": 62,
"query": "artificial intelligence employers"
},
{
"date": "2025/06/17",
"position": 1,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 61,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 34,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 44,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 4,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 5,
"query": "future of work AI"
}
] |
Message from CEO Andy Jassy: Some thoughts on Generative AI
|
Message from CEO Andy Jassy: Some thoughts on Generative AI
|
https://www.aboutamazon.com
|
[
"Andy Jassy",
"Ceo Of Amazon"
] |
Think of agents as software systems that use AI to perform tasks on behalf of users or other systems. ... There will be billions of these agents, ...
|
As we go through this transformation together, be curious about AI, educate yourself, attend workshops and take trainings, use and experiment with AI whenever you can, participate in your team’s brainstorms to figure out how to invent for our customers more quickly and expansively, and how to get more done with scrappier teams. When I first started at Amazon in 1997 as an Assistant Product Manager, I worked on leaner teams that got a lot done quickly and where I could have substantial impact. We didn’t have tools resembling anything like Generative AI, but we had broad remits, high ambition, and saw the opportunity to improve (and invent) so many customer experiences. Fast forward 28 years and the most transformative technology since the Internet is here. Those who embrace this change, become conversant in AI, help us build and improve our AI capabilities internally and deliver for customers, will be well-positioned to have high impact and help us reinvent the company.
| 2025-06-17T00:00:00 |
2025/06/17
|
https://www.aboutamazon.com/news/company-news/amazon-ceo-andy-jassy-on-generative-ai
|
[
{
"date": "2025/06/17",
"position": 83,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 76,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 86,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 84,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 43,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 64,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 84,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 83,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 77,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 40,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 70,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 86,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 71,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 43,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 68,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 79,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 51,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 97,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 67,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 50,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 79,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 45,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 68,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 84,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 50,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 45,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 93,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 45,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 67,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 90,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 79,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 48,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 82,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 48,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 87,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 99,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 77,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 51,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 81,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 46,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 67,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 68,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 82,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 49,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 81,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 45,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 68,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 69,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 45,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 71,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 47,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 48,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 45,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 79,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 67,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 78,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 48,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 81,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 45,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 68,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 48,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 86,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 44,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 61,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 82,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 76,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 76,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 49,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 70,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 74,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 54,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 77,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 68,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 75,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 44,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 45,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 76,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 80,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 70,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 88,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 69,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 79,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 79,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 48,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 73,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 85,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 48,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 67,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 75,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 46,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 73,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 81,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 48,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 40,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 71,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 73,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 38,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 80,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 42,
"query": "generative AI jobs"
},
{
"date": "2025/06/17",
"position": 73,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 85,
"query": "AI impact jobs"
},
{
"date": "2025/06/17",
"position": 84,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 48,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 75,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/17",
"position": 31,
"query": "AI employers"
},
{
"date": "2025/06/17",
"position": 70,
"query": "AI employment"
},
{
"date": "2025/06/17",
"position": 53,
"query": "AI layoffs"
},
{
"date": "2025/06/17",
"position": 80,
"query": "AI replacing workers"
},
{
"date": "2025/06/17",
"position": 8,
"query": "AI workers"
},
{
"date": "2025/06/17",
"position": 41,
"query": "generative AI jobs"
}
] |
Australian Framework for Generative Artificial Intelligence (AI) in ...
|
Australian Framework for Generative Artificial Intelligence (AI) in Schools
|
https://www.education.gov.au
|
[] |
... Education Ministers endorsed the 2024 Framework Review, undertaken by the National AI in Schools Taskforce (the Taskforce) in consultation ...
|
The Australian Framework for Generative AI in Schools (the Framework) seeks to guide the responsible and ethical use of generative AI tools in ways that benefit students, schools, and society. The Framework supports all people connected with school education including school leaders, teachers, support staff, service providers, parents, guardians, students and policy makers.
In June 2025, Education Ministers endorsed the 2024 Framework Review, undertaken by the National AI in Schools Taskforce (the Taskforce) in consultation with representatives of all jurisdictions, school sectors and national agencies.
| 2025-06-17T00:00:00 |
https://www.education.gov.au/schooling/resources/australian-framework-generative-artificial-intelligence-ai-schools
|
[
{
"date": "2025/06/17",
"position": 54,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 60,
"query": "artificial intelligence education"
},
{
"date": "2025/06/17",
"position": 53,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 63,
"query": "artificial intelligence education"
},
{
"date": "2025/06/17",
"position": 62,
"query": "artificial intelligence education"
},
{
"date": "2025/06/17",
"position": 53,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 53,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 55,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 54,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 54,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 53,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 62,
"query": "artificial intelligence education"
},
{
"date": "2025/06/17",
"position": 68,
"query": "artificial intelligence education"
},
{
"date": "2025/06/17",
"position": 34,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 30,
"query": "AI education"
},
{
"date": "2025/06/17",
"position": 58,
"query": "artificial intelligence education"
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.