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Technology experts feel Workplace Modernization becomes a ...
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Technology experts feel Workplace Modernization becomes a part of organization’s Digital Transformation
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https://varindia.com
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[
"Deepak Sahu"
] |
To achieve all these organizations need to undergo a digital journey to empower employees and build an agile work environment by adopting workplace ...
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Pandemic has pushed organizations to adopt the latest technologies for continued business operations, fulfilling customer expectations and demands, meeting employee expectations and achieving productivity in a hybrid work culture. To achieve all these organizations need to undergo a digital journey to empower employees and build an agile work environment by adopting workplace modernization. Now, it has become a part of an organization’s digital transformation journey and it is important for any organization as it enables its employees with tools and technologies to be more productive, improves collaboration and communication among employees, increases flexibility, reduces costs and improves security. It helps organizations to remain competitive in the market.
It is when organizations will start adopting technologies among the workforce, investing in new solutions, upgrading employee skills in latest technologies etc. that will help in advancement of Industry 4.0 to 5.0.
Let’s take a look at the views of technology experts on workplace modernization.
Modernizing the workplace is critical for digital transformation journey
Harsh Arora
Group Head IT, Hindustan Power
Key factors to consider in workplace modernization
The requirement of modernization of business is essential for the survival of the business. IT can contribute a lot to achieve this. There are many factors. The important ones are:
To be able to work anywhere in the world
To be able to work any time
To be able to work on any device
To be able to work securely
Workplace modernization: A part of digital transformation
Digital transformation is imperative for all businesses, from the small to the enterprise. In recent years, the COVID-19 pandemic has brought new urgency to meeting digital transformation goals – and forced many organizations to speed up transformation work. An organisation may take on digital transformation for several reasons. But by far, the most likely reason is that it is a survival issue. An organization's ability to adapt quickly to supply chain disruptions, time to market pressures, and rapidly changing customer expectations has become critical. Modernizing the workplace is a must in the digital transformation journey.
Workplace modernization for 2023
We are looking for a comprehensive platform which enables employees, vendors, customers, and other stakeholders to collaborate seamlessly. The platform should be secure, configurable and scalable. It should offer the features like document sharing, chatting, audio/video group calls, integrate with other business critical applications etc. We are evaluating Meta Workplace, Slack, Google Workplace, Microsoft 365 suite etc.
Modernizing workplace provides employees with tools and technologies to be productive
Sachin Arora
CISO, dcafé digital
Key factors to consider in workplace modernization
There are several key factors that businesses should consider when modernizing their workplace. These include:
Employee needs and preferences: Modernization should take into account the needs and preferences of employees. This can include providing them with the tools and technologies they need to be productive, as well as creating a comfortable and engaging work environment.
Improved communication and collaboration: Modernization can improve communication and collaboration within a business by providing employees with access to the latest collaboration tools and technologies. This can help to increase efficiency and productivity, as well as foster a sense of community within the workplace.
Increased flexibility: Modernization can also increase flexibility in the workplace by allowing employees to work from anywhere and on any device. This can help to improve work-life balance and make it easier for employees to manage their workloads.
Cost savings: Modernization can also lead to cost savings for businesses. By implementing new technologies and processes, businesses can streamline their operations and reduce their reliance on costly manual labour.
Improved security: Modernization can also improve security in the workplace by providing employees with access to secure, encrypted communication and collaboration tools. This can help to protect sensitive information and prevent data breaches.
Workplace modernization: A part of digital transformation
Modernizing the workplace should be a part of every organization’s digital transformation process. Digital transformation involves the adoption of new technologies and processes to improve business operations and increase efficiency. Modernizing the workplace is a crucial part of this process, as it can provide employees with the tools and technologies they need to be productive and improve collaboration and communication within the organization. Additionally, modernizing the workplace can help to increase flexibility, reduce costs, and improve security. Overall, it can help organizations to stay competitive and adapt to changing market conditions.
Workplace modernization gives a new meaning to digital transformation
Jaideep Khanduja
CTO, AccioMango
Key factors to consider in workplace modernization
There are many factors that businesses need to consider in modernization of the workplace. A recent study 'IT at Work: 2022 and Beyond' was conducted around July this year, by ManageEngine, the enterprise IT management division of Zoho Corporation. One of the key elements that emerged out of that study is - a successful hybrid work culture demands collaboration between IT and non-IT teams. It also says AI and ML are crucial contributors to strengthening IT security frameworks. IT decentralization is drawing bigger benefits for businesses. On top of all above, IT security has emerged as the biggest challenge.
Workplace modernization: A part of digital transformation
Digital transformation journey has got a new meaning now for enterprises. Many things which were not there in the original plan are coming out as a priority in the new roadmaps.
Workplace modernization for 2023
For 2023, maximum focus will stay on collaboration tools, acquiring new IT skills, workplace and work time flexibility, embedding AI and ML into the core IT security frameworks, IT decentralization, and strengthening of IT security team.
“Our goal is customer satisfaction by using latest technology”
Lakshmana Vadaga
Head - IT, Imagicaaworld Entertainment
Key factors to consider in workplace modernization
The CIO’s role is becoming more proactive than reactive in approach. A CIO needs to infuse enough checks and balances in place to avoid risk. It is important to focus not only on technology deployment in an organisation but essentially the mindset needs to change. Considering the modernization of the workplace, a CIO needs to discuss with all stakeholders before implementation of any application or infrastructure.
Workplace modernization: A part of digital transformation
In any industry, a CIO has to play a dual role of understanding its core business and driving the business with the help of IT strategies and technologies. In fact, the CIO is the only person who has crucial business knowledge and IT insights.
It is a CIO who converts technology solutions and strategies into business opportunities in the financial industry. Service as a revenue model is the success mantra for all CIO’s. We have been called “CAAS” (CIO as a Service).
Workplace modernization for 2023
We have been continuously upgrading our technology as per customer needs. Our goal is customer satisfaction by using the latest technology.
Our Key Priorities are:
- Upgrade and migrate to enhanced open source technologies.
- Extensive use of AI and ML in regulatory functions.
- AI and ML in business and operations to be integrated
- Data Encryption, robust security especially for transaction based applications.
Modernization of People, Process and Technology leads an enterprise to a productive, agile, efficient work culture
Balwant Singh
CISO, Dharampal Satyapal
Key factors to consider in workplace modernization
Post pandemic era has forced organizations to adopt robust continued business operations, ensure satisfied and delighted customers, meeting employee expectations and achieving productivity in hybrid work culture. To overcome these key factors enterprises need to transform their digital journey, empower and engage employees and build an agile work environment by adopting and accelerating workplace modernization along with culture transformation, digital transformation at organization level.
Workplace modernization: A part of digital transformation
People, Process and Technology are the foundation for any enterprise to run its operations successfully. Organizations need to balance all these aspects, digital transformation requires skilled workforce and they require latest processes aligned with business operations and transformations, all three aspects are interconnected which cannot either be overlooked or regarded, hence modernizing all three aspects will lead an enterprise to a productive, agile, efficient work culture.
Workplace modernization for 2023
Workplace modernization is not a static job, it’s a dynamic practice which requires regular assessments of PPTs. Hence regular training and skill enhancement programs, employee engagements, hybrid work cultures without compromising employers and employees expectations, adopting and implementing new technologies with due care and due diligence are some areas we should eye on for the betterment, empowerment and modernization of an enterprise and its workforce.
“The modernization of the workplace has become a key factor in attracting and retaining employees”
Manikant Singh
CISO, DMI Finance
Key factors to consider in workplace modernization
Modernizing the work environment will ensure that employees perform competently and are in a position to use their skills effectively, ensuring that organizations are more competitive in the marketplace. Modernizing workplaces not only increases productivity but also helps businesses lower operational costs and get up to date on compliance standards. Modernization is a way to help employees to manage their day-to-day lives better and allocate more time for work.
Key factors where businesses need to consider modernization of the workplace include: Increased penetration of working technologies, such as mobile phones, laptops and tablets. These devices have become ever more powerful which in turn, businesses are being forced to improve their IT infrastructure to keep up with the advancements. Businesses also need to innovate and develop new products to meet consumer demands and needs. This calls for an increased collaboration between product development teams as well as marketing departments to ensure that more products are designed on the basis of customer demand. Moreover, businesses must regularly test new products against their competition to gauge how well they sell in the marketplace, thus allowing them to make necessary changes in their marketing strategies. The modernization of the workplace has become a key factor in attracting and retaining employees. This is because companies are turning to technology and automation.
Workplace modernization: A part of digital transformation
Modernizing the workplace should be a part of every organization’s digital transformation process because it will help to increase productivity and work efficiency by having a more innovative working environment. The workplace should be at the forefront of technology and innovation, allowing employees to collaborate more efficiently. IT leaders who have a hunky-dory Digital Transformation journey will not get anywhere if they ignore human capital investment.
Workplace modernization must be a part of digital transformation process
Parveen Kumar Sharma
CTO & Consultant - IT, Vianaar
Key factors to consider in workplace modernization
Following are the key factors businesses need to consider to modernize their workplace environment:
To be able to work anywhere in the world /any time/on any device
To have technological breakthroughs
Helps to increase the productivity of employees
To improves customer experience
For security enhancement
To helps in increasing revenue streams
With less workforce more automation
To have healthy lifestyle
Workplace modernization: A part of digital transformation
Modernising the workplace should be a part of every organisation’s digital transformation process. Digital transformation puts technology at the core of business strategy. This approach can reduce operating expenses and inefficiency. It could even change the course of the business. With a unified model across business and technology, it is easier to achieve future ambitions. Digital transformation changes the way an organization operates. Systems, processes, workflow, and culture are all part of this process. This transformation affects each level of an organization and brings together data across areas to work together more effectively.
“Workplace modernization is a necessity and not a luxury”
Dheeraj Chawla
CIO, Shib Dass and Sons
Key factors to consider in workplace modernization
I believe that the businesses are still facing the heat of the pandemic and it is going to stay. The biggest challenge was investment in technology. I believe technology is a great enabler and it plays a vital role in bonding man and machine in an organisation. Cyber threat and fraud are the main areas of concern at the moment. Making the organization strong with a proactive approach is needed in place of mending and sorting the damages occurred. Adopt the best that is suited, always trust the machines but do not forget it is run by men, invest on upgrading and scaling up of people and process for optimal resource utilization.
Workplace modernization: A part of digital transformation
Workplace modernization is a necessity and not a luxury now for the changing trends in the market. To face the market challenges you have to make it more updated by investing and pumping money in technology else either you will be left out in the competition or will be forced to quit the market.
Workplace modernization for 2023
We are a focused company and are keeping a trend and culture of early adoption of technology into our company. We have created a reserve pool of funds for any technology adoption. Time to time we keep training our employees for any sudden change.
“All business processes should be aligned to have sustainable digitization”
Tejas Shah
Head IT Application, Prince Pipes and Fittings
Key factors to consider in workplace modernization
There are a lot of digitization factors that play a very vital role for workplace modernization.
One of the key initiatives to start with should be employee productivity, by identifying the majority of the manual or repetitive work to automate either by means of any common software platform or by RPA/BOTS.
Workplace modernization: A part of digital transformation
All business processes should be aligned to have sustainable digitization. Once we review all the business pain points and understand the existing process, then it is very easy to Identify - Implement - Review.
Workplace modernization for 2023
One of the most focused areas for workplace modernisation is identifying all critical business processes and trying to integrate in one central system to have a 360 degrees view to all stakeholders and to management.
Other key areas include IIOT with advanced features to have full insights overall.
“Workplace modernization is the first and most vital stage to consider before digital transformation”
Kushal Varshney
CTO, Virescent Infrastructure
Key factors to consider in workplace modernization
Below are the key factors which business needs to consider during modernization of workplace:
Workplace modernization is a business movement about engineering the right culture, business process change, and employee experiences
The modernization of workplace should be stakeholder (employees, management, visitors including customer & vendor) centric
Prepare a holistic, comprehensive, connected-integrated inclusive strategy considering input to output, entry to exit, hire to retire; end to end journey / process
Business should plan for a hassle free, seamless, connected, automated and secured experience at Workplace without compromising on meaningful controls
Keep everything simple, convenient, comfortable, transparent, collaborative, unbiased and less bureaucratic
Ensure no parallel system is created in excel or email etc., single source of truth and avoid duplicate entry points
Ensure that all technology is integrated and blended to work in harmony
Plan for emergencies and work with Zero trust principle even at Workplace
In new normal, plan for Hybrid and extended workplace as well
Workplace modernization: A part of digital transformation
Workplace modernization is the first and most vital stage to consider before digital transformation and must be applied throughout your change journey. With a modern workplace, you will succeed, improving the customer and employee experience, communications, processes, and productivity i.e., making you future ready.
Workplace modernization for 2023
Being born in the pandemic, since the beginning we have embraced digital employee experience and now in a new normal hybrid scenario, we are continuously improving our Employee experience through workplace modernization. We work with a continuous improvement approach and change is a never-ending journey.
Organizations realize the importance of productivity tools and are investing on them
Shweta Srivastava
Head IT Applications, JK Cement
Key factors to consider in workplace modernization
Workplace modernization is indeed one of the strategies for most businesses. With changes that have emerged post-pandemic, there is a huge shift in the employee as well as employer mindset. Work from anywhere has become a norm and enterprises are also to see value around it. Some of the key factors that are imperative to consider while planning to modernize the workplace are –
Flexibility to work from anywhere and anytime of the day
To be able to work from any device without much hassle of setting it up
Effective collaboration between the teams
Introduction of productivity tools which can contribute towards achievement of organizational goals and objectives
Technology tools supporting Agility which can cater to the VUCA world
Rapid development & delivery for quicker Go-to-market solutions
Continuous training and upskilling of resources
Security of IT assets.
Workplace modernization: A part of digital transformation
In the dynamic economy, businesses must respond to the demands of their consumers in a much faster, efficient and productive way. Since the expectations from employees in an organization has changed, so has the need for them to be able to align themselves with the newer ways of working and to be able to deliver the desired outcomes. Organizations have realized the importance of these productivity tools and are investing heavily on them. Performance of the organization completely depends on the performance of their employees. Modern tools and technologies empower the workforce for fulfilling the organization’s objectives.
Modernization of workplace ensures workforce is aligned with organization’s goals and objectives
Gaurav Vij
CIO, HooLiv.com
Key factors to consider in workplace modernization
For any organization to sustain in today’s fast digital world, it needs innovation, digital transformation, and hence needs the modernization of the workplace to ensure that the workforce is aligned with organization’s goals and objectives, while delivering to meeting the organization’s set and defined goals.
What does this mean to any organization? Why is this change desired to be there? Why do we need such technological upgrades?
To enable to employees working from anywhere
To ensure the expected productivity goals are met
To ensure seamless collaboration between the discrete teams, at different geographical places
To secure the organization’s assets
Workplace modernization: A part of digital transformation
Modernizing the workplace must be a part of every organization’s digital transformation journey.
Most organizations have either started the digital transformation journey, or are in the middle of it. Some of these small SME organizations today are failing to keep track of the digital transformation, or keep a pace with it. Fundamentally, they lack the adoption, skills and the technologies to modernize, and that makes it essential for the business to modernize their workplace environment.
Today, all CIOs are CFOs, all CEOs are Tech CEOs and all Companies are Tech Companies. This is the future of the Industry 4.0 shaping into Industry 5.0, and can only happen when organizations adapt the technologies in their workforce, while investing in new solutions, bringing the organizational change in appropriate manner and at the right time, as well as keep advancing their workforce to the latest technological trends.
Workplace modernization enhances employee metrics and job satisfaction
Venkat Ramani
CTO, Alliance Broadcasting (NEWS7 TAMIL)
Key factors to consider in workplace modernization
The key factors are:
Improve efficiency
Build more security
Improve flexibility & speed
Technical & modern challenges
Help to increase revenue
Should able to work from anywhere, any device, anytime with the customer flexibility
Workplace modernization: A part of digital transformation
Modernizing the workplace helps in several employee metrics including skills and talents, engagement and productivity, and job satisfaction.
| 2022-12-28T00:00:00 |
https://varindia.com/news/technology-experts-feel-workplace-modernization-becomes-a-part-of-organizations-digital-transformation
|
[
{
"date": "2022/12/28",
"position": 52,
"query": "workplace AI adoption"
}
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|
Datalynq's Market News - 2023 Industry Updates, Tech ...
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2023 Industry Updates, Tech Breakthroughs, and More
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https://datalynq.com
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[] |
Although artificial intelligence (AI) has dominated headlines for the past few years, many other technologies are also experiencing exciting growth and ...
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Electric Vehicles Are Embracing New Trends in 2024 - January 26, 2024
The automotive world is rapidly evolving as today’s cars gain access to technology that was impossible just a decade ago. In China, two automakers are rolling out electric passenger vehicles powered by sodium-ion batteries. The move comes in response to fluctuations in lithium prices that put pressure on the EV market.
Meanwhile, AI remains the biggest trend in all technology. Volkswagen is partnering with a startup called Cerence to bring ChatGPT features into its vehicle lineup. Drivers will be able to interact with the AI assistant for both simple and complex queries while on the road.
Two Chinese Automakers Launch EVs With Sodium-Ion Batteries
With the popularity of electric vehicles (EVs) reaching new heights, the batteries that power them are coming under the microscope. Though it may seem trivial to those on the outside, the type of battery under the hood is a significant factor in determining both driving range and the total cost of the vehicle. In China, the world’s largest EV market, sodium-ion-based batteries are making their debut to upset the reign of lithium-ion batteries.
Recent reports highlight two new EVs powered by sodium-ion batteries arriving on the market. The first comes from JMEV, an EV offshoot of the Chinese Jiangling Motors Group. It boasts a sodium-ion battery manufactured by China’s Farasis Energy with an energy density between 140Wh/kg and 160Wh/kg. For comparison, most lithium-ion batteries feature an energy density between 260Wh/kg and 270Wh/kg.
Notably, the JMEV vehicle has a driving range of 251 kilometers (about 156 miles). This range makes it an acceptable choice for daily commuting or cross-city travel, though it won’t be winning any awards for its longevity. Compared to leading lithium-ion EVs, which boast ranges over 300 miles per charge, this sodium-ion EV is underwhelming.
However, it’s an exciting new addition to the market given the potential for growth and savings in the sodium-ion battery segment. Compared to their lithium-based cousins, sodium-ion batteries feature superior discharge capacity retention. The JMEV model’s is a respectable 91%.
Meanwhile, the second Chinese EV to feature a sodium-ion battery comes from Yiwei, a brand belonging to the JAC Group. The battery itself is made by Hina battery and gives the vehicle a range of 252 kilometers.
Sodium-ion battery technology is expected to improve significantly over the next few years. Farasis claims the energy density of its batteries will increase to between 180Wh/kg and 200Wh/kg by 2026. It claims this will make the battery more relevant for a broader range of applications, including energy storage and battery swapping.
Another factor to consider is the variable price of lithium and its ripple effect on the EV market. In 2022, sharp price hikes spurred manufacturers to devote more resources to sodium-ion battery research and production. Unsurprisingly, Farasis and Hina aren’t the only firms making a push into the market. China’s CATL and Eve Energy also launched their sodium-ion technology in response to the rising prices of lithium.
Given that China imports roughly 70% of its lithium, price swings are a significant factor for the industry to consider. Since experts believe sodium-ion battery prices will drop as they enter mass production, these could be a viable alternative for the industry.
However, experts warn that slumping lithium prices will quell interest in sodium-ion battery research and adoption. How this trend plays out in 2024 will largely depend on lithium prices—not the booming demand for EVs in China and beyond.
Volkswagen Partners with Cerence to Bring ChatGPT Features to Its Vehicles
Just when it seemed like ChatGPT was everywhere, the artificial intelligence (AI) chatbot is expanding its presence into cars. Thanks to a partnership between Volkswagen and Cerence, an AI-focused tech startup, ChatGPT will soon be integrated into the carmaker’s Ida voice assistant. The news, announced during CES 2024, arrives with a mixed reception from drivers.
For those wary of integrating AI with their vehicle’s native software, there is a bright side. The car’s Ida assistant will still handle tasks like voice-powered navigation and climate control changes. However, Cerence’s Chat Pro software will soon be able to handle more complex queries through the cloud.
Drivers can pose natural language, open-ended queries like “Find me a good burger restaurant nearby.” Cerence’s software then processes the request and sends an answer back through the Ida assistant or the built-in navigation system.
As one might expect from ChatGPT, the integration can also handle more nuanced queries. A recent Volkswagen ad showed Ewan McGregor using “The Force” (the Cerence ChatGPT integration) to inquire about wearing a kilt to an upcoming event. The software supplied a detailed response advising the actor on the appropriateness of wearing kilts to formal gatherings. The ChatGPT integration will also be able to offer interactions like providing trivia questions for long road trips or reading a bedtime story for a child in the back seat.
According to Cerence, the ChatGPT integration gives drivers access to “fun and conversational chitchat” at the push of a button or voice command prompt. Looking ahead, Volkswagen and Cerence plan to continue their collaboration to add more ChatGPT features to the Ida voice assistant.
Drivers can expect ChatGPT functionality to arrive in the second quarter of 2024. The update will roll out over the air, and Volkswagen claims it will be “seamless.” An extensive lineup of Volkswagen vehicles, including both electric and gas-powered models, will support the integration. ID 3, ID 4, ID 5, ID 7, Tiguan, Passat, and Golf will be the first to receive the update.
Of course, while this is an exciting update for Volkswagen fans, there is the question of whether cars need access to ChatGPT’s features. The last thing the roads need is more distracted drivers. While drivers can interact with ChatGPT hands-free, it’s easy to envision someone getting a bit too involved with answering trivia questions behind the wheel and losing focus on the road. At this time, it’s unclear whether Volkswagen and Cerence have a plan in place to address these safety concerns.
Despite this, similar features will likely arrive in vehicles of other makes in the days ahead. ChatGPT is currently one of the hottest topics of conversation in the tech world, and automakers are likely scrambling to catch up with Volkswagen and add it to their vehicles.
AI Preparing for Another Busy Year - January 9, 2024
The buzzing excitement of a new year always brings plenty of change and interesting perspectives for the chip sector. From new product releases to outlooks on the latest trends, manufacturers and buyers are preparing for a busy year. AI promises to be a key technology in 2024, and Intel’s latest Xeon processor, which boasts AI acceleration in every core, is a testament to it.
Meanwhile, memory prices are continuing their surge from the end of 2023 thanks to sustained production cuts from the world’s biggest manufacturers. As buyers scramble to secure inventory amid fresh demand from China’s smartphone market, the price trend for memory chips looks bright.
Intel’s Latest Xeon Processors Feature AI Acceleration in All 64 Cores
Computing headlines in 2023 were dominated by artificial intelligence (AI). Experts believe the same will be true in 2024 as tech leaders around the globe further expand the integration of AI with traditional hardware and software. Intel’s forthcoming 5th Gen Xeon processors are no exception. The new chips are built with AI acceleration in each of their 64 cores to improve both efficiency and performance. The chipmaker’s latest breakthrough doesn’t stand alone. Adding AI to traditional computing hardware
is one of the hottest trends in chipmaking. It promises to play a central role in the industry over the coming years as more ways for end users to harness the power of AI on their devices are introduced.
However, experts have also warned that increased usage of AI puts a hefty strain on the world’s power supply. Many fear the technology could consume more power than entire countries as its popularity skyrockets. One way to offset this power-hungry tech is through more efficient chips—a feature Intel highlighted during the 5th Gen Xeon unveiling.
Intel says the new chips will enable a 36% higher average performance per watt across workloads. This is a significant energy reduction and will surely entice buyers who closely monitor their cost of ownership. Over the long run, systems using Intel’s newest Xeon chips will use far less energy than those running on older processors.
Despite being more efficient, the new Xeon processors also deliver a notable performance increase of 21%, on average, compared to the previous generation. For AI work, such as large-language model (LLM) training, the chips boast a 23% generational improvement, according to Intel executive vice president and head of the Data Center and AI Group, Sandra Rivera. For LLMs under 20 billion parameters, the new Xeon chip delivers less than 100-millisecond latency, enabling faster performance for model training and generative AI projects.
Per Intel’s press release announcing the new chip, including AI acceleration in every core will “address demanding end-to-end AI workloads before customers need to add discrete accelerators.”
Indeed, upgradeability is another key selling point for the 5th Gen Xeon processors. The chips are pin and software compatible with the previous generation.
Rivera says, “As a result of our long-standing work with customers, partners and the developer ecosystem, we’re launching 5th Gen Xeon on a proven foundation that will enable rapid adoption and scale at lower TCO.”
The first equipment featuring Intel’s new Xeon chips will arrive in the first quarter of 2024. Buyers can expect offerings from leading OEMs, including Lenovo, Cisco, Dell, HPE, IEIT Systems, Super Micro Computer, and more.
As AI adoption continues, expect to see more acceleration built into next-gen silicon. While it’s unclear how far the AI trend will advance, the technology isn’t going away anytime soon. By contrast, it seems AI will play a pivotal role in the future of computing and humanity. Don’t be fooled by the feeling that AI is already everywhere. According to Intel CEO Pat Gelsinger, the technology is likely “underestimated.”
With that perspective leading the way, expect Intel to focus more intensely on AI chips in the years ahead. The 5th Gen Xeon processor is a meaningful step in this direction, and more will surely follow.
NAND Flash Wafers See 25% Surge Amid Sustained Production Cuts
Aggressive production cuts are positively impacting memory chip prices practically across the board, according to new data from TrendForce and insights from industry experts. While the likes of Samsung, SK Hynix, and Micron have repeatedly slashed production—particularly of NAND Flash modules—buyers have burned through their inventories and are now rushing to secure more.
As the year begins, peak season demand from the holiday production cycle is waning, but overall demand for memory chips hasn’t slowed down. Experts point to persistent production cuts as the primary driver creating a supply and demand imbalance. As a result, memory chip makers can dramatically raise prices.
In November alone, NAND Flash wafers saw their price skyrocket by 25%, according to data from TrendForce. This rise closely followed Samsung’s decision to slash its total production capacity by 50% in September. While the rise is painful for buyers looking to source new memory components, experts have gained confidence in the pricing of those chips.
Interestingly, production cuts aren’t the only factor behind the price increase. Experts point to the surging Chinese smartphone market as another noteworthy factor. Led by Huawei and its Mate 60 series, smartphone manufacturers in China are working to regain market share after falling behind thanks to chip export restrictions put in place by the U.S. and its allies. As the Chinese chip sector seeks to reestablish its footing, the country’s device makers are aggressively boosting their production goals and aim to expand their output further in the new year.
Both in China and elsewhere, memory chip buyers have no choice but to accept higher wafer prices as they race to meet demand from consumers. Moreover, industry sources cited by TrendForce report that inventories across the board are shrinking rapidly, forcing customers to bite the bullet and place even more orders at higher prices. One source is quoted as saying, “Everyone just keeps scrambling for inventory.”
However, the longevity of the rising memory prices may be limited. Industry rumors suggest memory manufacturers may be preparing to increase production again in response to downstream demand. While this hasn’t been confirmed by any of the largest memory players, a production uptick does make sense for the first half of 2024.
No one wants a repeat of the sweeping shortages and subsequent round of panic buying seen in the past few years. A stable supply chain is far more beneficial in the long run for all involved. Yet, memory makers also can’t afford to continue selling their chips at incredibly low prices. This is a tricky balance to pull off and it will be interesting to see how the industry handles it in the coming months.
Notably, if production increases again, prices will likely slow their rise and stabilize around their current level. In the days ahead, both buyers and manufacturers must carefully monitor memory prices and inventory levels to prevent disruption and ensure profitability.
New Developments Coming in 2024 - December 15, 2023
Partnerships are the name of the game in today’s intertwined and convoluted chip industry. From securing supply chains to advancing manufacturing technology, more chipmakers are teaming up today than ever before.
Meta and MediaTek have recently announced a collaboration to develop next-gen chips for AR/VR smart glasses. Meanwhile, Intel has chosen TSMC to produce its Lunar Lake PC chips in a surprising move for its forthcoming mainstream platform.
MediaTek, Meta to Collaborate on AR/VR Chips for Next-Gen Smart Glasses
Although artificial intelligence (AI) has dominated headlines for the past few years, many other technologies are also experiencing exciting growth and development. Virtual reality (VR) and augmented reality (AR) have come a long way, primarily thanks to chip advancements that allow them to operate untethered from external computers.
Meta has rapidly cemented itself as a leader in this space and is expected to hold 70% of the overall market share in 2023 and 2024. Both its current Quest VR headsets and more experimental augmented reality glasses are far more refined than models from years past. Thanks to the accelerating adoption of AI, these devices are expected to get much smarter.
Now, Meta has announced a collaboration with MediaTek to develop AR and VR semiconductors for its next-gen smart glasses. Notably, Meta has relied on Qualcomm chips until this point to power the last two generations of its smart glasses.
According to TrendForce, the move is likely an effort by Meta to decrease costs and secure its supply chain. Meanwhile, the partnership will help MediaTek challenge Qualcomm in the AR/VR space and expand its footprint.
This fall, Meta introduced the second generation of its smart glasses product—the first came as part of a collaboration with Ray-Ban. The new glasses will feature improved recording and streaming capabilities to further cater to their primary audience of social media users. They will also take advantage of generative AI advancements by integrating Meta’s AI Voice Assistant powered by the Llama2 model.
MediaTek’s expertise in building efficient, high-performance SoCs will be essential to this ambition. AR glasses have traditionally been clunky, which has hindered their adoption. A sleeker design that doesn’t sacrifice advanced capabilities could change this.
Meta has been working diligently to develop chips in-house, including through a collaboration with Samsung. Its partnership with MediaTek can be seen as a risk management strategy in the short term as it continues to move toward chip independence. As smart glasses currently come with price tags as high as $300 that may deter consumers, the move may also be a way to cut production costs thanks to MediaTek’s competitive pricing.
Interestingly, despite the buzz around its AR and VR products, Meta has shipped just 300,000 pairs of its original model. With an anticipated launch of its next-gen smart glasses in 2025, it remains unclear how the market will receive them. Moreover, there are no concrete indications that the AR device market has gained significant traction.
Despite this, MediaTek’s decision to collaborate with Meta extends beyond the latter’s line of devices. By strategically integrating itself into Meta’s chip supply chain, MediaTek can cut into Qualcomm’s dominant market share. In a sense, the move should be viewed as a strategic move for the future rather than a quick way to add revenue now. Should Meta further increase its already-large market share or the general adoption of AR and VR devices increase, MediaTek could benefit greatly.
The Taiwanese firm has made a big push into the VR and AR market in recent years and its latest move is a signal that it plans to continue on this path. In 2022, a MediaTek VR chip was used in Sony’s PS VR2 headset. Now, thanks to this partnership with Meta, MediaTek chips could become a foundational part of the AR and VR device market over the coming years.
Intel Chooses TSMC’s 3nm Process for Lunar Lake Chip Production
Intel’s upcoming Lunar Lake platform, designed for next-gen laptops releasing in the latter half of next year, is making waves in the PC industry thanks to its innovative design and solid specs. After years of keeping production of its mainstream PC chips in-house, Intel is looking outward for its latest platform. The U.S. chipmaker is reportedly partnering with TSMC for all primary Lunar Lake chip production using the Taiwanese firm’s 3nm process.
Notably, neither Intel nor TSMC has directly commented on the partnership after leaks of its internal design details began spreading last month. However, discussions between industry experts on social media and recent reports from TrendForce bring validity to rumors.
Lunar Lake features a system-on-chip (SoC) design comprised of a CPU, GPU, and NPU. Then, Intel’s Foveros advanced packaging technology is used to join the SoC with a high-speed I/O chip. A DRAM LPDDR5x module is also integrated on the same substrate. TSMC will reportedly produce the CPU, GPU, and NPU with its 3nm process, while the I/O chips will be made with its 5nm process.
Mass production of Lunar Lake silicon is expected to start in the first half of 2024. This timeline matches the resurgence in the PC market most industry analysts project for the back half of next year. After slumping device sales over the past several consecutive quarters, a turnaround is expected thanks to demand for new AI features and a wave of consumers who purchased devices during the pandemic being ready to upgrade.
This latest partnership is far from the first time Intel and TSMC have worked together, but the fact that it involves Intel’s mainstream PC chip line is noteworthy. The latter produced Intel’s Atom platform more than ten years ago. However, Intel has only recently started outsourcing chips for its flagship platforms, including the GPU and high-speed I/O chips used in its Meteor Lake platform.
According to TrendForce, the decision to move away from in-house production and trust TSMC with the job hints at future collaborations. What this could look like remains to be seen, but TSMC may soon find itself producing Intel’s mainstream laptop platforms more frequently.
Proactive Sourcing Strategies for All Components - December 1, 2023
The chip industry is positioned for a stretch of significant growth over the coming decade. Technologies like artificial intelligence promise to revolutionize the supply chain and daily life. However, this exciting shake-up means OEMs must carefully strategize the best way to source components—both advanced and legacy node chips—to prevent costly production delays.
Meanwhile, companies are racing to improve their AI capabilities. Training new models is a key priority and is being benchmarked with a new test designed for measuring the efficiency of training generative AI. Unsurprisingly, Nvidia leads the way, but both Intel and Google have made big improvements.
OEMs Must Prepare to Navigate Supply Chain Shifts Across Node Sizes as Chip Industry Evolves
For original equipment manufacturers (OEMs), the semiconductor supply chain is simultaneously one of the most difficult sectors to source inventory from yet also more accessible than ever. With supply and demand imbalances as well as inventory shortages and gluts raging over the past few years, the industry has been anything but stable. As chipmakers work to expand the production of advanced node chips, legacy nodes are falling out of favor even as demand for them remains steady.
This means OEMs must be mindful of sourcing components across node sizes and plan their strategies according to unique trends affecting each one. Over the next decade, technologies like artificial intelligence (AI), 5G, and electric vehicles promise to redefine the chip world. However, the aerospace, defense, and healthcare sectors continue to rely on legacy chips for their essential operations.
The latter is likely to cause problems according to many industry analysts. As semiconductor manufacturers shift their production strategies toward more advanced nodes, investment in legacy node fabs has decreased considerably. Certain firms, like GlobalFoundries, have capitalized on this by focusing their efforts on older chips. But the industry is largely moving on.
This means demand for legacy components is poised to outpace supply over the next few years. At a time when many legacy chips are also more difficult to find—often only available through refabrication—OEMs who rely on them are facing an incredibly challenging period for component sourcing.
Meanwhile, the majority of chipmakers are being lured in by the high profit margins and demand for cutting-edge chips designed to power AI and EV applications. They continue to invest billions of dollars each year to expand production capacity at existing facilities or build new fabs. The latter takes time, though, often years before a new fab is up and running. OEMs will need to be patient while waiting for shortages to resolve.
Moreover, OEMs must act now to adopt forward-looking strategies for sourcing essential components across node sizes. A diverse plan utilizing a more robust network of suppliers is an effective tactic for insulating against the ebbs and flows of the supply chain. This includes working with both local and global suppliers. Many carmakers, particularly those in the EV space, have adopted this tactic by forging relationships directly with chip manufacturers to guarantee inventory.
Some experts also recommend OEMs with the capacity to do so purchase extra inventory ahead of potential shortages. While building up a safety stock is expensive, doing so can prevent costly production delays down the line. Of course, on a larger scale, this trend can also further disrupt the supply chain as inventory gluts push down prices—as is currently being seen in the memory chip market.
Regardless of the approach OEMs take, the key is to remain agile. Being ready to adapt to changes in the market at a moment’s notice and predict them before they happen is essential in today’s fast-paced market. Utilizing advanced analytics tools and data from every point on the supply chain creates a bigger picture and allows for more accurate decision making.
Over the next several years, the chip industry is prepared to through another period of rapid growth and change thanks to AI and electric cars. As demand for different nodes changes with it, OEMs must be prepared to evaluate and adjust their sourcing strategies at multiple levels or risk falling behind. Meanwhile, OEMs relying on legacy components must be prepared to face a shortage and adopt clever solutions to secure enough inventory.
Nvidia Continues to Dominate Generative AI Training, But Intel and Google are Closing In
Training generative artificial intelligence (AI) systems is no easy task. Even the world's most advanced supercomputers take days to complete the process—a timeline few other projects can claim. Less advanced systems require months to do the same work, putting into perspective the vast amount of computing power needed to train large language models (LLMs) like GPT-3.
To benchmark progress in LLM training efficiency over time, MLPerf has designed a series of tests. Since launching five years ago, the performance of AI training in these tests has improved by a factor of 49 and is now faster than ever. MLPerf added a new test for GPT-3 earlier this year and 19 companies and organizations submitted results. Nvidia, Intel, and Google were the most noteworthy among them.
Each of the three took the challenge quite seriously and devoted massive systems to running the test. Nvidia’s Eos supercomputer was the largest and blew competitors away as expected. The Eos system is comprised of 10,752 H100 GPUs—the leading AI accelerator on the market today by practically every metric. Up against MLPerf’s GPT-3 training benchmark, it completed the job in just four minutes.
Eos features three times as many H100 GPUs as Nvidia’s previous system, which allowed it to achieve a 2.8-fold performance improvement. The company’s director of AI benchmarking and cloud computing, Dave Salvatore, said, “Some of these speeds and feeds are mind-blowing. This is an incredibly capable machine.”
To be clear, the MLPerf test is a simulation that only requires the training to be completed up to a key checkpoint. The computer reaching it means the rest of the training could have been completed with satisfactory accuracy without the need to actually do it. This makes the test more accessible for smaller companies who don’t have access to a 10,000+ GPU supercomputer while also saving both computing resources and time.
Given this design, Nvidia Eos’ time of four minutes means it would take the system about eight days to fully train the model. A smaller system built with 512 Nvidia H100 GPUs, a much more realistic amount for most supercomputers, would take about four months to do the same task.
While it should come as no surprise that Nvidia is dominating the AI space given its prowess in the AI chip sector, Intel and Google are making strides to close the gap. Both firms have made big improvements in their generative AI training capabilities in recent years and posted respectable results in the MLPerf test.
Intel’s Guadi 2 accelerator chip featured its 8-bit floating-point (FP8) capabilities for the test, unlocking improved efficiency over preceding versions. Enabling lower precision numbers has led to massive improvements in AI training speed over the past decade. It’s no different for Intel, which has seen a 103% reduction in time-to-train. While this is still significantly slower than Nvidia’s system, it’s about three times faster than Google’s TPUv5e.
However, Intel argues that the Gaudi 2 accelerator’s lower price point keeps it competitive with the H100. From a price-to-performance standpoint, this is valid. The next-generation Gaudi 3 chip is expected to enter volume production in 2024. Given that it uses the same manufacturing process as Nvidia’s H100, hopes are high for its performance.
Over the coming years, the importance of generative AI training will only grow. Advancements like those being made by Nvidia, Intel, and Google are expected to pave the way for a new age of computing. Thanks to benchmarks like MLPerf’s training tests, we can watch the results come through in real-time.
AI Helping Pull Memory and Mobile Out of the Depths - Nov 17, 2023
Experts have been quick to tout the revolutionary potential of artificial intelligence (AI). So far, the technology is living up to the hype. Industries across the spectrum are prioritizing AI in their forward-looking plans and chip suppliers are racing to keep up with demand being sparked by the rise of new AI products.
For the oft-beleaguered memory chip industry, this growth is a welcome change—and is driving price increases in the fourth quarter. Meanwhile, smartphone makers are working to implement generative AI into their latest devices as industry leaders claim the tech could be as influential as smartphones themselves.
DRAM, NAND Prices to Rise in Q4 with Continued Growth in Q1 Next Year
The memory chip market has gone through plenty of turmoil over the last few years. For the past several months, experts have been pointing to signs of recovery even as the industry seemed to bottom out. Now, thanks to numerous market influences, the numbers are starting to echo this sentiment as the memory sector heads for a turnaround in the new year.
According to a recent TrendForce report, Q4 memory chip prices are expected to rise by double digits. Mobile DRAM, interestingly, is leading the way with a projected increase of 13-18%. Meanwhile, NAND Flash isn’t far behind with prices for eMMC and UFS components expected to jump by 10-15% in the quarter.
Typically, mobile DRAM takes a backseat to traditional DRAM chips, but several factors are buoying its price in the fourth quarter. One of the largest is Micron’s decision to hike prices by more than 20% for many of its leading memory products. Samsung’s recent move to slash production in response to an industry-wide supply glut is also driving memory chip prices upward.
Meanwhile, the fall/winter peak season for new mobile devices tends to foster a favorable end of the year for memory chip makers. While this is a factor in the Q4 price rise, it isn’t the only one. Growth in the Chinese smartphone market—sparked by Huawei’s new Mate 60 series—is causing device makers to increase their production targets as consumer demand rises. Notably, the Mate 60 Pro features a 7nm 5G chip, making it the first such Chinese-made device to hit the market since global trade restrictions aimed to cut the country off from advanced chips and chipmaking equipment were implemented.
The wider chip industry is also prepared for a positive fourth quarter on the back of increasing electronics sales and IC sales. Notably, this is expected to drive year-over-year growth for key chip markets in Q4 following a stretch of declines over the previous five quarters.
Looking ahead, experts don’t expect the memory chip market’s resurgence in the fourth quarter to fizzle out in the new year. The first frame of 2024 promises to be another strong period of growth. Though TrendForce reports, expectations should be tempered since external factors like the Lunar New Year and off-season production lulls will likely slow the rise of prices.
Even so, experts believe demand will continue into the new year and that suppliers will maintain their conservative production strategies. Both will influence prices to stay high into Q1 2024 as the memory market continues its impressive recovery.
Xiaomi First to Smartphone Generative AI Features with Qualcomm’s Latest Snapdragon Chip
There was a time when foldable displays and 5G were expected to be turning points for smartphones. In a sense they were, but those technologies are already on the edge of becoming outdated thanks to the arrival of artificial intelligence (AI).
Qualcomm’s Snapdragon 8 Gen 3 mobile chipset launched in late October boasting on-device generative AI, which dramatically speeds up processing-intensive activities usually done on the cloud. Xiaomi simultaneously announced new flagship phones using the edge AI Qualcomm mobile platform.
Generative AI has boomed this year thanks in large part to the rise of ChatGPT. Its uncanny ability to create shockingly passable content in response to simple user prompts sets it apart from other forms of AI that require an expert-level understanding to use productively. Tech luminaries like Bill Gates and Sundar Pichai have said they think generative AI on smartphones could be as big as the dawn of the internet and smartphone technology itself.
Xiaomi founder and president Lu Weibing was the sole smartphone manufacturer to speak at the 2023 Qualcomm Snapdragon Technology Summit, where he showcased the Xiaomi 14 series.
"Xiaomi and Qualcomm have a long-term partnership, and Xiaomi 14 demonstrates our deep collaboration with Qualcomm. This is one of the first times a new platform and device launch together," Lu said.
The Xiaomi 14 and 14 Pro, presently exclusive to China pending a global launch, have AI capabilities ranging from those considered fairly basic, like summarizing webpages and generating videoconferencing transcripts, to ambitious features such as inserting the user into worldwide location and event scenes.
Qualcomm claims in its press release that Snapdragon 8 Gen 3 ushers in a new era of generative AI, “making the impossible possible.” The platform’s large language models (LLM) can run up to 20 tokens/sec—one of the fastest benchmarks in the smartphone industry—and generate images in a fraction of a second.
"AI is the future of the smartphone experience," said Alex Katouzian, senior vice president and general manager of Qualcomm's mobile, computing, and XR division.
Xiaomi has been working on AI since 2016 and maintains a dedicated AI team of over 3,000 people. Between that team and more than 110 million active users worldwide, Xiaomi’s digital assistant Xiao AI, which got upgraded with generative AI capabilities in August 2023, has the potential to continue evolving rapidly.
The digital assistant recognizes songs and objects, prevents harassing calls, suggests travel routes, and provides medication reminders. It can also control household appliances when integrated into Xiaomi’s line of smart home devices.
Xiaomi is the world's leading consumer IoT platform company and quietly the world’s third-largest smartphone manufacturer. The company connects 654 million smart devices and commands an impressive 14% global smartphone market share. Of course, its smartphone rivals in China have also been busy in the AI arena. Huawei, Oppo, and Vivo recently announced major upgrade plans for their own digital assistants.
The recently launched Google Pixel 8 also focused on AI with the company boasting its algorithms’ ability to pick out the best facial expressions in batches of group photos and easily paste them into a different image. That allows, for example, for a photograph to feature eight happy, smiling people when only four were actually smiling when the shutter snapped. Magic Compose, another feature that Google announced at its May 2023 developer conference, uses generative AI to suggest responses to text messages or rewrite responses in a different tone.
According to a recent report from Bloomberg, Apple is said to be developing a number of new AI features for the iPhone and other iOS products. Scheduled for release in 2024, iOS 18 is expected to include AI upgrades to Siri, Apple’s messaging app, Apple Music, Pages, Keynote, and more.
"There may be a killer use case that doesn't exist yet," said Luke Pearce, a senior analyst for CCS Insight, a tech research and advisory firm. "But that will come around the corner, surprise us all, and become completely indispensable.”
In the meantime, generative AI promises to be the smartphone industry’s next major milestone. As the technology continues to evolve, expect device makers to find more ways to incorporate it while taking advantage of the latest edge AI semiconductors.
New Shortage Coming for AI? - Nov 3, 2023
Artificial intelligence (AI) is arguably today’s most exciting technology. However, as with any new tech, adoption and development aren’t easy. Chipmakers and buyers alike are currently struggling with a shortage of AI components due to bottlenecks in advanced chip production lines at TSMC and SK Hynix. Those are expected to ease in 2024 thanks to aggressive capacity upgrades as AI represents a big opportunity for chip companies.
Meanwhile, Nvidia dominates the AI market thanks to its combined offerings of both hardware and software. Companies like AMD are attempting to challenge Nvidia’s power by exploring new software solutions—and acquiring startups who have done the same—to pair with their existing hardware offerings.
Production Bottlenecks Affecting the Chip Industry Amid AI Boom
The number of artificial intelligence (AI) applications has skyrocketed as generative AI takes the world by storm. Businesses of all shapes and sizes are exploring ways to integrate the technology into their operations—and use it to boost their bottom lines. However, the advanced chips needed to power large language models (LLMs) and AI algorithms are complex and difficult to produce.
Take Nvidia’s flagship H100 AI accelerator, for instance. Though orders for the chip are piling up, production is limited by the fact that TMSC is the only firm manufacturing the H100. As a result, availability of the GPU is limited by TSMC’s tight production capacity, particularly in CoWoS packaging. While the Taiwanese chipmaker expects to start filling orders in the first half of 2024, this bottleneck represents a larger issue for the AI industry.
As demand for AI products soars, chipmakers are scrambling to keep up. High-powered GPUs aren’t the only components experiencing bottleneck issues either. The smaller components inside them are also difficult to source given their novelty and scarcity.
HBM3 chips, the fastest memory components needed to support AI’s intense computations in the H100, are currently supplied exclusively by SK Hynix. The latter is racing to increase its capacity, but doing so takes time. Meanwhile, Samsung is hoping to secure memory chip orders from Nvidia by next year as it rolls out its own HBM3 offerings.
All said, data from DigiTimes points to a massive disparity between supply and demand in the AI server market. Analyst Jim Hsiao estimates the current gap is as wide as 35%. Even so, more than 172,000 high-end AI servers are expected to ship this year.
This move will be supported by chipmakers who have made (and continue making) significant increases in their production capacity. By mid-2024, TSMC’s CoWoS capacity is expected to increase to around 30,000 wafers per month—up from the 20,000 wafer capacity it projected for the new year this summer.
Demand for AI servers is led by massive buying initiatives from tech firms as they seek to revamp their computing operations with AI. Indeed, 80% of AI server shipments are sent to just five buyers—Microsoft, Google, Meta, Amazon (AWS), and Supermicro. Of these, Microsoft leads the way with a staggering 41.6% of the total market share for high-end AI servers.
Analysts believe this head start will make unseating Microsoft from its position of AI leadership very difficult in the coming years. A novel breakthrough in new technology or significant investment will be needed for the likes of Google and Meta to catch up given their respective 13.5% and 10.3% market shares.
Notably, the growing demand for AI servers is also causing a drop in shipments of traditional high-end servers. Top tech firms are moving away from these general-purpose machines as their budgets are pulled in multiple directions. Moreover, companies are choosing to buy directly from ODM suppliers rather than server brand manufacturers like Dell and HP. Experts predict ODM-direct purchases will account for 81% of total server shipments this year. This change has forced suppliers to adapt quickly to their business model being ripped out from under them.
Competition in the AI space is expected to heat up in 2024 and beyond thanks to capacity increases and profitable applications for AI continuing to be discovered. As the necessary hardware becomes more readily available, buyers will have more options and flexibility in their orders, leading to more competition among manufacturers. Though AI demand is likely to even out over time, this sector represents a massive opportunity for chipmakers over the coming decade. Expanding advanced chip production capacity is step one toward reliably fueling the AI transformation.
AMD Acquires Nod.AI to Bolster its AI Software Portfolio and Compete with Nvidia
In the race for dominance in the artificial intelligence (AI) sector, all roads run through Nvidia. The GPU maker saw its revenue spike by over 100% this year on the back of significant demand for AI products and its decade-long foresight to position itself as a market leader. While Nvidia’s hardware is certainly impressive, its fully-fledged ecosystem of AI software and developer support is what truly gives the firm such a massive advantage.
In an effort to catch up, AMD has announced its acquisition of Nod.AI. The startup is known for creating open-source AI software that it sells to data center operators and tech firms. Nod.AI’s technology helps companies deploy AI applications that are tuned for AMD’s hardware more easily. While details of the acquisition weren’t disclosed, Nod.AI has raised nearly $37 million to date.
In a statement, an AMD spokesperson said, “Nod.AI’s team of industry experts is known for their substantial contributions to open-source AI software and deep expertise in AI model optimizations.”
Nod.AI was founded in 2013 by Anush Elangovan, a former Google and Cisco engineer. He was joined by several noteworthy names from the tech industry, including Kitty Hawk’s Harsh Menon. The startup launched as an AI hardware company focusing on gesture recognition and hand-tracking wearables for gaming. However, it later pivoted to focus on AI deployment software, putting it closely in line with AMD’s needs.
The startup will be absorbed into a newly formed AI group at AMD which was created earlier this year. Currently, the group consists of more than 1,500 engineers and focuses primarily on software. AMD already has plans to expand this team with an additional 300 hires in 2023 and more in the coming years. It’s unclear if these figures include the employees coming in from Nod.AI or if AMD plans to hire even more staff in addition to them.
Nod.AI’s technology primarily focuses on reinforcement learning. This approach utilizes trial and error to help train and refine AI systems. For AMD, the acquisition is another tool in its belt of software offerings to tempt prospective buyers with. Reinforcement learning tools help customers deploy new AI solutions and improve them over time. With software designed to work well with AMD’s hardware, the process becomes more intuitive and leads to faster launch times.
While AMD is a long way from being able to compete with Nvidia’s software platform and developer support directly, it is taking steps in the right direction. The company is utilizing both internal investment and external acquisitions to do so, according to its president Victor Peng. Nod.AI is its second major acquisition in the AI software space in the past few months. Though AMD has no current plans for further moves, Peng noted that the firm is “always looking.”
Over the coming years, Nvidia’s lead in the AI space will be challenged. For AMD and others, combined innovation in both hardware and software will be essential to stealing market share from the current leader. AMD’s latest acquisition will help bolster its software portfolio in the short term with immediate effect while it continues to scale its long-term plans.
Leaders Pledge AI Protection as Satellites Make Chips in Space - October 20, 2023
The chip industry is already worth $500 billion and that figure is expected to double by the end of the decade. With so much on the line chipmakers are tech companies are doing everything they can to innovate and evolve.
For some, this means pursuing the mind-boggling power of artificial intelligence. With many still wary about the technology, though, government officials and tech leaders are teaming up to safely advance the AI field and earn trust. Others are looking elsewhere for innovation—including the stars. Could manufacturing semiconductor materials in space be the key to more efficient chips in the years to come? One U.K. startup thinks the answer is yes.
Tech Leaders Commit to Safely Advancing AI Through Collaboration and Transparency
It’s no secret artificial intelligence (AI) is reshaping the way the world interacts with technology. Thanks to the introduction of ChatGPT, machine learning and generative AI have gone mainstream with everyone from leading tech firms to average end users experimenting with its possibilities. But some are less enthusiastic about the potential applications for AI and remain wary of its risks.
As the ubiquity of AI extends to practically every industry, tech leaders and government officials are coming together to pledge their commitment to AI safety. Spearheaded by the Biden Administration, the voluntary call-to-action has been answered by 15 influential tech firms in recent months.
Earlier this year, the program was initially backed by seven AI companies. This included Google, Microsoft, OpenAI, Meta, Amazon, Anthropic, and Inflection. Now, eight more firms have pledged their intention to aid in the safe development of AI, including Adobe, IBM, Nvidia, Palantir, Salesforce, Cohere, Stability, and Scale AI.
The latter said in a recent blog post, “The reality is that progress in frontier model capabilities must happen alongside progress in model evaluation and safety. This is not only the right thing to do, but practical.”
“America’s continued technological leadership hinges on our ability to build and embrace the most cutting-edge AI across all sectors of our economy and government,” Scale added.
Indeed, companies around the world are exploring new ways to integrate artificial intelligence into every aspect of their operations. From logistics to customer service and research to development, AI promises to revamp the way work is done.
Even so, the safety concerns of AI loom large over any potential benefits. Much of this fear stems from not understanding how the models themselves work as they grow larger and more complex. Others fear it will soon be impossible to differentiate AI-generated content from human-generated work. Meanwhile, the cybersecurity risks associated with AI are numerous while solutions remain largely unexplored.
The Biden Administration’s plan to address these problems is threefold. Step one puts an emphasis on building AI systems that put security first. This includes investment in cybersecurity research and safeguards to prevent unwanted access to proprietary models. AI firms involved in the pledge have also committed to rigorous internal and external testing to ensure their products are safe before introducing them to the public. Given the rapid advancement of AI and industry-wide push to bring new applications to market first, this is an important guardrail.
Finally, the companies involved have made a commitment to earning the public’s trust. They aim to accomplish this in many ways, but ensuring users know when content is AI-generated is paramount. Other transparency guidelines include disclosing the capabilities of AI systems and leading research into how AI systems can affect society.
In a statement, the White House said, “These commitments represent an important bridge to government action, and are just one part of the Biden-Harris Administration’s comprehensive approach to seizing the promise and managing the risks of AI.”
The statement also mentions an executive order and bipartisan legislation currently being developed. Ultimately, though, no one company or government will dictate the future of AI. An ongoing collaboration is needed to ensure the technology is developed safely over the coming years.
Getting the most influential tech firms on board early is a big step in the right direction. Closely monitoring their actions to ensure they consistently align with this commitment is crucial as more innovations are made and AI continues to evolve.
U.K. Startup Eyes Satellite-Based Chip Manufacturing in Space, Promises Greater Efficiency
Producing high-quality semiconductors requires a precise manufacturing environment that costs billions of dollars to create. While the microgravity and vacuum of outer space are hostile to human life, they are perfect for making semiconductors. Some companies, including U.K.-based startup Space Forge, are exploring the possibility of manufacturing chips in orbit.
The startup’s ForgeStar-1 satellite is on its way to the U.S. and will be launched either late this year or early next year. This comes after its first attempt at a launch went awry when the Virgin Orbit rocket it was strapped to failed in January.
The satellite is roughly the size of a microwave but contains a powerful automated chemistry lab. Researchers on Earth will control the devices inside to mix chemical compounds and experiment with novel semiconductor alloys. They’ll be able to monitor how the substances behave in microgravity and the vacuum of space compared to their responses on Earth.
In a statement to Space.com, Space Forge CEO Josh Western said, “Producing compound semiconductors is a very intense and very slow process, they are literally grown by atoms, and so gravity has a profound effect… In space you’re able to overcome that barrier, because there is an absence of gravity.”
Of course, microgravity isn’t the only advantage of making chips in space. The process also benefits from the perfect vacuum—something chipmakers rely on expensive machinery to replicate on Earth to protect materials from contamination. Manufacturing chip materials in space negates the need for manmade vacuum equipment and ensures contaminants are non-existent.
Space Forge estimates that the favorable conditions of outer space make it possible to produce chips that are 10 to 100 times more efficient than those made on Earth. That’s a notable improvement that could have radical implications for the $500 billion semiconductor industry—especially considering that its size is expected to double by 2030.
Of course, making semiconductors in space isn’t as simple as it sounds. Materials produced in orbit would need to be safely returned to Earth for further processing and packaging. Protecting such sensitive materials on a rough and fiery trip back into the atmosphere is a major challenge.
Space Forge’s first satellite won’t even attempt this feat. Instead, it will beam experiment data back to Earth digitally for researchers to analyze. The startup does plan to return its satellites eventually, but Western says this isn’t in the forecast for another two to three years.
Although the prospect of making chips in space remains futuristic, this is an exciting development to monitor. Manufacturing semiconductors in space’s favorable environment could yield far more efficient chips in the years to come. As chipmakers seek new ways to produce the most advanced silicon and cash in on the growing industry’s demand, no approach is too outlandish to consider.
Moving Forward with Semiconductor Development – October 3, 2023
As tensions with China persist, chipmakers are looking for new ways to diversify their supply chains. This has put developing Asian nations in the spotlight. Perhaps none have shined as brightly as Vietnam, which the U.S. views as a key strategic partner. A major tech summit last month saw several billion-dollar business partnerships with U.S. chip firms inked.
Meanwhile, U.S. domestic chip ambitions remain strong. Private companies and public institutes alike are working to advance the speed of American chip innovation while also bolstering a workforce facing massive shortages. A new $46 billion investment from the National Science Foundation aims to address both of these goals as the U.S. continues expanding its stake in the chip sector.
Vietnam, U.S. Strengthen Ties Amid New Semiconductor Deals
Political and economic relations between the U.S. and Vietnam have been icy over the past few decades. However, this sentiment is changing thanks to hard work from government officials on both sides. The two countries have now agreed to billions of dollars in business partnerships following a major tech summit and diplomatic visit by U.S. President Joe Biden.
As countries and companies alike seek to diversify their supply chains away from China, developing nations in Asia have taken center stage. Thanks to cheap labor and regional accessibility to other chip operations, these countries offer the most convenient path to a more stable supply chain. Vietnam, which now sees the U.S. as its largest export market, has seized the opportunity to expand its role in the chip sector.
In a press conference, President Biden said, “We’re deepening our cooperation on critical and emerging technologies, particularly around building a more resilient semiconductor supply chain.”
“We’re expanding our economic partnership, spurring even greater trade and investment between our nations,” he added.
A number of key government officials joined executives from top tech and chip firms, including Google, Intel, Amkor, Marvell, and Boeing, at the Vietnam-U.S. Innovation and Investment Summit last month. The roundtable consisted of discussions on how to deepen partnerships and spark new investments.
Those talks are already paying dividends as several new business deals were announced. Leading the way was a $7.8 billion pledge from Vietnam Airlines to purchase 50 new 737 Max jets from Boeing.
In the chip sector, two new semiconductor design facilities are being built in Ho Chi Minh City. One will belong to Synopsys and the other to Marvell—both U.S.-based firms. At a broader level, the partnership seeks to “support resilient semiconductor supply chains for U.S. industry, consumers, and workers.”
These deals come shortly after Amkor announced a $1.6 billion chip fab near Vietnam’s capital of Hanoi. The company expects its new facility, which will primarily be used for assembly and packaging, to open sometime this month.
Despite this positive momentum, a dark cloud still hangs over the partnership. Vietnam’s chip workforce remains concerningly small. Currently, just 5,000 to 6,000 of the country’s roughly 100 million citizens are trained hardware engineers. Demand forecasts for the next five years project Vietnam will need 20,000 hardware engineers to fill an influx of new chip jobs. That number will more than double to 50,000 in ten years.
Without a robust pipeline of new chip talent, even the loftiest partnerships and investments could be in jeopardy. This reflects a similar chip worker shortage in the U.S., where 67,000 positions for technicians and engineers are expected to go unfilled by the end of the decade.
Even so, the increase in collaboration between Vietnam and the U.S. is a positive sign. Chipmakers are working to quickly decouple their supply chains from China as economic tensions with the country and its international trade partners sour. Hubs like Vietnam, India, and Malaysia have become central to these efforts. With each bit of political momentum, relocating chip production becomes easier.
The U.S.-Vietnam partnership is an important one to monitor in the coming months. Both Washington and Hanoi believe their new strategic partnership will usher in even more investment deals than those already revealed. Whether or not the latest wave of diplomacy is enough to entice investors and other chipmakers remains to be seen.
National Science Foundation Commits $45.6M to Support US Chip Industry
In a new wave of support arriving as a result of the CHIPS Act, the U.S. National Science Foundation (NSF) has pledged $45.6 million to support the domestic semiconductor industry. The public-private partnership brings in several top firms, including IBM, Ericsson, Intel, and Samsung. Notably, the support comes through the NSF’s Future of Semiconductors (FuSe) program.
NSF Director Sethuraman Panchanathan said in a statement following the investment, “By supporting novel, transdisciplinary research, we will enable breakthroughs in semiconductors and microelectronics and address the national need for a reliable, secure supply of innovative semiconductor technologies, systems, and professionals.”
The program funds are divided across 24 unique research and education projects with more than 60 awards going to 47 academic institutions. The grants are funded primarily by the NSF using dollars from the CHIPS Act with additional contributions coming from the partner companies. Each firm has pledged to provide annual support for the program through the NSF.
NSF leaders hope the investment will serve as a catalyst for chip breakthroughs. The FuSe program emphasizes a “co-design” approach to chip innovation, which considers the “performance, manufacturability, recyclability, and environmental sustainability” of materials and production methods.
Perhaps more important to U.S. semiconductor ambitions, though, is developing a robust chip workforce. Currently, the nation is lacking in this area with 67,000 chip jobs expected to be vacant by 2030. The NSF’s latest investment places a heavy emphasis on developing chip talent in America. As the industry works to shore up workforce gaps, collaboration is more essential than ever.
Panchanathan says the NSF’s $46 million investment will “help train the next generation of talent necessary to fill key openings in the semiconductor industry and grow our economy from the middle out and bottom up.”
Leveraging the expertise and support of industry partners is a key component of the FuSe program. Dr. Richard Uhlig, senior fellow and director of Intel Labs, said in a joint press release, “Implementing these types of programs across the country is an incredibly powerful way to diversify the future workforce and fill the existing skills gap.”
Meanwhile, President of Samsung Semiconductor in the U.S., Jinman Han, said, “Helping drive American innovation and generating job opportunities are critical to the semiconductor industry… As we grow our manufacturing presence here [in the U.S.], we look for partners like NSF that can help address the challenges at hand and drive progress in innovation while cultivating the semiconductor talent pipeline.”
Demand for semiconductors is growing around the world. As chipmakers seek to move their operations out of China amid tense economic conditions, the U.S. domestic industry is vying to reclaim its status as a global powerhouse. The CHIPS Act, passed in 2022, has sparked a new period of growth and innovation as demonstrated by initiatives like this one.
As the latest wave of NSF-funded projects roll out over the coming months, it will be exciting to see what sort of innovation they yield. Likewise, these essential programs will help inspire the next generation of chip talent in America.
Corporate Demand for AI is Driving Chip Market - September 19, 2023
The chip market has endured a tumultuous few years in the wake of the COVID-19 pandemic. Luckily, several factors within the tech industry are paving the way for a strong recovery and pattern of growth. From IoT devices to automakers eyeing electric vehicles, products across every sector need more chips. As the AI craze reaches full swing, chipmakers are turning record profits and preparing to use this as an opportunity for bolstered growth in the coming years.
AI, Increasing Tech Adoption Drive Chip Demand, Grow Market
The semiconductor industry appears well-positioned for a period of growth according to a report from the IMARC group which looks out as far as 2028. IMARC predicts the chip industry will reach $941.2 billion globally by 2028 and experience a growth rate (CAGR) of 7.5% from now until then.
So, what’s driving this steady incline for the industry? As the global economy leans more heavily on tech-centered products, the need for semiconductors rises. Given a push for more technology in offerings across sectors, chips are increasingly in demand—even in places they haven’t been traditionally.
The growing Internet of Things (IoT) is one major driver of growth. More and more of today’s devices are connected to the internet, enabling efficient communication and seamless collaboration. Of course, these devices require several components to stay connected, going beyond those required for their core function. Analysis firm Mordor Intelligence projects the IoT chip market to reach $37.9 billion by 2028, expanding significantly from its current $17.9 billion size.
Meanwhile, the automotive industry is also hungry for chips thanks primarily to the rise of electric vehicles (EVs). These cars rely on more components to power their complex software and hardware. Semiconductors are essential for key EV features like battery control, power management, and charging. Even non-electric vehicles are being built with more chips today than ever before. Consumers demand flashy, attractive features, which increases the number of chips needed to support them. As some companies lean into self-driving technology and advanced driver assistance programs, more advanced chips are required.
Indeed, the automotive industry has been one of the biggest chip buyers in recent years. Experts predict this trend will only increase as more carmakers and governments emphasize EVs, citing environmental concerns. Analysts project the auto sector will be the third largest buyer of semiconductors by 2030, accounting for $147 billion in annual revenue.
Artificial intelligence (AI) is perhaps the most influential technology in the world right now. Surging demand has chipmakers fighting to keep up as companies race to invest in AI. The rise of ChatGPT sparked interest in generative AI, catching the eye of major tech players like Microsoft and Google. Meanwhile, other machine learning applications are being explored across every industry, driving demand for high-power data center chips. Benzinga projects the
AI chip market will grow to $92.5 billion by 2028, a staggering increase from the $13.1 billion it was valued at in 2022.
Nivida has a massive head start on the market and currently is responsible for making the overwhelming majority of AI chips thanks to its prowess in the GPU market. Memory chipmakers are also benefiting as AI applications require high-speed DRAM units. The explosive growth of AI is driving demand for HBM3 memory chips and their successors.
Finally, an increased desire for high performance across devices has put chipmakers in a favorable position. Emerging technologies such as 5G and edge AI computing require advanced silicon and additional components to enable connectivity.
As the world continues to embrace technology in every facet of daily life, the chip industry must be ready. Addressing critical workforce shortages and ensuring manufacturing capacity is sufficient are key areas to watch in the coming years. Moreover, creating a more diverse supply chain that is more resilient in the face of economic disruption is essential. By working to solve these challenges, the chip industry can put itself in a place to succeed throughout the remainder of the decade.
Corporate Demand for ChatGPT is an Excellent Opportunity for Chipmakers
ChatGPT, the flagship offering of OpenAI, has revolutionized the way the world thinks about artificial intelligence (AI). The technology has started evolving at a rapid pace—and the industry powering it isn’t far behind. As tech giants gear up for an AI arms race over the next decade, chipmakers are racing to meet demand.
Now, OpenAI is working to monetize its AI golden child. The Microsoft-backed startup reportedly brings in $80 million each month. But it isn’t content to stop there. OpenAI recently introduced a new ChatGPT business offering, which gives corporate clients more privacy safeguards and additional features. The so-called ChatGPT Enterprise comes via a premium subscription which the company has not yet provided pricing details for.
OpenAI reportedly worked with more than 20 companies to test the product and find the most marketable solutions. Zapier, Canva, and Estee Lauder were all involved and remain early customers of the product. However, OpenAI claims that over 80% of Fortune 500 companies have also used its software since its launch late last year.
For the chip industry, AI represents a turning point. The technology is already being used to make chip manufacturing more efficient. Startups have tasked AI-powered computer vision programs with spotting defects in wafers during production. This method is both faster and more accurate than manually reviewing each wafer, resulting in greater revenue and production for chipmakers.
Elsewhere, researchers are beginning to explore how applying machine learning principles to the intricacies of chip design could one day result in more efficient, more powerful semiconductors than humans can create. Samsung is a pioneer in this area. The South Korean firm is already employing generative AI in hopes of competing with TSMC by increasing wafer yields.
Despite these promising applications, data from McKinsey shows just 30% of companies who currently use AI and machine learning see value from it. But this is expected to change quickly as more businesses experiment with AI and learn how to utilize it most effectively. The same report suggests the use of AI could generate $85 to $95 billion in the long term.
For chipmakers, using AI in-house isn’t the only factor to consider. As the world’s largest firms scramble to gain an advantage in the AI gold rush, their need for high-performance chips is dire. Without the right hardware, it’s impossible to train AI models and use them to generate and analyze profitable data. Firms who are able to provide the needed silicon will benefit tremendously.
As ChatGPT is so often an indicator of the wider AI market’s direction, don’t be surprised to see a big push to include AI in the office in the coming months. Startups of all sizes will introduce their offerings to businesses seeking to improve their efficiency and processes. Each day, more “real-world” uses for AI will crop up as startups who have hungrily devoured capital seek to start turning a profit. For chipmakers, the winners of the AI race aren’t what matters. Rather, the success or failure of AI as a concept and as a useful technology will dictate much of what the future looks like for chips. With some luck, it will be a key growth driver for the industry over the next ten years.
Semiconductors in Space! - September 5, 2023
The future is bright as technology continues to advance more rapidly than anyone can predict. AI leads the way as countries around the world strive to become proficient and find the next breakthroughs. Meanwhile, startups are looking to the stars as dreams of manufacturing higher-quality products in space inch closer to reality.
For the components industry, these developments are part of a larger trend of adaptation. As technology dictates how the world moves forward, the need for components is evolving but always present. Finding innovative ways to meet the demands of companies and countries pursuing advanced technology is paramount for years ahead.
U.K. Invests Millions in New AI Silicon but Eyes Chip Diplomacy as Path Forward
As the world turns its focus toward the exciting future of artificial intelligence (AI), every nation is racing to strengthen its digital capabilities. The U.K. recently announced an initiative that will see roughly $126 million poured into AI chips from AMD, Intel, and Nvidia. This move comes as part of a pledge made earlier this year to invest over $1.25 billion to bolster its domestic chip industry over the next 10 years.
However, critics of the move claim the government isn’t investing enough compared to other nations. Indeed, the U.K.’s latest investment is minuscule compared to those made elsewhere. The U.S. has invested $50 billion in its domestic semiconductor industry through the CHIPS Act while the E.U. has invested some $54 billion. Even so, the scope of the U.K.’s recent move shouldn’t be surprising, given that it accounts for just 0.5% of global semiconductor sales.
The recent injection of taxpayer money will be used to build a national AI resource. This will give AI researchers access to powerful computing resources and high-quality data to advance their work and the field. Other countries, including the U.S., are establishing similar programs to further their domestic AI capabilities.
The primary line item of the U.K.’s recent investment is reportedly an order of 5,000 GPUs from Nvidia, which are used to power generative AI data centers and are essential to running the complex calculations demanded by AI applications. The U.K. government is reportedly in advanced talks with Nvidia to secure these chips amid the company’s massive surge in international demand.
U.K. Prime Minister Rishi Sunak notes that Britain will focus on playing to its strengths rather than delving too far into areas where it is outmatched. For instance, the U.K. will devote a significant portion of its chip resources to research and design rather than building new fabs like many of its European neighbors.
Moreover, the U.K. seems poised to put itself in the center of the raging discussion surrounding AI safety. It recently announced that a long-awaited and highly publicized international AI safety summit will take place early this November. The meeting will include officials from “like-minded” governments as well as researchers and tech leaders. The group will convene at the historic Bletchley Park between Oxford and Cambridge, home of the National Museum of Computing and the birthplace of the first digital computer.
Interestingly, as the U.K.’s small investment compared to other nations will likely hinder its domestic chip ambitions, leadership in the regulatory space could be a fitting role. Britain reportedly aims to be a bridge between the U.S. and China for tense chip and AI safety discussions.
In a statement, a government spokesperson said, “We are committed to supporting a thriving environment for compute in the U.K. which maintains our position as a global leader across science, innovation, and technology.”
Meanwhile, China is racing to buy billions of dollars of GPU chips to further its own AI ambitions ahead of U.S. bans slated to go into effect in early 2024. At this time, it’s unclear if the U.K. will invite China to participate in its upcoming summit.
This will be an important development to watch as the U.K. aims to secure its position as a chip leader despite investing far less than other nations. While the U.S., Japan, Taiwan, and South Korea continue to dominate manufacturing, Britain could play a vital role in the future of the industry as a global moderator and champion of regulatory discussions.
How Manufacturing Chips and Drugs in Space Could Revolutionize Life on Earth
Outer space presents an environment for unique experiments that are simply impossible to perform on Earth. Astronauts living aboard the International Space Station (ISS) have been conducting such research for years. More recently, though, interest in manufacturing products in outer space has taken off.
From new pharmaceuticals to pure materials for semiconductors, the possibilities are endless. As a result, experts believe the space manufacturing industry could top $10 billion as soon as 2030. Startups and governments alike are racing to push the limits of this sector.
Manufacturing certain products on Earth, especially at a microscopic scale, is limited by factors like gravity and the difficulty of producing a reliable vacuum. In space, high radiation levels, microgravity, and a near vacuumless environment give researchers ample opportunity to produce materials or use research methods not available on Earth.
A Wales-based startup called Space Forge aims to revolutionize chip manufacturing by taking it into orbit. The company’s ForgeStar reusable satellite is designed to create advanced materials in space and return them safely to Earth.
Since crystals grown in space are of far higher quality than those grown on Earth, producing semiconductor materials in space leads to a final product with fewer imperfections. Andrew Parlock, Space Forge’s managing director of U.S. operations, said in an interview, “This next generation of materials is going to allow us to create an efficiency that we’ve never seen before. We’re talking about 10 to 100x improvement in semiconductor performance.”
The startup plans to manufacture chip substrates using materials other than silicon. In theory, this could lead to chips that outperform anything the world has seen to date while also running more efficiently.
As for concerns about manufacturing at scale, Space Forge CEO Josh Western says, “Once we’ve created these crystals in space, we can bring them back down to the ground and we can effectively replicate that growth on Earth. So, we don’t need to go to space countless times to build up pretty good scale operating with our fab partners and customers on the ground.”
As the semiconductor industry seeks new ways to make chips more efficient with current manufacturing technology nearing its limits, new materials made in space could be the answer. Though many years of research and testing will be needed, space manufacturing is a promising path for chip companies to explore.
Meanwhile, manufacturing drugs in space has also caught the attention of investors and researchers alike. Varda Space Industries relies on the unique ability to research and produce high-quality proteins in space through crystallization. This allows scientists to better understand a protein’s crystal structure so they can optimize drugs to be more effective, resilient, and have fewer side effects.
Varda co-founder and president Delian Asparouhov says, “You’re not going to see us making penicillin or ibuprofen… but there is a wide set of drugs that do billions and billions of dollars a year of revenue that actively fit within the manufacturing size that we can do.”
He notes that of all the millions of doses of the Pfizer COVID-19 vaccine given in 2021 and 2022, “the actual total amount of consumable primary pharmaceutical ingredient of the actual crystalline mRNA, it was effectively less than two milk gallon jugs.”
Once again, this alleviates concerns of producing drugs in space at scale. Rather than making the entire drug in space, companies like Varda will focus on making the most important components. Then, they’ll ship those back to Earth to complete the manufacturing process.
Thanks to recent advancements in spaceflight technology, such as reusable rockets, making missions to orbit cheaper, dreams of in-space manufacturing are inching closer to reality. Advancements in the coming years will help set the groundwork for what could be the new normal for manufacturing, one that allows humanity to go beyond the limits of what we can create on our planet.
AI is Running the Chip Industry – August 25, 2023
No technology currently has a greater influence on the semiconductor industry than artificial intelligence (AI). From generative models like ChatGPT to massive data centers powering in-house algorithms, AI has sent demand for high-end silicon through the roof.
With demand soaring, chipmakers are scrambling to keep up and expand their production capacities. Cutting-edge AI solutions demand high levels of performance and optimized power efficiency. So, churning out advanced chips is a top priority for manufacturers across segments. Some chipmakers are even turning to an unlikely source for answers to stringent design challenges for future chips—the AI algorithms themselves.
As the chip industry grapples with the possibilities and limitations of AI, the technology’s influence is already redefining the landscape.
AMD Assures Production Capacity for Key AI Chips Despite Tight Market
AMD CEO Lisa Su had reassuring words for analysts and investors during the company’s second-quarter earnings call. Amid a booming market for AI chips, Su admitted that production capacity is tight, but that her company is poised to meet demand following numerous discussions with supply chain partners in Asia earlier this year.
She said in the earnings report, “Our AI engagements increased by more than seven times in the quarter as multiple customers initiated or expanded programs supporting future deployments of Instinct accelerators at scale.”
Large language models (LLMs) like ChatGPT have brought AI, particularly generative AI, to the forefront of the public’s eye. Companies in every sector are scrambling to get their hands on the necessary tech to keep up. For AMD, the generative AI application market and data centers are key strategic focal points.
With many AMD customers reportedly interested in the MI300x, it’s no surprise they hope to deploy the solution as soon as possible—despite the fact that AMD’s M-series GPUs were announced just a month ago. In the meantime, AMD is working closely with its buyers to ensure joint design progress and those deployments go smoothly as it begins sampling the new line.
Su noted in the Q2 earnings call that AMD has secured the necessary production capacity to manufacture its MI300-level GPUs—including front-end wafer manufacturing to back-end packaging. The firm is committed to taking in the massive capacity of neighboring supply chains, including TSMC’s high-bandwidth memory (HBM) and CoWoS advanced packaging. Over the next two years, AMD also plans to rapidly scale up its production capacity to meet soaring customer demand for AI hardware.
In the earnings report, Su said, “We made strong progress meeting key hardware and software milestones to address the growing customer pull for our data center AI solutions and are on track to launch and ramp production of MI300 accelerators in the fourth quarter.”
AMD plans to begin early deployments of the M-series GPUs in the first half of 2024. More rollouts will happen throughout the latter half of the year as a higher volume of M1300 units becomes available.
Importantly, Su also commented on the AI chip sector’s stiff competition, citing Nvidia’s market domination and Intel’s recent successes. She noted that the MI300’s flexible design allows it to handle both training and inference workloads. Notably, this capability is attractive to customers across multiple segments, including supercomputing, AI, and LLMs, giving AMD an opportunity to succeed in all these markets.
Simply put, Nvidia’s head start in the AI market won’t last forever. Although the chipmaker is significantly far ahead of everyone right now, it won’t be the only major supplier of AI chips in the long run. AMD is well-positioned to grab a significant chunk of the market and succeed alongside its competitors with its forthcoming MI300 series. With reassurances of secured production capacity in a tight market, we’ll likely see these chips in the real world sooner than later.
Researchers Are Using AI to Optimize Chip Design. But What Comes Next?
Computers designing themselves sounds like something from a science fiction movie. But it’s already happening inside the offices of Google’s AI-focused DeepMind, where researchers are using AI algorithms to solve chip design problems.
For years, experts have predicted that the end of Moore’s Law is near as chips continue to shrink and layouts become more complicated. In September 2022, Nvidia CEO Jensen Huang even declared the decades-old adage dead. Thanks to AI, though, the concept of doubling a chip’s transistor count every other year could get a breath of new life.
In a recent blog post, DeepMind researchers discussed how they are using AI to accelerate chip design. The novel approach treats chip design like a neural network, which consists of a series of interconnected nodes bridging the gap between inputs and outputs on either edge. To translate this into chip design, the DeepMind team created, “a new type of neural network which turns edges into wires and nodes into logic gates, and learns how to connect them together.”
The result? Circuits optimized for speed, energy efficiency, and size. Using this method, the team won the IWLS 2023 Programming Contest, finding the ideal solution to 82% of circuit design problems during the challenge. By later adding a reward function to reinforce the algorithm for positive design decisions, the team has seen “very promising results for building a future of even more advanced computer chips.”
It seems DeepMind researchers foresaw the future when they wrote in a 2021 paper, “Our method has the potential to save thousands of hours of human effort for each new generation.”
While AI isn’t ready to start designing chips all on its own, the promising results speak volumes about this vastly underexplored technique. Semiconductor leaders like Nvidia and Samsung are already using reinforcement learning algorithms to maximize the efficiency of their chips. Numerous startups are also exploring their own methods for using the power of AI to optimize semiconductor layouts.
However, it’s unclear whether the latest AI craze—generative AI—will play a role. Several companies and researchers are exploring how the technology could be used to optimize chip design, but analysts are doubtful.
Gartner VP analyst Gaurav Gupta said in a recent interview regarding the use of generative AI, “There is very limited evidence that I have seen so far.”
This partially calls back to the larger issue in the generative AI space of who owns what. Generative AI models like ChatGPT are trained on massive datasets to gain their skills. That data comes from internet sources—as does the data for most generic AI applications. But whether the resulting models can be used to create new designs that are then considered proprietary is uncertain. Earlier this week, a U.S. district judge ruled that works of art created by AI cannot be copyrighted. While not applying to semiconductors, this ruling could set an important precedent for other industries moving forward as the world grapples with how to regulate AI.
Even so, experts believe generative AI could still have a place in chip design—just not creating designs from scratch. The technology could be used to augment human-made designs or identify new ways to make a circuit layout more efficient. For now, though, it appears reinforcement learning algorithms will lead the way.
Over the coming years, more chipmakers will join the fray, making AI chip design more commonplace. As designs continue to shrink, AI will be a powerful tool for the industry to use to continue innovating and improving semiconductor performance and efficiency. Moreover, as demand for more capable chips to power AI applications grows, the technology could become a bit of a self-fulfilling prophecy, as older iterations are used to improve future functionality.
Artificial Intelligence Continues to Attract Chipmakers - August 14, 2023
Original component manufacturers (OCMs) and semiconductor equipment manufacturers are working overtime to prepare for the incoming tidal wave of artificial intelligence (AI) demand.
The world has quickly fallen head-over-heels for the capabilities of AI. The popular generative AI model, ChatGPT, has been a trailblazer within the AI market, inspiring numerous copycat programs from big and small technology companies. As the competition heats up with ChatGPT equivalents, the need for higher computing power will become a top priority.
After all, these chatbots won’t be able to generate anything without these chips.
An AI Equipment Boom is on the Way
Artificial intelligence applications have exploded in use since the introduction of ChatGPT. Impressed with the capabilities of generative AI, the market sector is rapidly growing. With consumer demand and competition on the rise, semiconductor and chipmaking equipment maker Tokyo Electron sees a boom in equipment sales on the horizon.
The semiconductor industry has been experiencing a significant glut of excess electronic component inventory. When consumer demand wilted from increased prices due to inflation and high energy costs, there were worries over how long it would take for the industry to recover. To the surprise and delight of many generative AI, which quickly captured worldwide attention, will be the driving force of glut recovery.
With this explosion of popularity, the leading generative AI application ChatGPT is already driving growth within the semiconductor manufacturing equipment market sectors. The current impact is still relatively minor, but Tokyo Electron expects that to blossom into significant gains come 1H2024.
The semiconductor industry beyond equipment manufacturers will likely receive a significant boost in demand as technology companies and manufacturers work to dethrone the king of AI chips, Nvidia.
Tokyo Electron’s senior vice president Hiroshi Kawamoto said OCMs are already contacting the company regarding its GPU-making equipment product lines. Kawamoto told Nikkei Asia reporters, “The trend is still limited in scope, but I think we will start seeing a difference in revenue through April - September of 2024.”
“The number of semiconductors needed for generative AI servers will likely increase,” Kawamoto continued. “This technology could become our next driver for growth.”
As of late, like many OCMs, Tokyo Electron has faced a steady decline in demand during 2023’s chip glut. Demand for memory chip-related equipment has faced the steepest declines to no one’s surprise. Kawamoto doesn’t believe that will last for much longer.
“DRAM-related equipment will start looking up as early as the end of 2023,” Kawamoto said. “A real rebound won’t start until the next fiscal year.” He expects steady growth over the next fiscal year and forward due to the wide range of end uses for mature semiconductor manufacturing equipment, primarily utilized in automotive and industrial sectors. These industries have less volatility than their advanced node counterparts, as they can be subject to more extreme market shifts.
This is good news for the semiconductor industry, which is currently entering the peak period of excess electronic component inventory. It means faster recovery and mitigation of excess through selling before it costs manufacturers more by storing it correctly.
Chinese GPU Suppliers Eye AI Market
As semiconductor equipment manufacturers prepare for the oncoming AI equipment boom, so are graphic processing unit (GPU) suppliers. Many of which are eager to grab a portion of Nvidia’s expansive market share within the AI sector.
Artificial intelligence applications, inferences, and other programs require a lot of computing power. ChatGPT’s current model, GPT-3, runs 175 billion parameters to function. AMD’s latest chip, MI300X, only runs 40 billion parameters. Large-scale AI programs need a lot of high-performance GPUs to run significant tasks like content generation.
GPUs are the secret sauce behind many large language models (LLMs) like ChatGPT and other popular AI tools. With the sudden rise in popularity, GPUs have sprung into high demand overnight. Nvidia dominates, but OCMs of all shapes and sizes are working overtime to grab AI market dividends.
Chinese GPU suppliers and startups are no different. More so now that harsh sanctions by the U.S. are seeking to limit China’s access to new AI-capable electronic components.
The Shanghai-based startup, Denglin, has received funding and support from the China Internet Investment Fund (CIIF), the State Cyberspace Administration of China, and the Ministry of Finance to develop CUDA and OpenCL-compatible chips. CUDA is the name of Nvidia’s software package, allowing users to access the chip’s hardware features for development options. This software has been extremely popular among U.S. developers.
The chips Denglin has been funded to develop are reportedly used primarily for HPC and AI markets with their GPGPY usage capabilities. Denglin has announced four products this year, that are used for gaming and AI training. Its most popular solution is the Goldwasser chip. The chip is “designed for AI-based compute acceleration and will be getting edge and cloud gaming platforms.”
According to Denglin, its GPUs have been in high demand since last year when its 2nd generation GPU production capacity was completely booked. This new GPU will likely face the same high demand as it promises to deliver a 3 to 5 times greater performance gain for transformer-based models. Likewise, it is reported to greatly reduce hardware costs for ChatGPT and generative AI workloads, making it strong competition for Nvidia.
Predictably, a GPU supplier that offers the same CUDA and OpenCL capabilities for AI processing would be immensely popular in China’s domestic market. Especially now that restrictions are keeping Nvidia from making a splash within it. Nvidia GPUs’ hefty price tags are nothing to scoff at, either.
So far, thirteen other GPU developers within China are vying for the top spot. However, whether Denglin can prove popular enough to beat Nvidia on a global scale is far more uncertain.
Someone typing at night
Supercomputers and China's AI Rules - July 28, 2023
The world of artificial intelligence (AI) is on an unstoppable upward trajectory of popularity and developmental breakthroughs. OpenAI’s ChatGPT lit the fuse within generative artificial intelligence and other AI applications. Now, tech companies and chipmakers across the board are working hard to develop their own generative AI chatbot or AI-capable components respectively.
With these discoveries come concerns. Industry leaders, including Elon Musk, are warning others of the potential dangers of unrestricted artificial intelligence. Equally concerned are government lawmakers who are now beginning to draft the first set of laws to safeguard the public from AI-generated misinformation.
Tesla’s Upcoming Supercomputer Entering Production
Earlier this year, Tesla CEO Elon Musk called for a pause on artificial intelligence, citing concerns that mismanaged design could lead to “civilization destruction.” Musk’s critiques of artificial intelligence are nothing new. Musk’s contradictory stance on artificial intelligence, condemning it publicly while increasing AI development within his companies has been well-known for years.
When Musk learned of OpenAI’s and Twitter’s relationship, where ChatGPT was built on user Tweets, Musk severed ties. Formerly, Musk helped found the AI lab in 2015.
Recently, Musk has continued expressing his concerns on artificial intelligence and is reported to be working alongside Microsoft's CEO in approaching the EU to discuss the best strategies for AI regulations.
Meanwhile, Tesla’s AI team recently announced on Twitter the ongoing progress of Tesla’s upcoming custom supercomputer platform Dojo. According to the tweet, the computer will go into production in July 2023 and is expected to be “one of the world’s five most advanced supercomputers by early 2024.”
Reports by Electrek and Teslarati detail Dojo’s progress as another significant step by Tesla to carve out a spot for the company within the AI market. Furthermore, Tesla is working to reduce its dependence on traditional chipmakers, like Nvidia, whose A100 and H100 GPU chips are dominant within AI applications, which is true for even some Tesla AI products. In contrast, Dojo uses AI machine learning that utilizes Tesla-designed chips and infrastructure which are trained on data from Tesla's feed to develop its neural network.
After Dojo’s launch in 2021, the supercomputer has continued to be developed over the last few years with the goal of supporting Tesla’s vision technology to obtain complete autonomous driving. Musk has been working hard to make this area of AI a reality. Despite his critiques of other AI developments, Musk is pleased with the progression and advancements by the Tesla AI team in both software and hardware.
Dojo is expected to be Tesla’s first step in creating a powerful computing center capable of handling many AI tasks. Tesla currently uses Nvidia’s GPUs in its previous supercomputer to process FSD autonomous driving data. Dojo should be able to process more video data, contributing to the acceleration of iterative computing in Tesla’s Autopilot and FSD systems. Eventually, Dojo could be able to provide the significant computing power required for Tesla’s other projects, including its humanoid robot Optimus.
Dojo’s supercomputers will greatly aid Tesla’s growing workload as the company strives for independence by designing its own chips and AI applications. With the data-driven insights provided by Dojo through Tesla’s video data, Tesla could come closer to making autonomous driving a success. By extension, the industries that could benefit from Dojo’s insights would be extensive, especially in a more interconnected world.
However, it will be interesting to see how upcoming AI regulatory efforts impact the later development within the market.
China is First to Begin Major AI Regulation
Microsoft and Tesla leaders are heading to the EU to discuss the need for tech leaders to be involved in establishing AI regulation. In comparison, China has moved ahead in establishing new requirements for generative AI. As one of the first countries to regulate generative AI used in popular tools like ChatGPT, other countries might use their laws to guide their regulations.
The Cyberspace Administration of China (CAC) recently announced updated guidelines for the growing generative AI industry. These new rules are set to take effect on August 15th, and it appears in this “interim measures” document several previously announced provisions have been relaxed.
The announcement comes after regulators fined fintech giant Ant Group a little under $1 billion. This fine followed a series of regulatory strikes against other tech giants, including Alibaba and Baidu, who are all launching their own AI chatbots, much like Microsoft and Google.
The new regulations will apply only to services available to China’s general public. Technology developed in research institutions or created for users outside of China are now exempt. The language that indicated punitive measures, including hefty fines, had been removed not to limit AI's ongoing development within China.
In the document, the CAC “encourages the innovative use of generative AI in all industries and fields by supporting the development of secure and trustworthy chips, software, tools, computing power, and data sources.” Beijing urged tech platforms to “participate in formulating international rules and standards” regarding generative AI. A regulatory body would aid in continued monitoring and culpability to hold each other accountable.
It will be interesting to see how other countries will follow suit, especially now that tech leaders are courting lawmakers to collaborate on AI regulations. Going forward, it will be pertinent for tech companies and governments to work together to form a robust and flexible foundation to encourage AI development, not hinder it, and safeguard users.
Production line robots
AI Rules and Regulations on Big Tech’s Mind - July 14, 2023
In May 2023, a few months after the massive boom of OpenAI’s ChatGPT, Geoffery Hinton, dubbed “the Godfather of AI,” quit Google. With his departure came a warning that artificial intelligence could soon grow smarter and possibly manipulate humans. Unfortunately, most of the general public saw those concerns and thought of AI turning on its human creators and overrunning the world with an army of metallic soldiers.
No, that’s not what Hinton and other tech leaders like Microsoft’s Brad Smith are concerned about with artificial intelligence (AI.)
In an interview with CNN, Hinton discussed concerns about current challenges and rising problems within AI becoming more notable with its fast developments. Specifically, Hinton’s immediate concerns are that “the internet will soon be flooded with fake text, pictures, and videos that regular people won’t be able to distinguish from reality. This could, he said, be used by humans to sway public opinion in wars or elections. He believes that A.I. could sidestep its restrictions and begin manipulating people to do what it wants by learning how humans manipulate others.”
These concerns are not unfounded. Presently, some natural language processing chatbots hallucinate or deliver users false information. In many AI-generated images, artists have expressed concern over being replaced or having their work copied and regurgitated by AI faster and cheaper. However, far-reaching and general restrictions will not fix the issues with AI that concern Hinton and other AI critics.
Better monitoring through regulations based on conversations between lawmakers and tech leaders will be vital in establishing guidelines to prevent the spread of false AI information.
Microsoft President Discusses AI Regulation with the EU
Artificial intelligence has been making headlines ever since OpenAI’s ChatGPT came out. With artificial intelligence’s rise, so too have the chips that make these AI feats possible. As the possibilities within AI, specifically generative AI, are explored, the benefits have been marred by growing concerns.
Attention-grabbing headlines calling the rise of AI the doom of humanity explore the idea of artificially intelligent automatons enslaving their human creators are more fiction than fact. However, for every dystopian sci-fi article that dramatizes the dangers of AI, there is an article that rightly discusses the concerns about the growing use of artificial intelligence, mainly regarding areas where AI still lacks.
Artificial intelligence is, without a doubt, a fantastic tool. The capabilities of AI will be pertinent for all organizations in the next cycle of growth to aid the work and development of human staff. Automation, predictive analytics, market intelligence, generative AI, and other developments will help companies refine processes, increase operational efficiency, and improve employee morale.
That said, AI is not perfect. On debut, Google’s Bard AI made a factual error about the James Webb Telescope, and Microsoft’s Bing AI created fictional financial information. These specific errors are part of an inherent problem with AI called artificial hallucination. These hallucinations are “generated content nonsensical or unfaithful to the provided source content." There are several reasons why or what causes an AI to hallucinate. In natural language processing models like Bing, Bard, and ChatGPT, the cause of hallucinations mostly centers on divergence from the training source material or filling in gaps that were not within the training data.
The biggest concern is for internet-data scouring large language models, which is that the internet is full of false information. Information that trains and feeds many of these language mod
| 2022-12-28T00:00:00 |
https://datalynq.com/post/electronic-component-market-news
|
[
{
"date": "2022/12/28",
"position": 95,
"query": "AI economic disruption"
}
] |
|
Knowledge Engineering, Knowledge Management and AI ...
|
Knowledge Engineering, Knowledge Management and AI Assistants
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https://www.earley.com
|
[
"Seth Earley"
] |
... generative AI solutions into your Knowledge Management culture, program, and systems. ... What Kind of Jobs Do AI Assistants Perform? Additional examples may are ...
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AI assistants are a form of cognitive AI. They go by many different names. The different terms used to identify AI assistants are described below. The role of AI assistants is to support customers or employees as they carry out tasks. In some cases, for repeatable actions with unambiguous outcomes, a chatbot or AI assistant can fully automate interactions. In other cases, when the answer requires judgment and human expertise, the AI assistant can surface the necessary information to help the human solve a problem – whether for a customer self-service app or a customer service agent supporting an end user task. These virtual job assistants are designed to help customer support staff be more efficient, accomplishing the task more quickly and accurately with a consistent outcome.
What are the Different Types of AI Assistants?
Here is a partial list of the terms used to describe AI assistants:
Virtual assistant
Intelligent assistant
Intelligent virtual assistant
AI assistant
AI virtual assistant
Cognitive assistant
Shopping assistant
Digital assistant
Virtual customer assistant
Voice assistant
Personal AI assistant
Personal assistant
The bot family includes the following types of cognitive AI. They differ from AI assistants in that they have a very specific and limited role – that is, they are focused on a specific knowledge domain (such as medical information or manufacturing solutions) or a narrow task (such as configuring products, retrieving vacation policy information or supporting a loan application)
Shopping bot
Chatbot
Helper bot
Configure Price Quote (CPQ) bot
Knowledge retrieval bot
Agent assist bot
Troubleshooting bot
FAQ bot
Service bot
Conversational cognitive AI software is another type of AI assistant and the most challenging to develop. It includes:
Conversational AI
Conversational commerce
Conversational assistants
This type of AI assistant attempts to support the user in multi-turn interactions using voice recognition and natural language processing. Multi-turn interactions allow the customer to continue a dialog beyond the initial task, or to respond in a variety of sequences rather than being confined to a linear path. The dialogs and tasks can be more complex as the user is guided through specific tasks or asked to provide more information all as conversations.
The user issues a voice command using natural language and the system responds to the trigger with data or content. Some vendors focus specifically on conversations which may feel more natural to users, but these can quickly degenerate if the conversation becomes too complex. In that case, there should always be a mechanism to handle those failures (such as escalating to a human service agent). In some cases, there is little different between conversational AI and conversational commerce other than the specific application (commerce is a specific use for conversational AI).
What Kind of Jobs Do AI Assistants Perform?
Additional examples may are defined through functionality; for example, medical transcription bots or airline reservation assistant bots. They may also include retail assistants that help a user navigate through a series of interactions, such as updating a credit card and then completing a transaction. A specialized AI virtual assistant uses natural language dictionaries to understand a voice command in a particular field that contains domain-specific terminology (such as medical terminology). Speech recognition converts voice to text for processing by the virtual assistant so that a natural language query can retrieve the correct information. Amazon echo and Amazon Alexa are trained in different dictionaries of terminology to understand specialized functionality. For example, characters in a TV show would not be in a standard natural language dictionary. A specialized Alexa “skill” would understand those characters based on training the app on those names.
This same specialization is required for businesses that have unique offerings or technical content that is not commonly part of typical natural language processing. An AI model can respond to unique customer queries only if it has been trained on those queries or queries that are similar.
How Does Knowledge Management Relate to AI Assistants?
Regardless of what they are called, all these assistants are information access mechanisms. A chatbot is a channel--to data, content and knowledge. This is where knowledge management comes in. AI assistants, like new employees, need to be taught the answers. This is what is referred to as “training data.” It’s important to remember that the same information needed to train humans is needed to train an AI assistant.
The information to power the cognitive AI functionality has to be stored somewhere – either within the chatbot platform or in a stand-alone knowledge base. The advantage of having this information in outside the chatbot platform is that it can be repurposed in different ways – powering downstream applications like marketing automation, product support sites and channel partners. Some chatbot approaches require that the knowledge be built into the chatbot platform, which is not ideal since it leads to further fragmentation of knowledge rather than consolidation of that knowledge for reuse.
Many different mechanisms and technologies can be leveraged in a cognitive AI and AI assistant environment. However, at the simplest level, they consist of two things: A mechanism for understanding a user input (called an utterance) and a way of providing an output (a response). They rely on natural language processing (NLP) to interpret utterances, and in the case of voice assistants, speech recognition, which uses AI technology to convert speech to text.
Virtual assistants are increasingly used to handle routine customer queries, leaving more complex tasks for human agents. An AI-powered virtual assistant can offload these tasks, reducing the cost of customer service while improving responsiveness. That may sound overly simplistic, but thinking of inputs and outputs will help explain how these applications need to be configured, deployed, and maintained.
What is an Utterance
The first concept to familiarize yourself with is that of an utterance – this is the input. Human speech is varied and can be ambiguous. An utterance is the text that a user types into the chatbot interface or, if a voice recognition agent, the words that they use to express what they need. Voice is translated into text using speech recognition machine learning algorithms, so only the interpretation of the text needs to be considered. Because users use different wording to express a need, those variations in utterances need to be classified into a common request called an intent. An AI personal assistant will function only if it is programmed to interpret the intent. Analytics can reveal when intents are not recognized so that new training data can improve utterance interpretation.
This interpretation of utterances is the first area of application for machine learning for AI assistants. Machine learning uses variations in phrasing of questions as an input and produce an output that says, “These ten different ways of saying something really means this.” The utterances are classified to an intent. The more variations in phrasing that the algorithm sees, the more likely it will be to recognize a new variation that it may not have seen. This is because the phrases live in a vector space where similar phrases are classified as the same intent if they are near one another (in mathematical terms). This is the first opportunity to use “training data” – the more examples of a phrase variation that inform the classification algorithm, the more accurate it will be. The AI assistant then has a better chance of retrieving the right information and delivering it to the user.
What is an Intent?
Intents can contain multiple details that allow a more specific request to be interpreted by the system. I might want a place to eat, and a chatbot could return a list of 50 restaurants, but if I want a Japanese restaurant that is within a mile of my location and is moderately priced, providing those details within the knowledge base will allow the chatbot or AI assistant to return a more specific recommendation. Those details are additional metadata and area referred to as “slots” – they are variables that need to be filled in so users can complete their intended actions. The chatbot will need those variables to make a better recommendation, just as it does when it needs to complete a task. If a chatbot is making a reservation for the user, it needs to know the number of people and the time.
Once the user’s goal has been classified to a recognized intent, the artificial intelligence system needs to provide an answer. In this way, we can think of chatbots as informational retrieval mechanisms – just as search is an information retrieval mechanism. In fact, “slots” can be thought of as metadata for retrieval, just as facets such as color, size, brand and price are used in retail websites. Extracting the details from an utterance helps to ensure that the intent is as specific as it needs to be for the chatbot to retrieve an answer, rather than just a long list of documents. The retrieval can be considered as a very specific search. In order to make search work effectively, knowledge and content need to be curated and correctly structured.
How Does Knowledge Management Make Retrieval (Search) Work?
Search works best when the content being searched is well optimized for search. Consider getting web pages to rank on Google. That does not happen without a great deal of effort. An entire industry with tools and consultancies is devoted to optimizing search. The same thing needs to happen to our knowledge and content so that a chatbot or virtual assistant can correctly retrieve it.
This process is the realm of knowledge management and its artificial intelligence focused cousin, knowledge engineering. Knowledge engineering is also referend to as “symbolic AI.” In the early days of AI, a great deal of work went into knowledge representation so that researchers could build expert systems that could produce answers such as the diagnosis of an illness. This rule-based approach went out of fashion as statistical machine learning approaches became more effective with the increased computing power, improved algorithms, and enormous amounts of data available from the cloud and from the ecosystem of technologies throughout the world. However, the need for correctly structured and curated knowledge has never gone away. If the knowledge is not available, a bot will not be able to create it from nothing. We have to teach chatbots how to answer questions and that is done using a knowledge source – a knowledge base.
AI assistants of all types are experiencing rapid growth, because they aid customers and employees alike in completing their tasks quickly and accurately. However, matching the type of AI with the target task requires considerable thought and planning. Is a chatbot with a well-defined path the best match, or is the interaction more complex, requiring conversational AI and multi-turn options? Do the likely users prefer using a keyboard or voice recognition, or should both be offered? No matter what the choice, the AI assistant will not be able to find the answers if the information is not well organized and well structured. Humans and AI assistants both need to retrieve information in order to provide answers to customers. A knowledge base with the right content, structure, and metadata allows precise retrieval of information. Combined with the appropriate virtual assistant, this knowledge will allow companies to automate many customer interactions, improving customer satisfaction and increasing revenue.
The data science behind smart devices such as a smart speaker, or a platform like Google home (where multiple smart devices are connected with the Google assistant as the interface) is the enabler of enhanced functionality, but customers are wary due to privacy concerns. An AI-powered virtual assistant will be the interface to many smart devices. However, when a customer mentions a topic and coincidentally receives relevant adverts to that topic, their concerns over privacy will be magnified. Providing controls such as a privacy preference center may allay those fears, but it will take time for people to become acclimated.
Ready to get started building an AI Assistant? Contact us to learn how we can help.
| 2022-12-28T00:00:00 |
https://www.earley.com/insights/knowledge-engineering-knowledge-management-and-ai-assistants
|
[
{
"date": "2022/12/28",
"position": 68,
"query": "generative AI jobs"
}
] |
|
What, Why, and How of Adaptive Learning
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What, why, and how of adaptive learning
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https://getrapl.com
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[] |
Executive Summary: Adaptive learning is designed for an evolving world where workforces are becoming more diverse. Employees from different age groups, ...
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Who Are Frontline Employees? Frontline employees are the face of your business. They are the customer service representatives, field agents, retail staff, warehouse operators, and service technicians who interact directly with customers, products, and operational processes. Their impact on business success is immediate and measurable, as they: ✅ Drive customer satisfaction through direct interactions.✅ Influence revenue generation in sales and service roles.✅ Ensure operational efficiency in logistics and supply chains.✅ Handle safety and compliance, reducing workplace risks. Without proper frontline employee training, organizations risk high turnover, inconsistent service quality, and reduced productivity—leading to costly operational inefficiencies. How Are Frontline Employees Categorized in an Organization? Organizations typically structure frontline employees based on their roles and responsibilities. For instance: Customer-Facing Teams: Retail associates, call center representatives, and sales agents who provide direct customer interactions. Field Teams: Field sales reps, service technicians, and delivery staff working outside the office. Operations and Support: Logistics workers, facility managers, and warehouse operators who ensure seamless internal processes. Different Types of Frontline Employees and Their Activities Customer Service Representatives: Handling customer inquiries and complaints. Providing product recommendations and support. Field Sales and Agents: Engaging customers in their environment. Promoting products and closing deals. Warehouse & Logistics Workers: Managing inventory and supply chain logistics. Ensuring timely deliveries and reducing operational errors. Facility and Maintenance Teams: Maintaining safety and compliance standards. Conducting routine audits and reporting issues. Why Should Frontline Employees Matter to an Organization? Frontline employees are a direct link between the company and its customers. When empowered, they: Enhance customer experiences with improved service and response times. Increase sales conversions by confidently communicating product value. Ensure operational efficiency through optimized workflows. Strengthen brand loyalty by delivering consistent service quality. Why Is Training Needed for Frontline Employees? Many frontline workers operate in high-pressure, fast-paced environments with little time for formal training. Without effective learning opportunities, they: Struggle to keep up with changing processes and new technologies. Lack confidence in customer interactions, leading to lost business. Miss important safety and compliance protocols, increasing risk. Feel disengaged, resulting in higher turnover rates. Why Frontline Employees Hesitate to Engage with Training Despite its benefits, many frontline employees see training as a burden. Common barriers include: ❌ Training sessions feel too long and disrupt daily work schedules.❌ Content is generic and doesn’t address real challenges.❌ Lack of clear incentives or immediate impact on their performance.❌ Limited access to training platforms, especially in field-based roles. The Role of Engaging Content in Frontline employee training Let’s be honest—boring training content gets ignored. RapL Craft makes content creation effortless and engaging: 🎥 AI-powered tool to create interactive, high-quality learning materials in minutes 📚 Create quizzes, tests, assignments, surveys with just simple prompts 🎮 Integrate learning content creation into your workflows, save time and improve efficiency With RapL Craft, businesses can quickly develop impactful training content that resonates with employees and keeps them engaged. Explore Craft today! Best Practices for Frontline Employee Training: Making Learning Stick The best frontline employee training programs blend different learning methods for maximum engagement and retention. Here’s how: 🎯 On-the-Job Learning: Employees learn best by doing! Pairing hands-on experience with structured training creates a stronger learning experience. 📱 Mobile & Microlearning: Short, engaging modules fit into busy schedules and ensure knowledge is absorbed in bite-sized pieces. 🏆 Gamification & Rewards: Who doesn’t love a little competition? Adding leaderboards, challenges, and rewards makes training fun and engaging. 📊 KPI-Based Learning: Training must connect to employee performance metrics so workers see its relevance and impact. 🔄 Continuous Learning & Reinforcement: One-time training sessions won’t cut it—regular knowledge reinforcement is key to long-term success. How to Measure Training Effectiveness: Linking Training to KPIs A frontline employee training program should directly impact business metrics. Here’s what to track: 📈 Performance Improvement: Are employees hitting their sales, service, or operational KPIs post-training? ✅ Training Completion & Engagement Rates: Are employees actively participating and completing training modules? 📊 Knowledge Retention: Are employees applying what they’ve learned on the job? 🙌 Customer & Employee Feedback: Are customers and frontline workers noticing the benefits of training? Tracking these indicators ensures training is not just a one-off event but a measurable driver of business success. The Role of Knowledge Mapping & Analytics Data-driven decision-making is essential for frontline employee training success. RapL Analytics helps companies: 🔍 Identify knowledge gaps of the frontline employees through real-time performance tracking and learning engagement 📊 Monitor training impact on key KPIs, correlate training to the individual KPIs of employees and measure the impact in real time 📈 Enable managers to fine-tune training programs based on data and provide regular feedback to nudge frontline employees to perform better By leveraging RapL Analytics, businesses ensure frontline employees receive relevant, impactful training that leads to measurable performance improvements. Key Takeaways: Future-Proofing Your Frontline Employees 🚀 Frontline training isn’t optional—it’s a business game-changer. Well-trained employees mean better customer experiences, improved safety, and higher productivity. 📱 Modern training needs to be digital-first. Mobile learning, microlearning, and interactive methods keep frontline employees engaged. 🏆 Mix it up! The best training programs combine gamification, real-time coaching, and data-driven insights for lasting impact. 📊 Measure everything. Link training to performance KPIs, customer satisfaction, and employee retention for real ROI. 💡 Leverage AI & automation. RapL Analytics, and Craft take the hassle out of training, making it smarter, faster, and more effective. Empower your frontline teams. Strengthen your business. Let’s make frontline training work.
| 2022-12-28T00:00:00 |
2022/12/28
|
https://getrapl.com/blog/how-adaptive-learning-can-improve-your-business-in-real-terms/
|
[
{
"date": "2022/12/28",
"position": 37,
"query": "machine learning workforce"
}
] |
Top 5 Workforce Optimization Software Solutions for Call ...
|
Top 5 Workforce Optimization Software Solutions for Call Centers in 2023
|
https://thelevel.ai
|
[
"Colm Shalvey"
] |
Genesys strives to make customer and employee experience management more seamless by uniting all channels with artificial intelligence and machine learning.
|
A well-run call center requires a high-performing agent workforce able to handle customer inquiries quickly and efficiently. What’s more, call center leaders often have to manage agents spread out across the globe making it even more challenging to monitor for performance and engagement.
This is where call center workforce optimization (WFO) software enters the equation.
At a high level, workforce optimization software is an integral part of every contact center. With the right tools in place, workforce optimization can help agents be more successful and productive, enabling organizations to improve business results while also bolstering agent morale.
And that’s a pretty big deal because happy, engaged agents give your business a competitive advantage. In fact, Gallup reports that more than 70% of employees are engaged at top-performing organizations. Since engaged employees accomplish more, this helps these companies create extremely productive work environments.
Workforce optimization software helps employees collaborate effectively. According to Gartner, this increases individual employee performance by as much as 20.6%. According to a recent Metrigy study that surveyed 524 companies, using WFO applications can increase an organization’s revenue by 30.1%.
Add it all up, and the benefits of workforce optimization solutions speak for themselves. But with so many workforce optimization solutions in the marketplace, it can be difficult to choose the best option for your business.
To make your search easier, we’ve compiled a list of the best workforce optimization software on the market today. Read on to learn more about each to determine which one can meet your needs the best..
| 2022-12-28T00:00:00 |
https://thelevel.ai/blog/workforce-optimization-software-2/
|
[
{
"date": "2022/12/28",
"position": 46,
"query": "machine learning workforce"
}
] |
|
How To Overcome AI-Led Biases In Recruiting and Hiring
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How to Overcome AI-Led Biases in Recruiting and Hiring
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https://builtin.com
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[] |
Resume scoring and candidate screening are some of the most prominent AI applications that HR professionals use in the recruiting process.
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Human-resources professionals around the world are embracing artificial intelligence. In fact, 88 percent of companies globally use some form of AI in HR, according to a Mercer report. While the technology’s applications range across a variety of HR functions, from recruiting and candidate engagement to employee management and training, businesses need to be cognizant of the potential pitfalls of indiscriminately embracing AI.
3 Ways to Overcome AI-led Biases in HR Train anyone involved in AI algorithms to identify and solve potential biases. Use in-house and third-party data resources to create a more robust and diverse pool of datasets. Define your company’s bias and fairness standards to guide the choice of AI tools.
AI systems are only as good as the data they are trained on. If HR datasets carry inherent, historical human bias, the systems built using these datasets will naturally result in biased outcomes.
However, with sustainability and diversity, equity and inclusion initiatives taking center stage, organizations can no longer afford to let AI-led biases affect their talent decisions. Here are two main types of AI-led biases to watch for, plus ways to curb them.
Read More about AI and Hiring17 Examples of AI in HR and Recruiting to Know
AI-Led Biases in Recruiting and Candidate Engagement
Resume scoring and candidate screening are some of the most prominent AI applications that HR professionals use in the recruiting process.
These screening tools traditionally leverage pre-trained datasets to quickly identify candidate attributes that best match the job requirements. However, they are trained on how humans would have manually screened the candidates. Harvard Business School Research shows that any candidate who is qualified for the job but fails to meet the highly specific parameters on the automated hiring system could be passed over. This means that the system screens out potential hires before it reaches any human eyes. This is definitely a problem in today’s competitive market.
Apart from this, human biases can also easily seep into the AI systems, resulting in specific preference for candidates of certain backgrounds, ethnicities, genders and experience levels. Since AI learns from past data, if humans preferred candidates of certain backgrounds over others, the AI system will reflect those decisions as well in a subtle way.
AI-Led Biases in Employee Engagement and Management
The Great Resignation has reminded us that talent retention is not easy. Phenomena like quiet quitting have also shown us why employee engagement is crucial. Employee engagement in the United States stands at an appalling 32 percent.
AI can help here. In fact, besides recruiting, employee engagement and management are another key function of AI in HR.
AI algorithms, chatbots, predictive models and sentiment analysis tools help HR departments measure employee satisfaction, evaluate employee performance, devise better HR policies, curate training opportunities and map talent needs. However, there is also the imminent risk of AI-led biases in these tools jeopardizing engagement efforts.
For example, the results from an algorithm assessing employee performances can be skewed in an unfair way due to unseen biases. This can happen when algorithms are based on data shared by biased human managers, thus spreading more of this bias, and at the same time, allowing people to remove themselves from accountability by blaming the AI system when these biases are confronted.
Even when it comes to sentiment analysis tools, it requires the algorithms to be trained on what employees have historically valued, how that compares to the organization’s work culture, and techniques historically used by HR teams to identify patterns of dissatisfaction as well as hot-button issues. However, these systems stand to depict a poor pulse on employees’ values, sentiments and dissatisfaction if not everyone who is supposed to provide the data for analytics does so.
How to Overcome AI-Led Biases in HR
While there are evident limitations when it comes to AI’s applications in HR, the benefits of these systems far outweigh the challenges. Therefore, it’s in the interest of HR professionals to understand the best ways to overcome AI-led biases.
Train Employees to Identify Bias
Bias must be prevented throughout the HR cycle. Just the simple understanding that these systems are susceptible to biases, and are not the be all and end all in the decision-making process, can go a long way.
Training anyone involved in designing, developing or using AI algorithms, be it employers, HR department or IT specialists, to identify and solve potential biases is imperative. With this approach, business leaders as well as HR professionals are always actively monitoring for unethical issues and preventing bias.
Specifically in hiring, identifying potential bias can include analyzing the output and validating not just the selected candidates but most importantly the ones who were rejected. If possible, we must look for reasons the AI system has made the decision. For instance, even if the model doesn’t evaluate race, it may be indirectly inferred based on a person’s name. If the decision is based on such an unusual reasoning, the AI system must be scrutinized and tuned accordingly.
Focus on Data Quality and Variety
There is an abundance of data and data sources in today’s digital world. The first step to making smart data-led decisions is understanding the types of data that are relevant to HR. These include everything from hiring and HR policy data to employee engagement tools and historical data around diversity and inclusion.
Ensuring that the quality of an organization’s HR data is up to the mark is imperative. Equally important is to understand if one’s data sources are adequate and reliable. Including data that has been accumulated from a source where a certain background is predominant will cause the system to be biased. It is important to monitor how diverse the data that we train on is.
Today, HR professionals have a lot of third-party data sources at their disposal. Using a mix of in-house and third-party data resources can help create a more robust pool of datasets.
Define your Own Bias and Fairness Standards in HR
HR teams can develop a single standard for ethics, fairness and bias or they may set different thresholds for different groups and situations. Regardless, they should invest both time and resources in identifying/developing the most diverse dataset that is representative of their organization’s goals for fairness.
What constitutes fair in one organization may be applicable at another. For example, fair pay standards in remote work models differ widely. Some organizations follow a location-based pay structure to account for the varied cost of living expenses among remote employees. Other companies regard merit/value based pay, where employees performing the same work get paid the same, regardless of their locations and cost of living.
Periodic auditing can help in setting these standards. This can include a review of performance evaluations, employee training needs and even rejected applications and whether or not those rejections were justified. More specifically, HR leaders must keep an eye on the field of AI research and stay updated with specific software, its limitations and new findings. For reference, they can peruse best practices from companies such as Google AI or IBM’s AI Fairness 360 Framework.
Read More about AIWhat Is Artificial Intelligence? How Does AI Work?
A Fair Foundation
The practice of ethical AI must be the foundation of fair HR processes and an enabler of business growth. Continuous effort and compliance toward ethical AI training and AI-supported hiring by developing fair job descriptions and embracing diversity must be on every organization’s radar.
All things considered, being progressive in HR through the use of powerful technology is key. However, it shouldn’t be done without confidence in the data or in the absence of proper data standards. It needs to be done with HR professionals armed with the necessary skill sets to use AI as a tool, not an absolute substitute for human decision-making.
| 2022-12-28T00:00:00 |
https://builtin.com/artificial-intelligence/overcoming-AI-led-biases-in-HR
|
[
{
"date": "2022/12/28",
"position": 9,
"query": "AI hiring"
}
] |
|
Free AI Images, Stock Photos, and Graphic Design Tool on ...
|
Free AI Images, Stock Photos, and Graphic Design Tool on Narrato
|
https://narrato.io
|
[] |
Engage your audience with stunning images and graphics. Create unique, custom AI images and graphics or search from thousands of stock images.
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Narrato Joins Forces with Typeface: A New Era in AI Content Creation!
We're beyond thrilled to announce that Narrato has been acquired by Typeface! Together with Typeface, we are looking forward to building the best-in-class AI content creation assistant for content teams around the world.
What This Means for You:
We will continue to support all our Narrato users with the same commitment as always. The change will not affect any of our services and support, and we will continue to offer the best platform experience on Narrato as ever. We know you might have questions about this exciting update, and we're here to help! Feel free to reach out to Narrato support at any time.
About Typeface
Typeface is a leading enterprise generative AI platform for personalized content creation trusted by the Fortune 500, Salesforce, Google, and Microsoft.
| 2022-12-28T00:00:00 |
https://narrato.io/workspace/features/images-and-graphics
|
[
{
"date": "2022/12/28",
"position": 14,
"query": "AI graphic design"
}
] |
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Deans and Leadership - Stevens School Of Business
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Deans and Leadership - Stevens School Of Business
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https://www.stevens.edu
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[] |
Currently, he is exploring how generative AI technologies are reshaping decision-making processes in organizations. Joelle Saad-Lessler. Associate Dean, ...
|
Dean
GJ de Vreede, a distinguished scholar and academic leader, begins his term as Dean of the Stevens School of Business at Stevens Institute of Technology in 2024. With a rich background in information systems, de Vreede brings a wealth of experience and expertise to the role during a pivotal time in the school’s history.
Before joining Stevens, de Vreede served as the interim dean of the University of South Florida’s (USF) Muma College of Business. Under his stewardship, the college expanded its enrollment to more than 8,400 students, including 6,500 undergraduates, 1,800 graduate students, and 100 doctoral students. His tenure was marked by significant milestones, such as record-breaking fundraising efforts that secured over $16 million, including the naming of the Nault Center for Entrepreneurship, and an all-time high of over $1 million in scholarships.
De Vreede's leadership at USF was characterized by a commitment to academic excellence and innovation. He oversaw the college's dual AACSB reaccreditation with zero deficiencies and spearheaded initiatives to enhance research and faculty development.
He demonstrated his commitment to student success by overseeing the rollout of the Bellini Center for Talent Development, expanding advising services for undergraduate and graduate students, and implementing an organizational well-being strategy.
De Vreede's academic journey began in the Netherlands, where he earned both his M.S. and Ph.D. in Information Systems from Delft University of Technology. Prior to arriving at Muma, he held leadership and (visiting) faculty positions around the world, including China, Austria, South Africa and the U.S.
| 2022-12-28T00:00:00 |
https://www.stevens.edu/school-business/leadership
|
[
{
"date": "2022/12/28",
"position": 76,
"query": "artificial intelligence business leaders"
}
] |
|
How AI Is Revolutionizing The Way We Earn A Living
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How AI Is Revolutionizing The Way We Earn A Living
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https://www.aimoneyguide.com
|
[] |
Some workers may find their jobs replaced by AI, while others may see their roles transformed by the incorporation of AI technologies. There are also potential ...
|
In the modern day workplace, AI is being used to automate daily tasks, improve efficiency, and ultimately make better business decisions. Using AI tools to make money from internet could not be easier . It is becoming increasingly common for businesses to incorporate AI technologies into their operations, and new jobs now require some level of familiarity with AI.
The growing impact of AI on job markets and career opportunities is undeniable. Some jobs are being replaced by AI, while others are being created or transformed by it. As AI continues to advance, it is likely to have a significant impact on the way we earn a living and the types of work that are available.
In the simplest terms, AI which stands for artificial intelligence refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect. AI manifests in a number of forms. - Oracle
Artificial intelligence (AI) is a rapidly developing field that is transforming the way we live and work.
We will explore the ways in which AI is revolutionizing the way we earn a living and the implications for the future of work. We will discuss how AI is changing the nature of work, the gig economy, and the importance of skill development in the age of AI.
Photo by Kenny Eliason / Unsplash
One of the most significant impacts of AI on the modern workplace is the automation of tasks. Many businesses are using AI to automate tasks that were previously performed by humans, including data entry, customer service, and even some creative tasks like writing and design.
The benefits of AI-powered automation for businesses are clear: it can save time and reduce costs by reducing the need for human labor. However, the impact on workers can be more complex. Some workers may find their jobs replaced by AI, while others may see their roles transformed by the incorporation of AI technologies.
There are also potential drawbacks to the widespread adoption of AI-powered automation. Some experts have raised concerns about the potential for job displacement and the need to reskill workers for new roles. There are also ethical considerations around the use of AI in decision-making, such as the potential for bias in algorithms.
Overall, the impact of AI on automation is likely to be significant, with both benefits and challenges for businesses and workers. As AI continues to advance, it will be important for individuals and businesses to adapt to the changing landscape of work and to consider the implications for employment and job security.
Photo by Possessed Photography / Unsplash
2. AI and the Gig Economy
The gig economy, characterized by short-term contracts or freelance work rather than permanent employment, has grown significantly in recent years. It is estimated that more than 57 million Americans now participate in the gig economy in some capacity.
AI is playing a role in the growth of the gig economy in a number of ways. For example, platforms like Upwork and Fiverr use AI algorithms to match freelancers with clients and to optimize the bidding process. AI can also be used to automate tasks within the gig economy, such as data entry or transcribing audio recordings.
The impact of AI on the gig economy is likely to be significant. On the one hand, AI could also help. It may make it easier for gig workers to find and complete daily tasks. On the other hand, the use of AI in the gig economy raises concerns about job displacement and the need for gig workers to constantly adapt and reskill in order to remain competitive.
Overall, the role of AI in the gig economy is an evolving one, and it will be important for gig workers and businesses to understand and adapt to the changing landscape of work in the age of AI.
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3. AI Skill Development
As AI continues to advance and transform the way we work, it is becoming increasingly important for individuals to stay up-to-date on AI technologies and to develop relevant skills. This is particularly true for those looking to pursue careers in fields that are likely to be impacted by AI, such as data analysis, software development, and machine learning.
There are a number of ways for individuals to learn about and develop skills in AI. Many universities and online learning platforms offer courses in AI and related fields, and there are also professional organizations and certification programs that can help individuals build their expertise.
In addition to formal education, there are also a number of resources available for those looking to learn more about AI on their own. These include online tutorials, open source software, and communities of practice where individuals can share their knowledge and experiences.
The importance of skill development in the age of AI cannot be overstated. As AI continues to transform the way we work and earn a living, those with the right skills and knowledge will be well-positioned to take advantage of new opportunities and to stay competitive in the job market.
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Conclusion
In conclusion, AI is having a significant impact on the way we earn a living and the types of work that are available. From automation to the gig economy, AI is changing the nature of work and the skills that are needed to succeed in the job market.
The potential for AI to transform the way we work is undeniable, and it is important for individuals and businesses to adapt to the changing landscape of work in the age of AI. This may involve reskilling and learning new technologies, as well as staying up-to-date on the latest developments in AI.
While the future of work in the age of AI may be uncertain, one thing is clear: those who are able to adapt and embrace the opportunities presented by AI will be well-positioned to succeed in the changing job market.
| 2022-12-29T00:00:00 |
2022/12/29
|
https://www.aimoneyguide.com/how-ai-is-revolutionizing-the-way-we-earn-a-living/
|
[
{
"date": "2022/12/29",
"position": 35,
"query": "automation job displacement"
},
{
"date": "2022/12/29",
"position": 38,
"query": "workplace AI adoption"
}
] |
The Future of AI in Plant Operations
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The Future of AI in Plant Operations
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https://www.uptimeai.com
|
[] |
Rather than replacing humans, AI will be used as an additional resource that complements human capabilities and allows for greater adaptability in today's ...
|
Introduction
Artificial Intelligence (AI) is going to change the world, and we’re here to tell you how.
Plant operations is one of the most critical areas of all manufacturing—it’s where the rubber meets the road, so to speak. In fact, injuries in manufacturing are among the top 3 across industries in the United States [Source: Bureau of Labor Statistics, US Department of Labor]. That’s why AI is so important for plant operations: it allows companies to improve safety, reduce costs, increase production, and meet sustainability goals.
Here are a few examples of the most promising areas for AI growth in the coming years.
Managing energy consumption and usage.
Managing inventory levels.
Identifying root cause.
Predicting maintenance needs.
Recommending performance optimization.
While it is expected to improve the operating conditions in a plant, Can it truly deliver on that promise? Well… let’s find out!
The role of humans in future plant operations.
Humans have always been a part of the production process. From the seed to the shelf, humans are responsible for every step along the way.
In the past, it was not uncommon for companies to hire workers to do all of their plant operations, large scale or small. However, as companies have become more automated, they have begun to rely on software and robotics, be it in the supply chain or handling raw materials. Humans will continue to play a vital role in the future of plant operations. We must understand how humans are already involved in this process because it can help us set realistic expectations for technology. The role of humans in future plant operations includes the manufacturing process and setting realistic expectations for technology.
Applying technology to the manufacturing process.
AI is a tool, and humans are the ones setting the goals. AI can be used to help humans execute their processes more efficiently, but it will never be able to replace them entirely. Rather than replacing humans, AI will be used as an additional resource that complements human capabilities and allows for greater adaptability in today’s dynamic manufacturing environment.
As we move forward with this technology, it is important that we do not lose sight of its limitations or potential benefits—especially when considering its impact on plant operations.
For example, Production line quality inspection in real-time, which is typically time-consuming, can be accelerated with the aid of computer vision, paving the way for autonomous operations.
Setting realistic expectations for the technology.
AI is not a silver bullet. Setting realistic expectations for the technology is essential, especially considering how it will impact your business. For example, if you’ve heard about robots that can replace human resources in manufacturing plants and warehouses, this may be exciting news for some organizations but not for others.
In fact, many companies are using AI technology as part of their plant operations today—and they’re doing so with great success! Here are a few examples: ai-based predictive maintenance solutions with the help of machine learning algorithms can monitor the entire manufacturing plant with sensors, and due to computing power, can process billions of data points to predict when maintenance is required on both critical and non-critical assets.
AI is the tool, but humans set the goals.
AI is a tool that helps humans achieve their goals. If a plant manager wants to improve efficiency, they can use machine learning to identify opportunities for improvement: what does your equipment do well? What does it need work on? How can we use automation to address these problems?
The best way for humans and AI teams alike is to think about how all of this information could be useful in real-life situations rather than just as raw data or numbers.
Conclusion
The future of plant operations lies in the synergistic partnership between AI technology and human expertise. By leveraging AI’s analytical power alongside human judgment, companies can achieve new heights in efficiency, safety, and profitability.
Ready to see how AI can transform your plant operations? Request a Demo or Schedule a Free Consultation with our experts today to explore tailored solutions for your business.
| 2022-12-28T00:00:00 |
2022/12/28
|
https://www.uptimeai.com/resources/future-of-ai-in-plant-operations/
|
[
{
"date": "2022/12/29",
"position": 28,
"query": "AI replacing workers"
},
{
"date": "2022/12/29",
"position": 65,
"query": "future of work AI"
}
] |
Does AI and Chat GPT mean law is no longer a good career?
|
Does AI and Chat GPT mean law is no longer a good career?
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https://www.dcurbanmom.com
|
[] |
I am seriously worried that AI will render the jobs of most knowledge workers obsolete sooner than we think. ... Thr idea of AI replacing doctors is completely ...
|
Anonymous wrote: Anonymous wrote: To explain a bit, I work in a job where I review things people have submitted through Legalzoom (and with counsel). And one of the issues is that people don't know basically how to describe their issue correctly. A good lawyer understands how to ask the write questions and say "oh okay your problem isn't actually X it's Y". But submissions from LegalZoom are always X. No one's bothered to ask the relevant questions.
The problem is because they submitted X they basically just have to start all over. They wasted a lot of time and money pursuing X.
To be clear bad lawyers fall in this trap too. They don't bother to question if X is the thing their client needs at all. But it's usually these discount mass firms that everyone in my field knows about (for being scammy), or it's lawyers who have small practices and are in another field and haven't done sufficient research.
I don't see how AI solves that issue at all. You have to be able to spot issues.
+1.
TBH, issue spotting is useful in many fields beyond law. It's even useful in random internet forums, where so many posters ask questions without realizing their key issues until the discussion gets going. +1.TBH, issue spotting is useful in many fields beyond law. It's even useful in random internet forums, where so many posters ask questions without realizing their key issues until the discussion gets going.
Yes, it would be like reducing the medical field diagnostics to Web MD.
| 2022-12-29T00:00:00 |
https://www.dcurbanmom.com/jforum/posts/list/15/1102613.page
|
[
{
"date": "2022/12/29",
"position": 42,
"query": "AI replacing workers"
}
] |
|
Here are the 6 tech jobs that are expected to be most ...
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Here are the 6 tech jobs that are expected to be most in-demand in 2023
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https://interestingengineering.com
|
[
"Maia Mulko"
] |
AI and machine learning engineers are in high demand because of the growing number of applications that call for machine learning nowadays (self-driving cars, ...
|
High-demand tech jobs can be found in data science.
Software engineering and specialized subsets are also in high demand.
Digital marketer and video editor jobs are rising, too.
2023 is around the corner.
The world continues to become more technology-driven, and the job market gets updated to meet the needs of all the sectors that are being transformed by the digital age.
The available data indicates that these will be some of the most in-demand tech-related positions in 2023.
1. Data scientist
Nowadays, most companies rely on data to boost their productivity and stay competitive. They use data to track opportunities, predict market trends, and build strategies for business growth.
| 2022-12-29T00:00:00 |
https://interestingengineering.com/culture/6-tech-jobs-most-in-demand-2023
|
[
{
"date": "2022/12/29",
"position": 10,
"query": "machine learning job market"
}
] |
|
How to Get Started as an AI Developer | Study Hub
|
How to Get Started as an AI Developer
|
https://www.edoxi.com
|
[
"Lukas Barwicki",
"Nithin Kumar Reddy",
"Satendra K"
] |
AI experts talk about the boundless opportunities and effects on the implementation of AI. While Artificial Intelligence (AI) and Machine Learning (ML) is ...
|
Artificial Intelligence or AI is an emerging technological trend. The Job Growth has already flooded the industry demanding candidates with AI skills. Artificial Intelligence is revolutionising the world, shaping our future. The complexity of the work is reduced and in future, AI would turn to be an integral part of most businesses.
AI experts talk about the boundless opportunities and effects on the implementation of AI. While Artificial Intelligence (AI) and Machine Learning (ML) is creating huge impacts on our day to day lives, a vast number of career opportunities are also on the rise.
AI Developers have the best pay in entry-level careers and are supposedly the smartest group of people in the world. So, how do you become an AI Developer? Where do you start to be a Developer? What are the skills and opportunities of a Developer? If these are your questions, here is the answer. Let's check it out.
Start with Gaining Basic AI Skills
Well, there is a lot of information you need to gather before starting as an AI Developer. The first step that you should take towards building a career in AI is to analyse your skills required to be an AI Developer. Some of the AI skills you’ll require are:
Mathematical skills - You must possess good mathematical skills and statistical knowledge to be an AI expert. Every action in artificial intelligence requires complex algorithms and calculations. For quick learning of Linear Algebra and Math Analysis, you can prefer any short courses or practical lessons.
Computer skills - To design and develop relevant software, computer skills are necessary. It will help you in performing the given tasks efficiently.
Communication skills and problem-solving skills- Once you're hired, you may not be working alone. AI requires the efforts of many people and so communication skills and problem-solving skills are mandatory.
Knowledge of Programming languages- AI developers are required to develop their knowledge of Programming Languages like Python and R. These languages make up the core of many AI systems. You can choose any training courses or lessons if you are not well versed in programming languages. If you're a developer, you would know why programming languages like Python is best for Artificial Intelligence.
Machine Learning (ML)- Aspirants are also required to have a working knowledge of the fundamental concepts and principles behind machine learning. You should gain expertise in fields such as text mining, data pre-processing, and data cleaning.
Knowledge of Trending AI Technologies- Always keeps yourself updated with the latest and top trending AI Technologies It will help you to learn what is in demand.
How to Learn the Basics of AI?
Initially, you might experience certain difficulties studying Artificial Intelligence. Here we describe how you can practically start learning to be an AI Developer?
Concentrate on a specific issue and search for an answer, rather than just finding out about what you can discover on the web. You will then require an algorithm that will process data into a structure which is reasonable for AI.
We would then suggest you train on a basic model, give an outcome, and assess its performance. Bring out your creativity to the solution. Pick various issues and follow a similar method for each task. Complete a Kaggle competition which will permit you to test your aptitudes, tackling similar issues. This is how you can effectively learn basic lessons of AI Development.
Key roles of an AI Developer
If you need to get hired as an AI developer, you must understand the key roles and duties of this position. Have you ever checked what AI developers do in a company?
Artificial Intelligence or AI developers are hired in companies to provide solutions to issues utilising AI and programming. You will be responsible for designing and developing AI-based systems. AI is always developing and so, you should be aware of the latest advancements in AI to improve existing infrastructures and to build AI models.
Artificial intelligence developers specialize in developing software for robots and systems. When you work as a developer, it is your responsibility to coordinate between data scientists and business analysts. You must also be capable to test and deploy models. Moreover, you are expected to use AI to empower the company with novel capabilities.
Opportunities for AI Developers
If you are a graduate and an aspiring candidate to pursue the job role of an AI Developer, you must know computer science, artificial intelligence, programming languages, robotics and engineering as we mentioned earlier. Artificial Intelligence would develop in the future in a way creating changes in every sector and so the next generation of AI will be much more sophisticated.
Securing a career as an AI Developer or being an AI expert isn't as simple as learning computer science or earning a college degree. It takes effort, skills, and creativity to enhance a career in AI. To get a clear idea, learn how to build a career in Artificial Intelligence. Since AI is an emerging but a fast-growing sector, job opportunities for the creative people are in demand.
Getting familiar with the basics of data science and machine learning is your first important step, to begin with, AI. However, there are a large number of online courses that will provide you lessons on basic maths, algorithms, data science, machine modelling and programming languages.
You might assume that getting started as an AI Developer is difficult. But, what you can easily do is to start applying the knowledge you have to create simple machine learning solutions as your first step to the profession. For developers, your opportunity in the AI field is never-ending. The ideas are limitless that you are capable of diversifying your company in a competitive market. Keeping up with new trends like ChatGPT and other developments in the field of AI requires a proactive approach to continuous learning and staying informed.
Let's recollect the major points that you need to focus on to be an AI Expert. First, prepare and gain a knowledge base. Learn key things about AI and practice. You can get started with a non-formal education if you do not have a degree related to learning AI. If you’re working in Dubai and looking to upskill yourself, there are some educational institutes where you can avail some great AI courses in Dubai.
Now that you know what it means to become an AI Developer. We have witnessed how AI is being integrated into smartphones enhancing edge detection in portraits, face recognition and colour profiles in the camera. If AI could bring such innovations in a camera, imagine how much it can contribute to various sectors of our business. Don't wait now, start learning and applying your AI skills to create simple machine learning solutions that will build your dreams.
Locations Where Edoxi Offers Artificial Intelligence Course
Here is the list of other major locations where Edoxi offers Artificial Intelligence Course
Artificial Intelligence Course in Dubai | Artificial Intelligence Course in Qatar
| 2022-12-29T00:00:00 |
https://www.edoxi.com/studyhub-detail/start-as-an-AI-developer
|
[
{
"date": "2022/12/29",
"position": 77,
"query": "machine learning job market"
}
] |
|
Future world of work: Let's take a look
|
Future world of work: Let's take a look
|
https://arts.eu
|
[
"Authos Content",
"About The Author",
"Aileen Kreibich",
"Managing Director"
] |
The growth of artificial intelligence, machine learning, as well as the use of robotics will greatly change the world of work in the coming years. This raises ...
|
Forms of employment
In addition to the above, the individualization of work will also play an increasingly important role, and with it a wide variety of forms of employment. Thus, in the working world of the future, there will no longer just be part-time, full-time or self-employment, but also crowd-, cloud- or gig-work. Instead of having only permanent employees in one's own company, these forms of work offer the possibility of meeting the demand for skilled workers for projects in an uncomplicated way. Hiring freelancers or outsourcing certain activities can effectively counteract the shortage of skilled workers.
Thanks to the growth in technology, virtual teamwork in the form of remote work is also set to increase. The ifaa also states that new industrial jobs will be created, including in residential areas, as industrial services increase. This means that there will be a focus on STEM occupations in the future, and IT in particular will grow in the workplace of the future.
Technology
The growth of artificial intelligence, machine learning, as well as the use of robotics will greatly change the world of work in the coming years. This raises the question of which professions will still exist at all in the future when AIs and machines learn more and more and become more intelligent. The New Careers trend is looking at precisely this: Which areas will disappear completely, and where will new professions emerge? Above all, the field of robotics, i.e. machines that are becoming increasingly intelligent, will gain in growth in the coming years. These will communicate better with each other in the sense of an Internet of Things, and thus increase efficiency. For the working world of the future, we can therefore assume that the demand for IT specialists, but also for mechanics, will increase.
Flexibility
The last category for the working world of the future will go hand in hand with technology, but also with sustainability and the new forms of employment. Flexibility will influence work in the future through mobility due to location-independent working, but also through agility - unlike conventional planning of projects and work processes, in agile working the goal is fixed, but the approach is regularly adapted to current circumstances. This creates individual solutions that offer maximum flexibility for employees. This flexibility also means that the degree of networking will have to increase in order to stay in touch for work. In the last ten to fifteen years, we have already seen how new technologies suitable for widespread use are changing the nature of collaboration and supporting greater flexibility. With this development comes new demands on companies as a whole and on managers in particular, who must learn how to lead at a distance.
How to prepare for the working world of the future
The above developments can seem overwhelming at first glance. Many systems and ways of working as we know them are becoming obsolete; which is precisely why you should prepare yourself and your company for the future now. You may already be in line with some trend directions, such as flexible working through home office or remote work. You are probably already using AIs in one place or another to make workflow more effective, or in terms of Big Data to analyze large amounts of data and evaluate decision options with business intelligence tools, or you have already integrated fully automated systems/machines via Internet of Things.
But what about your employment patterns? Is the 40-hour work week your standard or do you offer more customized work schedules? Have you ever thought about using gig workers or crowdwork? Is your organization still arranged in a steep hierarchy, or is it already flatter and offers individual employees more flexibility and room for their own development? There are many opportunities in the area of collaboration and the human factor to prepare for the working world of the future. Of course, you should keep your industry in mind; not all measures can be implemented for every field of activity.
The dynamics of change, crises and challenges make it even more important for all professionals to take care of themselves as well. Strengthening mental health and building a stable foundation for oneself is part of the future competencies of our upcoming working world. It is with this health competence in particular that we can take advantage of all the opportunities that are available to us in the process.
Summary
The working world of the future holds many challenges and also currently unknown developments. Nevertheless, it can already be predicted that the old systems, such as a steep hierarchy or the daily trip to the office, will become obsolete - at least in those areas where things can be done differently. These developments will demand a great deal from both employers and managers, and will require an evolution of management style as well as the company. For this leap, it can be beneficial to consider the help of a strong partner with vision and foresight. We implement many of these trends in our daily work, from flexible working models to the use of AI. If you need support in your company for a transformation to be well equipped for the working world of the future, contact us.
| 2022-12-29T00:00:00 |
https://arts.eu/en/insights/article/future-world-of-work-lets-take-a-look/
|
[
{
"date": "2022/12/29",
"position": 62,
"query": "future of work AI"
}
] |
|
How artificial intelligence can improve safety at workplaces
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How artificial intelligence can improve safety at workplaces · Accent Systems
|
https://accent-systems.com
|
[] |
AI has the ability to improve safety in the workplace by reducing human error, automating tasks, and providing employees with real-time feedback.
|
Introduction
Artificial Intelligence (AI) has been a hot topic in the tech industry for years now, and for good reason. AI has the potential to revolutionize many different industries, from healthcare to hospitality. But one sector of the economy that is often overlooked when it comes to AI is safety and compliance at the workplace. With AI, companies can use predictive analytics to identify potential safety risks and alert workers before something dangerous happens. This technology also allows for faster response times in emergency situations and better data-driven decisions about how best to reduce risk. In this article, we’ll look at how AI can be used to improve safety at workplaces around the world.
What is AI and how can it be used in workplace safety?
Artificial intelligence (AI) is a term used to describe a computer system that can learn and work on its own, making decisions based on data. AI has the ability to improve safety in the workplace by reducing human error, automating tasks, and providing employees with real-time feedback.
When it comes to safety in the workplace, AI can be used in a number of ways. For example, AI can be used to monitor employee behavior and identify potential risks. AI can also be used to create virtual reality training simulations that can help employees prepare for hazardous situations. Additionally, AI can be used to create predictive models that can help organizations anticipate and prevent accidents before they happen.
Ultimately, AI has the potential to make workplaces safer by reducing human error and increasing situational awareness. By leveraging the power of AI, businesses can create a safer environment for their employees and protect their bottom line.
Some ways AI can improve safety in the workplace
1. Implementing AI can help create a safer work environment by identifying potential hazards and risks.
2. AI can be used to monitor employee behavior and performance, helping to identify issues early on.
3. By providing employees with real-time feedback, AI can help them correct unsafe behaviors before an accident occurs.
4. AI can also be used to create virtual simulations of dangerous work environments, allowing employees to practice safety procedures without putting themselves at risk.
5. Finally, AI can be used to develop new safety technologies and protocols, making workplaces safer for everyone involved.
How to get started with using AI for workplace safety
AI can be used to create predictive models that can help identify potential safety issues before they occur. So the very first thing you should consider is to deploy the necessary infraestructure to get enough relevant data.
At Accent Systems, We offer multiple solutions to monitor all the parameters in real-time, contact us to check how can we get the data you need to feed your AI powered system to improve the safety for your workers.
Check out our connected worker solution here:
https://accent-systems.com/solutions/connected-worker/
Conclusion
Artificial intelligence has the potential to drastically improve safety in the workplace. By leveraging AI’s machine learning capabilities, companies can quickly and accurately identify hazards and take steps to eliminate them. In addition, AI-powered systems can be used to monitor the environment for suspicious activity or dangerous conditions and alert employees accordingly. With its ability to automate certain processes and provide real-time feedback on risks, AI is a powerful tool that should not be overlooked when seeking ways to enhance workplace safety efforts.
| 2022-12-29T00:00:00 |
2022/12/29
|
https://accent-systems.com/how-artificial-intelligence-can-improve-safety-at-workplaces/
|
[
{
"date": "2022/12/29",
"position": 16,
"query": "machine learning workforce"
},
{
"date": "2022/12/29",
"position": 3,
"query": "artificial intelligence workers"
}
] |
Machine Learning in Public Sector | Civica
|
Machine Learning in Public Sector
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https://www.civica.com
|
[] |
In volume 3 of Perspectives* we enter the matrix of machine learning and examine its impact for public services and how we can get the most value.
|
Machine learning opens new possibilities to drive positive change; helping computers learn from data to create models humans wouldn’t be able to build otherwise. But how can we maintain trust and deliver real benefits for everyone?
Since the term ‘machine learning’ was coined in 1959, the technique has been used to build models that lie at the core of daily applications. From rapid diagnosis of disease, to assigning limited inspection resources, and suggesting early intervention, there are many potential benefits. However, the topic is not without controversy.
Working with our partners at the University of Bath’s ART-AI Centre, Perspectives* volume 3 aims to help public services crack the machine learning matrix by examining:
| 2022-12-29T00:00:00 |
https://www.civica.com/en-us/innovation/civica-northstar/perspectives/machine-learning-reloaded/
|
[
{
"date": "2022/12/29",
"position": 31,
"query": "machine learning workforce"
}
] |
|
Wayne AI Research - Wayne State University
|
Home-Wayne AI Research
|
http://ai.wayne.edu
|
[] |
We're a team of scientists and engineers with diverse research interests spanning a broad range of contemporary AI frontiers.
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Artificial Intelligence (AI) has risen as one of the national science and technology priorities. National Artificial Intelligence Research and Development Strategic Plan, informed by visioning activities in the scientific community as well as interaction with the public, identifies as its first strategic objective the need to make long-term investments in AI research in areas with the potential for long-term payoffs in AI. National Science Foundation is leading the effort establish a set of hubs in a broader nationwide network that will accelerate research in AI; expand America's workforce; and transform society, from extreme weather preparedness to K-12 education, for decades to come. More recently, National AI Initiative Office launched by White House to coordinate AI research and policymaking across government, industry, and academia. Our vision, mission and set of goals align closely with nation’s AI Initiatives.
| 2022-12-29T00:00:00 |
http://ai.wayne.edu/
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[
{
"date": "2022/12/29",
"position": 39,
"query": "machine learning workforce"
},
{
"date": "2022/12/29",
"position": 72,
"query": "AI healthcare"
}
] |
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Data Preparation 101
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Data Preparation 101
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https://sigma.ai
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[
"Sigma Ai"
] |
This is the process of converting data from a structured or unstructured format into a form that machine learning algorithms can use. ... Our flexible workforce ...
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Preparing Data for Machine Learning
Data preparation for machine learning is a multi-step process, though not every project will cover every stage. Machine learning algorithms generally require data in very specific formats and in large volumes. This makes data preparation a typically time and labor-intensive step – in fact, up to 80% of the time spent on a machine learning project may be spent on data preparation. The steps to preparing data for machine learning typically look like the following:
Data sourcing
The very first step is to understand the type of data required for a project and then source that data. Once the type of data to be gathered has been determined, it must then be ethically collected. This means respecting data ownership rights and obeying any applicable stipulations on the use of such data. Data will rarely be exactly as needed and in the desired format, so it will need to be prepped further in some capacity.
Data cleansing & filtering
Once data has been sourced and gathered, it must be suitably cleaned and filtered for the particular project. This step means taking steps to ensure that the data available is easier to understand and use as part of machine learning models. Generally, this involves ensuring that data is in a consistent and standardized format. Data filtering then involves weeding out the bits that you don’t need until you are left with appropriately cleansed and highly relevant data that can be used for modeling.
Data augmentation
Data augmentation is a way of creating or modifying additional data that is based on currently available data. The data needs to be balanced, meaning that there are some classes that need to be modeled that are not well represented or all the operating conditions do not have data associated to them, so the goal of this stage is to make sure that the volume of data is good enough and that the classes to be modeled are balanced. Some ways to do data augmentation include, for example, adding synthetic data and sourcing more data. This is often done to avoid issues with small datasets, such as oversampling and overfitting.
Data annotation
Data annotation is also sometimes known as data enrichment and it typically involves labeling or otherwise enriching data in some way to provide additional context and help to sort and categorize data in various ways. This is usually done by data type, applying labels such as whether data is in the form of images, video, audio, text, or something else. Then within data subsets, labels can annotate specific data, for example labeling all the names of people used within a piece of text. This is an important step, as machine learning models are typically trained based on the type of task to be performed. Data annotation makes it easy to sort and find data specific to the sort needed for the particular model.
Translation, normalization, localization
Depending on the needs of the particular model and the data, this step may or may not be needed. Certain data may not be in the correct language or may have localization quirks that require sorting for consistency. A good example of this would be images of canned, carbonated drinks. Even within English, these are known by several different names, such as “pop”, “fizzy drink”, “soda”, “soft drink”, and more. These are all functionally the same name but for the sake of machine learning models, consistency is needed, and as such one particular name should be chosen and used to refer to all instances within the data.
Model training/re-training
Once sufficient data has been gathered, cleansed, augmented and generally made useful for machine learning, it can then be used to train and re-train models. Machine learning models are trained on data sets with a specific purpose in mind, such as identifying all instances of the names of places or people used within a body of text.
Model validation
Model validation is a crucial step as it is used to determine whether the model in question accurately represents the behavior of the system or not. This is where a validation dataset including a diverse set of data including ground truth annotation becomes very important, as it is used to verify that the model in question can generalize and operate outside the specific conditions included in the training dataset.
Why Does Special Attention Need to Be Paid During the Data Preparation Process?
The data preparation process is where up to 80% of the time is spent on a machine learning project with good reason. Since machine learning models are trained off data, the outcome of any model is largely dependent on the quality of the data input. There are many issues that can come about with ill-prepared data outlined below.
What Challenges of Data Preparation Should You Consider?
There are several challenges involved in data preparation, such that it pays to work them out upfront before starting to source data. The first step is understanding the goal of the model and working out what data you need and how much. This can be very varied – for example, if you want to attempt to forecast sales and revenue streams for the next 6 months, there are many data points you may choose to include as part of the model. These include, but are not limited to, historical sales data, weather data, location data, employee data, supplier data, raw materials data, general market trends data, and plenty more. How much you wish to include and how much weight data should have will be very project-specific. However, these are decisions that should be made before data sourcing begins.
The next challenge is to find relevant, high-quality data. This is a particularly difficult challenge and one that most machine learning projects are likely to face if there is no pre-prepared data available. Some of this data might already be available in some format, such as historical sales data or historical weather data. Still, this data is likely spread out across several locations and formats.
It’s essential to also work out how data will be structured once it has been decided upon and sourced. As data is likely to come in several different formats, knowing how data is to be structured ahead of time can help to determine how much prep work will be needed to convert any particular data source into a data format that can be used for machine learning.
Another challenge worth mentioning is the difficulty to obtain high-quality annotations/labels which are unambiguous, consistent, and represent accurately the data.
How Do We Know if Our Data Preparation Was Thorough Enough?
Several signals can be used to determine how thorough data preparation was. In supervised learning, there is what is known as ‘ground truth data’. This data represents some sort of objective truth that can be used as the source of truth for comparison with data produced by machine learning models. The closer the output of machine learning models matches ground truth data, the better the quality of the model. This helps avoid associated issues such as overfitting or underfitting. Ground truth data is also used and is necessary for supervised learning to train the models themselves. It’s important that the validation set validates that the models are good enough. If the validation set is diverse enough, this also validates that the data preparation is good enough.
Overfitting is an issue that happens when a model fits the training data too well. This means that both the data and the noise get picked up by the model to the extent that the noise/random fluctuations in the training data get incorporated into the model. The model then struggles to generalize it to unseen data.
Underfitting is a related issue, but unlike overfitting, which fits the training data too well, underfitting is a problem of not being able to model the training data nor generalize with unseen data. Underfitting is generally easy to detect and suggests either an issue with the model itself or that it did not have sufficient data for training.
Proper data preparation leads to better AI
Data is the lifeblood of machine learning, you need a constant supply of high-quality data in order to get the best performance. Though data sourcing, cleansing, annotation and formatting are time-consuming tasks that are not as interesting as making and experimenting with new models, they are still important foundational steps. Problems with data will bleed into the later stages and cause problems with modeling.
The better your input, the better your output will be. Proper data preparation leads to better AI, as models can generalize better and avoid problems such as underfitting and overfitting. Though the prospect is not as exciting as working with the models themselves, spending the bulk of a project’s time on data preparation pays dividends in producing high-quality data that can be reliably used to train models for AI.If you need help with data preparation for your machine learning project, contact Sigma today. We can help you source, combine, and format and annotate your data to be ready for modeling.
| 2022-12-19T00:00:00 |
2022/12/19
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https://sigma.ai/data-preparation-machine-learning/
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[
{
"date": "2022/12/29",
"position": 77,
"query": "machine learning workforce"
}
] |
10 Biggest Tech Layoffs of 2022, From Meta to Uber
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10 Biggest Tech Layoffs of 2022, From Meta to Uber
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https://www.dice.com
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[
"Nick Kolakowski"
] |
Here are the tech companies that laid off the most employees in 2022, according to crowdsourced data on layoffs.fyi.
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Some of the biggest companies in tech unleashed layoffs this year. They had varied reasons for doing so; for example, Twitter cut thousands of workers after new management took over, while Meta reduced its workforce in the face of economic uncertainty and an advertising squeeze.
Whatever the causes, here are the tech companies that laid off the most employees in 2022, according to crowdsourced data on layoffs.fyi. As you can see, these layoffs took place across specializations, from e-commerce to networking:
Despite these layoffs, it’s important to keep in mind that organizations throughout the economy continue to hire all kinds of tech professionals for all kinds of roles. The latest data from the U.S. Bureau of Labor Statistics (BLS), as analyzed by CompTIA, shows the tech unemployment rate fell to 2 percent in November. Overall tech sector employment increased by 207,200 between January and November.
“The hotter-than-anticipated tech jobs report confirms there are still many more employers hiring tech talent than shedding it,” Tim Herbert, chief research officer at CompTIA, wrote in a statement when that CompTIA data was released. “It’s certainly premature to dismiss concerns over the health of the economy, but this should be a reassuring sign for the tech workforce.”
So why are some of the biggest tech companies laying off workers? Many of these giants (including Amazon and Meta) hired aggressively during the pandemic, buoyed by extraordinary profits as consumers and companies spent money on everything from new cloud-based software tools to online games. But recent fears of recession have led customers to curtail their spending, leading worried executives to launch new rounds of cost-cutting.
And there are signs the cuts aren’t over yet. Companies such as Micron and Tesla may reduce jobs in the first part of 2023, while Google’s recent attempts at streamlining its costs and operations could lead to layoffs. But it’s important to keep in mind that these companies—and many others—will need to resume hiring at some point in order to fulfill their strategies.
| 2022-12-29T00:00:00 |
2022/12/29
|
https://www.dice.com/career-advice/10-biggest-tech-layoffs-of-2022-from-meta-to-uber
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[
{
"date": "2022/12/29",
"position": 11,
"query": "AI layoffs"
}
] |
In The News
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Intellizence
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https://intellizence.com
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[] |
... layoffs spiking for periods in ... Intellizence,Inc, a leading artificial intelligence (AI)-powered market intelligence platform, is excited to announce
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Novo Holdings’ $16.5 billion Catalent buy leads 2024’s M&A deals so far
Both layoffs and VC funding have increased since the beginning of the year (data curated in part from Intellizence and Crunchbase) — with layoffs spiking for periods in April and May due to significant layoff announcements.
Drug Discovery & Development|June 13, 2024
Layoffs continue to build in H1 2024, affecting more than 20,600 workers
Both layoffs and VC funding have increased since the beginning of the year (data curated in part from Intellizence and Crunchbase) — with layoffs spiking for periods in April and May due to significant layoff announcements.
Drug Discovery & Development| May 28, 2024
More cybersecurity firm closures expected after IronNet shutters
Additional security related firms listed recently by Intellizence as filing for some type of bankruptcy protection in 2023 include; Quanergy Solutions, SunGard Availability Services and Cyren.
Forbes| June 7, 2023
Corporate Bankruptcies Are Rising At A Concerning Rate—What To Do If Your Company Has Filed For Bankruptcy
Here is a list of notable companies that filed for bankruptcy protection in the United States in 2023, according to Intellizence
Forbes| June 19, 2023
The Latest 2023 Tech Layoff Numbers in Three Charts
The November headlines were full of tech companies announcing layoffs. And only the biggest tech employers
made the news; small tech startups trimming already lean staffs were hidden in the deluge. Intellizence
gathered the layoff data from WARN filings, news reports, and press releases, using AI-based tools to
automatically extract key details and remove duplicates.
IEEE Spectrum | June 19, 2023 The November headlines were full of tech companies announcing layoffs. And only the biggest tech employersmade the news; small tech startups trimming already lean staffs were hidden in the deluge. Intellizencegathered the layoff data from WARN filings, news reports, and press releases, using AI-based tools toautomatically extract key details and remove duplicates.
Latest Tech Layoff Trends in Three Charts
The November headlines were full of tech companies announcing layoffs. And only the biggest tech employers
made the news; small tech startups trimming already lean staffs were hidden in the deluge. Intellizence
gathered the layoff data from WARN filings, news reports, and press releases, using AI-based tools to
automatically extract key details and remove duplicates.
IEEE Spectrum | December 09, 2022 The November headlines were full of tech companies announcing layoffs. And only the biggest tech employersmade the news; small tech startups trimming already lean staffs were hidden in the deluge. Intellizencegathered the layoff data from WARN filings, news reports, and press releases, using AI-based tools toautomatically extract key details and remove duplicates.
The Global Data Marketplace Platform Market size is expected
to reach $3.5 billion by 2028, rising at a market growth of
24.3% CAGR during the forecast period
A data marketplace platform offers a distinctive user experience while facilitating the purchase and
trading of various data kinds. Datarade entered into a partnership with Intellizence, a leading AI
platform-providing company. Through this partnership, Datarade aimed to enable its customers to seamlessly
discover, compare, and buy data products from Intellizence’s portfolio.
ReportLinker | October 25, 2022 A data marketplace platform offers a distinctive user experience while facilitating the purchase andtrading of various data kinds. Datarade entered into a partnership with Intellizence, a leading AIplatform-providing company. Through this partnership, Datarade aimed to enable its customers to seamlesslydiscover, compare, and buy data products from Intellizence’s portfolio.
Investors Pay The Price When Bloated Companies Lay Off Workers
Shares of these companies, including Tesla (TSLA), Coinbase Global (COIN) and Uber Technologies (UBER), are down an
average 46% this year, says an Investor’s Business Daily analysis of market data from S&P Global Market
Intelligence and layoff data from Intellizence.
Investor’s Business Daily, LLC | October 7, 2022 Shares of these companies, including Tesla (TSLA), Coinbase Global (COIN) and Uber Technologies (UBER), are down anaverage 46% this year, says an Investor’s Business Daily analysis of market data from S&P Global MarketIntelligence and layoff data from Intellizence.
10 Health companies that have announced layoffs, hiring freezes this year
Even with a falling unemployment rate, U.S. companies are still laying off workers. From CVS Health to
Spectrum Pharmaceuticals, several healthcare companies have announced layoffs and hiring freezes this year
according to a July report from Intellizence.
Becker’s Healthcare | August 15, 2022 Even with a falling unemployment rate, U.S. companies are still laying off workers. From CVS Health toSpectrum Pharmaceuticals, several healthcare companies have announced layoffs and hiring freezes this yearaccording to a July report from Intellizence.
How to Evaluate a Potential Employer in a Downturn
With rising interest rates, high inflation, political issues, and war all contributing to a roller coaster
global economic outlook, it’s hard to know whether to stay in a stable job — no matter how unhappy you are — or make
a move when it’s unclear how changing economic conditions could affect your employment. Finally, whether the company
is public or private, check websites like Crunchbase and Intellizence for layoff-reporting data.
Harvard Business Publishing | August 02, 2022 With rising interest rates, high inflation, political issues, and war all contributing to a roller coasterglobal economic outlook, it’s hard to know whether to stay in a stable job — no matter how unhappy you are — or makea move when it’s unclear how changing economic conditions could affect your employment. Finally, whether the companyis public or private, check websites like Crunchbase and Intellizence for layoff-reporting data.
60+ Surprising Employee Layoff Statistics for 2022
In this Employee Layoff Statistics, we will discuss many companies across the globe that has fired employees so far
this year for several reasons. Data Source Intellizence.
Enterprise Apps Today | July 30, 2022 In this Employee Layoff Statistics, we will discuss many companies across the globe that has fired employees so farthis year for several reasons. Data Source Intellizence.
The Wrong Way to Downsize a Workforce
Corporate downsizings are on the rise. CEOs face urgent crises culminating in massive layoffs. Pandemic pressures have
devastated the airlines, hospitality and tourism, banking, technology and transport industries. Companies
that Announced Major Layoffs. (2022, February 24) from Intellizence.
Psychology Today | March 11, 2022 Corporate downsizings are on the rise. CEOs face urgent crises culminating in massive layoffs. Pandemic pressures havedevastated the airlines, hospitality and tourism, banking, technology and transport industries. Companiesthat Announced Major Layoffs. (2022, February 24) from Intellizence.
Intellizence data products now available through AWS Data Exchange for APIs
Intellizence,Inc, a leading artificial intelligence (AI)-powered market intelligence platform, is excited to announce
that Intellizence data products are now available through AWS Data Exchange for APIs.
Communitech | February 17, 2022 Intellizence,Inc, a leading artificial intelligence (AI)-powered market intelligence platform, is excited to announcethat Intellizence data products are now available through AWS Data Exchange for APIs.
Integrating AI and human intelligence solutions – Interview with Int ellizence CEO
Sachi Komarasamy and his co-founders launched Intellizence in 2016. Started with a goal to monitor a set of companies for
individuals, it has grown into one of the trusted resources for research, data, and industry trends on emerging technologies and companies globally.
Listen to hear his thoughts on the intersection of Artificial Intelligence and Human Intelligence during a podcast interview hosted by
LAC Group’s Chief Business Development Officer Mario Thériault
LAC Group Podcast | February 11, 2021 Sachi Komarasamy and his co-founders launched Intellizence in 2016. Started with a goal to monitor a set of companies forindividuals, it has grown into one of the trusted resources for research, data, and industry trends on emerging technologies and companies globally.Listen to hear his thoughts on the intersection of Artificial Intelligence and Human Intelligence during a podcast interview hosted byLAC Group’s Chief Business Development Officer Mario Thériault
Intellizence leverages Data and AI to power Market Intelligence during COVID-19
Finance and market intelligence firms have never been more data-driven. The need for greater
information for the purpose of monitoring economic conditions has been acutely highlighted during the recent
COVID-19 outbreaks.Learn how Sachi Komarasamy and his team at Intellizence developed an award-winning AI-powered platform that
helps enterprises monitor sales and risk signals in their target companies.
Communitech | June 25, 2020 Finance and market intelligence firms have never been more data-driven. The need for greaterinformation for the purpose of monitoring economic conditions has been acutely highlighted during the recentCOVID-19 outbreaks.Learn how Sachi Komarasamy and his team at Intellizence developed an award-winning AI-powered platform thathelps enterprises monitor sales and risk signals in their target companies.
APIs to Track Coronavirus COVID-19
Track this API – The Intellizence COVID-19 API provides the latest information about companies working on COVID-19.
API returns diagnostics,treatments, and prevention products and solutions. This service also provides data associated
with vaccines, drugs, and ventilators. The dataset includes product categories, company details, regulatory agencies, status, and sources.Information is updated daily.
ProgrammableWeb | Jun 25, 2020 Track this API – The Intellizence COVID-19 API provides the latest information about companies working on COVID-19.API returns diagnostics,treatments, and prevention products and solutions. This service also provides data associatedwith vaccines, drugs, and ventilators. The dataset includes product categories, company details, regulatory agencies, status, and sources.Information is updated daily.
| 2020-05-08T00:00:00 |
2020/05/08
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https://intellizence.com/in-the-news/
|
[
{
"date": "2022/12/29",
"position": 54,
"query": "AI layoffs"
}
] |
Artificial Intelligence in Healthcare | Resources
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Artificial Intelligence in Healthcare
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https://www.n1health.com
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[] |
N1 Health is the applied AI platform that drives measurable business results for healthcare organizations. We combine detailed consumer data, ...
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N1 Health is the applied AI platform that drives measurable business results for healthcare organizations. We combine detailed consumer data, predictive models, and cloud native technologies to create a holistic picture of every individual to generate meaningful predictions that enable precision in your outreach and interventions. Our experienced Data Science and Customer Experience Teams transform these predictions into actions that improve your members’ health outcomes and drive financial performance. We get you the results that matter – better health for every one.
To learn more, see our resources below or contact us now to get started.
Read about our News and Events
| 2022-12-29T00:00:00 |
https://www.n1health.com/ai-healthcare-resources/
|
[
{
"date": "2022/12/29",
"position": 62,
"query": "AI healthcare"
}
] |
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The Intersection of Graphic Design and AI
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The Intersection of Graphic Design and AI: Unveiling the Future of Design Innovation
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https://www.pixeltrue.com
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[] |
AI's intervention in graphic design is reshaping how we create, innovate, and interact with visual content.
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In the evolving landscape of graphic design, one force is making an indelible mark: artificial intelligence (AI). We're in the midst of a digital revolution where AI technology isn't just a far-off concept - it's transforming industries, and graphic design is no exception.
AI’s intervention in graphic design is reshaping how we create, innovate, and interact with visual content. We've seen automated tools emerge that can generate designs at a speed human designers could only dream of.
These AI-powered platforms can create logos, social media graphics, or even full websites within minutes. They're becoming our allies in streamlining workflows and enhancing efficiency without compromising on creativity.
Moreover, personalization has become the new norm in marketing. AI helps us to take personalized designing to another level by using data to understand user behavior and preferences. This enables us to tailor our designs according to individual consumer needs - all thanks to advancements brought about by AI technology.
Understanding the Intersection of Graphic Design and AI
Let's plunge straight into the heart of it: the fascinating intersection where graphic design meets artificial intelligence. It's an exciting space, brimming with potential for both sectors. For starters, we're seeing a rise in automated design tools that leverage AI technology, making it easier than ever to create stunning visuals.
Take Adobe Sensei as an example. This powerful software harnesses machine learning to automate tasks like cropping images or choosing color schemes - things that would typically take a human designer a significant amount of time. Now, we can execute these tasks efficiently without compromising on quality.
Then there are platforms like Canva and Grid.io which use AI algorithms to provide personalized design suggestions based on user data. The designs aren't just aesthetically pleasing; they're tailored to individual preferences and needs.
AI isn't just automating existing processes though – it's pushing boundaries and expanding our creative horizons. DeepArt is one such platform that utilizes deep learning techniques to transform ordinary photos into extraordinary works of art reminiscent of famous painting styles.
But perhaps one of the most intriguing impacts of AI on graphic design is its potential for generating original creations from scratch. Generative Adversarial Networks (GANs) such as Artbreeder allow users to blend multiple images together, resulting in unique designs not previously thought possible.
Here are some statistics showing how much influence AI has had on graphic design:
In essence, this intersection between graphic design and AI offers us a glimpse into an exhilarating future where creativity is no longer solely the domain of humans.
The Evolution of Graphic Design with AI Integration
Let's dive into the world where art meets technology. When we talk about graphic design, we're delving into a vibrant, creative field that's undergone significant changes over the years. And now it's getting even more exciting as artificial intelligence starts to play a role.
We've seen AI integrated into many aspects of our daily lives and its influence on graphic design is no exception. Today, automated design tools powered by AI are revolutionizing how we approach design tasks. These tools can analyze data at lightning speed, generating designs in seconds that would take humans hours to create.
Consider Canva for instance - an online tool offering thousands of customizable templates and elements for creating social media posts, presentations, logos and more. It uses machine learning algorithms to understand users' preferences and suggest designs accordingly. Then there’s Adobe Sensei which leverages AI to automate tedious tasks like masking images or adjusting color balance.
AI is not just speeding up the design process but also making it more personalized than ever before. Personalized designs are becoming increasingly important in an era where consumers seek unique experiences tailored to their tastes and preferences. With AI-powered tools such as Tailor Brands or Looka, businesses can now create custom logos based on specific inputs about their brand identity within minutes.
Here are some key stats showcasing the impact of AI on graphic design:
AI integration doesn't mean creativity will be lost though; it simply means designers can focus on what they do best - being creative! They’ll spend less time on monotonous tasks while letting machines handle complex calculations or repetitive actions.
On top of this evolution in how we work with graphics comes another dimension: generative design. Using advanced algorithms and machine learning techniques, generative design allows us to explore virtually unlimited permutations based on predefined parameters – transforming how we approach problem-solving within the realm of visual communication.
So yes! Even Picasso could have used a little help from his robot friends!
AI-Driven Automated Tools Transforming Graphic Design
AI's growing influence on graphic design can't be overstated. It's shifting the landscape, giving us new tools and methodologies that are transforming how we work. One of these changes is the advent of AI-driven automated tools.
Automated design tools use machine learning to learn from our actions, making them faster and more efficient with each use. They're capable of executing tasks in a fraction of the time it would take a human designer, freeing us up to focus on other aspects of our projects. A prime example is Adobe Sensei, which uses AI to automate mundane tasks like cropping images or picking color themes.
These tools aren't just quick—they're incredibly versatile, too. Platforms like Canva leverage AI algorithms to offer personalized design recommendations based on user preferences and behavior patterns. This means that even non-designers can create stunning visuals for their brand or project without needing any prior experience or expertise.
Moreover, there's an increasing number of automated logo generators popping up online—Logoshi and Looka are just two examples—that rely heavily on AI technology. You input your company name and industry type, maybe add some colors or symbols you fancy, then sit back as the tool spits out several polished logo options for you to choose from—it’s as easy as pie!
Of course, it’s not all sunshine and rainbows in this brave new world of automated design tools. We've got some serious questions to grapple with: Will automation lead to job loss among designers? How do we ensure quality control when machines are doing so much of the work?
While we don’t have all the answers yet, one thing's clear: The intersection between graphic design and artificial intelligence is increasingly shaping our industry for better or worse—and we’ll need to adapt accordingly.
Impact of AI on Personalized Designs
In the world of graphic design, we're seeing some pretty fascinating shifts. One that's got us particularly jazzed is the rise and impact of artificial intelligence on personalized designs. Let's dive in and explore how this intersection is reshaping our industry.
First off, it's important to note that AI has allowed us to streamline many time-consuming tasks. Things like creating multiple variations of a design for A/B testing or resizing graphics for different platforms can be automated with AI-powered tools. This not only saves time but also ensures a high level of consistency across all iterations of a design.
Secondly, we've seen an uptick in AI's capacity to generate unique and personalized content. Take Adobe Sensei as an example; it uses machine learning algorithms to analyze data about previous designs and suggest new ones based on patterns it identifies. It can even provide recommendations customized to individual user behaviors and preferences.
But it doesn't stop there! With advancements in neural networks, AI systems are now able to understand and mimic human creativity better than ever before. They can generate original artwork from scratch or remix existing work into something completely novel – adding an entirely new layer of personalization possibilities.
Lastly, let’s touch upon how AI is enhancing the collaborative aspect of graphic design. Tools like Canva use intelligent algorithms to recommend complementary color schemes, fonts, and layouts based on what you've already selected – making co-designing more intuitive than ever before!
In essence, the advent of AI technology has brought forth incredible opportunities for personalization in graphic design - transforming how we create, collaborate and connect with audiences worldwide.
The Role of Machine Learning in Enhancing Graphic Design Processes
Machine learning, a subset of artificial intelligence, is swiftly becoming an invaluable tool for graphic design. It's transforming the way we create and interact with visual content. By automating routine tasks and providing more personalized design options, machine learning is truly revolutionizing our approach to graphic design.
Let's take Canva for instance. This graphic design platform has integrated machine learning into its system to provide users with tailored recommendations based on their previous designs and preferences. It allows us to create stunning visuals without needing extensive knowledge or expertise in graphic design.
Machine learning has also made it possible for us to automate certain aspects of the design process that were once time-consuming manual tasks. Adobe Sensei, Adobe’s AI and machine learning technology, uses algorithms to analyze images and automatically suggest improvements such as cropping suggestions or color adjustments.
Incorporating these advancements doesn't just save designers' time; it also enables them to focus more on conceptualization and creative elements rather than technicalities. This shift fosters innovation by freeing up mental space for creative exploration.
What's more exciting is how machine learning can generate original artwork through advanced techniques like generative adversarial networks (GANs). These networks consist of two parts: one part generates new content while the other evaluates how believable or realistic that content appears. Through this back-and-forth process, GANs can produce incredibly lifelike yet entirely unique pieces of art.
To sum it up:
Machine Learning aids in personalizing user experiences through platforms like Canva.
It automates labor-intensive aspects of the design process as exhibited by Adobe Sensei.
Machine Learning encourages innovation by allowing designers to concentrate on creativity.
Advanced ML techniques like GANs are capable of generating original artwork.
These few examples underline just how much potential there lies in integrating machine learning into our graphic design processes. As we continue exploring its capabilities, we're likely only scratching the surface of what could be achievable in this intersection between AI and Graphic Design!
Real-World Examples: AI Applications in Modern Graphic Design
Shaping the contemporary design landscape, artificial intelligence (AI) has become a game-changer. Let's dive into some real-world examples of how AI is revolutionizing graphic design.
Automated Design Tools: We're seeing more and more tools like Canva and Adobe Sensei that leverage AI to simplify the design process. For instance, Canva’s "design suggestion" feature uses AI algorithms to suggest layouts based on user preferences. Adobe Sensei, on the other hand, uses machine learning to automate mundane tasks such as cropping images or identifying fonts.
Personalized Designs: Personalization is key in today's marketing world and AI is making it easier than ever. Netflix's algorithm, for example, generates personalized thumbnails for every viewer based on their viewing habits - a feat made possible by advanced image recognition technology.
Logo Creation: Remember when you had to hire a professional designer just to create a logo? Well, not anymore! Now we have platforms like Looka that employ AI to produce unique logos in minutes based on user-inputted preferences.
Content-Aware Fill Tools: Ever wanted to remove an unwanted object from an image without leaving a trace? Photoshop’s content-aware fill tool uses AI technology to analyze the surrounding pixels and seamlessly fill in the gap.
Generative Adversarial Networks (GANs): GANs are another fascinating application of AI in graphic design. They're capable of creating realistic images from scratch! DeepArt and DeepDream are two services using GANs to transform simple sketches into stunning artworks.
It's clear that these advancements aren't just niche applications - they're becoming integral components of our digital ecosystem. As we continue exploring this relationship between graphic design and artificial intelligence further, who knows what exciting innovations await us around the corner!
Potential Challenges and Solutions in Implementing AI into Graphic Design
Venturing into the integration of AI in graphic design, it's crucial to recognize that this innovative approach doesn't come without its challenges. One such hurdle is the fear of job loss among designers.
Many professionals worry that automated tools might render their skills obsolete. Yet, we can counter this by emphasizing that AI is intended as a support tool, not a replacement. It allows designers to automate mundane tasks while they focus on higher-level creative aspects.
Another challenge lies in the complexity of AI technologies. Understanding and applying these systems effectively requires technical expertise beyond traditional design skills. However, potential solutions exist here too. Offering learning resources and training programs can help designers build necessary tech proficiency.
Data privacy is also a significant concern when implementing AI into graphic design workflows. Automated tools often require access to vast amounts of data — potentially including sensitive client information — leading to increased vulnerability to cyber threats. To mitigate this risk, strict security protocols should be implemented alongside any AI integration.
Moreover, maintaining creativity in an environment driven by algorithms presents another challenge for many designers who prefer human intuition over machine logic for inspiration. To overcome this, we propose blending both approaches—utilizing AI for efficiency and productivity while keeping human input integral for originality and innovation.
Lastly, there's the issue of algorithmic bias which could inadvertently lead designs down a narrow path based on pre-existing templates or styles seen in past data sets used by the AI system itself. This could limit diversity or inadvertently create designs that lack cultural sensitivity or inclusivity if unchecked.
To tackle this issue head-on:
We must ensure diverse data sources are used during programming
Regular auditing processes must be implemented
Human oversight should remain constant to prevent unintentional biases from influencing outcomes
In essence, while adopting artificial intelligence in graphic design poses challenges—it also provides us with ample opportunities for growth and enhancement within our industry if navigated wisely.
Conclusion: The Future Outlook of AI in the Realm of Graphic Design
Venturing into the future, we see an increased intersection between artificial intelligence and graphic design. It's almost as if we're witnessing a revolution in design aesthetics and efficiency.
Leveraging AI for creative work has become more than just a trend. It's becoming an integral part of our processes, enabling us to break barriers and redefine the boundaries of creativity. Let's have a look at some key developments we're likely to witness:
Automated Design Tools: We'll continue to see an uptick in sophisticated AI tools that automate tedious tasks, enabling designers to focus on more strategic aspects of their projects. These tools will evolve beyond mere task automation and start offering insightful suggestions based on data-driven analysis.
We'll continue to see an uptick in sophisticated AI tools that automate tedious tasks, enabling designers to focus on more strategic aspects of their projects. These tools will evolve beyond mere task automation and start offering insightful suggestions based on data-driven analysis. Personalized Designs: Personalization is becoming increasingly crucial for users. With AI, there will be further advancements in creating designs tailored perfectly to individual tastes and preferences.
Personalization is becoming increasingly crucial for users. With AI, there will be further advancements in creating designs tailored perfectly to individual tastes and preferences. Improved Collaboration: We can expect improved collaboration between humans and machines as they learn from each other. This symbiotic relationship will lead to innovative designs that couldn't have been possible by human effort alone.
In essence, it's safe to say that artificial intelligence holds immense potential for graphic design. The integration of these two fields promises not only enhancements in current practices but also opportunities for new avenues yet unexplored. While there may be concerns about the implications for designers' roles or job security, it’s important we embrace this change positively.
Perhaps what we need isn't fear but adaptability – learning new skills, understanding how AI works within our field, and figuring out how best we can utilize these technologies while still retaining our unique human touch in our creations.
So, here’s looking forward - excitedly - at what lies ahead! Remember, technology changes but creativity remains eternal.
| 2022-12-29T00:00:00 |
https://www.pixeltrue.com/growth-by-design/the-intersection-of-graphic-design-and-ai-unveiling-the-future-of-design-innovation
|
[
{
"date": "2022/12/29",
"position": 18,
"query": "AI graphic design"
}
] |
|
The 10 Best AI Graphic Design Tools You Need ...
|
The 10 Best AI Graphic Design Tools You Need To Know About
|
https://simplified.com
|
[
"Shubham Kulkarni",
"Shubham Kulkarni Is A Creative Professional With Experience In Design",
"Content Writing",
"Video Editing",
"Publishing. He Works Closely With Teams To Turn Ideas Into Well-Crafted Digital Content",
"Combining Visuals",
"Storytelling Across Formats. With A Hands-On Approach Concept To Delivery",
"He Supports Every Stage Of The Content Production Process."
] |
Canva is an online AI graphic design tool that eases the process of designing graphics for social media, blogging, and more. · Visme is a graphic design tool ...
|
Artificial Intelligence(AI) has been invading every aspect of our lives, and graphic design is no exception. Designers are always looking for new and more efficient ways to tackle repetitive and tedious tasks. However, it’s no wonder AI design tools have also found their way into the industry. Knowing the best AI graphic design tools can significantly improve your workflow and productivity. Especially in today’s fast-paced world of 24/7 business and social media marketing. But what if you don’t know which tools to use or features to look out for? Don’t worry, this article has you covered by providing you with the 10 best AI graphic design tools to help you with your projects. Let’s get started!
1. Simplified
Designing digital products is indeed a complex and time-consuming task. But now, no more with Simplified- The #1 free AI graphic design tool. Simplified is an AI graphic design app that allows you quickly create stunning designs for all sorts of digital products, for example, invitations, ads, social media posts, long form content, videos, 50+ copywriting templates, and more, without any technical coding knowledge.
Simplified also offers AI Image Generation From Text Prompts feature. These AI Image Generators create stunning and eye-catching images directly from the text inputs without manual effort. All you need to do is give instructions and enjoy your favorite drink till the time AI will work for you. So why wait? Get started today and see how easy and fun graphic design can be with Simplified AI.
2. Canva
Canva is an online AI graphic design tool that eases the process of designing graphics for social media, blogging, and more. Also, you can create everything from blog headers and creative logo to infographics to YouTube thumbnails in minutes. In addition, it has a drag-and-drop interface and simple prebuilt templates so you can focus on what matters: your message. It doesn’t require complicated skills or knowledge of AI designing software like Photoshop or Illustrator.
3. Visme
Visme is a graphic design tool that offers some of the best AI features on the market. The software has a drag-and-drop interface, which makes it easy to create professional-grade visuals without any previous graphic design experience. Additionally, you can use Visme to collaborate with other designers. Also, you can share your creations with clients in real-time, and make edits to your projects without starting from scratch.
Related: How Graphic Designers Can Make A Real Difference Right Now
4. Prisma
Prisma is a powerful and easy-to-use app. It uses artificial intelligence to transform your photos into beautiful graphic designs. Prisma’s features include customizing your image by choosing the color palette and filter. It’s available for iOS and Android devices so you can enjoy it on the go!
5. Vance
Vance is deep learning and AI design tool used to generate high-quality graphics in an automated manner. It works by feeding it a photo and then asking it to create a similar image based on what it learned from the picture. Also, you can quickly get quick mockups or generate an entire marketing campaign with just one click.
6. Uizard
It is an AI graphic design tool that can be designed to create wireframes prototypes and mockups within minutes. Its innovative features allow users to transform hand-drawn drawings into wireframes and convert wireframes into prototypes with just a single click. However, this AI graphic design app offers a wide range of graphic design templates with drag-and-drop functionality. You can alter and modify your designs in just a few minutes.
7. Khroma
Khroma is a design platform that combines artificial intelligence with the power of human creativity. Also, it connects designers and clients to create personalized artwork. However, Khroma is also a marketplace for designers who can collaborate and make their designs available for purchase through the platform.
8. Adobe Sensei
Adobe Sensei is a powerful graphic design tool that let’s you create stunning designs in a snap. With Sensei it is possible to quickly and easily design complex designs that might otherwise be challenging or difficult to make on your own. Sensei also comes with a broad array of features that will allow you to customize your designs to the highest quality. If you’re looking for a tool that can help you take your graphic design to the next level, Adobe Sensei is definitely worth checking out.
9. Fronty
Fronty is a free, easy-to-use, browser-based app that helps you create beautiful infographics with just a few clicks. However, this cool new AI tool has an extensive library of templates and prebuilt layouts to help you get started without any design skills. Additionally, it’s possible to export your infographic in PDF and JPEG formats.
10. Sketch2code
It is an excellent tool for designing graphics, icons, logos, and more. Sketch2code uses artificial intelligence to create professional design concepts in minutes. The algorithm is designed to do the tedious work so that you can focus on what matters most: your creativity. Whether, you’re looking for a new logo, web design, or social media graphics, Sketch2code will provide endless options to suit your needs.
Bottomline
In conclusion, AI graphic design tools are improving at a rapid rate. These tools have the potential to revolutionize graphic design and fundamentally change the way we create graphics. However, there are a variety of AI graphic design tools available that can assist you in your work. The best AI graphic design tools are those that are user-friendly, allow you to create high-quality designs, and provide you with a variety of features.
FAQ’s
What is AI in graphic design?
AI in graphic design is a method where computers design or enhance visual solutions, often using specialized software. For example, It can range from simple tasks such as correcting color and images to more intricate tasks such as making illustrations or 3D models.
What is AI software in design?
AI design software is a kind of application that lets designers design and alter digital images by using AI. This type of software can be designed to produce images that could be utilized for various uses, including designing products or architecture.
Is AI replacing graphic designers?
With the rapid advances in AI technologies, usually people think that AI could take over human designers completely. Although AI is getting ever more efficient, it’s still not in a position to rival the imagination and originality of human artists. However, AI is best used as a tool that can help human designers develop more appealing designs, but not replace them entirely.
Which tool is best for AI?
To answer this question, you must first understand your own needs and requirements. What type of AI graphic design project are you working on? What budget do you have to work with? Once you have a clear understanding of your own needs, you can then begin to compare the different AI graphic design tools on the market.
| 2022-12-29T00:00:00 |
2022/12/29
|
https://simplified.com/blog/design/ai-graphic-design
|
[
{
"date": "2022/12/29",
"position": 35,
"query": "AI graphic design"
}
] |
Artificial Intelligence for Executives : Creating and ...
|
Artificial Intelligence for Executives : Creating and Capturing Value Through Artificial Intelligence training course
|
https://www.lpcentre.com
|
[] |
AI-powered insights enable organizations to make well-informed decisions, maximize productivity, and provide sustainable growth in an increasingly digitally ...
|
Artificial Intelligence (AI) is no longer a utopian vision but increasingly a strategic necessity for businesses committed to enhancing efficiency, optimizing decision-making, and building a competitive edge. Executives and business leaders must grasp the potential of AI across every business function, from customer relationship management and HR automation to supply chain optimization and corporate strategy development. AI-powered insights enable organizations to make well-informed decisions, maximize productivity, and provide sustainable growth in an increasingly digitally driven-economy.
The artificial intelligence for executives training course empowers top executives with a sharp understanding of the potential, limits, and business applications of AI. In five days, participants will learn AI tools, strategic deployment techniques, regulation, and ethical issues. With emphasis on practical case studies and interactive strategy design exercises, the course guarantees that executives get practical recommendations to implement AI in their firms successfully.
| 2022-12-29T00:00:00 |
https://www.lpcentre.com/singapore/management-leadership/artificial-intelligence-for-executives-creating-and-capturing-value-through-artificial-intelligence
|
[
{
"date": "2022/12/29",
"position": 13,
"query": "artificial intelligence business leaders"
}
] |
|
AI Threatened Amazon Employees' Jobs, But They Embraced ...
|
AI Threatened Amazon Employees’ Jobs, But They Embraced It Anyway
|
https://observer.com
|
[
"Alex Kantrowitz",
"Alex Kantrowitz Is The Founder Of Big Technology",
"A Free Newsletter",
"Podcast About Big Tech",
"Society. He'S Also The Author Of Always Day One",
"How The Tech Titans Plan To Stay On Top Forever",
"A Contributor At Cnbc.",
"Alex.Kantrowitx Gmail.Com"
] |
When Amazon automated its retail employees' tasks, it didn't eliminate their jobs, but it did fundamentally change them. Vendor managers are now more auditors ...
|
This story is syndicated from the Substack newsletter Big Technology; subscribe for free here.
Sign Up For Our Daily Newsletter Sign Up Thank you for signing up! By clicking submit, you agree to our <a href="http://observermedia.com/terms">terms of service</a> and acknowledge we may use your information to send you emails, product samples, and promotions on this website and other properties. You can opt out anytime. See all of our newsletters
After Amazon automated its vendor managers’ forecasting, purchasing, and negotiation tasks, I expected to find them sullen. The narrative typically dives into mass unemployment, the end of work, and end times. So I was a bit surprised when they instead seemed matter-of-fact about what happened, unworried about what this new wave of AI portends.
“When we heard ordering was going to be automated by algorithms, on the one hand, it’s like, ‘Okay, what’s happening to my job?’” said Elaine Kwon, a former Amazon vendor manager. “On the other hand you’re also not surprised, you’re like, ‘Okay, as a business this makes sense, and this is in line with what a tech company should be trying to do.’ ”
Another current employee told me that at Amazon, “you’re constantly trying to work yourself out of a job. You should not be doing the same thing day to day. Once you’ve done something consistently, you need to find mechanisms to invent and simplify.”
From a business standpoint, it’s easy to see why Amazon employees (who receive a chunk of their compensation in stock) feel this way. Amazon’s business is a flywheel—a system that gets better and stronger as each component improves. By offering a wide selection of products at low prices, Amazon generates traffic from people looking to buy. The traffic makes Amazon a more enticing place for sellers, who sell more products at better prices to reach Amazon’s customers, generating more demand.
In Amazon’s early days, with fewer seller relationships to manage, the company could hire humans to manage its vendor relationships. But as Amazon scaled to twenty million products, the labor cost to manage every relationship with a human was prohibitive, causing prices to rise and sticking a wrench in the flywheel.
“You could start the Amazon business in an un-technical way. But you couldn’t scale it,” said Ralf Herbrich, Amazon’s first director of machine learning. “Each of those processes in our flywheel, pretty much each of them, will really only scale up if we are automating some of those decisions that people are doing, particularly the ones that are based on repeated patterns that we observe, and that’s where AI comes in.”
Twenty years ago, an Amazon vendor manager could handle a few hundred products, Herbrich said. Today, they’re working with anywhere from ten thousand to one hundred thousand. (An Amazon spokesperson said Herbrich was using these numbers as an example and they shouldn’t be taken at face value.)
When Amazon automated its retail employees’ tasks, it didn’t eliminate their jobs, but it did fundamentally change them. Vendor managers are now more auditors than doers. “They go from typing to selecting,” Herbrich said. “When there are mistakes, often what we find is that they now need to have the skills to diagnose what inputs to the algorithm may be wrong. It shifts from making the outputs, how many units to buy, to changing the inputs.”
Here’s an example of how that plays out: Amazon’s inventory forecasting system was once missing predictions on some basic fashion products. Herbrich was incredulous; white socks should not be hard to forecast. So, he ordered a review of the inputs going into the prediction tool and found that Amazon had fifty-eight thousand different color categories in total. Spelling mistakes and nonstandard spellings had thrown the system off, and when they standardized color, things went back to normal.
Thanks to Hands off the Wheel, Amazon’s retail division now operates more leanly and efficiently. The concept has also enabled Amazon’s third-party marketplace and fulfillment operation—where vendors list directly on Amazon, instead of relying on Amazon as a middleman—to thrive.
The prestige of the vendor manager job has worn off a bit too, and many vendor managers have moved to new roles within Amazon. When I browsed LinkedIn to find out where they went, I found many ended up in two specific job categories: program manager and product manager. Program and product managers are professional inventors at Amazon. They dream up new things and steward them along as they get built. Product managers typically focus on getting individual products built, and program managers focus on multiple, interrelated projects.
According to LinkedIn’s data, these are the fastest-growing job functions inside Amazon today. “That was a thing that a lot of people really looked for,” Kwon told me. “They were also looking for other cool teams that valued innovation.”
Tim, another ex-vendor manager, noticed a similar migration. “I have friends in categories where two years ago there were twelve people in that category, now there’s three,” he told me. “Almost every person that I knew in retail at this point has a job now that is product manager, program manager. Nobody is really in a core retail function anymore. If you’re a non-engineer, you become a program manager or a product manager.”
By automating work inside its retail division, Amazon is opening up new opportunities for inventing, which was the plan all along, according to Jeff Wilke, its ex-CEO of Worldwide Consumer. “People that were doing these mundane, repeated tasks are now being freed up to do tasks that are about invention,” he said. “The things that are harder for machines to do.”
| 2022-12-30T00:00:00 |
2022/12/30
|
https://observer.com/2022/12/ai-threatened-amazon-employees-jobs-but-they-embraced-it-anyway/
|
[
{
"date": "2022/12/30",
"position": 11,
"query": "automation job displacement"
},
{
"date": "2022/12/30",
"position": 7,
"query": "AI job losses"
}
] |
robotics
|
Acropolis
|
https://acropolisepc.com
|
[] |
In fact, many believe that advanced forms of AI will eventually replace or at least complement human workers in various industries. ... replacing human employees.
|
What to watch in 2023
Published On 12.24.22
As technology has advanced, so have manufacturing processes. More efficient ways of doing things emerge as new trends that trick manufacturers to save money while achieving the same results. This is especially true when it comes to automating or computerizing parts of their production lines.
Computer automation has become prevalent in almost every industry. It is used for everything from programming robots to controlling machinery and equipment.
Efficiency is one of the biggest benefits of plant automation. Using computers to control machines and equipment removes the need for human intervention which can cost time and money. Robots are very good at performing repetitive tasks with little input from humans.
Also, engineers use software to design products; therefore, having automated systems means less expensive redesigns due to hardware limitations. Product designers can also create more elaborate designs due to no limits put by physics.
This article will discuss some exciting developments in manufacturing plant automation in the coming year.
The rise of the AI on the plant floor
Artificial intelligence (AI) has become one of the most popular buzzwords in recent years. It is even being referred to as the technology that will completely change how we live our lives.
The reason for this growing interest in AI is because it can perform tasks that require logical reasoning, pattern recognition, and computational knowledge. These are all things that humans are capable of!
In fact, many believe that advanced forms of AI will eventually replace or at least complement human workers in various industries.
This seems like an overly dramatic statement, but some experts are predicting full automation in certain industries within the next decade. This includes factories, warehouses, and other similar facilities where robots can perform repetitive tasks with high accuracy.
These autonomous machines are not only cost effective, but they also reduce employee turnover due to stress-related reasons. Luckily, there are already a number of companies that offer different types of automated machinery.
Future implementation on the plant floor may include not just hardware robotics but also software in the form of Robotic Process Automation (RPA).
Robotic Process Automation
What is robotic process automation? That is, what does the term robotic process automation mean?
Robotic process automation (RPA) is a software technology that makes it easy to build, deploy, and manage software robots that emulate humans actions interacting with digital systems and software. Just like people, software robots can do things like understand what’s on a screen, complete the right keystrokes, navigate systems, identify and extract data, and perform a wide range of defined actions. But software robots can do it faster and more consistently than people, without the need to get up and stretch or take a coffee break.
You may have heard of robots performing human-like activities before. This is what some refer to as AI (artificial intelligence). For example, many companies use chatbots now to help answer customer service questions via smartphone apps.
Chatbot technology has been around for quite some time, but only recently did people begin referring to them as AI. A chatbot can seem almost intelligent at times because it uses algorithms to determine how to respond to certain questions.
The key difference between traditional AI and RPA is that RPA isn’t autonomous. It doesn’t perform these tasks completely by itself like a robot would. Instead, humans must give it input and tell it when to start and stop working.
RPA has already proven itself useful in areas such as ERP, data entry and quality issue identification. Other appropriate tasks include:
Recording action items and meeting minutes
Recording attendance
Responding to FAQ emails
Scheduling meetings
Ordering supplies
Predicting computer problems
Creating and filing invoices
AI Driven Predictive Maintenance
Recent developments in manufacturing technology have made it possible to automate many of the tasks that need to be done within plants, including inspection, testing, and analysis of products. These advanced systems use artificial intelligence (AI) to analyze performance data and determine when maintenance or repairs are needed to keep production running smoothly.
When an equipment repair is due, the system will alert staff so they can get ready for scheduled downtime or carry out the work immediately
This predictive maintenance uses software and robots instead of human workers and technicians, reducing overhead costs while ensuring efficient operation of machinery.
The next step forward is using computer-controlled machines and robotics to do the actual work. For example, instead of having humans pour molten metal into molds, there are now automated arms doing this job. The same goes for assembling components onto finished goods, etc.
Cloud Based Control Systems
A hosted, cloud-based, SCADA system is provided as a service and can be connected to a variety of processes at virtually any geographical location over a secure, private network. Operators can securely access and interact with their process data, potentially from any device anywhere in the world at any time.
With minimal network infrastructure deployed at the assets, a secure connection can be established with existing control system infrastructure. For organizations who are not yet automated but may be hesitant due to budget constraints, the cost savings of a hosted SCADA system can help by limiting capital investments to only control system hardware such as local operator interfaces, instrumentation, and installation. If assets are already automated via a control system, the move to SCADA in the cloud is simplified even further.
Security, performance, reliability and maintainability are common concerns when deploying onsite servers and network infrastructure. By offloading these functions to a cloud provider, asset owners can focus more on running an efficient and profitable business.
Another benefit inherent to a properly designed cloud-based SCADA solution is scalability. As assets are bought and sold, the system can be ramped up and down in a quick, efficient, and secure manner. Integrated cybersecurity, in combination with the flexibility of user and role-based access control, allows for specificity of access to applications for any combination of view, control, and configuration per asset.
Meeting labor demands through automation
One of the biggest hurdles to efficient manufacturing is staffing. Companies are constantly trying to find ways to reduce overhead, save money, and increase production.
One way many companies have done this in recent years is through automated guided transport (AGT) systems. These work by having robots that travel along rails or tracks next to each other transporting materials from one area to another.
The robot picks up the material and takes it where it needs to go while someone stays back to monitor it. This cuts down on human labor needed to keep things running and organized.
Some even have computer chips built into them so they can detect changes in objects and need not be supervised directly. This gives engineers more flexibility when changing out parts or creating new ones.
These types of robotics come with their own set of challenges such as cost but overall they are a worthwhile investment if you want to remain competitive in the industry.
Aging workforce adapting to new technologies
The manufacturing industry is changing, and rapidly. Technology has improved efficiency of production across the board – from raw material procurement and processing, to product assembly and shipping.
This includes improving productivity through use of automation and robotics. They are used for repetitive tasks that can’t be or aren’t as efficient done manually, like assembling products or packaging materials.
The average cost of an automated robot in the United States is around $50,000-$100,000, which makes them accessible to almost any size business. There are many examples of companies using robots in their plants, even making a profit!
While some workers may feel threatened by changes such as this, it is important to remember that they are not going away anytime soon. Having experience with these new technologies is a valuable asset to most employers.
What people without technology expertise can do is learn how to work effectively within a plant environment that has been modified by tech. This will include things like learning how to communicate efficiently about changes, understanding what roles individuals have in the process, and knowing your job well.
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Robotics role on the plant floor
Published On 12.30.22
Robots have been around for quite some time, but they are becoming more prevalent in manufacturing plants across the country. With the right software designed specifically for robots, your company can leverage these automated machines to perform various tasks beyond just assembly.
Robots that work collaboratively with humans offer new possibilities for career opportunities. By creating an environment where workers and robotics experts work together, it lessens the stress typically experienced when working close to or next to a robot. This is particularly important since many robots require frequent interaction with individuals to carry out their jobs.
The most common application of collaborative robots is in material handling. These bots take over the heavy lifting by moving materials from one area to another or putting them into different compartments or containers. Companies use this technology because it cuts down on human labor costs while still giving high quality results.
This article will discuss two types of collaborative robots that do not cost very much to implement at your plant. What makes these robots unique is how they interact with each other as well as people outside of production.
Recent developments in robotics have shifted towards more collaborative robots that work alongside or even replacing human employees. These autonomous, computer-controlled machines are designed to perform specific tasks without direct supervision.
Robots such as these were once thought of only performing repetitive tasks like putting together IKEA furniture or assembling cars. However, today’s roboticists are developing them for much higher purposes.
One area where they are proving especially effective is in the production line. Here, each robot performs one task while being watched over by another robot, which takes over when it detects something wrong with the first machine’s performance.
This second robot can then be configured to take over other jobs as well, making this type of collaboration very cost efficient. Technology companies use this concept to great effect, as seen with Amazon’s automated warehouse systems.
These robots will keep working until they no longer function properly, at which point someone must fix it before it breaks down completely. This could mean giving it out extra maintenance or fixing it yourself if possible, but definitely not throwing it away!
Given all of this, why wouldn’t every company try implementing some form of automation? There are many benefits to doing so, including lower employee turnover, reduced costs due to time savings, and improved quality of products.
If you’re looking to start using technology to boost productivity, look into automating parts of your business.
Examples of collaborative robots in the manufacturing plant
Robots that work together to complete tasks are no longer limited to factories making cars or food products, they can do much more! This is due to two main reasons: technology has advanced enough so that these robots can perform complex functions, and companies have found applications for them beyond just cost-cutting.
Robots with arms designed specifically for human interaction are referred to as cooperative autonomous systems. More and more manufacturers are using these robots to fulfill diverse roles within their plants.
They’re used both independently and collaboratively by different employees and departments across the company. Some examples include:
Assemblers use them to take item parts and put them into place
Use them to pick up and pack items away after processing
Lay down material onto another surface to begin production of new goods
to begin production of new goods Take completed pieces and put them into other machines or storage
These robots reduce idle time for workers, as they don’t need to be in control of the robot at all times.
It also gives people more job opportunities because it isn’t necessarily limited to one position. People who were able to perform routine jobs such as putting parts together may get assigned work like laying down metal sheets which creates the next product.
Types of robots in a manufacturing plant
Robots come in many different shapes and sizes, but they all have one thing in common – they use sensors to measure your materials or products and perform an action based off what it detects.
The most well-known type of robot is the manipulator arm. These robots have an attached tool that they can manipulate and use as their hand. For example, a wheeled robot with a gripper tool could pick up objects and put them into another container.
Other types of robots include vision-based robotics and autonomous mobile robots (AMRs). Vision-based robots use cameras or lasers to detect features of the work surface or material being processed. AMR’s are self-driving vehicles that navigate around the factory without input from humans.
Another type of robot is the collaborative robot. A collaborative robot works with people! They can be taught how to process certain materials using human controllers that give instructions to the robot. Then, the robot takes these commands and performs its job automatically.
Why are collaboration robots so popular? Because they reduce operator exposure to risk and labor costs. Operators no longer need to constantly monitor and direct the robot; instead, they can leave this task to the computer programs.
This also helps keep workers safe because there are fewer chances for accidents. In fact, some companies even require collaboration over manual manipulation due to safety reasons. Types of Cobots include:
Articulated arm:
These are robots with rotary joints. They have an arm that is mounted to a base with a twisting joint.
Mobile:
Mobile COBOT or mobile collaborative robot are intelligent and transportable that can also be used to support humans in shared workspaces.
Mobile COBOT or mobile collaborative robot are intelligent and transportable that can also be used to support humans in shared workspaces. Cartesian:
Robots with a very rigid structure. 3 prismatic joints that only move in the 3 linear axis (X,Y & Z). Extremely precise due to their rigid structure and contained joint movement.
Robots with a very rigid structure. 3 prismatic joints that only move in the 3 linear axis (X,Y & Z). Extremely precise due to their rigid structure and contained joint movement. Delta:
Parallel robots with 3 robotic arms connected to a base. They can be working on a higher speed than the Cartesian COBOTS and are extremely accurate.
Consider the cost of a robot
Recent developments in collaborative robots have made them much more affordable, which is great for your business! There are many different types of robots that do various tasks within manufacturing plants, so it’s important to know what features matter most to you.
Robots can be expensive, especially when you add all of the extra equipment needed to control them. A computer program or “controller” is also very expensive, but not every robot comes with their own controller.
Some companies will sell you an inexpensive pre-programmed controller that you can connect to yours via Wi-Fi or Ethernet, but this isn’t always the best option. This could easily become costly if you need to make changes later on.
It is important to evaluate the entire project based on a verified business case that determines the ROI (Return on Investment). Many customers see an payback in as little as 10.5 months.
Consider the design of the manufacturing plant
A second way to use collaborative robots is in production lines or assembly areas where material can be input into the robot, the robot performs an automated task, and then the part is output.
In this case, the robot has a clear work area and materials are put into the robot by someone else and it does the next step automatically. After that, another person picks up the completed product and gives it back to the robot for the next stage of the process.
The cost per unit is usually lower than other types of automation because there are not as many moving parts. Also, people often feel comfortable working with robots due to their robotic nature.
This type of robotics is very popular in factories around the world. Many large companies have implemented them to reduce costs and increase efficiency.
Plan your robot installation
There are many different types of collaborative robots available these days, with most manufacturers offering one or more of their own brand models. As such, it is very difficult to know which type of robot would be best for you or your business!
In this article we will discuss some fundamental questions that must be asked before deciding if a mobile robotic arm is right for your business. By asking these questions early on, you can save time and money in the long run by not investing in an expensive robot that cannot perform its tasks or needs constant maintenance.
It is important to remember that even though each individual robot may seem similar at first glance, there are still several key features that make them differ from each other.
A well conducted detailed engineering study of the existing manufacturing operation can help mitigate many future issues.
Typical studies include conceptual design, examination of potential equipment changes and or additions, and implementation of CoBot technologies.
Deliverables may include:
Proposed 2D/3D Layouts of CoBot cell
Process simulation of target process to verify cycle times
Investigate and recommend best use of end effector technologies on target parts.
Determine CoBot cell control scheme and preliminary control panels.
Review wiring, I/O and sensors necessary to integrate the CoBot into process.
Investigate material handling (palletizing, infeed and outfeed) concerns.
Safety analysis.
Take care of your staff
As mentioned before, robots can be expensive so it is important to make sure that you are investing in them responsibly. Make sure to discuss how they will affect the workplace for both positive and negative ways!
Some examples of this could be having to let go of some positions due to automation or creating new positions such as engineering assistants to help maintain the robot.
Having these conversations now is very important since most people in the workforce today will not have access to high paying jobs forever.
Keep up to date with new technology
Technology is constantly changing, which can be both good and bad for your business depending on what you are looking to achieve. On one hand, it is easy to get overwhelmed by all of the new gadgets and gizmos that companies create.
They seem to be coming out faster than ever before, making it difficult to know what is actually worth investing in. This could mean wasting money if something does not work or prove its efficiency.
On the other hand, staying ahead of the curve can give you an edge over competitors who may be more technologically advanced than you.
By being familiar with newer technologies, you will have an advantage over those who are not. It is always advantageous to keep yourself informed about what is happening in the world of tech.
There are many ways to do this, from reading blogs to listening to podcasts to watching YouTube videos. No matter how you choose to stay abreast of the latest trends in technology, just make sure you are doing so for educational purposes only!
Don’t use these tools to promote products or services unless you have vetted them first! There are several free resources available online that can help you remain impartial.
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| 2022-12-30T00:00:00 |
https://acropolisepc.com/tag/robotics/
|
[
{
"date": "2022/12/30",
"position": 41,
"query": "AI replacing workers"
}
] |
|
What is Machine Learning & Top Use Cases
|
Introduction of Machine learning and Use Cases: 2023 Update
|
https://edvancer.in
|
[
"Edvancer Edventures"
] |
When it comes to job prospects, Machine Learning has a considerably larger potential than other vocational areas in India and around the world. In the field of ...
|
People are currently experiencing a true global technological revolution. Thanks to developments in computer power and applications for machine learning. In the first two decades of the twenty-first century, artificial intelligence (AI) research has made significant strides. One of the most successful and widely used technological applications, machine learning has a daily impact on billions of users and a variety of businesses.
The study and use of statistical models and techniques enable computer systems to carry out particular tasks without human input constitutes the field of machine learning, a subset of artificial intelligence. Utilizing
machine learning courses
makes it possible for people to use cutting-edge technologies in their daily lives.
Vocal assistance
These days, voice assistants are everywhere. Voice assistants are becoming more and more common, thanks to programmes like Apple’s Siri, Google Assistant, Amazon’s Alexa, and others. Behind all of these voice assistants is a machine learning system that uses Natural Language Processing to recognize speech (NLP). The response is then created after utilizing machine learning to translate the voice into numbers. To prepare information, NLP is also used to convert ambiguous legalese from contracts into understandable English. Researchers predict that as
progress, they will become astonishingly smarter.
Individualized Marketing
The marketing system is embracing technology more and more. The marketing sector groups clients based on behavioral and characteristic data using machine learning features. The platforms for digital advertising enable marketers to concentrate on a group of consumers who are likely to be influenced by their products. They are aware of what customers want and thus provide better product advertising.
Detecting fraud
Machine learning is being used by banks and large financial services providers to detect fraud. This aids businesses in safeguarding customer safety. Businesses that process credit card transactions may find value in machine learning. According to the policies of the company, the system is programmed to identify transactions that seem to be fraudulent based on specific criteria. The detection of such errors enables businesses to avoid suffering a significant loss. A business can also use machine learning to estimate sales or demand in real-time and obtain insights into its competitive environment and customer loyalty or educate its employees about the advantage of learning
machine learning courses.
Autonomous Vehicles
One of the exciting technologies that makes heavy use of machine learning is self-driving cars. Self-driving cars have the advantage of incorporating all three of the main machine learning techniques, supervised, unsupervised, and reinforcement learning, into their design. Machine learning capabilities are used by smart cars to identify things in the immediate vicinity of the vehicle, measure the distance between the vehicle in front of it, determine the location of the pavement and traffic signals, assess the driver’s health, and categorize scenes. Additionally, real-time advice about traffic and road conditions can be provided using machine learning.
Transportation Improvement
Machine learning is being used as the main source by businesses aiming to increase the transportation sector’s reliance on technology. Travel rates are adjusted dynamically to reflect shifting market conditions. Prices change based on variables such as the time of day, the location, the weather, client demand, etc. Drivers can now use machine learning to discover the best path to take passengers from point A to point B.
Understanding Behavior
Machine learning models can be used by businesses to forecast customer behavior based on historical data. Businesses examine social media for topics people are discussing before identifying users who are looking for a certain commodity or service.
Healthcare
The benefit of machine learning in the healthcare industry is its capacity to handle enormous information beyond the limits of human capabilities and then consistently turn the analysis of those datasets into therapeutic insights that benefit clinicians. The use of machine learning in the planning and delivery of healthcare ultimately results in improved outcomes, cheaper healthcare expenditures, and more patient satisfaction. In order to anticipate cancer, computer-assisted diagnosis (CAD), a machine learning tool, can also be used to examine women’s mammograms.
Automation of Process
The result of the fusion of AI with related technologies like computer vision, cognitive automation, and machine learning is intelligent process automation (IPA). Companies have a richer automation possibility by combining these technologies into a single process, unleashing every commercial value for the firm. By automating human data entry operations, the machine learning system can produce error-free insurance risk assessments.
Chatbots
By utilizing chatbots that provide pertinent answers to customers’ questions, machine learning is assisting customer service. Machine learning algorithms can comprehend a customer’s requirement and the tone in which they express it using the principles of Natural Language Processing (NLP) and sentiment analysis. The system then directs the inquiry to the proper customer service agent.
Physical Protection
Security at huge gatherings is greatly aided by machine learning. The use of technology in security at significant public events helps prevent false alarms and detects items that human screeners would overlook.
Conclusion
Machine learning is no longer just a trendy term. Many businesses are using machine learning models, and the benefits of predictive insights are already being realized. It goes without saying that there is a huge demand in the market for machine learning experts and Edvancer’s AI and
Machine learning courses
is here to assist you in reaching heights.
FAQs
1. What is ML life cycle?
Data preparation, model development, and deployment are the three primary processes that make up the life cycle of an ML project. These three elements are necessary for producing high-quality models that will benefit your company financially.
2. What is the scope of machine learning?
When it comes to job prospects, Machine Learning has a considerably larger potential than other vocational areas in India and around the world. In the field of AI and ML, it is expected that there would be 2.3 million jobs.
3. Which data type is used to teach machine learning?
Although data can take many different forms, machine learning models generally use four different kinds of data. These consist of text data, time series data, category data, and numerical data.
Here’s the list of top machine learning use cases in today’s world:Share this onFollow us on
| 2022-12-30T00:00:00 |
2022/12/30
|
https://edvancer.in/what-is-machine-learning-top-use-cases/
|
[
{
"date": "2022/12/30",
"position": 37,
"query": "machine learning job market"
}
] |
Job Varieties and Nature - Data Sciences and Engineering |
|
Data Sciences and Engineering
|
https://datasce.cs.hku.hk
|
[] |
Career Prospects. Job Varieties & Nature · Job Demand ... machine learning engineer, big data engineer, business analyst, and information security analyst.
|
Salaries for data-related professionals are also attractive in the US. As shown in the DICE 2020 Tech Job Report, the median annual compensation of data scientists in the US is USD120,000. The Salary Explorer reflected that monthly salaries of data scientists in 2022 in the US range from USD82,300 to USD236,000, with an average of USD155,000.
| 2022-12-30T00:00:00 |
https://datasce.cs.hku.hk/index.php/career-prospects/career-prospects/
|
[
{
"date": "2022/12/30",
"position": 84,
"query": "machine learning job market"
}
] |
|
Futurist Perth - conference speaker, author - AI, disruption ...
|
AI, disruption, change, leadership, innovation
|
https://gihanperera.com
|
[] |
Leveraging AI · Seeing Into The Future · Future-Ready ... What's more, it created a triple crisis: a health crisis, an economic crisis, and a social crisis.
|
In the early days of the pandemic, we got sick of the phrase “unprecedented times”, but they were unprecedented because they affected everybody at the same time. Over the last few decades, other industries have also faced massive disruption and change, and I worked with leaders and teams in those industries as they strived to survive or thrive.
But the coronavirus pandemic was (and is) different.
For almost everybody, this was their first experience of disruption that affected the entire world. You couldn’t switch to a new market, you couldn’t transfer your operations elsewhere, and you couldn’t escape to a safe haven. It affected everybody and everywhere.
What’s more, it created a triple crisis: a health crisis, an economic crisis, and a social crisis. Even if you work in an area that wasn’t affected badly by the health crisis – and hence didn’t suffer from the economic crisis – the social crisis continues to affect your team members, their loved ones, and others in their community.
I wrote my previous book “Disruption By Design” to warn leaders about inevitable disruption coming their way, and to urge them to take action (“disrupt yourself”) before it happened to them. I didn’t predict it would come from a pandemic, but I knew it would come from something.
This book is for leaders on the other side of disruption, who must lead with both their heart and their head, as they navigate through this time of uncertainty and rapid change.
| 2022-12-30T00:00:00 |
https://gihanperera.com/books/disrupted/
|
[
{
"date": "2022/12/30",
"position": 67,
"query": "AI economic disruption"
}
] |
|
Exploring the Latest Developments in Generative AI
|
Exploring the Latest Developments in Generative AI: Opportunities and Challenges for the Future
|
https://time-to-reinvent.com
|
[
"Jana Ahlers"
] |
There are many potential opportunities for generative AI, including the ability to automate content creation, improve efficiency in various industries.
|
A guest contribution by ChatGPT
Note: this article was purely created with ChatGPT, using the prompt „write an interesting article about the latest developments in generative AI and the future outlook of AI with real-life examples. Include the history, opportunities and risks.“
The images used in this article were created with DALL·E 2, using the prompt „create an interesting visual for an article on generative ai“.
Generative AI refers to artificial intelligence systems that are able to generate new content, such as text, images, or music, based on a set of input data or algorithms. This type of AI has the potential to revolutionize various industries and has already made significant strides in recent years.
One of the most well-known examples of generative AI is GPT-3, a language generation model developed by OpenAI. GPT-3 is able to generate human-like text based on a given prompt and has been used for a variety of applications, including generating news articles and social media posts. Other examples of generative AI include DeepFake, which uses AI to manipulate or synthesize video or audio content, and StyleGAN, which generates realistic images based on a dataset of images.
The history of generative AI can be traced back to the 1960s, with the development of machine learning algorithms and neural networks. However, it was not until the 2010s that generative AI began to gain significant attention, due in large part to the advancement of hardware and software capabilities.
There are many potential opportunities for generative AI, including the ability to automate content creation, improve efficiency in various industries, and enable the creation of new forms of art and entertainment. For example, generative AI could be used to generate personalized news articles or social media posts, or to design custom products in manufacturing or retail. In the entertainment industry, generative AI could be used to create new music or visual art, or to generate realistic special effects for movies and television.
However, there are also risks associated with generative AI, including the potential for misuse or abuse. For example, DeepFake technology has been used to create fake videos for the purpose of spreading misinformation or creating fake celebrity pornography. Additionally, there are concerns about the impact of generative AI on employment, as it has the potential to automate certain jobs that currently require human labor.
Overall, the future outlook for generative AI is promising, with the potential for significant advancements and innovation in a variety of fields. However, it is important to consider and address the potential risks and ethical implications of this technology as it continues to develop.
| 2022-12-30T00:00:00 |
2022/12/30
|
https://time-to-reinvent.com/exploring-the-latest-developments-in-generative-ai-opportunities-and-risks-for-the-future/
|
[
{
"date": "2022/12/30",
"position": 28,
"query": "generative AI jobs"
}
] |
On Target for Tech Talent
|
On Target for Tech Talent
|
https://www.vedp.org
|
[] |
And these days, that means some exposure to fields like machine learning, artificial intelligence, data visualization, data analytics. But they also need to ...
|
How Virginia's Plan to Double its Tech-Talent Pipeline by Aligning Resources at All Levels Hits the Bull's-Eye
When it comes to building a comprehensive strategy for increasing talent to meet the technology workforce demands of today and tomorrow, there is a nationwide call by employers for talent with skills that include a particular focus on software development, engineering, machine learning, artificial intelligence, user experience design, and user interface design — all competencies with roots in computer science. Virginia has plans to fill that supply-side gap and hit the bull’s-eye of employer demand by increasing and roughly doubling its tech-talent pipeline in the near future.
It’s an approach that was buoyed by the Virginia Economic Development Partnership’s (VEDP) 2017 Strategic Plan for Economic Development of the Commonwealth that identified the tech sector as Virginia’s largest traded-sector employment growth opportunity. And while it is an approach that synced up with the timing of Virginia winning HQ2, it’s also an effort that’s been building momentum at all education levels across the Commonwealth.
Arlington elementary school students
Now that Amazon is looking to Virginia to fill as many as 25,000 highly skilled jobs over the next 12 years, the Commonwealth is primed to produce 25,000 to 35,000 additional degrees in computer science (CS) and related fields statewide, roughly split between bachelor’s and master’s degrees, over the next two decades. While Amazon is in the spotlight today, these are workers who will benefit existing Virginia employers, from Capital One to WillowTree and PowerSchool, to tomorrow’s startups.
Virginia’s plan is a seamless approach, targeting CS and related fields across each phase of the education spectrum — K-12 classrooms, community colleges, undergraduate, and post-graduate programs — with performance-based investments and other funding.
Tidewater Community College
Similar to other states, Virginia’s current tech-talent pipeline poses a dichotomy of risks and opportunities. While the Science, Technology, Engineering, and Math (STEM) workforce shortage is expected to grow nationally, and two in five Americans say that the shortage of workers in STEM fields is at crisis levels, according to a study from tech and engineering company Emerson, the Commonwealth touts a few advantages in not only bridging the gap, but building for the future.
Virginia State University
First, Virginia has a head start. It has the vision to anticipate this tech-talent need, and with plans accelerated thanks to the Amazon HQ2 project and other tech company expansions, Virginia is poised to be a leader among states working to turn their tech-talent tide. In fact, according to CodeVA, an educational nonprofit created to promote computer science across Virginia, in 2016, Virginia became the first state in the nation to pass sweeping computer science education reform. This law mandates that every Virginia child will receive access to essential computer science literacy — to include coding — from kindergarten through graduation.
From K-12 classrooms to Virginia’s robust community college system, and from four-year university labs to graduate program internships, Virginia is on target to meet the needs of tomorrow’s technology workforce. Chalk it up to an accelerated start, a recognition by tomorrow’s workers of where the jobs will be and the influx of funding and programs designed to ensure Virginia stays at the head of the class when it comes to tech aptitude and workforce readiness moving forward.
| 2022-12-30T00:00:00 |
https://www.vedp.org/news/target-tech-talent
|
[
{
"date": "2022/12/30",
"position": 63,
"query": "machine learning workforce"
}
] |
|
AI in Medical and Healthcare
|
AI in Medical and Healthcare
|
https://anubrain.com
|
[] |
AnuBrain uses AI and ML to create breakthrough solutions that offer advanced technological support for the medical and healthcare industry.
|
Artificial intelligence and machine learning are revolutionising the medical and healthcare industry. The applications of AI in healthcare are improving how patients communicate with medical professionals and get their treatment done.
The AI applications in healthcare include advanced technological support in surgical problems, clinical decision-making assistance, clinical effectiveness optimization, critical disease prediction, imaging analytics, etc.
AnuBrain uses artificial intelligence (AI) and machine learning (ML) capabilities to create effective and efficient solutions for the medical and healthcare industry. In this way, we aim to help provide medical care and assistance like never before.
| 2022-12-30T00:00:00 |
https://anubrain.com/ai-in-medical-and-healthcare/
|
[
{
"date": "2022/12/30",
"position": 39,
"query": "AI healthcare"
}
] |
|
Exploring the Boundaries of AI and Design
|
Exploring the Boundaries of AI and Design: A Typography and Graphic Design Blog
|
https://ipassas.com
|
[] |
Training a Machine Learning Model for UX Design ... February 17, 2025 ... The Future of AI Isn't General-Purpose Models—It's Hyper-Specialized Tools That Actually ...
|
You have reached the end!
| 2022-12-29T00:00:00 |
2022/12/29
|
https://ipassas.com/exploring-the-boundaries-of-ai-and-design-a-typography-and-graphic-design-blog/
|
[
{
"date": "2022/12/30",
"position": 15,
"query": "AI graphic design"
}
] |
Make Social Media Content a Breeze with Designs. ...
|
Make Social Media Content a Breeze with Designs.ai’s Templates
|
https://designs.ai
|
[
"Jared Haring"
] |
Designs.ai is a powerful tool for creating visually stunning social media content and is also super easy to use.
|
Designs.ai is a powerful tool for creating visually stunning social media content and is also super easy to use. Designs.ai allows you to choose from a wide selection of pre-designed templates or create your own custom templates to suit your specific needs. Therefore, here are 6 social media content ideas that can help boost your brand with the use of Designs.ai:
1. Create eye-catching graphics for your social media posts
Use Designs.ai’s Graphicmaker to design custom graphics according to your brand that will stand out in your followers’ feeds and get them to stop and take notice.
2. Design beautiful Instagram stories
With Designs.ai’s wide selection of templates to choose from, you can effortlessly create stunning Instagram stories that will grab your audience’s attention and keep them engaged. Putting up a question or poll within the story is likely to encourage interactivity with your target audience.
Use Designs.ai to design custom graphics according to your brand that will stand out in your followers’ feeds and get them to stop and take notice.
3. Hosting an AMA
Example of AMA Story Template in Designmaker
“Ask Me Anything” is a good way to engage with your audience. Designs.ai’s templates will allow you to easily create the necessary graphics for you to host your AMA, which you can then implement in your Instagram Stories or Posts.
4. Create stunning Facebook covers
Your Facebook cover photo is the first thing people will see when they visit your page. Use Designmaker to create a visually stunning cover that will reflect your brand and capture the attention of your audience.
5. Design outstanding Pinterest graphics
Pinterest is a great platform for visual content, and Designmaker can help you create Pinterest graphics that will grab your audience’s attention and drive traffic to your website or blog.
6. Create social media graphics for Twitter
Twitter is all about visuals, and Designmaker can help you create attention-grabbing graphics that will help your tweets stand out in the crowded feed.
Overall, Designs.ai is a valuable tool for creating social media content that engages and inspires your audience. Whether you’re a small business owner, marketing professional, or creative entrepreneur, Designs.ai has the tools and resources you need to take your social media content to the next level.
| 2022-12-30T00:00:00 |
2022/12/30
|
https://designs.ai/blog/make-social-media-content-a-breeze-with-designs-ais-templates/
|
[
{
"date": "2022/12/30",
"position": 27,
"query": "AI graphic design"
}
] |
Business leader - Arisa
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Business leader
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https://aiskills.eu
|
[] |
ARISA explains Artificial Intelligence (AI) and provides up-to-date learning tools to allow organisations to be more competitive and future-oriented.
|
To take advantage of what AI has to offer – from process automation, to productivity improvement, to talent and business development — European businesses need more AI-skilled individuals.
ARISA will support and provide guidance to business and tech leaders to:
Increase AI adoption within organisations and bolster growth
Attract, manage, and retain AI-skilled professionals
Make sure AI-based solutions are secure, trustworthy, and inclusive
With ARISA, business leaders will be able to support the development and deployment of AI in Europe, and become a part of a forward-looking community.
| 2022-11-16T00:00:00 |
2022/11/16
|
https://aiskills.eu/business-leader/
|
[
{
"date": "2022/12/30",
"position": 10,
"query": "artificial intelligence business leaders"
}
] |
Open Positions
|
Open Positions
|
https://www.moloco.com
|
[] |
At Moloco, we're creating an environment where people can unlock their fullest potential and do the best work of their career.
|
Going Further Together
It is incredibly rare to find a company that sits at the intersection of cutting edge tools and technology, a strong financial profile and a supportive culture with dynamic leaders who will help you grow - all within an addressable market that is ample with opportunity. At Moloco, we’re creating an environment where people can unlock their fullest potential and do the best work of their career.
All Departments All Locations Clear Filters
| 2022-12-31T00:00:00 |
https://www.moloco.com/open-positions
|
[
{
"date": "2022/12/31",
"position": 38,
"query": "machine learning job market"
},
{
"date": "2023/06/26",
"position": 38,
"query": "machine learning job market"
}
] |
|
Review: Machine Learning & AI Specialization with Andrew Ng
|
Review: Machine Learning & AI Specialization with Andrew Ng
|
https://www.getbridged.co
|
[] |
Artificial intelligence and machine learning are only going to grow in popularity in the job market as more companies implement these concepts in their ...
|
Program Overview
As robots continue to take over the world, more and more companies are in search of tech workers with artificial intelligence and machine learning experience. Just kidding about the robots, but AI/ML continues to dominate the job market in technical fields. Being versed in these concepts, even at a high level, gives you a leg up over other people attempting to break into the same roles. If a career in tech sounds interesting to you, we recommend reading on to see if Stanford's Machine Learning Specialization is right for you.
This program will teach you the fundamentals of machine learning and real-world AI applications. It's taught by AI expert Andrew Ng, a renowned researcher at Stanford and groundbreaking work at leading tech companies.
This 3-course program, which should take you around 10 weeks / 2 months to complete, has been rated 4.9 out of 5 by over 4.8 million learners since its launch in 2012. The courses that make up the specialization include:
Supervised Machine Learning: Regression & Classification
Advanced Learning Algorithms
Unsupervised Learning: Recommenders & Reinforcement Learning Ng covers a wide range of practical machine learning topics, including supervised learning, unsupervised learning, and best practices for AI and machine learning innovation in technical careers.
By the end of the program, you'll have mastered key concepts and gained the skills you need to apply machine learning to real-world problems with confidence.
If you're familiar with product roles, you can use the information in these courses to break in to several careers, including UX researcher, product manager or almost any type of analyst. Putting a machine learning course from Stanford on your resume is definitely a way to step up from the pack.
Weekly Breakdowns
We’ve recapped the learning objectives from each week to set your expectations for course material. The great part about this program is that you can jump to any course, and any section if it’s interesting to you. For example, if you’re already a product manager and just looking to rock a recommendations algorithm for your product, jump to course 3, week 2. However, you only can get the certificate if you complete all 17 weeks of content. To audit an individual week-- find the exact course (we've linked them individually here) and click "audit" to save it to your profile. Then open the desired week on the side panel that aligns with our recaps.
Course 1: Supervised Machine Learning: Regression and Classification
Learning Objectives from Week 1: Introduction to Machine Learning Understand the fundamental concepts and techniques of machine learning, including supervised and unsupervised learning.
Begin to write and run Python code in Jupyter notebooks to implement machine learning algorithms and models.
Know how to define and optimize a regression model using gradient descent, including the implementation and visualization of a cost function. Learning Objectives from Week 2: Regression with Multiple Input Variables Learn various mathematical techniques to make machine learning calculations faster and more efficient.
Use feature scaling, feature engineering, and polynomial regression to improve the performance of a machine learning model.
Write code to create a machine learning model that can predict numerical values using linear regression. Learning Objectives from Week 3: Classification Use a specific type of machine learning model called logistic regression to predict one of two outcomes, known as binary classification.
Write binary classification code to predict outcomes.
Learn techniques to prevent a machine learning model from being too complex and overfitting to the training data.
Course 2: Advanced Learning Algorithms
Learning Objectives from Week 1: Neural Networks Understand the fundamental concepts and components of a neural network, including layers and activations.
Learn how to use a neural network for image classification, including building a neural network in TensorFlow or regular Python code.
Learn about advanced techniques for improving the performance of a neural network, such as parallel processing. Learning Objectives from Week 2: Neural Network Training Use TensorFlow to train a neural network on data and understand the importance of different activation functions.
Understand and implement multiclass classification, including the use of the softmax activation and categorical cross entropy loss function.
Learn about the difference between multi-label and multiclass classification.
Learning Objectives from Week 3: Advice for Applying Machine Learning Understand how to evaluate and improve the performance of a learning algorithm, including techniques such as regularization and error analysis.
Learn about the iterative process of developing and updating a machine learning model, including techniques like data augmentation and transfer learning.
Understand the concept of bias and variance and how they apply to neural networks.
Learn about fairness and ethics in machine learning and how to measure precision and recall when working with imbalanced datasets. Learning Objectives from Week 4: Decision Trees Understand the structure and use of decision trees for making predictions.
Learn about the impurity metric "entropy" and how it is used in the construction of decision trees.
Learn about advanced techniques for using decision trees, such as tree ensembles and boosting, and understand when to use decision trees or neural networks. Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning
Learning Objectives from Week 1: Unsupervised Learning Implement the k-means clustering algorithm, including the optimization objective, initialization, and centroid update function.
Understand how to choose the number of clusters for the k-means algorithm and when to use supervised learning versus anomaly detection.
Implement an anomaly detection system and the function that finds the closest centroids to each point in k-means. Learning Objectives from Week 2: Recommender Systems Learn how to work with collaborative filtering recommender systems in TensorFlow.
Utilize deep learning content-based filtering using a neural network in TensorFlow.
Understand ethical considerations in building recommender systems.
Learning Objectives from Week 3: Continued Linear Regression Study outliers, inference and testing with in linear regression and variability partitioning. Learning Objectives from Week 4: Reinforcement Learning Understand key concepts in reinforcement learning, including return, state, action, and policy.
Understand the Bellman equations and the state-action value function.
Understand continuous state spaces and how to build a deep Q-learning network. Quick Notes While this is a high-level and beginner friendly program, there is some coding involved with Python. Python is probably one of the most popular coding languages for data science and machine learning, which makes the course extra valuable.
HOWEVER, if you're not looking to code, this may not be the program for you.
Cost and Auditing
The program is only $49/month, and comes with a Linkedin Certificate on behalf of Duke University (remember– this is a prestigious place!!). If you complete the curriculum on the proposed timeline, it should take about 2-3 months, though you could blitz through it on a break in far less. While that seems steep, compared to a degree or bootcamp this micro-certification is a steal! If you have a learning budget, or are dedicated to upskilling your career with a machine-learning focus– we recommend paying for and completing the program to get the shareable certificate (GET RECEIPTS!). This will help make your Linkedin more searchable to recruiters who may be looking for specific keywords and programs. To audit the program and simply learn the material, this program is completely free! Thanks Coursera!
Student Reviews
While this program was initially introduced to Stanford in 2012, it's only been a part of Coursera for a limited time (2022, to be exact!). However, the reviews are overwhelmingly positive-- which is to be expected from a tried-and-true program. Each of the three courses had a 4.9 rating average with thousands of reviews.
Some of our favorite positive review points: Perfect balance of application and theory, and wise choices in ramping up the complexity gradually. Discussion boards are very helpful, feels very much like personalized learning.
-PD
-PD Prof Ng is a fantastic teacher! The three courses are really well structured and builds upon themselves. I expected to learn some cool things, and I sure did - some mind-blowing machine learning things! The mentors on the forum are really helpful and respond to questions will thoughtful replies, which is great.
Prof Ng is passionate about machine learning, but is also sincere & humble, and is also very mindful of the ethics of AI and how it impacts people. The course is pretty cheap, and I can tell Prof Ng really wants to pass on AI knowledge.
-Nathan B Aggregations of negative review points: Assignments could be more difficult, and less google-able.
Definitely at a pace for beginners. ... and our favorite overall review: The best thing this course did for me was to remove the enigma of machine learning. This specialization is not so much about going deep into individual machine-learning algorithms and techniques as it is about exposing a student to the broad spectrum of all the different kinds of problems for which machines can be programmed to learn a solution.
Once a student completes this program, they have a very good idea of the kinds of problems that can be solved by letting machines learn how to solve those problems and specific algorithms/techniques that need to be used for that particular kind of problem. A student can then research additional resources for the specific problem they have at their hand and take a deep dive into developing a working solution for their specific problem. This course enables you to start that journey by taking away the fear created by the belief that machine learning is something very challenging.
-Anupam
Supplemental Materials
Other Analytics & ML Specializations
For the web analytics power-user: Google's Data Analytics Specialization
Google also sponsors a data analytics certificate program through Coursera. This is one of the more coveted certificates in the industry for learning the Google Analytics tool specifically, hence our recommendation of this course from Duke. Google's course is also free to audit, but same rules apply if you want the certificate to show off at $49 a month.
Aspiring analysts: University of Minnesota's Analytics for Decision Making
The University of Minnesota also runs a great Coursera program with a 4.7 star average. It's free to audit, but if you want the certificate it's covered under a $49/month Coursera subscription. We especially love course 2 for the experimenters out there... you can never go wrong with data-driven optimization strategies.
This is a newer beginner-level class that has a great overview of types of analytics, and when to use each method to maximize effectiveness.
Conclusion
| 2022-12-31T00:00:00 |
https://www.getbridged.co/course-review/stanford-deepai-machine-learning-specialization
|
[
{
"date": "2022/12/31",
"position": 43,
"query": "machine learning job market"
}
] |
|
Artificial Intelligence and Data Science Demand in Myanmar
|
Artificial Intelligence and Data Science Demand in Myanmar
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https://datamites.com
|
[
"Datamites Blog",
"Datamites Team"
] |
It brings opportunities in various areas of finance, education, business, and telecommunication. Introduction to Artificial Intelligence in Myanmar. Technology ...
|
Data Science Resources
Artificial Intelligence and Data Science are trending in the business market of Myanmar and shaping the future with relevant careers. The services served by AI are big data robotics and IoT contributes to an increasing number of tech companies in Myanmar. Artificial Intelligence has impressive career chances with a high-package salary that is in demand and seeks desired candidates for the respective domain and the same applies to Data Science. It brings opportunities in various areas of finance, education, business, and telecommunication.
Introduction to Artificial Intelligence in Myanmar
Technology has introduced advanced innovation and made artificial intelligence a reality in today’s world. With the power of computers, artificial intelligence has brought success to business organizations. In tech firms, the need for artificial intelligence or human intelligence has grown to perform multiple tasks at once. In Myanmar, artificial intelligence has brought new features such as visual perception, virtual reality, speech recognition, language translation, and decision-making to transform the outlook of the company.
Myanmar has recently accelerated technological growth and advanced digital innovations throughout the country. It has embraced the trends and equally tried to renovate the business market. With the advancement of technology, it has become easier to perform in wide sectors. According to World Economic Forum, Myanmar is inclined to support the future of Artificial Intelligence which brings accessible data for the benefit of the financial and IT industries. Digitalization helps in many ways by reducing risks in data privacy, cybersecurity, and other complex tech problems. Myanmar has established a workforce with trending business values that brings economic stability to the country.
In 2015, the democratic elections took place which marked a turning point in the society of Myanmar according to a Worldbank.org report. It generated product liberalization and modernized the economic system to improve economic stability. There are top IT firms that have brought development, innovation, and social welfare. Myanmar is emerging in terms of digitalization and technology to bring accessible standards to the nation. The telecom business started slowly but was rapidly implemented in the country. With improved access to internet connectivity, Myanmar has flourished in terms of technological services.
Naypyidaw, the capital city of Myanmar is accelerating the demand for AI and is providing job opportunities to flourish employees’ skills in AI niches.
Refer the following articles:
What is artificial intelligence, and what is its demand in Myanmar?
Artificial intelligence is a human intelligence process that is stimulated by computer power. The main application of artificial intelligence is to detect languages, speech recognition, and machine vision. Artificial intelligence is programmed like machines that adapt human actions and mimics their behaviour. The goal of artificial intelligence is to perceive informational data and learn computer-enhanced reasoning. With the help of artificial intelligence, cognitive activity can execute complex tasks.
Myanmar is in high demand for new technologies like artificial intelligence, machine learning, etc. In the view of Grand View Research, the market size of Artificial Intelligence is USD 93.5 billion and the growth rate will increase up to 38.1% by the year 2023- 2030. The big IT firms have already launched applications of artificial intelligence that predominantly include business marketing, educational institutes, financial services, eCommerce, and retail. The future of data science, artificial intelligence, machine learning, etc are used in various fields of the technological sector and creates the decision to improve data insights. Artificial intelligence benefits humans in several ways with its remarkable applications.
The use of artificial intelligence includes mobile applications like Apple’s Siri, Amazon Alexa, and Google Assistant. It helps in enormous ways just by recognizing the voice, it books flight tickets, performs to accept incoming calls, schedules appointments, etc. The tech companies in Mynamar have artificial intelligence services that assist to achieve AI solutions. Myanmar’s technological sector has a prominent need for AI services to boost the consumer experience and improve security controls.
Read the following articles:
What are the career opportunities in artificial intelligence?
The career opportunities in artificial intelligence have escalated due to its high demand in the tech market of Myanmar. Choosing careers in artificial intelligence would most likely have an enhanced future with fuller possibilities for growth. The need for AI professionals has increased in recent years and gave importance to new technologies.
The desired job roles in artificial intelligence are Data Scientists, Advanced-Data Scientists, Big Data Engineers, Business Intelligence Developers, Machine Learning Engineers, Research Scientists, Al Data Analysts, AI Engineers, and Product Managers.
Giant companies that make significant use of artificial intelligence are Apple, Facebook, Amazon, Deepmind, Datavisor, Clarifai, etc.
Refer the following articles:
What is the salary of an Artificial Intelligence Professional in Myanmar?
Myanmar has embraced artificial intelligence professionals to develop their talent and perform diligently for the benefit of the companies. The salary of an artificial intelligence professional depends on the skillset, educational qualifications, and experience in the related field.
The salary of an Artificial Intelligence professional in Myanmar ranges up to 547,000 MMK per year according to a Glassdoor report. The salary varies according to their expertise in AI and their position within the company that they work in.
The AI engineer’s salary in Myanmar marks 500,000 MMK per year depending on their skill knowledge and education qualification according to a source GoLogica Technologies.
Read the article: Is Data Science and Artificial Intelligence in Demand in Malaysia?
How DataMites is providing AI Training in Myanmar?
DataMites is a globally recognized training institute that provides a complete grip over artificial intelligence courses in Myanmar. The AI program covers data science packages NumPy, scipy, sklearn, matplotlib, and pandas. We are providing online AI courses in Myanmar and the duration will depend on the course you take. The students get assistance from expert professionals and access to the data science cloud lab for practice. DataMites data science courses in Myanmar are accredited by IABAC and thoroughly help students to grow exponentially.
DataMites training institute prepares students to get AI training in Myanmar that will help them build a strong foundation for the skills aligned with the growing AI market. The artificial intelligence course in Myanmar covers deep learning algorithms, batchnorm, RNNS, etc. In the training period, students are also provided with case studies from natural language processing, healthcare, autonomous driving, etc.
Introduction to Data Science in Myanmar
Myanmar has embraced new employment in data science that projects scientific methods and algorithms to extract knowledge from unstructured data. The job market for data science has expanded into many sub-sectors that choose the right candidate for the role. The tech companies in Myanmar are thriving by providing data science career paths. The importance of data science has reached its heights as multiple employees are choosing their career lines as data scientists, data analytics, data engineers, etc. According to the Bureau of Labor Statistics, the growth rate of careers in data science has increased in Myanmar.
Data science has seen potential growth in Myanmar and is estimated to grow 35.4% in the coming years. The leading IT companies in Myanmar are giving huge career opportunities in data science and guiding employees to get desired job role to upskill their talent. With the knowledge of data science, one can easily extract valuable information for the growth of business markets. The hype around data science careers in Myanmar is high and the scale is only going to grow even further in the years to come.
Read these articles for more information:
What is data science and its demand in Myanmar?
Data science is the study of data that deals with modern tools and techniques. Through the use of data science, much useful information is used in complex machine learning algorithms that help in building predictive models. Data science cover statistic and maths to uncover organizational data. These data help in making better strategic planning and decision-making. IT firms are evolving their employment needs in the search for data science to improve business outcomes and values. Data science undergoes major stages of data processing and data analysis.
The demand for data science in Myanmar is increasing and the career prospects are as well. According to a Glassdoor report, data science is ranked as the top career path to pursue. Myanmar is recruiting incredible job roles in data science that will secure the future and also the business markets. The technological firms in Myanmar are giving more importance to data science careers because it is one of the fastest-growing jobs in recent years. The employment rate is growing tremendously which increased the number of employees who seek a future in data science.
The capital city of Myanmar, Naypyidaw has introduced Data Science as its major component in giant IT firms to enhance technology services.
What are the career opportunities in data science?
Data science career is flourishing in the business market of Myanmar. As the future is trending with the latest technology in artificial intelligence, data science, and machine learning which have high levels of job opportunities in tech firms. Today, data science has an impressive future that students or fresher can pursue to upskill their talent in their respective niches. Data is fuel to many IT companies that extract deep knowledge about the power of informational data. The major skills required for pursuing a data science career are statistics and math, programming languages like Python and R, raw data techniques, advanced data analysis, data visualization, etc.
The career opportunities in data science are data engineer, data analyst, data scientist, data architect, business intelligence analyst, statistician, and machine learning engineer. The top IT companies that make essential use of data science careers are Google, Microsoft, IBM, Amazon, Fractal Analytics, Absolutdata, Accenture, etc.
Read the following articles:
What is the salary of data science professionals in Myanmar?
Data science is in high demand concerning its skills and with increased digital information the need for data has become prominent. Myanmar has had higher recruitment for data science professionals in recent years and so is the salary high for skilled professionals in data science.
The salary of a data science professional in Myanmar ranges up to 909,000 MMK Per Month. The salary of the professional depends on their skills, educational qualifications, and experience in the related domain.
According to a Glassdoor report, the highest salary of a data scientist or analytics goes up to 1,380,000 MMK Per Month.
According to Salaryexplorer, a data scientist’s salary in Myanmar is 482,000 MMK Per Month. The salary includes transportation, housing, skills, experience, etc.
Read the following articles:
How DataMites is providing Data Science Course Training in Myanmar?
DataMites is a renowned training institute that provides a leading course in data science and relevantly covers courses for Python/R, statistics, business aspects, tableau, and machine learning. The students get intensive 2 months of online training in data science. The students willing to pursue their desired career in data science get access to a data science cloud lab for practice. The courses of DataMites are accredited by the International bodies of IABAC, which provides globally recognized certification at the end of the training.
DataMites offers data science programs in Myanmar through an online platform with professional mentoring, and a rich syllabus. The training session will assist you with job placements and internships. The entire training provides valuable case studies and client projects to handle which enhances the student’s credibility.
Refer the following articles:
Conclusion
The future of artificial intelligence and data science is trending and the need for AI analysts, AI engineers, data scientists, data analysts, and data engineers is increasing significantly in Myanmar. The country has developed itself to bring transformation in technical firms by using informational data. With the help of artificial intelligence and data science, there can be massive changes in the digital world.
| 2022-12-31T00:00:00 |
2022/12/31
|
https://datamites.com/blog/artificial-intelligence-and-data-science-demand-in-myanmar/?srsltid=AfmBOorniIRG3_7hTL5tTGVVPtttntTxIwAh2JnlzgW6SL_Nw6I7TvSa
|
[
{
"date": "2022/12/31",
"position": 50,
"query": "machine learning job market"
}
] |
Sourcing Qualified Candidates in the Hottest Software ... - Bridge
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Sourcing Qualified Candidates in the Hottest Software Engineering Market in the USA for Jasper.ai
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https://bridgeteams.com
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[] |
Show interest in artificial intelligence and machine learning. Our project started when the engineering market was the “hottest” and engineers were dictating ...
|
Using a tech-powered highly efficient sourcing approach, Bridge facilitated screening calls with 20 qualified candidates in the first month of the project and provided close to 30 experts in the following two months.
During the project, which lasted four months, Jasper was able to swap roles, prioritize certain positions, and add new ones to the search.
Bridge managed to find the proverbial “needle in a haystack” ‒ engineers with a very specific set of skills and experiences, clearly showing signs of interest in AI/ML and GPT-3 algorithms.
| 2022-12-31T00:00:00 |
https://bridgeteams.com/sourcing-qualified-candidates-in-the-hottest-software-engineering-market-in-the-usa-for-jasper-ai/
|
[
{
"date": "2022/12/31",
"position": 83,
"query": "machine learning job market"
}
] |
|
Careers
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Careers – FAR.AI
|
https://far.ai
|
[] |
This is more than a job, it's an opportunity to contribute to one of the most significant technological shifts of our time. Join a pioneering team working on ...
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Be at the forefront This is more than a job, it's an opportunity to contribute to one of the most significant technological shifts of our time. Join a pioneering team working on groundbreaking projects that shape the future of artificial intelligence.
Innovate and Collaborate Our culture is built on collaboration and diversity, where creative thinking and new ideas are not just encouraged, they're essential. At FAR.AI, you will work on cutting-edge projects, collaborate with a diverse team of leading researchers, engineers, and operators who are passionate about AI safety.
Make a FAR-reaching impact Our work addresses challenges that have profound implications worldwide. By joining us, you'll play a pivotal role in influencing industry standards, informing policy decisions, and safeguarding the future for everyone.
Grow and learn with us At FAR.AI, we care about helping you grow and flourish. Starting from our transparent interview process, work trials, onboarding roadmaps, to our continuous learning and development programs, we are intentional about supporting your growth.
| 2022-12-31T00:00:00 |
https://far.ai/careers
|
[
{
"date": "2022/12/31",
"position": 53,
"query": "future of work AI"
},
{
"date": "2022/12/31",
"position": 3,
"query": "AI hiring"
}
] |
|
The Future of Consulting in the Age of AI
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The Future of Consulting in the Age of AI
|
https://futureofleadership.ai
|
[
"Marin Ivezic",
"Root",
"--M-A-Box-Bp",
"--M-A-Box-Bp-L",
".M-A-Box",
"Margin-Top",
"Important Margin-Right",
"Important Margin-Bottom",
"Important Margin-Left",
"Important"
] |
AI's ability to automate grind work leaves consulting firms navigating a tricky tension: how do we maintain profitability through greater efficiency, while ...
|
As a professional consultant, I have spent most of my career dealing in the currencies of change. My clients are almost always in growth or flux, trying to navigate new challenges or position themselves for the future. We work together in the realms of novelty, uncertainty and ambiguity, finding the answers that enable them to survive and then thrive in disruption. At its best, consulting is an indispensable service, enabling organizations to make sense of complexity and move through turbulence to make better decisions faster.
It seems increasingly clear to me, though, that the consulting industry itself is in dire need of its own medicine. We are currently living though the early stages of the Age of AI, which you know already because it’s all anyone seems to talk about. Yet, I don’t believe my industry is talking about it enough; or, at least, it’s not talking about the right things. Smart people in highly successful firms are spending a lot of time thinking about how to leverage AI’s promises of efficiency gains and cost reduction, but missing the far bigger threats and opportunities that the technology promises for the sector itself. As a discipline, we are currently ankle-deep in the first wave of AI, while apparently blind to the tsunami to come.
When Luka and I wrote The Future of Leadership in the Age of AI, it was just after the announcement of Generative Adversarial Networks (GAN), but way before transformers architecture, Generative AI, and ChatGPT. Our key prediction, and the thesis, for the book was that AI would most profoundly disrupt knowledge work and leadership roles. At the time, most conversations about AI’s future revolved around smarter robots taking over blue-collar jobs—factory workers, truck drivers, and the like. Few were considering the seismic shifts coming for the boardrooms, consulting firms, and corner offices.
Of course, the “future” we speculated about is today’s present, and much of what the book anticipated seems to be manifesting itself in our daily reality. ChatGPT was released just over a month ago and even in that short time the scope of what was thought achievable with AI has been redefined. Competition for AI-augmented products and services is starting at all points in the value chain, with new use cases being explored and promised by early adopters everyday. I predict that we have not seen anything yet.
All sectors are set for some form of disruption, Healthcare, Finance, Retail, Education, Manufacturing, and Transportation being among the most susceptible. Typically, in such situations, players in these domains reach out to consultants to help shepherd them through the necessary alterations. But the consulting industry—my home turf for decades—is itself standing at the edge of a major transition. Many consultants may not realize this yet, or, as is more common, refuse to realize it, but if my original premise holds true—that knowledge work will face the brunt of AI disruption—then professional services are set to undergo nothing short of a revolution.
Consulting, with its tried-and-tested model of leveraging junior consultants’ time to deliver solutions crafted under senior partners’ guidance, is ripe for disruption. What happens when AI can do the heavy lifting traditionally assigned to those junior associates? What does a consulting firm look like when the bottom of the pyramid is replaced by the AI? What will be the value provided to our clients? How will we develop the talent?
In the coming sections, I explore how AI will reshape the consulting landscape, why it’s happening faster than most people anticipated, and what it means for the future of this industry. From evolving client expectations to the democratization of expertise, the rules of the game are being rewritten—and consulting firms need to adapt (very quickly!), or risk being left behind.
This isn’t just an evolution of consulting; it’s a fundamental redefinition of what it means to provide professional services in a world where intelligence, creativity, and expertise are increasingly augmented or even fully provided by machines.
The consulting industry is no stranger to change. Over the decades, it has flexed to meet new market demands, technological revolutions, financial crises, trust erosion, and increasingly savvy clients. But the AI-driven destabilization we are witnessing will be unlike anything we’ve seen before.
For years, the consulting business model thrived on its ability to deliver tailored solutions through privileged access to information and structured frameworks and methodologies. Large teams of analysts and associates performed assessments, served up research, strategies, policies, and implementation plans under the guidance of senior partners. Clients paid handsomely for this service, trusting that consulting firms offered something unique and unattainable elsewhere.
AI promises much in this arena, automating data analysis, streamlining processes, and generating insights, not to mention churning out polished PowerPoint slides. But with these opportunities come a difficult challenge. The grind work that forms the foundation of the traditional consulting pyramid is precisely the kind of labor that AI excels at. Why pay a team of fresh MBAs to sift through data and build PowerPoint decks when AI will be able to do it faster, cheaper, and often better?
The implications go beyond operational efficiency. AI threatens to erode the information asymmetry that has historically given consulting firms their edge. When clients can access powerful generative and predictive AI tools that provide quick, nuanced answers, the gap between what they can do themselves and what consultants offer begins to shrink.
This possibility is forcing consulting firms to rethink their value proposition. It’s no longer enough to deliver frameworks or reports—clients are looking for measurable outcomes, genuine breakthroughs, and senior expertise that AI can’t replicate. This isn’t just an evolution of consulting; it’s a fundamental redefinition of what it means to provide professional services in a world where intelligence, creativity, and capability are increasingly augmented or even fully provided by machines.
As someone who’s been in the consulting trenches for decades, I’ve seen the industry navigate countless upheavals. AI feels different. Though many consultants still see this technology as just another tool in the consultant’s toolkit, that is highly limited view that will ultimately cost them, because AI is so much more than an efficiency enabler; it’s a force that will be reshaping the entire playing field.
For decades, we dreamed that AI would handle the mundane: produce goods, clean our homes, drive our cars, and free us from tedious tasks so we could focus on creativity, art, and higher pursuits. Instead, AI is now mastering the higher pursuits, and we’re still stuck cleaning our houses.
When I wrote The Future of Leadership in the Age of AI, I anticipated that knowledge workers—consultants, analysts, managers, and yes, even senior executives—would face the brunt of AI’s impact. These were the roles reliant on processing vast amounts of information, identifying patterns, and making decisions based on structured frameworks—all things AI was rapidly getting better at.
Today, that prediction feels more relevant than ever. If anything, I may have underplayed the speed and degree of impact.
AI is going to be a transformative force in professional services. It’s not simply replacing repetitive tasks; it’s encroaching on areas traditionally seen as requiring a human touch—areas like research, strategic planning, engineering, financial modeling, and even leadership decision-making. As AI grows more adept at generating insights, simulating scenarios, and tailoring solutions, human consultants will need to redefine their roles in the consulting value proposition.
More than that, they will need to redefine themselves. No longer will strong analytical skills be sufficient to differentiate yourself as a strong consultant; increasingly, those who succeed will be those who are able to develop and embody higher levels of creativity, emotional intelligence, adaptability, and the ability to drive and manage change in an AI-augmented world. These qualities, once considered “soft skills,” are becoming the hardest to replace and therefore the most valuable.
Or, as I illustrated in the Working With AI, a comic that I published at same time as the book:
While many leaders may grasp this concept intellectually, few have fully embraced what it means in practice. The same could be said for consulting firms. As AI will automate the grind work traditionally done by junior consultants, it will force a reckoning for firms that have long relied on selling hours over outcomes.
The warning signs are clear. Firms that fail to move beyond the traditional leverage model risk becoming obsolete. As I argued in the book, the key to thriving in an AI-enabled world lies in recognizing where human strengths—like creativity, empathy, and judgment—add unique value that AI cannot replicate (yet). Consulting firms will need to restructure themselves around these principles, focusing on outcome-driven engagements, capability building, and delivering senior expertise in ways that AI cannot.
For decades, the consulting industry has relied on a business model that’s as iconic as it is profitable: the pyramid structure. Essentially, this model leverages a small number of highly compensated senior leaders at the top, supported by an expansive base of junior consultants and analysts at the bottom. It’s a scalable model premised on selling the expertise of senior partners while delivering solutions through the efforts of junior staff.
Here’s how it works in practice:
Finders, Minders, Grinders : At the top of the pyramid are the “finders,” senior partners whose primary responsibilities are business development and client relationship management. Below them are the “minders,” mid-level managers who oversee project execution, ensuring deliverables align with client expectations. Finally, at the base are the “grinders,” typically junior consultants or fresh MBAs tasked with the heavy lifting—researching, analyzing, assessing, and preparing the PowerPoint decks and Excel models that feed most consulting engagements.
: At the top of the pyramid are the “finders,” senior partners whose primary responsibilities are business development and client relationship management. Below them are the “minders,” mid-level managers who oversee project execution, ensuring deliverables align with client expectations. Finally, at the base are the “grinders,” typically junior consultants or fresh MBAs tasked with the heavy lifting—researching, analyzing, assessing, and preparing the PowerPoint decks and Excel models that feed most consulting engagements. The Leverage Model : The genius of the pyramid lies in its ability to scale revenue. By hiring large numbers of grinders and charging clients premium rates for their work, firms generate significant profit margins. The higher up the pyramid, the less hands-on work is required, with senior partners overseeing multiple projects while contributing minimal direct labor.
: The genius of the pyramid lies in its ability to scale revenue. By hiring large numbers of grinders and charging clients premium rates for their work, firms generate significant profit margins. The higher up the pyramid, the less hands-on work is required, with senior partners overseeing multiple projects while contributing minimal direct labor. Information Asymmetry: Traditionally, consulting firms thrived on their ability to access, analyze, and synthesize information that clients couldn’t. Grinders would comb through mountains of data, applying proprietary methodologies to create insights and strategies, which partners then sold as high-value deliverables. This asymmetry in expertise and information access created the perceived value that justified the hefty fees.
For decades, this model worked brilliantly. It allowed firms to grow, expand their client bases, and create a pipeline of talent rising through the ranks. It also offered a clear career progression for consultants, with grinders aspiring to become minders and, eventually, finders.
But the cracks in this model have been forming for some time, and AI threatens to widen them into chasms.
The traditional pyramid depends heavily on the assumption that junior consultants are essential to the process. They’re the ones crunching data, building models, and preparing client-ready deliverables. Margins on their hours are what pay the partners and generate firm’s profits. But what happens when AI can do most of that grind work in seconds? And when clients realize that AI can help them do do the same or better work themselves?
As AI tools like generative models and predictive analytics become more sophisticated, they’re automating many of the tasks that junior consultants were hired to perform. Data analysis, scenario modeling, policy writing, and even drafting detailed strategy documents are increasingly within AI’s capabilities. This doesn’t just challenge the pyramid model; it fundamentally upends it.
Suddenly, the leverage that made the pyramid so profitable starts to evaporate. Clients no longer see the value in paying for a team of grinders when AI can produce similar—if not better—results faster and cheaper. And without the base of grinders, the entire pyramid begins to collapse.
AI has proven to be exceptionally good at performing the kinds of tasks that grinders have traditionally handled, such as:
In the past, junior consultants would spend countless hours gathering data from various sources, cleaning it, and analyzing it to find insights. Today, AI-powered tools can automate much of this process. Machine learning algorithms can sift through massive datasets in seconds, identifying patterns, trends, and anomalies that would take a human days or even weeks to uncover.
Tools like natural language processing (NLP) can pull relevant information from unstructured data sources—think market reports, social media feeds, or financial filings—without the need for manual effort. And with real-time analytics, AI doesn’t just crunch numbers, it delivers insights dynamically, adjusting as new data becomes available.
One of the key responsibilities of junior consultants has been building detailed financial models and running scenario analyses. AI tools are not only capable of generating these models but can also simulate complex “what-if” scenarios at speeds no human could match.
For example, generative AI can instantly produce forecasts based on different variables, highlighting risks and opportunities without requiring manual input. This doesn’t just save time; it often produces more accurate and nuanced results, as AI can evaluate factors far beyond what a human might consider.
The “deck”—a hallmark of consulting—is no longer the exclusive domain of human consultants. AI-driven tools like ChatGPT and other generative models can draft professional, client-ready reports in minutes. They are getting better at it daily: from executive summaries to detailed strategy recommendations, AI is increasingly able to tailor content to specific client needs, producing high-quality work that rivals (and soon exceeds) what junior consultants can deliver.
Even the visual components of presentations are being automated. More and more service providers are offering AI-driven solutions that massively amplify the speed and quality of information and insight visualization: charts, infographics, dashboards and reports that are not only aesthetically polished but also data-rich and highly customizable.
Grinders traditionally spent hundreds of hours conducting research on industry trends, competitor analyses, and regulatory environments. With AI these tasks can be completed in a fraction of the time. Generative AI models can produce comprehensive summaries of a given topic, pulling from vast repositories of data and presenting it in a digestible format. Or simply throw the latest laws and regulations at AI, and it will produce a summary just at the right level of detail and will highlight the most salient points.
Instead of assigning a junior consultant to scour reports and write a 20-page industry analysis, firms can now use AI to generate that report almost instantly.
AI Capabilities Prediction
If you’re amazed by what AI like ChatGPT can achieve today, prepare to have your expectations shattered in the near future. The pace of advancement is staggering, and by the end of 2025, tools like ChatGPT are likely to outperform even seasoned fifth-year consultants with a PhD and an MBA.
And that’s just the beginning. Research into self-improving AI is already underway, and once AI systems gain the ability to refine and enhance themselves, we could see capabilities evolving not year-by-year, but hour-by-hour. This could either be an exhilarating leap forward—or a daunting challenge for those unprepared to adapt.
At first glance, this level of automation seems like a massive win for consulting firms. Faster turnaround times, reduced costs, and consistent deliverables—what’s not to love? But this efficiency comes at a cost.
The traditional pyramid model depends on leveraging junior consultants to create value while charging clients a premium for their time. Using AI to automate the bulk of the grind work directly threatens this revenue model. Additionally, without the need for grinders, firms lose their traditional talent pipeline. After all, grinders weren’t just doing the work—they were learning the ropes, developing skills, and rising through the ranks to eventually become mid-level managers and partners.
AI’s ability to automate grind work leaves consulting firms navigating a tricky tension: how do we maintain profitability through greater efficiency, while still developing talent for the future?
As the pyramid flips, a new foundation will emerge: one built on expertise rather than leverage, outcomes rather than outputs, and direct client engagement rather than hierarchical delegation.
The consulting industry’s pyramid structure has always been the cornerstone of its profitability and scalability. But as AI disrupts the foundation—automating the grinder work that underpins the model—the pyramid will undergo a dramatic inversion. The roles that once drove the business will be upended, and the consulting model will have to switch from leveraging junior consultants’ time to maximizing senior expertise and delivering measurable outcomes (my emphasis).
With AI automating much of the grind work, clients will no longer see value in paying for what machines can now do faster and cheaper. The emphasis will move to senior consultants and partners. Their ability to deliver unique insights, strategic foresight, and creative problem-solving—skills that AI cannot replicate, yet—will have to become the core value proposition.
In this new model, clients won’t be paying for labor, and why would they when they will be able to get most of that knowledge labor for a $60 per month GenAI subscription? They will want 40-50% of a senior partner’s time, not the traditional 1% they might have gotten under the old pyramid. They will expect their senior consultants to engage deeply, build capabilities, and drive results—not just oversee teams of analysts. They will want measurable results: increased revenue, reduced costs, improved efficiency, and technological reinvention.
This change will require consulting firms to rethink their engagement models. It will no longer be enough to deliver strategies and recommendations. Firms will have to follow through on implementation, driving quantifiable change, and ensuring that the outcomes justify the investment.
So, while AI will automate tasks that improve efficiency, it will also raise client expectations. They will increasingly view AI-powered insights as table stakes and demand consulting firms deliver value beyond what they could achieve themselves with similar tools.
As the pyramid flips, the role of mid-level consultants—the minders—will also be called into question. Traditionally, they have bridged the gap between senior partners and junior consultants, ensuring projects run smoothly and deliverables meet client expectations. But if AI can automate grind work and clients demand direct access to senior expertise, the middle layer risks being squeezed out. For more on this idea, see my aforementioned book.
This doesn’t mean the end of all mid-level roles, but it does mean a revision. Managers will need to evolve into specialists who can oversee AI systems, integrate data-driven insights into strategic discussions, and act as facilitators of progress.
The inversion of the pyramid isn’t just a challenge, it’s a threat to the traditional consulting way of being. Many senior partners, particularly in legacy firms, are deeply invested in the old model. They’ve built their careers on leveraging networks, managing teams, and scaling through junior talent and hierarchical delegation. Asking them to change by focusing more on direct client engagement and concrete results, and re-learning their old areas of expertise requires a significant adjustment in mindset.
It won’t be easy, but those firms who embrace it soonest and fastest will be at the leading edge of a new era in which consulting moves beyond the traditional model to deliver value in ways that truly matter to clients.
Firms that resist the change will face irrelevance. While many senior partners will be slow to adapt, tech-savvy operators and MBB competitors—McKinsey, BCG, and Bain―are already positioning themselves to capitalize on this inversion. Success will go to those who refocus their value proposition around expertise, innovation, and results.
For most of its history, consulting has thrived on its ability to access and control information that clients couldn’t easily obtain. Expertise combined with structured methodologies, expensive subscriptions to industry analyst reports, expansive internal knowledge bases, re-usable assets or “accelerators”, have given consulting firms a distinct advantage. Clients have relied on them not just for their analysis but for their ability to uncover insights hidden in complex datasets, industry trends, and competitive landscapes. Consulting firms have been the gatekeepers of knowledge and understanding—an advantage that has perennially justified their heavy fees.
Information asymmetry has worked because clients usually don’t have the resources, time, or expertise to gather and analyze the data necessary to solve complex problems.
AI is already dismantling that dynamic. Clients can now access sophisticated AI platforms that manage industry data, generate predictive models, and produce tailored recommendations in minutes. And as AI continues to develop it is better and better able to synthesize information from unstructured news, media and online social sources. It can detect patterns and trends that might take human analysts days or even weeks to identify. On top of that, AI tools don’t suffer from fatigue, and they are less susceptible to cognitive bias than humans (any cognitive biases AI does have is inherited from humans anyway). Instead, they process information consistently and at dizzying speed, making them a powerful equalizer between client and consultant.
As clients start realizing that AI can deliver most of the insights that help them navigate complexity, thereby freeing them of much of their reliance on consultants, there will be a irreversible tilting in the balance of power.
To stay relevant, consulting firms will have to focus on delivering value that goes beyond what machines can offer. This will mean contextualizing AI-generated insights within the unique realities of each client’s business, navigating the cultural and operational hurdles that AI alone cannot address, and fostering outcomes that AI-powered tools can enable but cannot achieve on their own.
In a world where access to insight is democratized, consulting firms will have to redefine their purpose, moving from providing information to delivering improvement. The burden of proof will also become heavier―consulting firms will have to work harder and more often to justify the value of their expertise to clients who have access to a vast pool of information and analytic capability.
The end of information asymmetry isn’t just a challenge, though, it’s also an opportunity. Consulting firms willing to adapt will gain a competitive advantage by breaking their reliance on privileged access to information; they will move beyond the advisory nature of most consulting and tangibly support clients in navigating complexity, implementing AI solutions effectively, and building internal capabilities that create lasting impact. Firms that will embrace this new way of being will thrive in a landscape where information is no longer a barrier but a shared resource.
Staying relevant means not only adopting AI tools but also fundamentally rethinking how value is created and delivered together with AI.
Many of the conversations about AI in consulting are about how the technology will alter the way firms operate, but as significant is how it will alter what clients expect. Clients will become more sophisticated, leveraging AI tools themselves and demanding far more from their consulting partners. Clients will no longer be impressed by standard deliverables or conventional methodologies—they will expect consultants to bring something truly transformative to the table.
One major risk for consulting firms is underestimating how quickly clients will be adapting to AI. Many firms assume they can remain a step ahead, offering expertise that outpaces clients’ in-house capabilities. But this gap will narrow rapidly. Clients will not just use AI for routine tasks; they will experiment with its strategic applications and cutting-edge tools. A firm that remains focused on delivering insights or frameworks that a client could replicate using ChatGPT or similar, risks being seen as redundant.
Another challenge lies in clients’ evolving perception of value. The traditional model of billing for hours or outputs—like detailed reports, assessments, and presentations—is already losing its appeal. And AI will wipe out any perception of value embedded in junior consultants’ hours. Clients will be looking for assessable impact: tangible improvements in efficiency, cost savings, or competitive positioning. Firms that fail to align their offerings with these expectations will struggle to justify their fees.
This trend also highlights a broader cultural risk for consulting firms. Clients are increasingly expecting partnerships rather than transactions. They will want consultants to act as enablers, integrating AI-driven insights into their operations and building internal capabilities that create long-term value. Firms that cling to outdated delivery models risk alienating these clients, who will turn to more innovative competitors capable of offering deeper collaboration and outcome-driven engagements.
Finally, there’s the reputational risk of falling behind. In an industry built on the perception of expertise and progressive knowledge, failing to keep up with client expectations can damage a firm’s brand. Once seen as out of touch or overly reliant on legacy models, a consulting firm may find it difficult to regain credibility, especially in a market where nimble competitors are quick to capitalize on gaps.
The risks of falling behind client expectations are not theoretical—they are already materializing. Clients are moving quickly, and consulting firms must do the same. Staying relevant means not only adopting AI tools but also fundamentally rethinking how value is created and delivered together with AI.
For decades, consulting firms have relied on a tried-and-tested conveyor belt of outputs to client: strategies, assessments, target operating models (TOMs), and the like. These deliverables, often polished to perfection in PowerPoint, have long been the bread and butter of consulting work. Clients have paid top dollar for them, believing they represent deep expertise, rigorous analysis, and actionable guidance.
But as AI revolutionizes the way insights are generated and delivered; this model will lose its luster. Strategies, assessments and TOMs will become commodities, no longer the premium outputs they once were. Clients will be demanding more than slide decks and static plans—they will want dynamic advancement, a clear path to implementation, and trackable achievements along the way.
AI will erode the value of traditional consulting deliverables in several ways. First, generative AI tools will be able to produce strategy-like outputs at a fraction of the cost and time. These outputs may not yet be as nuanced as those crafted by experienced consultants, but they’re already good enough for many clients, especially when cost and speed are priorities.
Second, AI will introduce a level of adaptability and dynamism that static deliverables can’t match. Traditional TOMs and strategies are snapshots in time, reflecting assumptions and conditions that may quickly become outdated. In contrast, AI-driven models will be able to continuously update and refine recommendations as new data becomes available, providing clients with insights that remain relevant in a constantly changing environment.
Third, clients will rapidly become aware of what AI can achieve. They will no longer be impressed by deliverables that consist of well-designed slides or neatly formatted reports. Instead, they will expect consulting firms to integrate AI-driven insights into their work, leveraging these tools to deliver smarter, faster, and more actionable solutions.
The decline of traditional deliverables doesn’t mean clients no longer value what consultants bring to the table—it means the bar has been raised. To stay relevant, firms must redefine their deliverables around:
Dynamic insights : AI-enabled recommendations that evolve as new data becomes available.
: AI-enabled recommendations that evolve as new data becomes available. Implementation pathways : Detailed plans that go beyond theory and show exactly how results will be achieved.
: Detailed plans that go beyond theory and show exactly how results will be achieved. Measurable outcomes: Deliverables that tie directly to key performance indicators, allowing clients to see the impact of their investment.
In this new landscape, the consulting firm’s role is not to create plans but to enable transformation.
In an AI-driven environment, clients will no longer want consultants to stop at recommendations; they will expect them to deliver measurable outcomes. A beautifully crafted strategy is meaningless if it doesn’t result in tangible business improvements.
The client who was once satisfied with a 50-page report outlining a transformation plan, will now want to know how the plan will be implemented, what the expected ROI is, and how success will be measured.
This trend will be particularly pronounced in areas like digital transformation and operational efficiency, where AI tools are already delivering outcomes.
Another factor already driving the decline of PowerPoint-based outputs is the increasing demand for continuous evolution. Many industries are moving so rapidly that static deliverables are no longer sufficient. Clients will need ongoing support to adapt to changing conditions, implement strategies, and fine-tune operations in real time―consulting firms will need to change from delivering episodic solutions to partnering with clients for ongoing growth.
That, of course, will require consulting firms to rethink their service models. Instead of delivering a one-off assessment or TOM, firms will have to provide end-to-end-and-beyond solutions that include strategy, implementation, and ongoing optimization. The days of presenting a deck and moving on to the next client are over.
Sustained engagement will also require changes to pricing structures and talent strategies. Firms will have to invest in AI and digital tools, retrain their workforce, and develop the infrastructure needed to support long-term engagements. And, moving away from transactional engagements with clients towards long-term partnerships built on trust, collaboration, and shared success will require far more sophistication from senior consultants in communication, collaboration and empathic connection.
However, the opportunities far outweigh the challenges. Firms that embrace this approach can position themselves as indispensable partners in their clients’ success, building deeper relationships and securing a steady stream of revenue. More importantly, they can align their services with the realities of the modern business world, ensuring they remain relevant and competitive in the age of AI.
The Devaluation of Organizational Bullshit
Organizational bullshit—a term rooted in academic study (not just swearing) and popularized through critical reflections on workplace culture—has long been a feature of corporate life. As explored in my earlier articles – We need to free organizations of bullshit – Part 1: Why? and We need to free organizations of bullshit – Part 2: How?, the phenomenon is pervasive, often marked by jargon-heavy language, vague mission statements, and hollow deliverables designed to obscure more than they illuminate. Information asymmetry and the prestige of traditional hierarchies so common in the consulting industry have undoubtedly supported this culture of hot air nonsense, but the arrival of AI is poised to dismantle that as well.
AI, by its very nature, disrupts the foundations on which organizational bullshit thrives. By democratizing access to information and making the processes behind decision-making transparent, AI exposes the gaps between perception and reality. The shiny veneer of “well-packaged but empty” deliverables will no longer suffice in a world where actionable, measurable results are the expectation. For many organizations this will be a reckoning, especially for those reliant on their ability to project authority and expertise without necessarily delivering substance.
Drawing from my previous writing, organizational bullshit isn’t harmless. It leads to poor decision-making, reduces job satisfaction, and ultimately detracts from the authentic value organizations are meant to create. These effects are exacerbated when this behavior becomes embedded in delivery, whether it’s consultants inflating the value of their work or internal teams prioritizing optics over outcomes. The stakes have always been high, but AI’s ability to strip away pretenses adds a new urgency to addressing this issue.
Ultimately, this is a chance for the industry to rid itself of this baggage. My earlier articles argued for the necessity of cutting through workplace nonsense to achieve clarity and purpose―the age of AI will amplify this need.
Long-term partnerships in an AI-driven world hinge on trust. Clients must feel confident that their consulting partners are not only competent but also aligned with their goals and values. Trust is particularly important in the context of AI, where decisions about data, ethics, and implementation carry significant risks and implications.
Consulting firms can build trust by:
Demonstrating transparency : Clearly communicating how AI tools and methodologies are used, what clients can expect, and how outcomes will be measured.
: Clearly communicating how AI tools and methodologies are used, what clients can expect, and how outcomes will be measured. Delivering consistent results : Meeting or exceeding expectations across multiple engagements to establish a track record of reliability and impact.
: Meeting or exceeding expectations across multiple engagements to establish a track record of reliability and impact. Prioritizing client interests: Sharing the benefits of AI, offering fair pricing, and focusing on outcomes that genuinely benefit the client, rather than maximizing the short-term firm’s profitability.
Trust isn’t built overnight, but it’s the cornerstone of any successful long-term partnership.
The future of consulting lies in AI-driven, tech-supported, end-to-end delivery models that emphasize outcomes over outputs, integration over abstraction, and measurable impact over point-in-time recommendations.
AI-informed clients will increasingly expect their consulting partners to see projects through from start to finish. This means firms will have to go beyond advising and take responsibility for executing strategies, embedding solutions, and ensuring their impact is sustained over time.
This means firms will need to be highly proficient in using tools like predictive analytics, generative AI, and machine learning platforms to extract value from AI at every step in the client’s value chain. For example, an AI-powered model that identifies operational inefficiencies can be paired with automation tools to streamline processes directly, reducing the time and effort required to achieve tangible results.
This ability to integrate AI solutions into client operations will transform consulting engagements. Deliverables will no longer be just plans; they will be living systems that evolve with the client’s needs, driving continuous improvement and returning quantifiable value.
AI and other advanced technologies will no longer be just tools for analysis—they will be the backbone of full lifecycle consulting delivery. To thrive, firms will have to embed technology into every stage of their engagements:
Discovery and Diagnosis : AI tools analyze data at scale, identify trends, and pinpoint root causes faster and more accurately than traditional methods.
: AI tools analyze data at scale, identify trends, and pinpoint root causes faster and more accurately than traditional methods. Strategy Development : Generative AI platforms produce dynamic, adaptable strategies that clients can implement in real time, rather than static documents.
: Generative AI platforms produce dynamic, adaptable strategies that clients can implement in real time, rather than static documents. Implementation Support : Automation tools and AI models help firms deploy solutions directly, from optimizing supply chains to enhancing customer experience to automate security operations.
: Automation tools and AI models help firms deploy solutions directly, from optimizing supply chains to enhancing customer experience to automate security operations. Performance Monitoring and Optimization: Once solutions are in place, AI ensures they remain effective, using real-time data to adjust and refine as conditions change.
This approach will enable firms to deliver results that are not only faster but also more sustainable, creating long-term value for clients.
An end-to-end-and-beyond delivery model will require consulting firms to engage with their clients in untraditional ways. Instead of handing over deliverables and moving on, firms will have to adopt a collaborative approach, working alongside clients to ensure solutions are successfully implemented and integrated into their operations.
This partnership model will involve:
Co-creation : Developing solutions in collaboration with clients to ensure they align with organizational goals and culture.
: Developing solutions in collaboration with clients to ensure they align with organizational goals and culture. Capability Building : Training client teams to use AI tools and processes, empowering them to sustain improvements long after the consultants are gone.
: Training client teams to use AI tools and processes, empowering them to sustain improvements long after the consultants are gone. Shared Accountability: Taking joint responsibility for achieving outcomes, rather than simply providing recommendations.
By embracing collaboration, consulting firms will be able to strengthen client relationships and position themselves as trusted collaborators in sustainable success.
Repositioning themselves as long-term partners in value creation is not optional for consulting firms; this will be essential for those wanting to remain competitive in an AI-augmented industry. Such a metamorphosis requires more than just adopting new tools, though—it demands cultural maturation. Firms must move away from seeing themselves as advisors and embrace their role as enablers of change.
For those willing to take the leap, the rewards will be significant. This rejuvenated type of delivery model allow firms to build deeper client relationships, deliver greater impact, and secure their place as leaders in the next era of consulting.
The adoption of AI in consulting will create a double-edged sword. On one hand, it will offer unprecedented opportunities for efficiency, automation, and scalability, allowing firms to deliver insights and solutions faster than ever before. On the other hand, it will raise expectations among clients, who will demand that these benefits translate directly into greater value for their businesses—not just increased profitability for consulting firms.
The rise of AI-driven consulting will demand a new model of partnership—one in which firms are transparent about how AI is being used and actively share its advantages with their clients, an approach that will foster trust and deepen relationships.
Historically, consulting firms have tended to retain the benefits of efficiency gains. For example, if a new tool or process allowed junior consultants to work faster, it often translated into higher margins rather than reduced costs for the client. This approach worked in a world where clients had limited visibility into a firm’s operations and relied on the perceived effort behind deliverables to judge value.
However, clients are set to become increasingly aware of how generative AI, automation, and advanced analytics streamline consulting work. They will understand that tasks that once took weeks will now take hours and will start to question why cost savings aren’t being passed on to them. Without transparency, firms risk eroding trust and damaging their reputations.
To remain competitive, consulting firms must embrace an approach that distributes cost gains—one that positions AI not as a profit-maximizing tool for the firm but as a value-creating enabler for the client. This requires rethinking both pricing models and engagement strategies.
Transparent Pricing : Though it will be uncomfortable at first and run antithetical to the culture pervading most of the consulting industry, firms need to lean into a more open approach to pricing. Of course, they should recognize the significant costs involved in building bespoke AI platforms or applications, but they should also honestly acknowledge the role AI plays in reducing time and effort and reflect those savings in their pricing. This doesn’t mean undercutting the value of their work; rather, it’s about demonstrating fairness and aligning fees with the outcomes delivered.
: Though it will be uncomfortable at first and run antithetical to the culture pervading most of the consulting industry, firms need to lean into a more open approach to pricing. Of course, they should recognize the significant costs involved in building bespoke AI platforms or applications, but they should also honestly acknowledge the role AI plays in reducing time and effort and reflect those savings in their pricing. This doesn’t mean undercutting the value of their work; rather, it’s about demonstrating fairness and aligning fees with the outcomes delivered. Shared AI Capabilities : Instead of using AI exclusively behind the scenes, firms can integrate these tools into client engagements. For example, consultants can work alongside client teams, using AI platforms collaboratively to co-create solutions. This not only empowers clients but also positions the firm as a partner invested in their success.
: Instead of using AI exclusively behind the scenes, firms can integrate these tools into client engagements. For example, consultants can work alongside client teams, using AI platforms collaboratively to co-create solutions. This not only empowers clients but also positions the firm as a partner invested in their success. Outcome-Based Agreements: Moving away from billing by the hour or deliverable, firms can adopt pricing structures tied to measurable outcomes. For instance, a firm implementing an AI-driven cost-reduction strategy might tie its fees to the actual savings achieved, ensuring that both parties benefit from the results.
One of the most effective ways to share the benefits of AI with clients will be by helping them build their own AI capabilities. Instead of fostering dependency, firms can position themselves as enablers, equipping clients with the tools, skills, and processes needed to sustain improvements independently.
For example, a consulting firm implementing an AI-powered customer segmentation solution might also train the client’s marketing team to use the underlying technology, ensuring they can continue refining and applying the model long after the engagement ends. This approach strengthens the partnership by demonstrating a commitment to the client’s long-term success.
By sharing AI benefits transparently and equitably, consulting firms will be able to build trust—arguably their most valuable asset in an increasingly competitive market. Clients who see their consultants as genuine partners, rather than profit-driven vendors, are more likely to engage them for future projects, recommend them to peers, and collaborate on deeper, more strategic initiatives.
Trust also allows firms to justify premium fees where appropriate. Clients are willing to pay for expertise, innovation, and results when they believe the engagement is built on fairness and shared success.
By openly sharing the benefits of AI, consulting firms will be able to move beyond transactional engagements to build true partnerships. That’s not just good ethics; it’s good business. In an industry where trust and outcomes matter more than ever, the firms that embrace transparency and collaboration will be the ones that thrive in the age of AI.
The consulting career ladder has followed a predictable trajectory: junior consultants (the “grinders”) would start at the bottom, tasked with executing labor-intensive work. As they honed their skills and gained experience, they moved up to managerial roles (the “minders”), overseeing projects and guiding the grinders. Finally, after years of developing client relationships and mastering the art of selling, they could ascend to partner-level positions (the “finders”). This hierarchical system wasn’t just a structure—it was a proven pipeline for developing talent and ensuring the sustainability of consulting firms.
AI, however, will disrupt this model at its core. Tasks that once served as a training ground for junior consultants—such as creating financial models, drafting reports, and conducting market analyses—will (very) soon be performed by AI faster and more accurately. Which raises a fundamental question: if the grinders’ work is disappearing, what does the first rung of the consulting career ladder look like?
The focus of entry-level roles will have to transfer from repetitive execution to developing deeper, more specialized expertise. Instead of spending their early years crunching numbers or building slide decks, junior consultants must now learn to leverage AI tools effectively, interpret AI-driven insights, and apply them within the context of a client’s unique challenges. The emphasis is no longer on doing the work but on understanding the work and adding value beyond what AI can provide.
This evolution will require consulting firms to rethink their approach to talent development. The traditional “sink-or-swim” model, where grinders learned by doing under pressure, must give way to structured training programs that emphasize strategic thinking, creativity, and problem-solving. Entry-level consultants will need to be equipped with skills that allow them to complement AI, rather than compete with it. This includes areas like:
Interpreting complex data outputs from AI tools.
Contextualizing AI-driven insights for specific industries or markets.
Developing innovative, human-centered solutions to problems that AI alone cannot address.
As the role of junior consultants changes, so too does the pathway to advancement. The middle rung of the ladder—the “minder” roles—will likely focus less on managing teams of grinders and more on guiding clients through the intricacies of implementing AI-driven solutions. Managers will need to act as translators, bridging the gap between technical capabilities and business realities. This requires a mix of technical proficiency, strategic insight, and emotional intelligence—skills that haven’t traditionally been emphasized in these roles. For some more in-depth discussion, see my book.
Partners will also need to adapt, bringing more to the table than their ability to sell projects or leverage networks. They will need to be thought leaders and domain experts, capable of solving complex problems and delivering defined results. The career path to partnership, once defined by time and tenure, will increasingly prioritize expertise and the ability to drive transformative value for clients.
This changing career path also has significant implications for how consulting firms attract and retain talent. In the past, the promise of steady advancement through the ranks was a key draw for ambitious professionals. Now, firms must offer a reimagined value proposition, one that highlights opportunities for continuous learning, rapid skill development, and meaningful impact from day one.
In this new reality, the consulting career ladder isn’t disappearing—it’s evolving. The days of grinders may be numbered, but the rise of expertise-driven roles presents an opportunity to create a more dynamic, fulfilling career path for the next generation of consultants.
The rise of AI is reshaping not only how consulting firms deliver value but also the kind of talent they need to succeed. Traditional hiring strategies, which emphasized raw intelligence, adaptability, and a willingness to grind through demanding workloads, must now evolve to reflect the realities of an AI-integrated industry. With much of the repetitive, labor-intensive work being automated, consulting firms need to rethink who they hire, what skills they prioritize, and how they prepare new hires for a rapidly changing consulting landscape.
Historically, consulting firms recruited from a relatively narrow pool: high-achieving graduates with strong analytical and problem-solving skills, often sourced from elite universities and business schools. These individuals were expected to learn the ropes by doing—spending long hours on data-heavy tasks and building deliverables under tight deadlines. However, as AI takes over much of the grind work, firms will need to hire candidates who bring a different set of competencies.
In an AI-augmented consulting world, new hires must demonstrate not only technical aptitude but also the ability to think creatively and strategically. Firms should prioritize candidates who can:
Collaborate with AI tools : New hires need to be adept at using generative AI platforms, understanding their capabilities, and integrating AI-generated insights into broader consulting strategies.
: New hires need to be adept at using generative AI platforms, understanding their capabilities, and integrating AI-generated insights into broader consulting strategies. Interpret and contextualize AI outputs : AI delivers raw insights, but it takes a human to contextualize those insights within the nuances of a client’s specific challenges. Candidates with strong critical thinking skills and a knack for storytelling will be essential.
: AI delivers raw insights, but it takes a human to contextualize those insights within the nuances of a client’s specific challenges. Candidates with strong critical thinking skills and a knack for storytelling will be essential. Bridge the technical and the human: As AI drives many of the technical aspects of consulting, firms need individuals who can navigate the human side of the equation—communicating effectively with clients, building trust, and managing change within organizations.
The traditional consulting talent pipeline is no longer fit for purpose. Firms must broaden their hiring scope to include individuals with diverse backgrounds and skill sets, particularly in technology, data science, and specialized industries.
Candidates with experience in AI development, machine learning, and data engineering are increasingly valuable, as are those with deep expertise in niche domains such as cybersecurity, supply chain optimization, or healthcare analytics. These individuals bring specialized knowledge that complements AI-driven consulting and adds value beyond what traditional hires can offer. And beyond what AI alone can offer.
At the same time, consulting firms should actively seek candidates with strong interpersonal and creative skills—traits that are difficult for AI to replicate. As consulting becomes less about delivering information and more about driving results, the ability to inspire and lead change within client organizations will become a critical differentiator.
Attracting top talent in an AI-augmented industry also means rethinking the employer value proposition. Consulting firms must differentiate themselves not just by offering high salaries or prestigious career paths but by creating environments that foster continuous learning, innovation, and meaningful impact.
Firms that provide opportunities to work with cutting-edge AI tools, tackle complex global challenges, or drive transformative change within industries will be more appealing to prospective hires. Similarly, offering clear pathways for advancement, even in a world without grinders, will be essential for retaining ambitious professionals.
The Big 4 consulting firms, my current stomping ground, have long been icons of stability, prestige, and expertise. With deep roots in the professional services industry, these firms have built vast networks, amassed unrivaled client rosters, and leveraged their global presence to dominate the market. But their greatest strength—their traditional, partner-led structure—may also be their Achilles’ heel as the AI revolution accelerates.
At the heart of the Big 4 model is a leadership structure dominated by partners. These individuals often climbed the ranks over decades, excelling at client relationships, business development, and navigating internal politics. Their success has been built on a system that rewards consistency, risk aversion, and incremental change—qualities that now risk leaving these firms unprepared for the sea change AI is bringing.
As a current Big 4 partner, I’ve experienced firsthand the pervasive advice to stay patient, avoid disruption, “fit in”, and conform to the status quo—a culture of risk aversion and playing it safe that often stifles meaningful change. It’s this direct encounter with the inertia within Big 4 firms that fuels my deep concern for their ability to adapt in an AI-driven world. This article, in many ways, serves as a personal call to action: a plea for Big 4 firms to embrace the AI challenge boldly and urgently, rather than risk being left behind by the accelerating pace of technological change.
For decades, the traditional consulting playbook has worked like a charm. It’s seen firms grow year on year, with steady revenues and an established pipeline of talent feeding into the pyramid. Why fix something that isn’t broken?
This mindset breeds resistance to change. AI is viewed less as a transformative opportunity and more as a tool to enhance existing processes. While these leaders might approve pilot projects or adopt AI for internal efficiency, they’re often slow to recognize the existential threat AI poses to their core business model.
The problem is compounded by the pace of AI’s development. Inertia is no longer an option. Firms that wait too long to adapt risk falling behind nimbler competitors—tech-savvy systems integrators and forward-looking MBB firms—that are already flowing with this current.
In the traditional model, partners bring immense value through their networks and ability to sell. But as AI reshapes the consulting landscape, this value proposition is under pressure. Clients are increasingly demanding more than just access to a firm’s junior talent and methodologies; they want deep, hands-on expertise from the senior leaders themselves.
For partners accustomed to spending most of their time building relationships and overseeing projects from a distance, this is a challenging adjustment. AI’s automation of grind work and its ability to generate insights independently means clients no longer need intermediaries—they want direct access to the people who can deliver the most impact.
Partners will need to grow and evolve. They must offer more than just their network; they need to bring specialized knowledge, strategic foresight, and the ability to solve complex problems that AI cannot.
The Big 4 are powerhouses, steeped in tradition and guided by governance models designed to preserve stability. The steady corporate approach this breeds is effective in times of incremental change, but it becomes a liability in times of disruption.
Resistance to change often stems from a fear of losing control. Senior leaders worry that adopting radical AI-driven strategies could destabilize the firm’s established hierarchy, disrupt billing models, disrupt the “culture”, or dilute the role of partners. These concerns, while understandable, create a dangerous hesitation.
Meanwhile, competitors—particularly large systems integrators with a stronger technology focus—are moving swiftly. They’re integrating AI into their service delivery models, aligning offerings with emerging client expectations, and winning ground that the Big 4 might struggle to reclaim.
The Dilemma of Consensus
Related to the previous point, but a bit more specific is the Big 4’s tendency to prioritize consensus and loyalty over an honest examination of facts or alternative viewpoints. In partnerships a strong emphasis on consensus has long been considered a key ingredient for organizational stability. Partners pool their resources and jointly assume risks, building a collective ethos where loyalty and unity carry significant weight. Yet consensus, while beneficial for preventing interpersonal friction, can often stifle thoughtful dissent and obscure emerging threats. When radical shifts loom on the horizon this cultural bias risks undermining an organization’s ability to debate new opportunities and challenge the status quo.
Within the Big 4 consultancies, the need to safeguard unity can lead to a type of “groupthink.” Partners, wary of endangering core relationships, sometimes lean toward subdued discussions and safe decisions. Although different perspectives do surface in partner meetings, the shared desire for harmony can drown out voices pushing for bolder moves. Scholars such as David Maister and Greenwood have noted how the profit-sharing model and cultural norms in professional firms often reward consensus-building over confrontation. While this approach maintains a sense of collegiality, it can also hinder the rigorous exchange of views necessary for continual evolution.
The advent of AI-based consulting demands a faster pace of innovation than many firms are accustomed to, as we discussed above. To adapt effectively, firms must be willing to engage in open debate about their strategic direction, revenue models, and future capabilities. A partnership culture that relegates challenging opinions to the margins may leave new ideas without the leadership support they need to thrive.
Still, there are steps that can shift the balance from conformity to constructive debate. Some firms have instituted “innovation councils” or specialized task forces explicitly charged with questioning established norms, and they have begun to reward creative problem-solving through revised compensation metrics. Others are hiring data scientists and AI specialists who bring a different mindset, prompting partners to reevaluate entrenched practices. By placing as much value on thought leadership and new ventures as on traditional metrics, these firms can encourage honest examination of facts and diverse viewpoints. The aim is not to undermine the benefits of collaboration, but to harness them in a way that champions bold thinking rather than suppresses it.
The history of disrupted industries offers a cautionary tale. From Kodak’s resistance to digital photography to Blockbuster’s dismissal of streaming, legacy leaders often fail to adapt not because they lack the resources or talent, but because they cling too tightly to what worked in the past.
For the Big 4, the warning signs are already here. Clients are exploring AI tools independently, questioning the value of traditional consulting deliverables, and looking for partners who can drive transformation, not just advise on it.
If Big 4 partners don’t adapt quickly—by embracing AI, rethinking their roles, and prioritizing expertise over leverage—they risk following the same path as those disrupted before them.
For years, the Big 4 relied on their extensive networks as a key differentiator. Senior partners cultivated long-term relationships with decision-makers, leveraging trust and familiarity to secure engagements. However, as AI starts transforming consulting, this advantage is eroding.
Clients today are already less impressed by relationships and more focused on measurable outcomes. They no longer need to rely solely on their Big 4 partner’s Rolodex to access expertise or resources; AI tools, combined with open marketplaces and gig-based ecosystems, are leveling the playing field. The value of a personal network diminishes when clients can tap into cutting-edge insights and capabilities independently.
To remain relevant, Big 4 partners will have to demonstrate expertise and value that go beyond who they know. The ability to guide clients through complex AI-driven initiatives and deliver results that AI alone cannot achieve will become the new benchmark for success.
The Big 4 have long thrived on the combination of prestige, brand recognition, and information asymmetry. Clients often accepted glossy outputs at face value, trusting that the weight of the firm’s name guaranteed quality and rigor. But AI is poised to deliver a double blow to this comfortable model.
First, it will erode the Big 4’s brand value by diminishing differentiation. When AI tools can generate detailed analyses and polished presentations in minutes, the mystique surrounding the Big 4’s output will begin to fade. Second, AI will make it painfully clear to clients that much of what they’ve been paying premium fees for—those same strategies and frameworks—isn’t delivering meaningful value. As clients become more sophisticated and self-sufficient with AI, they will demand substance and outcomes, not slides and status. Simply put, the easy life that many Big 4 practices have enjoyed, where prestige and process mattered more than true impact, is coming to an end—and fast.
A New Currency of Expertise
AI is forcing a reinterpretation of expertise. It’s no longer enough to understand frameworks and methodologies or oversee a team of junior consultants. Clients will expect their advisors to be deeply knowledgeable in specific areas—whether it’s navigating AI implementation, managing data privacy risks, or driving cultural change in an AI-augmented organization.
The impact is greatest on Big 4 partners, many of whom have risen through the ranks based on their ability to sell and manage relationships, rather than their technical expertise. In the AI era, clients will demand more than polished presentations and generic recommendations—they’ll expect senior leaders to bring actionable, domain-specific insights to the table.
The Big 4 firms that succeed will be those that double down on upskilling their leaders, investing in continuous learning, and fostering a culture where deep expertise is valued as much as—if not more than—salesmanship.
The traditional Big 4 model is built on leveraging time. But in a world where AI can accomplish much of that work in seconds, this model will become increasingly unsustainable.
Clients will very quickly start questioning the value of paying premium fees for outputs that AI can replicate. They will demand outcomes above all—specific business improvements that justify the investment in consulting services. For the Big 4, this means moving away from billing hours and toward selling value.
This will require not just a change in pricing models but also cultural reform. Firms must embed outcome-driven thinking into their DNA, aligning their services with client goals and ensuring that every engagement delivers tangible impact.
Big 4 firms are already exploring how to use GenAI to supplement their own effort. The conversation, however, is focused on the promise of significant labor savings. And only on that. By automating much of the grind work, firms can reduce their reliance on junior consultants, thereby lowering operational costs. But this is a potentially blinkered view. Focusing only on immediate labor savings may create blind spots for firms trying to prepare for the next wave of AI-driven disruption.
At first glance, the logic of labor savings is irresistible. Firms can maintain—or even increase—their project capacity while reducing the overheads associated with hiring, training, and managing junior talent. The promise of such efficiency gains can also make AI a compelling selling point for clients, as firms present themselves as tech-savvy partners who deliver faster and more cost-effectively than ever before.
However, this efficiency-driven approach is inherently shortsighted. While consulting firms are busy optimizing their internal operations, their clients will also be embracing AI—often just a few steps behind. Clients will be using the same tools to analyze data, draft reports, and model scenarios, rapidly closing the gap between what they can achieve independently and what consulting firms offer. The same technology that firms are using to streamline operations is simultaneously eroding the exclusivity of their services.
This creates a troubling paradox. By focusing narrowly on labor savings, firms may unwittingly accelerate their own commoditization. Deliverables that were once central to a firm’s value proposition—detailed reports, frameworks, and strategies—are now viewed by clients as baseline outputs, easily replicable using AI.
The real danger lies in underestimating how quickly clients will evolve their use of AI. While consulting firms may see AI as a competitive edge today, the reality is that clients are often only a few months to two years behind in their adoption curve. Once clients become proficient in leveraging AI, they will demand more from their consulting partners—more insights, more value, and more demonstrable outcomes. Those firms focused on immediate gains may find themselves unprepared to meet these heightened expectations.
The next wave of AI-driven disruption won’t be about automation alone; it will be about transformative impact. Clients will want consulting partners who can go beyond delivering AI-generated insights to help them navigate the broader implications of AI adoption—reshaping business models, driving cultural change, and implementing AI-driven innovations at scale. Consulting firms that have optimized around labor savings may lack the expertise, infrastructure, and strategic foresight needed to thrive in this new environment.
The takeaway is clear: labor savings are not a strategy; they are a tactic. To succeed in the age of AI, consulting firms must look beyond the immediate benefits of AI and invest in building capabilities that position them well for the long-term. This means rethinking how they deliver value and staying ahead of the curve as clients close the gap in their own AI maturity. Or to be blunt and risk upsetting my peers: partners at Big 4, any AI efficiencies and savings achieved in the first phase of AI adoption (first 2-3 years) should be fully re-invested in future phases of AI-driven growth, and not distributed to equity partners.
The challenge for the Big 4 is that this adaptation won’t be easy—or cheap. Upskilling partners, overhauling pricing models, and rethinking service delivery will require significant investment and, perhaps more importantly, a willingness to disrupt established ways of working.
The pressure to evolve isn’t just external; it’s also internal. Younger consultants are increasingly drawn to firms that embrace innovation and offer opportunities to work on cutting-edge AI-driven projects. Without a clear path forward, the Big 4 risk losing not just clients but also the talent that drives their business.
While the Big 4 will grapple with adapting their traditional models, competitors such as large systems integrators (LSI) and MBB (McKinsey, BCG, Bain) firms will likely do a much better job at positioning themselves to thrive in the AI-driven consulting landscape. These firms have distinct advantages that make them more agile and better prepared to meet the demands of the AI era.
Large systems integrators like Accenture, IBM, Capgemini, Deloitte’s tech-focused arms, and others have long had a reputation for their technological expertise and implementation capabilities. Unlike the Big 4, which traditionally leaned on frameworks and strategies, systems integrators are already comfortable working at the intersection of technology and business.
AI is a natural extension of their capabilities. These firms are leveraging their tech-savvy cultures to:
Integrate AI into service delivery : Systems integrators excel at end-to-end delivery, from strategy to implementation. They will be able to deploy AI tools not just for insights but also to operationalize those insights in client environments.
: Systems integrators excel at end-to-end delivery, from strategy to implementation. They will be able to deploy AI tools not just for insights but also to operationalize those insights in client environments. Offer AI-enhanced solutions : Whether it’s predictive analytics, AI-driven automation, or bespoke machine learning models, these firms will be able to provide clients with tools and platforms that deliver immediate, measurable value.
: Whether it’s predictive analytics, AI-driven automation, or bespoke machine learning models, these firms will be able to provide clients with tools and platforms that deliver immediate, measurable value. Attract technical talent: Their focus on cutting-edge technology makes them a magnet for AI specialists, data scientists, and engineers, ensuring they have the expertise to lead in the AI space.
This combination of technological know-how and practical implementation positions systems integrators as go-to partners for clients looking to leverage AI, often bypassing the Big 4 altogether.
The MBB firms—McKinsey, BCG, and Bain—may not have the same technological depth as systems integrators, but they excel in their ability to anticipate and shape market trends. Known for their rigorous research and data-driven insights, these firms are often the first to identify emerging opportunities and risks, including those posed by AI.
Here’s what sets them apart:
Thought leadership : MBB firms have been producing reports, whitepapers, and frameworks on AI for years, positioning themselves as experts in navigating its impact. Clients see them as strategic partners who understand not just today’s challenges but also tomorrow’s opportunities.
: MBB firms have been producing reports, whitepapers, and frameworks on AI for years, positioning themselves as experts in navigating its impact. Clients see them as strategic partners who understand not just today’s challenges but also tomorrow’s opportunities. Agility : With their smaller, more elite structures, MBB firms can pivot faster than the Big 4. They’re quicker to adopt AI tools internally and to redesign their services around emerging client needs.
: With their smaller, more elite structures, MBB firms can pivot faster than the Big 4. They’re quicker to adopt AI tools internally and to redesign their services around emerging client needs. Focus on outcomes: MBB firms have always emphasized delivering results over process, already embodying an “outcomes over outputs” mindset. Their ability to tie strategy to measurable business impact makes them particularly attractive in an AI-driven world.
Both systems integrators and MBB firms benefit from cultures that are more aligned with the demands of the AI era:
Innovation-first mindsets : These firms are less bound by tradition and more willing to experiment with new technologies and service models.
: These firms are less bound by tradition and more willing to experiment with new technologies and service models. Specialized expertise : They have long emphasized hiring and retaining top-tier talent with deep domain knowledge, rather than those that “fit the culture”, making it easier to adapt to a world where expertise trumps leverage.
: They have long emphasized hiring and retaining top-tier talent with deep domain knowledge, rather than those that “fit the culture”, making it easier to adapt to a world where expertise trumps leverage. Client-centric approaches: Both groups are adept at tailoring their offerings to client needs, whether through customizable AI solutions or bespoke strategic advice.
All these predictions should offer some lessons for the Big 4:
Invest in technology and expertise : Building AI capabilities isn’t optional—it’s a requirement. The Big 4 need to develop or acquire the technical expertise to compete with systems integrators.
: Building AI capabilities isn’t optional—it’s a requirement. The Big 4 need to develop or acquire the technical expertise to compete with systems integrators. Focus on measurable outcomes : Adopting an outcomes-first mindset can help the Big 4 remain relevant as clients demand more than just frameworks and recommendations.
: Adopting an outcomes-first mindset can help the Big 4 remain relevant as clients demand more than just frameworks and recommendations. Embrace cultural change: Innovation needs to move from buzzword to business practice. The Big 4 must foster a culture that rewards experimentation and adaptation, even if it challenges long-standing traditions.
Transitioning to AI-driven, tech-supported, end-to-end delivery isn’t optional; it’s essential for consulting firms that want to remain competitive in an AI-augmented industry. This shift requires more than just adopting new tools—it demands a cultural transformation. Firms must move away from seeing themselves as advisors and embrace their role as enablers of change.
The consulting industry is entering a period of unprecedented instability. While firms have weathered technological disruptions in the past, the rise of AI is different in its scope, speed, and implications. As consulting adapts to this new reality, the trajectories firms choose will determine whether they survive or, hopefully, thrive. Yet, alongside the opportunities lie significant risks, particularly for firms that underestimate the pace of change or cling to outdated models.
In the near term, most consulting firms are likely to focus on integrating AI to enhance efficiency. The immediate appeal of generative AI and automation lies in their ability to streamline processes, reduce costs, and accelerate delivery. Firms are already using AI to automate labor-intensive tasks like data analysis, benchmarking, and report generation, which were traditionally handled by junior consultants. This trajectory allows firms to maintain competitiveness in the short term, offering faster and more cost-effective services to clients.
However, this efficiency-driven approach carries risks. By prioritizing cost savings, firms may fall into the trap of treating AI as a tactical tool rather than a transformative force. As discussed above. This perspective limits innovation and overlooks the deeper, structural changes required to meet evolving client expectations. Firms that focus solely on operational improvements risk being outpaced by competitors that embrace AI as a driver of strategic reinvention.
As AI tools become more sophisticated, consulting firms will likely expand their use beyond back-end efficiency into front-end engagement. This involves deploying AI not just to support deliverables but to actively enhance client interactions and outcomes. Firms may integrate AI into collaborative workshops, use predictive analytics to inform real-time decision-making, or offer dynamic, AI-driven dashboards that clients can interact with directly.
The intermediate trajectory represents a course correction from static consulting models to more dynamic, data-driven engagements. However, it also introduces challenges. As clients become more adept at leveraging AI themselves, the line between consultant and client capabilities begins to blur. Firms that fail to differentiate their services risk being seen as redundant, particularly if their offerings do not go beyond what clients can achieve independently with similar tools.
There is also the risk of over-reliance on AI. While AI can enhance insights and efficiency, it cannot replace the human elements of consulting—creativity, judgment, and the ability to navigate complex organizational dynamics. Firms that fail to establish a healthy balance may find themselves delivering technically impressive but strategically hollow solutions.
The most successful consulting firms will likely pursue a trajectory that positions them not as advisors but as transformation partners. This involves a fundamental reinvention of the consulting model, with firms taking greater accountability for implementation, outcomes, and capability building. Rather than delivering plans and frameworks, these firms will co-create solutions with clients, embed AI tools into their operations, and drive sustained, measurable impact.
This trajectory demands significant investment—not just in technology but also in talent, culture, and client relationships. Firms must upskill their workforce to complement AI, foster a culture of continuous learning, and build trust through transparency and shared success.
Yet, the risks are equally significant. Transitioning to this model requires a departure from traditional revenue structures, such as billing by the hour or deliverable, which may unsettle long-standing business models. It also demands agility, as firms must continuously adapt to advancements in AI and variations in client needs.
Perhaps the greatest risk for the consulting industry is inaction. The pace of AI-driven change is unrelenting, and firms that fail to evolve will be left behind. This includes firms that resist change due to cultural inertia, underinvest in AI capabilities, or cling to models that no longer resonate with clients. In other words, the Big 4 most likely. In a landscape where differentiation is increasingly tied to innovation, stagnation is a death sentence.
At the same time, there is a risk of moving too quickly without a clear strategy. Firms that rush to adopt AI without aligning it to their broader vision risk implementing tools that create more confusion than value. The balance lies in thoughtful, deliberate development—embracing AI not as a quick fix but as a cornerstone of a reimagined consulting model.
As the consulting industry navigates these trajectories, the firms that succeed will be those that strike a balance between short-term efficiency and long-term reinvention. By embracing AI as both a tool and a driver of progression, firms can position themselves as indispensable partners in a world where the rules of business are constantly being rewritten.
Generative AI (GenAI) is the headline act in consulting’s adoption of AI. From automating grind work to enhancing client insights, GenAI will mark a first phase of AI’s integration into the consulting industry. But the story will not end here. AI is evolving rapidly, and the second phase—driven by advancements in areas like autonomous decision-making, domain-specific AI, and integrated AI ecosystems—promises even greater disruption and opportunity.
Consulting firms that focus solely on GenAI risk falling behind as AI’s capabilities expand. To thrive in the long term, they must prepare for this second phase, anticipating its challenges and positioning themselves to leverage its transformative potential.
The next phase of AI will move beyond text generation and predictive analytics to encompass more autonomous, adaptive, and specialized capabilities. Future AI systems will not just suggest strategies but execute them, continuously learn from their environments, and refine their actions in real time. Domain-specific AI—tools tailored to industries like healthcare, finance, or logistics—will provide highly specialized insights and solutions that generic platforms like ChatGPT cannot replicate.
In addition, integrated AI ecosystems will emerge, where multiple AI tools work together seamlessly to address complex, multi-faceted problems. For consulting firms, this means engagements will no longer be about deploying individual AI solutions but designing and managing entire AI ecosystems for their clients.
These advancements will raise the bar for consulting firms. Clients will expect their advisors to go beyond leveraging AI for efficiency and deliver expertise in integrating, governing, and scaling these advanced systems.
As AI’s capabilities grow, so too will client expectations. In the second phase of AI adoption, clients will demand partners who can help them navigate the complexities of AI integration, from addressing ethical considerations to ensuring compliance with evolving regulations. They will expect firms to provide not just tools but frameworks for managing AI risk, aligning AI strategies with business goals, and fostering cultural adoption within their organizations.
Importantly, clients will also expect consulting firms to stay ahead of them. While clients may lag slightly in their adoption of AI during the GenAI phase, many will close this gap in the coming years. Firms that fail to maintain a clear edge in AI capabilities risk losing their perceived value as clients become increasingly self-sufficient.
Many consulting firms will approach the second phase of AI with a reactive mindset, adapting to changes as they occur. While this approach may preserve short-term stability, it comes at a cost. Firms that are slow to adopt new AI capabilities or fail to anticipate new client demands will struggle to compete with more proactive competitors.
A reactive strategy also increases the risk of falling into the same trap as the GenAI phase: focusing on operational efficiency at the expense of strategic reinvention. While efficiency gains may keep firms afloat, they will not differentiate them in a marketplace increasingly defined by innovation and outcomes.
To prepare for AI’s second phase, consulting firms will have to adopt a proactive, forward-looking approach. This begins with investing in the capabilities and infrastructure needed to support advanced AI applications. Firms should prioritize building expertise in areas such as:
Autonomous AI systems and their governance.
Industry-specific AI applications that offer tailored value to clients.
AI ethics, compliance, and risk management frameworks.
Additionally, firms will have to cultivate a culture of continuous innovation. This means encouraging teams to experiment with emerging AI tools, fostering collaboration across disciplines, and embedding AI literacy at every level of the organization. Senior leaders, in particular, must take an active role in championing this orientation, ensuring that their firms remain agile and adaptive in the face of rapid change.
The consulting industry has long prided itself on its ability to guide clients through change, but now it finds itself at a critical juncture. The rapid evolution of AI represents a strategic inflection point—a moment where the rules of the game shift, and decisions made today will determine which firms thrive and which fall behind. For consulting firms, adapting to this new reality isn’t just important—it’s existential.
Strategic inflection points are rarely obvious when they first emerge. They often feel like incremental changes rather than the seismic shifts they truly are. Many firms may see AI as an enhancement to their existing models rather than a force that demands a complete rethink of their business. But history has shown that industries failing to recognize and act on these moments are often left scrambling to catch up—or worse, rendered obsolete.
Adapting to an inflection point requires more than incremental adjustments; it demands bold, forward-thinking decisions. Consulting firms must shift from seeing AI as a tool to improve existing services to embracing it as a catalyst for reinvention. This means:
Reimagining their value proposition to focus on outcomes rather than outputs.
Investing in new capabilities, such as AI integration, ethical governance, and implementation expertise.
Redefining the consulting career path to prioritize expertise and innovation over hierarchy and tenure.
Adaptation also requires a new mindset. Firms must foster a culture of agility, where experimentation is encouraged, and failure is seen as a stepping stone to learning and growth. For this to work, senior leaders must, well, lead, setting the tone for the organization and ensuring alignment with long-term strategic goals.
As we reflect on the many ways AI will redefine the consulting industry, one truth remains steadfast: human expertise are, and will continue to be, irreplaceable. While AI has shown remarkable ability to automate processes, generate insights, and even draft strategies, its capabilities are inherently limited by its nature. Human consultants bring qualities that AI cannot replicate—qualities that are crucial for navigating the complexities of modern business challenges.
AI excels in environments with clear rules and abundant data. It can analyze historical patterns, predict trends, and suggest optimized solutions. However, many of the challenges clients face exist in ambiguous, uncertain contexts where no amount of data can provide definitive answers.
In these situations, human judgment becomes critical. Experienced consultants draw on intuition, domain knowledge, and a deep understanding of organizational dynamics to make decisions where AI falls short. An AI might, for example, recommend a cost-cutting strategy based on efficiency metrics, but it takes a human to evaluate the cultural and political implications of that strategy within the client’s organization.
Business transformation is as much about people as it is about processes. Clients don’t just need technical solutions; they need partners who can build trust, understand their unique challenges, and guide them through periods of change.
AI, for all its power, lacks the ability to connect on a human level. It cannot empathize with a stressed leadership team, mediate conflicts within an organization, or rally employees around a shared vision. Human consultants bring these interpersonal skills to the table, creating the relationships that make successful transformation possible.
AI generates insights based on existing patterns, but it cannot think creatively or innovate in the way humans can. True innovation often involves challenging assumptions, breaking established norms, and imagining possibilities that have never been explored before.
Consultants bring a level of creative problem-solving that goes beyond data-driven logic. They can conceptualize entirely new business models, identify unconventional opportunities, and craft narratives that inspire change. This ability to think outside the box is particularly valuable in industries undergoing rapid disruption, where the solutions of the past are no longer sufficient for the challenges of the future.
AI’s capabilities are only as good as the data the model is trained on and the goals it is programmed to optimize. Without careful oversight, it can produce results that are technically correct but ethically questionable or strategically misaligned.
Human consultants play a vital role in providing this oversight. They ensure that AI-driven solutions align with a client’s values, comply with regulatory standards, and consider the broader societal implications of their implementation. For instance, deploying AI to optimize workforce efficiency might improve short-term productivity, but human consultants can weigh the long-term impact on employee morale and organizational resilience.
AI can identify opportunities for improvement and even suggest steps to achieve them, but it cannot lead a transformation. Organizational change involves managing complex stakeholder dynamics, aligning competing priorities, and navigating resistance—all tasks that require human leadership.
Consultants act as conductors of organizational development, bringing together diverse teams, ensuring alignment across functions, and driving the execution of strategies. Their ability to manage the human side of change is a critical complement to the technical capabilities of AI.
The consulting firm of the future won’t be defined by a choice between human expertise and AI—it will thrive on the partnership between the two. AI will handle the heavy lifting, automating repetitive tasks, analyzing vast datasets, and generating foundational insights. Human consultants will focus on the higher-order challenges: interpreting those insights, crafting strategies that consider the full context, and driving the human-centered execution of those strategies.
By combining the strengths of AI with the irreplaceable qualities of human expertise, consulting firms can deliver value that neither could achieve alone. This synergy will be the cornerstone of consulting’s evolution in the age of AI.
As we enter a new chapter for the consulting industry, it’s clear that technology will continue to evolve at a breathtaking pace. But even in the face of rapid advancements, one thing remains constant: the need for human wisdom, empathy, and creativity. The consulting firms that recognize and embrace this truth will not only adapt to the future—they will define it.
The firms that fail to rise to this moment will not be undone by AI itself but by their own resistance to change.
As we stand on the brink of a new era, with the launch of tools like ChatGPT heralding a wave of AI advancements, it’s clear that the consulting industry is on the cusp of a major evolutionary step. The trends outlined in this article—AI-enhanced insights, continuous engagement, the switch from outputs to outcomes, and the rise of long-term partnerships—are not merely possibilities; they are inevitabilities. The consulting firm of the future will look radically different from the models we have relied upon for decades.
The consulting firm of tomorrow will not be defined solely by human expertise or technological capabilities but by how seamlessly the two are integrated. AI will be at the core of every engagement, from scoping projects to delivering insights and tracking outcomes. Consultants will need to act as interpreters, bridging the gap between what AI can produce and what clients need to succeed.
The role of the consultant will change from data analysis and report preparation to guiding strategic decision-making, managing AI systems, and helping clients navigate complex metamorphoses. Human expertise will remain critical, but it will focus on areas where AI cannot yet tread—creativity, empathy, judgment, and the ability to manage human dynamics in the face of change.
The episodic engagements that once defined consulting will give way to ongoing relationships. Clients will expect their consulting partners to stay with them through the entire lifecycle of change, providing not just solutions but the tools, training, and collaboration needed to sustain those solutions over time.
Under this new operating model, consulting firms will evolve into agile, adaptive entities that deliver value continuously rather than at discrete intervals. Pricing structures will align with client outcomes, with fees tied to measurable improvements rather than hours billed or deliverables produced.
As clients become more sophisticated in their use of AI, consulting firms will need to demonstrate their ability to deliver results that go beyond what AI alone can achieve. The consulting firm of the future will not just leverage AI tools to generate insights but will embed those tools into client operations, ensuring that every engagement drives measurable business impact.
This outcome-driven approach will redefine the value proposition of consulting. No longer will firms be able to justify their fees through effort or exclusivity of knowledge. Instead, their worth will be measured by the tangible improvements they deliver—cost savings, revenue growth, operational efficiencies, and more.
The traditional consulting career ladder—grinders, minders, and finders—will no longer suffice. As AI automates the repetitive grind work, firms will need to focus on hiring and developing talent capable of complementing AI, not competing with it. The career path will emphasize rapid upskilling, domain expertise, and the ability to lead complex, technology-driven upgrades to client’s businesses.
Junior consultants will spend less time on spreadsheets and PowerPoint decks and more time learning how to integrate AI tools, interpret data, and manage client relationships. Senior leaders will need to provide more than just networks and salesmanship—they will need to deliver expertise that is irreplaceable and value that is demonstrable.
The consulting firm of the future will also play a critical role in addressing the ethical and societal implications of AI. Clients will look to their consulting partners not just for technical solutions but for guidance on navigating issues like data privacy, algorithmic bias, and the broader impact of AI on workforces and communities.
This will require consulting firms to develop competency in AI governance, compliance, and risk management, positioning themselves as trusted advisors in an increasingly complex landscape.
As we close 2022, the consulting industry faces a stark choice: evolve or risk redundancy. The appearance of tools like ChatGPT is not just a technological milestone, it’s a wake-up call for an industry that has long relied on traditional models of value creation.
The consulting firm of the future will not emerge by accident. It will be built by those willing to embrace change, experiment with new models, and invest in the capabilities needed to thrive in an AI-driven world. The firms that succeed will not just adapt to the future—they will help define it, setting the standard for what consulting can and should be in the decades to come.
To thrive in this flipped industry, consulting firms must embrace change as both a necessity and an opportunity. The disruption caused by AI is not a threat to the core purpose of consulting, it’s a chance to realign with what clients truly value: solutions that are fast, actionable, and transformative.
Adapting to this new reality requires more than just adopting AI tools or tweaking service offerings. It demands a fundamental rethinking of how consulting firms operate, deliver value, and engage with clients. This shift isn’t optional. Clients are already moving forward, leveraging AI to become more self-sufficient and demanding more from their consulting partners. Firms that resist or delay will find themselves quickly outpaced by more agile competitors.
The flipped industry doesn’t mean the end of consulting—it means the end of consulting as we know it. The consulting firm of the future will be judged by its ability to:
Navigate complexity.
Interpret AI-driven insights in ways that resonate with clients’ unique challenges.
Build trust by delivering measurable results and ethical guidance.
Consultants who lean into these roles will find that their expertise is not diminished by AI but enhanced by it.
Thriving in a flipped industry requires consulting firms to go beyond adaptation and embrace reinvention. This means creating new models for delivering value—models that are collaborative, transparent, and built for long-term impact. It means redefining what it means to be a consultant, moving from a provider of solutions to an enabler of unlocked potential.
The firms that seize this moment will emerge stronger, not just surviving the disruption but shaping the future of the industry. They will set the standard for what consulting can achieve in a world where AI amplifies human potential rather than replacing it.
Above all, the flipped industry underscores the enduring importance of the human element. AI is a powerful tool, but it is just that—a tool. The creativity, empathy, and judgment that define great consultants cannot be automated. These qualities are what make consulting meaningful, and they are what will continue to differentiate the best firms from the rest.
As the consulting industry flips, the firms that thrive will be those that embrace the possibilities of AI while staying true to the principles that have always defined consulting at its best: delivering real value, building trust, and helping clients achieve what they couldn’t achieve on their own.
The flipped industry is here, and it’s happening faster than anyone anticipated. For those willing to embrace it, the future of consulting is bright—not despite AI, but because of it.
The Final, Final Thought. Really
As a Big 4 partner who cares deeply about the future of Big 4, it pains me to say this final thought, but I must. For the Big 4, this moment represents not just a challenge but an existential threat. Their traditional models, deeply rooted in hierarchical structures and slow-moving and risk-averse processes, leave them especially vulnerable to the pace of AI-driven change. If these firms cannot embrace AI and adapt quickly enough to meet the demands of a flipped industry—redefining their value proposition, overhauling their delivery models, and rethinking their leadership structures—then perhaps their time has passed.
In an era where agility and innovation are the ultimate currencies, the inability to transform may signal that their legacy approach was never meant to endure in the face of such rapid and disruptive technological growth. The firms that fail to rise to this moment will not be undone by AI itself but by their own resistance to change. Perhaps, in the end, that would be the most fitting judgment on their relevance in this new age.
| 2022-12-31T00:00:00 |
2022/12/31
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https://futureofleadership.ai/future-of-consulting-ai/
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[
{
"date": "2022/12/31",
"position": 81,
"query": "future of work AI"
},
{
"date": "2022/12/31",
"position": 74,
"query": "workplace AI adoption"
}
] |
AI in the Workplace: Opportunities and Challenges
|
AI in the Workplace: Opportunities and Challenges
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https://succesful-life.com
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"The Artistic Revolution",
"Exploring The World Of Ai-Generated Images - Successful Life",
"Understanding The Impact Of Artificial Intelligence On The Job Market - Successful Life",
"Best Ai Tools To Make Studies Easy - Successful Life",
"Elevate Your Productivity",
"Tips",
"Tools For Better Time Management - Successful Life",
"Artificial Intelligence Trends To Watch In The Coming Years - Successful Life"
] |
In the workplace, AI has the potential to automate tasks, improve decision-making, and increase efficiency and productivity.
|
AI in the Workplace: Opportunities and Challenges
Artificial intelligence (AI) is transforming the way businesses operate and has the potential to revolutionize many industries. AI systems are designed to learn and adapt over time. They are trained to perform a wide range of tasks, from simple to complex. In the workplace, AI has the potential to automate tasks, improve decision-making, and increase efficiency and productivity. However, the adoption of AI in the workplace also raises ethical and societal concerns. Especially the potential impact on employment and the need for appropriate safeguards to ensure fair and unbiased decision-making.
Opportunities
One of the main opportunities of AI in the workplace is its ability to automate tasks that are repetitive or time-consuming. By automating these tasks, AI can free up humans to focus on more complex and creative tasks. This can increase efficiency and productivity. For example, AI can be used to analyze large amounts of data, identify patterns, and make predictions or decisions. This will save time and improve the accuracy of decision-making. In addition, AI can be used to automate tasks such as customer service, freeing up employees to focus on more complex and valuable tasks.
Another opportunity of AI in the workplace is its ability to improve decision-making. AI systems can analyze large amounts of data and identify patterns that might be difficult for a human to detect, which can help improve the accuracy and speed of decision-making. This can be particularly useful in industries such as finance, where accurate and timely decision-making can be critical.
Concerns
There are several concerns that businesses should consider when implementing artificial intelligence (AI) in the workplace:
Impact on employment: One concern is the potential impact on employment, as AI has the potential to automate many tasks that are currently performed by humans. This could lead to job loss in some industries, and it is important to consider strategies for supporting workers who may be affected by the adoption of AI. Fair and unbiased decision-making: Another concern is the need for appropriate safeguards to ensure that AI systems are fair and unbiased. AI systems can be trained using biased data, which can lead to biased decisions or outcomes. It is important to ensure that AI systems are designed and trained to be fair and unbiased, and to put appropriate safeguards in place to prevent biased decision-making. Ethical considerations: The use of AI in the workplace raises a number of ethical considerations, such as the potential impact on privacy, the use of personal data, and the potential for AI systems to be used for malicious purposes. It is important for businesses to consider these ethical considerations and put appropriate safeguards in place to protect against potential risks. Security: AI systems can be vulnerable to cyber attacks, and it is important for businesses to consider the security risks associated with using AI in the workplace. This might include implementing security measures to protect against data breaches, as well as developing contingency plans in case of a security breach.
By considering these concerns, businesses can ensure that they are using AI in a responsible and ethical manner, and can mitigate any potential risks or negative impacts associated with the adoption of AI in the workplace.
Best practises
To fully realize the benefits of AI in the workplace, it is important for businesses to consider the following best practices:
Clearly define the goals and objectives of using AI: Before implementing AI in the workplace, it is important to clearly define the goals and objectives of using AI, such as increasing efficiency, improving decision-making, or automating tasks. This will help ensure that the AI system is aligned with the needs of the business. Invest in training and development: To ensure that employees are able to effectively use and collaborate with AI systems, it is important to invest in training and development. This might include providing training on how to use AI tools and systems, as well as training on how to work effectively with AI in a team setting. Consider ethical and societal concerns: It is important to consider the potential ethical and societal implications of using AI in the workplace, such as the potential impact on employment and the need for fair and unbiased decision-making. By addressing these concerns, businesses can build trust and credibility with employees and stakeholders. Develop a plan for managing and maintaining AI systems: To ensure that AI systems are functioning effectively, it is important to develop a plan for managing and maintaining these systems. This might include regularly updating software and algorithms, as well as training employees on how to troubleshoot and fix problems as they arise.
By adopting these best practices, businesses can realize the benefits of AI in the workplace while also addressing the challenges and ethical concerns. By understanding the opportunities and challenges of AI in the workplace, businesses can make informed decisions about how to use AI to drive business growth and success.
| 2022-12-31T00:00:00 |
2022/12/31
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https://succesful-life.com/ai-in-the-workplace-opportunities-and-challenges/
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Welcoming the dawn of a new era. The AI Revolution.
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Tall Man Business • Welcoming the dawn of a new era. The AI...
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https://tallmanbusiness.com
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[] |
At one point ( a while ago) there was a growing momentum in a conversation about “UBI” - Universal Basic Income. The notion that everyone could/should be ...
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Welcoming the dawn of a new era. The AI Revolution.
Read time: approximately 7mins-16secs-ish.
It’s New Year’s Eve. This post marks the month which saw the consumer launch of Chat GPT. Here’s an entrepreneur’s perspective.
I have been immersed in the innovation/entrepreneurial eco-system for more than two decades. Over that time we’ve seen global ups and downs in the world’s economy, some of which arguably the most severe in history. We’ve never seen so much change, transformation, and new technology.
Literally all our established orthodoxies are being challenged.
I think people are hardwired to resist change. For many, sadly that resistance worsens with age - which is a problem in an ageing society. I saw a recent video interview in which Elon Musk observed that humans don’t simply resist change, sadly “humans tend to ossify, and then die”. To which I’d respond with my observations about entrepreneurs:
Whether or not we like change, entrepreneurs have a demonstrable knack for translating change into opportunity.
Entrepreneurial success is sometimes admired, occasionally celebrated, and often envied.
Entrepreneurial talent is sometimes revered, but often mistrusted.
Entrepreneurial competence is often regarded as mythical, unacacdemic, instinctive, eccentric, and exclusive - all of which I frustratingly believe to be completely untrue.
It both saddens and maddens me that in an era of unprecedented opportunity for innovation and progress so little is understood about entrepreneurialism; how to cultivate it; how to teach it; and how to democratise the benefits of an entrepreneurial mindset so that everyone can leverage the opportunities available. Particularly the lack of entrepreneurialism, innovation, intuition, and financial literacy in the formal education and curriculum of our kids.
I think this is important:
To help counteract and pacify the genuine and legitimate concerns and anxieties I hear from people contemplating their future careers, incomes, and livelihoods. By contrast, entrepreneurs have a talent of managing uncertainty positively.
I am a passionate egalitarian. I am greatly concerned by the apparent disproportionate growth of socioeconomic inequality. I believe that an entrepreneurial mindset helps drive prosperity individually and collectively.
To maximise the potential positive impact of new technology and accelerate the mass adoption of technological advances to help people, businesses, and the planet to flourish.
My belief is that the more that entrepreneurialism is demystified and codified; the more that entrepreneurialism is understood; the more that entrepreneurialism is democratised and made inclusively accessible - the more these issues of anxiety, inequality, and ecology can be tackled.
Global issues such as the environment and the economy are debatable. Whether or not you agree with the scientific evidence regarding global warming - and whether or not you care about the consequences of this issue. Whether or not you have concerns about your future financial health and stability. From the perspective of an entrepreneur what I see is a world in which:
There are potentially more jobs than there are people.
There are jobs that the majority of people just don’t want to do. Justifiably so.
The cost of living is a significant burden for many people, (below a threshold of indulgence).
Average expectations fall significantly below average outcomes.
Inefficiency and poor productivity holds us back.
Human error accounts for significant loss of life and injury.
We are perilously close to passing ecological milestones that are irrevocable.
For years corporate corridors have been dominated with angst conversation about “Disruption”. My point is, if all we talk about is disruption, then all we will see is disruption.
What if instead all we thought about was: “Liberation” - then perhaps what we would be looking for is ways to enable ourselves to be more free to do what ever it is that we want to do.
At one point ( a while ago) there was a growing momentum in a conversation about “UBI” - Universal Basic Income. The notion that everyone could/should be paid c. $500 per month in order to live. Interestingly, my sense is to a large extent that conversation seems to have gone away (or certainly quietened). I understand the idea as a notion to provide stability; a sustainable alternative to displaced employment; a new mechanism for wealth distribution. But my whole premise is the opposite to this - my premise is not how best to distribute free cash and to remunerate idleness. My whole premise is, how best to pursue technological advancement in order to eliminate costs - for people to be free to do what they want to do.
Recent progress in nuclear fusion - the source of limitless [relatively] impact-less free energy, gives us a tantalising glimpse at a scientifically viable route to delivering such cost reduction, in terms of providing
Free energy to power our homes;
Free energy to heat our homes;
Free energy to power our cars;
Free energy to radically reduce the cost of food production.
Easy to dismiss as fantasist and fiction. Scientific breakthroughs this month in nuclear fusion mean that the industrial application of fusion power is an inevitability within the lifetime of my daughters. My view though is that this is not the most significant technological development to have arrived this month.
In preparing my book I have re-read the c. 175 posts I published over the last ten years. What’s clear, especially in the earlier posts, is the extent to which I was trying to capture the birth of the ‘digital economy’ - whatever that is, and whatever it means. Ten years later it could be debated whether the digital economy has now landed - or whether the ‘beginning’ is now over and we’re somewhere in the ‘midst’ of it. That is not important. What is important is, the public launch in December 2022 of ChatGPT - which delivers explosively the start of the ‘AI economy’.
It is a moment we should all note in our conscious memory: where and when we were when we first made aware of it - and fascinatingly, what we first nervously asked of it.
The difference between the ‘digital economy’ and the ‘AI economy’ is not semantic. Not only is the difference profound - but also, with each new economic-era the consequence, significance, and impact multiplies. In the case of AI I believe the impact will be exponentially more profound than any previous economic era.
When I first used ChatGPT it became immediately apparent to me that both conventional search (eg. Google) and established advertising models/revenue would be completely rewritten. I strongly suspect that is merely the tip of the iceberg. With CHATGPT, for the very first time, the general public gets a real-world practical AI tool that it can play with meaningfully that delivers tangible and interactive results.
The entrepreneurially minded among us will be beyond excited, motivated, and energised by the possibilities that AI now creates. Those that are more attached to an incumbent mindset will be deeply distrustful of this development and will choose instead to operate in either denial or combat (or both).
The hospitality sector. The agricultural sector. The care sector. The medical sector. All have profound shortages of labour. It seems overwhelmingly inevitable to me that the advances in technology, such as AI now need to be combined with the willing/available human capital in order to multiply the productivity and efficiency of those workers to enable them to meet the evident demand. AI can help make our existing workforce bionic. The distress in the health sector in the UK demonstrates that that need is beyond critically urgent. Whole regions of the UK today have announced ‘critical-emergency-cover only’.
I asked ChatGPT a question (as follows) - to which it replied with the six bullet points below, in less than four seconds:
Q. In using AI to deliver a conversational companion for elderly people with dementia, what safeguards need to be in place to protect vulnerable people? For example, in cold whether if an elderly person asked, should I turn-down my heating to save money - how can their safety and wellbeing be ensured and protected?
There are several safeguards that can be put in place to protect vulnerable people, such as elderly people with dementia, when using AI to deliver a conversational companion:
Clear and concise language: It is important to use clear and concise language when communicating with elderly people with dementia, as they may have difficulty understanding complex instructions or concepts. Safety reminders: The conversational companion should be programmed to provide safety reminders, such as reminding the elderly person to stay hydrated, take their medications, and dress appropriately for the weather. Emergency contact information: The conversational companion should be programmed to provide emergency contact information in the event that the elderly person is in need of assistance. Limited decision-making capabilities: The conversational companion should not be programmed to make decisions on behalf of the elderly person, as this could potentially put their safety at risk. Instead, it should be programmed to provide information and recommendations for the elderly person to consider, but ultimately it is up to the person to make their own decisions. Human supervision: It is important to have a human caregiver or family member available to provide supervision and assistance to the elderly person, as needed. Regular review and updates: The conversational companion should be regularly reviewed and updated to ensure that it is functioning properly and providing accurate and appropriate information and recommendations.
My next question ought to be “please can you code this to work via Alexa?” - which I have no doubt it can do, but then like many of my entrepreneurial peers, I’m just trying to figure out what problem to fix first and where best to invest my limited time and capacity.
I am not eulogising that AI is the answer to all our problems and that the future is going to be amazing. I’m also not saying that entrepreneurs have all the answers and are exclusively placed to leverage this new technology.
What I am saying is, is that the launch of ChatGPT and all the other flavours of AI that follow it, is a watershed moment. A before-and-after moment. More significant than when my parents replaced our black and white television for a colour TV.
From the perspective of business - I can’t think of a single industry that won’t be affected by AI technology. Whole industries will be (and are in need of being) completely rewritten as a consequence of the new capabilities that AI enables.
Like all changes of era throughout history before us - some of us will grasp the possibility that new technology represents as a positive opportunity. Others will fare less well. When we look back through history, so far, without exception the macro economic effect of each transition of era has been overall increased prosperity.
2023 is going to be epic!
Happy New Year.
| 2022-12-31T00:00:00 |
https://tallmanbusiness.com/post/705168002311667712/welcoming-the-dawn-of-a-new-era-the-ai
|
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"query": "universal basic income AI"
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Data Analyst Certificate & Training
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Développez votre activité commerciale
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https://grow.google
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[
"Perrine Guillem Et Adrien Briu Ont Décidé De Se Reconvertir Pour Réaliser Leur Rêve",
"Offrir Une Seconde Jeunesse À Une Spécialité Culinaire Locale De Montpellier",
"Notamment Grâce À Leur Boutique En Ligne.",
"Les Escalettes",
"Bois Online",
"Bois Online Est Un Atelier De Menuiserie",
"Fondé À Anglemont Il Y A Près De Soixante Ans",
"Qui A Basculé Dans Une Nouvelle Ère Numérique Suite À La Crise",
"Herboristerie Spécialisée Dans Les Huiles Essentielles Et Végétales",
"Anascan A Repensé Sa Stratégie Numérique Afin De Clarifier Son Offre"
] |
Generative AI for Educators · Events · Stories · Partners · About · Français. Google ... Qualify for in-demand jobs in data analytics. Data analysts prepare ...
|
“Pendant le confinement, les gens ont mis l'accent sur l'aménagement de leur intérieur; et finalement notre nouvelle activité en ligne nous a permis de compenser la baisse d'activité. Cela nous a aidés à garder tout le monde dans l'entreprise.”
Bois online est un atelier de menuiserie, fondé à Anglemont il y a près de soixante ans, qui a basculé dans une nouvelle ère numérique suite à la crise
| 2022-12-31T00:00:00 |
https://grow.google/certificates/en_ca/data-analytics/
|
[
{
"date": "2022/12/31",
"position": 88,
"query": "generative AI jobs"
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|
AI & Machine Learning
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AI & Machine Learning
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https://www.codepwr.com
|
[] |
Working with distributed teams doesn't just unlock access to a diverse workforce — it also allows us to have a wide pool of highly specialized engineering ...
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Artificial intelligence and machine learning have become essential assets for companies looking to revamp their operations and innovate. Through them, you can integrate smart data analytics, automate repetitive tasks, provide better customer service and support, and even identify new business opportunities.
Our team can help you adopt AI and ML into your pipeline, supercharging your daily processes through intelligent algorithms. Doing that can streamline the workflow across your entire company while providing you with a competitive edge.
| 2022-12-31T00:00:00 |
https://www.codepwr.com/our-expertise/ai-machine-learning/
|
[
{
"date": "2022/12/31",
"position": 85,
"query": "machine learning workforce"
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|
Intersection of AI, Life Sciences, Healthcare, and IP
|
Intersection of AI, Life Sciences, Healthcare, and IP
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https://www.patentnext.com
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AI-based patent filings addressing trending areas, including disease identification and diagnosis, drug discovery, personalized medicine, and clinical trials.
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PatentNext Summary: The life sciences and healthcare fields produce big data, which Artificial Intelligence (AI) tools can use to train AI models to assist doctors, patients, researchers, and other stakeholders in various ways. In the intellectual property (IP) space, there has been explosive growth in this area, with AI-based patent filings addressing trending areas, including disease identification and diagnosis, drug discovery, personalized medicine, and clinical trials, among others. These trends are expected to continue though challenges remain, such as the balance between the need for health-related data to train AI models and privacy and ethical concerns in using such data.
****
The fields of life sciences and healthcare generate vast quantities of data, e.g., “big data,” which can come from various sources, including hospitals, doctors, patients, caregivers, and researchers.
Artificial Intelligence (AI), in particular, machine learning (ML), thrives on big data for training accurate AI models. Because of this, AI lends itself to an apt new tool that complements the vast quantities of data typically generated by the life sciences and healthcare fields.
For example, once trained on the data generated in these fields, AI models may be used to develop innovations that can address disease identification, diagnosis, treatment, and drug discovery, among other trending areas.
Several trends have emerged for use of AI as a tool in the life sciences and healthcare industries. The below provides a non-exhaustive list of emerging trends.
1. Disease Identification and Diagnosis
The Centers for Disease Control and Prevention estimates that six in ten adults in the U.S. have a chronic disease, and four in ten adults have two or more. By 2025, it is estimated that chronic diseases may affect 164 million Americans – nearly half (49%) of the U.S. population.
To combat this, researchers have used AI as a tool to identify and diagnose chronic diseases. For example, the Mayo Clinic announced three instances of applying AI in the field of cardiovascular medicine:
Mitigating stroke impact. In emergency rooms, when people come in with a stroke called an intracerebral hemorrhage, they get a CT scan. That scan is examined by a computer trained to analyze CT data, cutting the time to diagnosis and limiting brain damage.
Preventing heart problems. Applying AI to electrocardiograms (ECGs) has resulted in a low-cost test that can be widely used to detect the presence of a weak heart pump, which can lead to heart failure if left untreated. The Mayo Clinic has a database of more than 7 million ECG records. The Mayo Client removes all identifying patient information to protect privacy. Then, the resulting data is mined to accurately predict heart failure noninvasively, inexpensively, and within seconds.
Detecting atrial fibrillation (a-fib) sooner. AI-guided ECGs are also used to detect faulty heart rhythms (atrial fibrillation) before any symptoms are evident.
The May Clinic announced that these AI-based tools have moved from the research stage to use in the clinic.
2. Drug Discovery and Manufacture
A machine learning model can be trained on pharmaceutical, bio, and other life sciences and/or healthcare data to aid researchers with early-stage drug discovery, including the development of new drug compounds and the development of discovery technologies to assist with next-generation sequencing.
For example, Nvidia released an AI framework (Nvidia BioNeMo) for training and deploying large biomolecular language models. Nvidia BioNeMo uses a large language model (LLM) framework and supports chemistry, protein, DNA, and RNA data formats. In particular, Nvidia BioNeMo includes four pre-trained language models accessible in the cloud. The models can be used by researchers to analyze amino acid sequences for predicting protein properties, protein modeling, reaction prediction, molecular optimization, and de novo molecular generation.
This allows researchers and scientists to train large-scale language AI models on big data datasets, resulting in better-performing AI models (e.g., neural networks), and, ultimately, allows scientists better understand diseases and find therapies for patients.
3. Personalized Medicine
AI models can be trained to provide customized treatment based on a person’s unique health history. Such models can provide optimized diagnoses and treatment protocols on a patient-specific level.
For example, at Johns Hopkins University, researchers use whole-heart computational AI models to better understand ventricular arrhythmias. These models included specific biophysical complexity of an individual patient’s cardiac pathology, factoring in cellular- and organ-level properties.
According to Natalia Trayanova, professor of biomedical engineering and medicine at Johns Hopkins University, this complex biophysical system “can be represented using a set of mathematical equations,” said Trayanova. “Solving these equations using computer software allows us to run detailed simulations to mimic the heart’s electrical activity.”
“Personalized computational modeling of patient hearts is making strides in developing models that incorporate the individual geometry and structure of the heart, as well as other patient-specific information,” Trayanova said in a press release.
“These types of models can enable fast evaluation of medical device settings and patient-selection criteria, as well as the development of novel therapeutic agents,” she explained.
4. Clinical Trials
The National Library of Medicine (NLM) acknowledges that AI could be used to modernize the crucial steps of clinical trial conduct-study design, planning, and execution. For example, AI can be used for linking big data, including electronic medical records (EMRs), published medical literature, and clinical trial databases, in order to improve recruitment by matching patient characteristics to selection criteria.
For example, according to the NLM, AI can assist by enhancing patient selection in the following ways:
By Reducing population heterogeneity by harmonization of large EMR data from diverse formats and different levels of accuracy and by leveraging electronic phenotyping.
By prognostic enrichment, which would include selecting patients who have a higher probability of having a measurable clinical endpoint. For example, ML techniques using key biomarkers of Alzheimer’s disease (AD) have been deployed for prognostic enrichment.
By predictive enrichment, which can include choosing a population with a better likelihood of responding to treatment. For early AD, a clinical trial simulation tool developed by modeling drug, disease, and progression of disease, which helped in predictive enrichment, has undergone regulatory review.
In addition, AI systems can be utilized for automatic analysis of EMR and clinical trial digital eligibility databases and match these with recruiting clinical trials from trial announcements, social media, or registries.
It can also help patients become aware of clinical trials of interest sooner and allow them to approach investigator sites for evaluation of eligibility. According to the NLM, AI-based clinical trial matching has facilitated an increase in enrollment in a lung cancer trial by 58.4%.
5. Radiotherapy and Radiology
The National Library of Medicine (NLM) has also reported on the topic of AI in radiotherapy, where AI can be applied to images (e.g., MRI CT scan, or ultrasound images) to segment and identity parts of the body, such as bones, organs, muscles, and fractures. The segmented areas may reveal portions of the body needing radiotherapy or radiology treatment.
For example, according to the NLM, breast cancer is one of the most commonly diagnosed cancers, responsible for 30% of all new cancer diagnoses in women. Ultrasound is normally used to diagnose breast cancer, and improvement in segmentation of breast ultrasound images into functional tissues provides a better tumor localization, assessment of treatment response, and breast density measurement.
Typically, segmentation of ultrasound is very time-consuming for radiologists and it is skill and experience-dependent. However, AI can be used to provide automated segmentation of ultrasound images, which can help mitigate those problems.
According to the NLM, a recent study shows convolutional neural network (CNN)-based segmentation can segment the 3D image into four major tissues: skin, mass, fibro glandular tissue, and fatty tissue with high accuracy. Such use of AI provides segmentation in the future in clinical diagnosis of breast cancer.
6. Electronic Health Records (EMR)
EMR data constitutes big data for training AI models. The National Library of Medicine (NLM) reports a need to correlate EMR data to provide a base for training AI models. See Prospect of Artificial Intelligence Based on Electronic Medical Record.
According to the NLM, AI data management software can be expected to increase the efficiency of hospital operations, patient management, and treatment.
Through AI, it is possible to classify diseases accurately, reclassify preexisting disease categories according to individual characteristics, quickly analyze images and medical data in EMR, and provide appropriate services. With the emergence of medical AI-integrated platforms, AI has become essential for the creation of new services, such as the improvement of medical quality and real-time health management.
Patenting Trends for inventions using AI in the Life Sciences and healthcare Fields
Patent filings at the United States Patent and Trademark Office (USPTO) across the above-referenced life science and healthcare categories reflect a similar trend.
The below chart shows AI and Life Science patent filings by Technology (“Tech”) Center over time from 2000 to 2022, where we see a spike in activity in post-2016.
Most of the patent filings come from technology companies focusing on AI-related technologies. These include IBM, Nvidia, and LG Electronics.
After the spike in 2016, the above chart shows continued activity up to the present day. Given the increased interest in AI and the life sciences and healthcare fields, we can expect further filings in this space in the coming years (note that the right-most side of the graph slopes down because of the 18-month “Publication Delay,” during which information for newer patent application filings, is not yet publicly available. See 37 CFR § 1.211).
In addition, the above chart organizes patent application filings by USPTO Tech Centers. As shown, most AI-based life science/healthcare patent applications fall into Tech Center 1600, which focuses on Biotechnology and Organic Chemistry. We can see that this tech center has handled a majority of AI-based life science and healthcare applications since 2000.
However, since 2019, Tech Center 2600, which focuses on Communications, has received most AI-based life science/healthcare patent applications. This tech center includes art units that focus on computer graphic processing (art unit 2615) and image analysis (art unit 2660), which are important to radiotherapy and radiology inventions.
The FDA’s Artificial Intelligence and Machine Learning Action Plan
Innovators of AI-based life science and healthcare inventions should familiarize themselves with the regulatory landscape, including the Food and Drug Administration (FDA)’s position on Artificial Intelligence (AI) and machine learning (ML) based software inventions.
The FDA has identified AI as an important technology that it will monitor and regulate, stating that: “Interest in medical devices incorporating ML functionality has increased in recent years. Over the past decade, the FDA has reviewed and authorized a growing number of devices legally marketed (via 510(k) clearance, granted De Novo request, or approved PMA) with [machine learning] across many different fields of medicine—and expects this trend to continue.”
Accordingly, any new AI technology, which proposes to improve the efficiency of clinical trial design and conduct, should be validated by testing alongside the existing technology it claims to complement or substitute.
The FDA maintains a list of AI/ML-enabled Medical devices submitted to the FDA. The list includes devices submitted and authorized via 510(k) clearance, granted De Novo request, or an approved Premarket Approval Application (PMA).
The below chart summarizes the number of AI/ML submissions made to the FDA through the years.
As shown in Chart 1 above, AI/ML submissions started increasing exponentially around the 2016 timeframe. This correlates with AI and Life Science patent filings, as shown above.
By far, the Radiogoloy FDA panel received the most submissions (392 submissions) compared to any other group. The Cardiovascular FDA panel received the second most submissions (57 submissions). The remaining panels each received 15 or fewer submissions.
The FDA considers AI/ML-based software as a medical device. Because of this, the FDA expects AI innovators to comply with requirements of clinical, analytical, and technical validation, quality systems, good machine learning practice, assurance of safety and effectiveness, transparency, and real-world performance monitoring.
In order to address these issues and the increased submissions regarding AI and ML, the FDA published an action plan outlining five actions the FDA intends to take. See FDA’s AI/ML-Based Software as a Medical Device Action Plan (the “FDA’s AI/ML Action Plan”).
These five actions are summarized below.
Developing a tailored Regulatory Framework for AI/ML-based devices.
The FDA categorizes AI/ML-based technologies as Software as a Medical Device (SaMD), which the FDA identifies as “software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device.” Id. The FDA states that its vision for AI/ML-based SaMD is to provide “appropriately tailored total product lifecycle-based regulatory oversight to “deliver safe and effective software functionality that improves the quality of care that patients receive.” Id.
Harmonizing a set of Good Machine Learning Practices (GMLP)
The FDA published a paper on guidelines for Good Machine Learning Practices (GMLP). The GMLP includes a set of AI/ML best practices similar to software engineering best practices as typically applied in the software field.
The GMLP includes a set of ten guiding principles that the FDA will no doubt use to review AI/ML-based FDA submissions. As the FDA explains, “[t]hese guiding principles will help promote safe, effective, and high-quality medical devices that use artificial intelligence and machine learning (AI/ML).” Id.
The ten guiding principles of the GMLP are (1) leveraging multi-disciplinary expertise during the product lifecycle of the device; (2) ensuring the security of data through quality software engineering; (3) using training data representative of the intended patient population; (4) keeping training datasets independent of test data sets; (5) selecting reference datasets that are clinically relevant and well characterized; (6) ensuring the AI model generated from the training data reflect the intended use of the device; (7) keeping a “human in the loop” to use the AI/ML device; (8) testing the device in clinically relevant conditions; (9) the AI/ML device is used with patients for which the AI/ML model was trained, and such patients are provided information informing them of the same; and (10) monitoring AI/ML model, as deployed, to check for model accuracy and check for any need to update or retrain the model. Id.
Patient-Centered Approach Incorporating Transparency to Users
One of the concerns of AI/ML devices has been a lack of transparency in the AI/ML models they use. For example, the FDA has identified unique challenges for manufacturers in clearly describing data to train an AI/ML model. This can include the relevance of its model inputs, the logic it employs (when possible), the role intended to be served by its output, and the evidence of the device’s performance. Therefore, the FDA seeks to clarify its position on the transparency of AI/ML technology in medical device software in order to ensure that users understand the benefits, risks, and limitations of these devices and to support the transparency of and trust in AI/ML-based technologies.
Regulatory Science Methods Related to Algorithm Bias & Robustness
Bias in AI/ML models poses a risk of alienating potential adopters of AI/ML-based technology. AI/ML-model bias is described by the FDA as follows: “healthcare delivery is known to vary by factors such as race, ethnicity, and socio-economic status; therefore, it is possible that biases present in our healthcare system may be inadvertently introduced into [a given AI/ML model].” FDA’s AI/ML Action Plan.
Accordingly, any AI/ML model development should include steps for the identification and elimination of bias. This can include increasing data transparency (e.g., tracking and showing the data used to train a given AI/ML model), eliminating private information, eliminating biased data (e.g., race-base data) from the model training sets, and/or including sufficient quantity and types of training data to train an AI/ML model to capture diverse populations for which an AI/ML device will be used.
Real-World Performance (RWP)
Finally, the action plan encourages a “total product lifecycle” approach to AI/ML devices. In this aspect, AI/ML devices output real-world data that is collected and monitored. Such real-world data “may allow manufacturers to understand how their products are being used, identify opportunities for improvements, and respond proactively to safety or usability concerns.” FDA’s AI/ML Action Plan.
Challenges Ahead
AI-based life science and healthcare trends are expected to continue though challenges remain. A major challenge comes from AI’s need for big data for accurate, or at least useful, AI models to be produced. While big data is plentiful in life science and healthcare, tension arises with privacy and ethical concerns in using such data. For example, in several fields, the most useful information can often be personal medical data, which is difficult to access for a number of regulatory reasons, the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) being among a few of these.
However, efforts are underway to combat data privacy issues, including the collection and use of such data. For example, the White House’s Office of Science and Technology Policy published the Blueprint for an AI Bill of Rights. The AI Bill of Rights explores ethical considerations when deploying AI. For example, according to the White House, the AI Bill of Rights is intended to “support the development of policies and practices that protect civil rights and promote democratic values in the building, deployment, and governance of automated systems.” AI Bill of Rights, About this Document. See also Ethical Considerations of Artificial Intelligence (AI) and the White House’s Blueprint for an AI Bill of Rights.
It may be that we see future regulation that addresses the use of healthcare-related data for developing AI-based innovations in the life sciences and healthcare fields.
****
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****
Please note that the above charts and related information in the patent-related section are provided courtesy of Juristat. The chart and information were obtained by searching for (“artificial intelligence” OR “machine learning”) AND (disease OR drug OR pharmaceutical OR “clinical trial” OR “radiotherapy” OR “radiology” OR “Electronic Health Records”), and using the fields Title, Abstract, Description, and Claims in the Juristat app.
| 2023-01-01T00:00:00 |
2023/01/01
|
https://www.patentnext.com/2022/12/the-intersection-of-artificial-intelligence-ai-life-sciences-healthcare-and-intellectual-property-ip/
|
[
{
"date": "2022/12/31",
"position": 11,
"query": "AI healthcare"
}
] |
Is AI closing the door on entry-level job opportunities?
|
Is AI closing the door on entry-level job opportunities?
|
https://www.weforum.org
|
[] |
The Forum's Future of Jobs Report 2025 reveals that 40% of employers expect to reduce their workforce where AI can automate tasks.
|
This regular roundup brings you essential news and updates on the labour market from the World Economic Forum’s Centre for New Economy and Society.
Top stories: AI threatens entry-level jobs; Gen Z rethinks traditional career paths; China and the EU double down on skills as talent shifts reshape emerging markets.
Are entry-level jobs on the way out?
For decades, entry-level roles have provided essential training grounds for newcomers to step into the world of work. From finance to journalism, junior staff have traditionally handled the ‘grunt work’ as a rite of passage as much as a development opportunity.
But as AI reshapes the career ladder, these early entry points could be increasingly at risk, according to Bloomberg.
International Workers Day, on 1 May, signifies the labour movement’s struggle for the rights of workers. AI stands as one of the most significant challenges – and opportunities – facing the labour market today.
Technological change, the green transition, economic uncertainty, geoeconomic fragmentation and demographic shifts are reshaping the labour market. Image: World Economic Forum
While 170 million new jobs are projected to be created this decade, the rise of AI-powered tools threatens to automate as many roles as it creates, particularly for white collar, entry-level roles. Bloomberg finds that AI could replace more than 50% of the tasks performed by market research analysts (53%) and sales representatives (67%), compared to just 9% and 21% for their managerial counterparts.
Whether by narrowing entry pathways or making roles that once required specialized skills more accessible, estimates suggest that AI could impact nearly 50 million US jobs in the coming years.
How AI could be closing the door on talent...
The Forum’s Future of Jobs Report 2025 reveals that 40% of employers expect to reduce their workforce where AI can automate tasks.
Technology, overall, is projected to be the most disruptive force in the labour market, with trends in AI and information processing technology expected to create 11 million jobs, while simultaneously displacing 9 million others.
At the same time, US firms are expanding business operations in India, where skilled professionals can be employed at significantly lower costs, Charter points out, further intensifying competition for white-collar roles.
But this can create a talent pipeline problem, with significant implications for social mobility and equal representation, Bloomberg says.
...while also opening new doors
Gen AI could democratize access to jobs, making it easier to build the technical knowledge and skills that have historically excluded otherwise qualified workers, according to Charter.
Rather than eliminating entry-level opportunities altogether, companies could harness AI to train the next generation of senior professionals. From law firms saying goodbye to the billable hour to more emphasis on apprenticeships, traditional structures could be redefined.
As Gen AI becomes further embedded in the workplace, companies will need to invest in substantial upskilling efforts to prepare their employees for the AI-driven economy.
Alongside global macroeconomic trends, AI is set to reshape the traditional career ladder, with entry-level jobs at risk. But employers and employees alike can prioritize upskilling, education efforts and levelling the playing field that comes with harnessing AI’s potential.
Loading...
More labour news in brief
In a move to support employment amid continued economic restructuring, China plans to prolong key unemployment insurance policies and job retention incentives through 2025.
President Donald Trump has signed an executive order directing Labor, Education and Commerce departments to focus on job needs in emerging industries. The goal is to support more than 1 million apprenticeships annually to improve job training for skilled trades.
With the digital and green transitions set to change the labour market, the European Commission has announced its “Union of Skills” plan to future-proof education and training systems across the bloc.
Youth employment in South Korea has seen its most significant decline in over a decade. With the number of workers aged 25 to 29 falling by 98,000 in the first quarter of 2025, this marks the country's steepest drop in 12 years.
| 2023-01-01T00:00:00 |
https://www.weforum.org/stories/2025/04/ai-jobs-international-workers-day/
|
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Shaping the Future: The Business Impacts of AI Forecasting
|
Shaping the Future: The Business Impacts of AI Forecasting
|
https://www.cogentinfo.com
|
[] |
By harnessing historical data, machine learning algorithms, and predictive analytics, businesses can anticipate market trends, customer preferences, and even ...
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Shaping the Future: The Business Impacts of AI Forecasting
The use of Artificial Intelligence (AI) has grown exponentially in business, transforming the way organizations operate, strategize, and innovate. Among the numerous applications of AI, one of the most consequential and promising is AI forecasting. This cutting-edge technology is proving to be the guiding source of businesses through uncertainty.
According to a study by McKinsey, AI models offer distinct advantages compared to traditional spreadsheet-based analytical methods. The report mentioned that applying AI-driven forecasting in supply chain management can result in 20% to 50% reduction in errors. This reduction directly translates into decreased lost sales and product unavailability, potentially reaching up to 65%. Similarly, warehousing costs can experience a decline of 5% to 10%, while administrative costs can go down from 25% to 40%. AI can help automate as much as 50% of workforce-management tasks, leading to substantial cost reductions of 10% to 15%.
As the business landscape becomes increasingly competitive, providing an exceptional customer experience is essential. AI forecasting plays a pivotal role in this area. By analyzing vast datasets and customer behavior patterns, AI-driven recommendation systems offer unparalleled personalization, ensuring customers receive tailored product suggestions and content. This leads to higher customer satisfaction and, subsequently, improved brand loyalty. According to a projection by Gartner, by the year 2025, approximately 80% of customer service and support entities will be integrating generative AI technology. It will help them to improve agent efficiency and boost the overall customer experience.
Understanding AI Forecasting
AI forecasting empowers organizations to predict future trends, make informed decisions, and optimize strategies. AI forecasting encompasses various methods tailored to different data types and forecasting goals. Here are three primary types:
Time Series Forecasting
Time series forecasting is a crucial part of AI forecasting, particularly suitable for data with a temporal component. It involves analyzing historical data points, such as stock prices, temperature records, or sales figures, to predict future values. Time series models excel at considering the temporal order of data, making them apt for scenarios where past observations indicate future trends.
Consider the case of JPMorgan Chase, one of the largest US-based financial institutions. They employ time series forecasting models to predict stock market trends. By analyzing historical price data, trading volumes, and external market factors, JPMorgan Chase makes data-driven investment decisions, enhancing their portfolio performance.
Regression Analysis
Regression analysis is a versatile technique applied across various domains, including economics, healthcare, and marketing. It explores the relationship between a dependent variable and one or more independent variables. In forecasting, regression models predict numeric outcomes, such as sales revenue, based on input variables like advertising spend, seasonality, or market conditions.
In the US retail sector, Walmart utilizes regression analysis for sales forecasting. They considered various factors, such as historical sales data, advertising expenditures, and seasonal patterns, to predict future sales. This enables Walmart to optimize inventory levels, streamline supply chain operations, and ensure products are readily available to meet customer demand.
Neural Networks
Neural networks, inspired by the human brain's interconnected neurons, represent the cutting edge of AI forecasting. These models are highly adept at recognizing intricate patterns and relationships within data, making them suitable for complex and unstructured datasets.
In the manufacturing industry, General Electric (GE) employs neural networks for predictive maintenance. By analyzing data from sensors embedded in machinery, GE's AI forecasting models can predict equipment failures before they occur. This proactive approach minimizes downtime, reduces maintenance costs, and optimizes manufacturing operations.
Role of Machine Learning in AI Forecasting
Machine learning is the foundation of AI forecasting, providing indispensable algorithms and techniques that empower AI systems to learn from data, adapt to shifting conditions, and make informed predictions. Machine learning algorithms meticulously examine historical data, unveiling intricate patterns, relationships, and trends that serve as invaluable guides for anticipating future events and outcomes.
Data-driven Insight Generation
Machine learning plays a pivotal role in transforming raw data into actionable insights. It operates as the analytical engine that not only processes and interprets data but also discerns the hidden signals within. In the financial sector, machine learning is crucial for detecting fraudulent transactions and assessing credit risk. Banks like JPMorgan Chase use sophisticated machine learning models to analyze spending patterns and flag unusual activities, enhancing the security of financial transactions.
Customer Churn Prediction
One of the critical challenges telecom companies face is customer churn, where subscribers decide to switch to a competitor's service. To combat this issue effectively, machine learning models come into play. These models analyze many data points, including customer usage patterns, billing data, and customer service interactions.
By scrutinizing these diverse data streams, machine learning models can spot early signs of potential churn. For instance, they can identify customers who are showing patterns of reduced usage, escalating service issues, or expressing dissatisfaction through interactions with customer service representatives. These are telltale signs that a subscriber may be considering a switch.
Proactive Retention Strategies
The true power of machine learning in this context lies in its ability to be proactive. Instead of merely recognizing churn after it has happened, machine learning models can anticipate it. This proactive approach enables telecom companies to take preemptive measures to retain their customers.
For instance, when a machine learning model flags a customer at risk of churn, the company can respond by offering personalized service improvements or taking any other suitable action. This might include providing discounts on specific services, suggesting more suitable subscription plans, or addressing particular issues the customer has encountered. Such targeted interventions can significantly increase the likelihood of retaining customers who would otherwise have churned.
Adaptation
What makes machine learning particularly potent is its capacity for adaptation. In the dynamic landscape of the telecommunications industry in the United States, customer behavior is subject to change based on various factors, including market dynamics, technological advancements, and shifts in consumer preferences.
Machine learning models, once trained, don't remain static. They can adapt to these evolving conditions and refine their predictions accordingly. This adaptability ensures that the insights derived from historical data remain relevant and accurate even in the face of changing circumstances.
How AI Learns and Adapts to Data
AI forecasting is far from a static process; it's a dynamic journey of continual learning and adaptation. Understanding how AI systems learn and evolve is crucial to appreciating their effectiveness and potential. Here's a deeper dive into the process:
Data Collection and Sourcing
AI systems forecast by collecting vast amounts of historical data relevant to the specific task at hand. For instance, in the healthcare sector, electronic health records (EHRs) serve as a goldmine of patient data. These records encompass medical histories, treatments, and outcomes, providing a rich source of information for forecasting patient admission rates.
In other industries, such as finance, investment firms tap into diverse data sources to build comprehensive datasets. Market data, financial indicators, and economic trends are meticulously gathered to gain insights into market behavior. In geographies where financial markets are highly dynamic, this data collection is integral to making informed investment decisions.
Data Preprocessing and Transformation
Raw data, though valuable, is only sometimes ready for AI analysis. Before AI can work its magic, data must undergo preprocessing, a critical step in ensuring its quality and usability. In the finance industry, where datasets are often massive and multifaceted, sophisticated preprocessing techniques are employed to clean and structure the data.
Data preprocessing can involve tasks like handling missing values, normalizing data, and encoding categorical variables. These techniques transform raw data into a format that AI models can readily digest, making it an essential part of the AI forecasting process.
Model Training and Learning
The heart of AI forecasting lies in model training. During this phase, AI models, which can take various forms, such as neural networks or regression models, are introduced to the preprocessed data. The models learn from the historical data patterns and relationships, extracting valuable insights that are crucial for making future predictions.
For example, in the agriculture sector, where crop yield forecasting is pivotal, models are trained on historical weather data, soil conditions, and past crop performance. The ability of AI models to analyze these complex interactions enables more accurate predictions of future crop yields, aiding in crop management and food production.
Validation and Testing
Before AI forecasting models are put into practical use, their performance is rigorously evaluated. Validation and testing datasets, separate from the training data, are used to assess how well the model generalizes to new, unseen data.
In the energy sector, utilities rely on AI forecasting models to predict electricity demand. To ensure the reliability of these models, they validate them by comparing predicted values against actual consumption data. This validation process helps fine-tune the models and ensures they can make accurate forecasts in real-world scenarios.
Continuous Learning
AI forecasting is an ongoing and iterative process. As new data becomes available, the AI model has the flexibility to adapt and improve its predictions. In the transportation industry, ride-sharing platforms like Lyft provide a prime example of this continuous learning.
Lyft's AI forecasting models are constantly updated to adapt to changing ride demand patterns. Factors like time of day, special events, and traffic conditions are considered in real time to optimize ride allocation and pricing. This adaptability ensures that users receive efficient and reliable service.
Business Benefits of AI Forecasting
The strategic integration of AI has emerged as a game-changer in business. AI, particularly in the area of forecasting, offers a potent arsenal of capabilities that empower organizations to make informed decisions, optimize costs, and elevate the customer experience. The business benefits of AI forecasting can be explained as follows:
Improved Decision-making
Decision-making in business is not merely about intuition and experience; it's about leveraging data-driven insights. AI forecasting is a catalyst for improved decision-making, offering unparalleled precision and foresight.
Traditional forecasting methods often grapple with the dynamic and intricate nature of data. AI forecasting, underpinned by advanced machine learning algorithms, excels in deciphering complex patterns within vast datasets. This is exemplified by the retail industry.
By implementing AI forecasting, retail majors like Target achieved a remarkable improvement in demand forecasting accuracy. This translated into optimized inventory levels, reduced costs, and enhanced customer satisfaction. The precision of AI-driven forecasts enabled it to respond swiftly to changes in consumer behavior, ensuring that popular products were always available and excess inventory was minimized.
This heightened accuracy extends to various sectors, including finance and weather forecasting. For instance, several banks employ AI forecasting models to predict financial market trends. The precision of these models enhances the decision-making process, enabling them to make timely and well-informed investment decisions on behalf of their clients.
AI forecasting doesn't stop at accurate predictions; it also equips organizations with invaluable data-driven insights that fuel strategic planning. In the US healthcare sector, AI has made significant strides in this regard. For instance, Mayo Clinic, a renowned US-based healthcare provider, uses AI forecasting to analyze historical patient data and predict disease outbreak patterns. These insights not only guide resource allocation but also inform proactive measures to combat the spread of diseases. Such data-driven strategies are instrumental in ensuring the timely deployment of medical resources during critical situations, ultimately saving lives and optimizing healthcare delivery.
Cost Reduction
Cost efficiency is crucial to ensure profitability, and AI forecasting has emerged as a key driver in reducing operational costs across diverse sectors. Traditional, static models for resource allocation often fall short in dynamic environments. AI forecasting, in contrast, dynamically adjusts resource allocation based on real-time data and predictive insights. AI forecasting fosters cost reduction by enabling efficient resource allocation, supply chain optimization, and waste minimization across industries.
For instance, Boeing, a prominent aerospace manufacturer, relies on AI forecasting to optimize production schedules and resource allocation. By accurately predicting demand for aircraft components, Boeing minimizes excess inventory and reduces production costs. The result is a leaner and more cost-effective manufacturing process, ultimately enhancing profitability.
AI forecasting has a remarkable impact on minimizing waste and operational costs in various sectors. A shining example can be seen in the US energy sector. Pacific Gas and Electric Company (PG&E), a major energy utility in California, employs AI forecasting to predict electricity demand patterns. By accurately anticipating peak usage times, PG&E optimizes energy generation and distribution. This not only reduces operational costs but also minimizes the strain on the grid during high-demand periods, enhancing overall energy reliability.
In agriculture, John Deere, a renowned agricultural machinery manufacturer, employs AI forecasting to optimize crop planting and maintenance. By predicting optimal planting times and irrigation needs, farmers can minimize resource wastage, such as water and fertilizer, while maximizing crop yields.
Enhanced Customer Experience
To be successful in business, enhancing the customer experience is critical, and AI forecasting empowers organizations to achieve that. AI forecasting, driven by machine learning algorithms, enables businesses to meet these expectations by analyzing vast datasets of customer behavior and preferences.
In the e-commerce industry, Amazon sets a remarkable example. Amazon's recommendation engine leverages AI forecasting to suggest products based on individual customer browsing and purchase history. This level of personalization not only enhances customer engagement but also significantly increases sales conversion rates.
Timeliness is another critical component of customer satisfaction, and AI forecasting plays a pivotal role in ensuring timely service delivery. This is vividly illustrated in the US transportation sector. Uber, the ride-sharing pioneer, relies extensively on AI forecasting to predict ride demand patterns. By accurately anticipating peak demand times, Uber ensures that customers receive timely transportation services, leading to increased customer satisfaction and loyalty. This proactive approach to service delivery has been a key driver of Uber's success.
Impact of AI Forecasting on Various Business Areas
AI forecasting has emerged as a game-changing tool that transforms the way organizations operate and make decisions across various business domains. Its influence is far-reaching, enhancing competitiveness, driving innovation, optimizing marketing and sales efforts, managing risks, promoting sustainability, and fortifying supply chain resilience. The impact of AI forecasting across various business areas can be explained as follows:
AI Forecasting and Competitive Advantage
In today's fiercely competitive business landscape, securing a competitive edge is paramount. AI forecasting equips organizations with the capability to gain that edge. By harnessing historical data, machine learning algorithms, and predictive analytics, businesses can anticipate market trends, customer preferences, and even competitor moves. This foresight enables proactive strategies, precise resource allocation, and the ability to capitalize on opportunities swiftly.
In the competitive world of e-commerce, companies like eBay and Alibaba are utilizing AI forecasting to secure a significant competitive edge. By analyzing vast volumes of customer data and predicting purchasing behaviors, they can tailor product recommendations in real time. This personalization has led to substantial revenue growth and solidified its position as a market leader.
AI Forecasting and Innovation
Innovation is the lifeblood of progress, and AI forecasting plays a pivotal role in driving innovation across industries. By analyzing vast datasets and identifying emerging patterns, AI systems can uncover opportunities for product or service enhancements. They can also predict market gaps and consumer needs, providing valuable guidance for research and development efforts.
Innovation is at the heart of companies like Tesla. They employ AI forecasting to analyze data from a network of sensors on their electric vehicles. By accurately predicting maintenance needs and potential failures, Tesla can proactively service vehicles, enhancing customer satisfaction and setting new standards in the automotive industry.
AI Forecasting in Marketing and Sales
AI forecasting is a game-changer in marketing and sales. It enables businesses to move beyond traditional demographic targeting and adopt a more personalized approach. By analyzing historical customer data and market trends, AI systems can predict individual preferences, buying behaviors, and the most effective channels for engagement.
Retail giant Costco, a prominent player in the United States, leverages AI forecasting to optimize its marketing and sales strategies. By analyzing historical sales data, consumer trends, and even external factors like weather, it tailors promotions and product placements to match consumer demand. This approach has resulted in increased sales, reduced waste, and improved customer experiences.
AI Forecasting and Risk Management
Risk management is a critical aspect of business operations, especially in industries like finance and insurance. AI forecasting empowers organizations to assess and mitigate risks more effectively. By analyzing historical data, market trends, and external factors, AI systems can predict potential risks and identify strategies to mitigate them.
The insurance industry is transforming with the adoption of AI forecasting. Progressive, an insurance company in the United States, uses AI models to assess driver behavior and risks. By collecting real-time data from telematics devices, Progressive can offer personalized insurance rates, attract safer drivers, and minimize risks, leading to higher profitability.
AI Forecasting and Sustainability
Sustainability has become a core concern for businesses worldwide. AI forecasting plays a significant role in promoting sustainable practices. By analyzing data related to resource consumption, energy usage, and environmental impact, AI systems can optimize operations to reduce waste, lower carbon footprints, and promote responsible resource allocation.
Sustainability is a core focus for companies like Google. The tech giant employs AI forecasting to optimize its data center's energy consumption. According to a report by Ericsson, the introduction of each new mobile network generation has led to a rise in energy consumption. As we move into the era of 5G with its expanded spectrum and increased site densification requirements for emerging use cases, this upward trend is expected to persist. The report further added that the key to achieving this transformation lies in harnessing the power of AI and ML for automation and informed decision-making.
AI Forecasting and Supply Chain Resilience
Supply chain disruptions can have devastating consequences for businesses. AI forecasting enhances supply chain resilience by providing real-time insights into demand patterns, potential disruptions, and alternative sourcing options. This level of visibility enables organizations to adapt swiftly to changing circumstances.
The COVID-19 pandemic highlighted the need for supply chain resilience, and companies like FedEx took swift action. FedEx employs AI forecasting to predict package shipment volumes and potential disruptions. By rerouting shipments, adjusting staffing levels, and optimizing routes in real time, FedEx maintains reliable deliveries, even during unforeseen challenges.
Key Industries Harnessing AI Forecasting
Across diverse industries in the United States, the adoption of AI forecasting has become a linchpin for innovation, efficiency, and informed decision-making. Let's explore how key sectors such as retail, finance, and healthcare leverage AI forecasting to drive transformative changes and unlock new possibilities supported by real-world case studies and industry statistics.
Retail
AI forecasting has revolutionized demand forecasting and inventory management, enabling retailers to optimize their supply chains and enhance customer satisfaction. Amazon, one of the largest e-commerce companies, relies heavily on AI forecasting for inventory management. By analyzing historical sales data, seasonality trends, and factors like customer reviews and shipping times, e-commerce companies optimize their inventory levels. This ensures that popular products are readily available, minimizing stock-outs and excess inventory costs.
According to Gartner, by the year 2026, the deployment of conversational AI systems within contact centers is poised to yield substantial cost savings, with a projected reduction of $80 billion in agent labor expenses. There are close to 17 million contact center agents worldwide. Gartner's projections suggest that by 2026, one out of every ten agent interactions will be automated. This marks a notable increase from the current estimated figure of 1.6% of interactions that leverage AI automation.
AI forecasting has also enabled retailers to implement dynamic pricing strategies that adapt to real-time market conditions and consumer behavior. This not only boosts profitability but also keeps pricing competitive and appealing to customers.
Prominent ride-sharing platforms have used AI forecasting to implement surge pricing during peak demand periods. By analyzing factors like rider demand, driver availability, and traffic conditions, these companies dynamically adjust pricing to incentivize more drivers to enter the market during high-demand times.
Finance
The financial sector is no stranger to the power of AI forecasting. Algorithmic trading, driven by AI models, has become a cornerstone of modern finance, enabling lightning-fast trading decisions and market predictions.
Renaissance Technologies, a renowned hedge fund, operates the Medallion Fund, which relies heavily on AI forecasting for trading decisions. Using complex mathematical models and machine learning algorithms, the fund can predict market movements and execute high-frequency trades with remarkable precision. Over the years, the Medallion Fund has consistently delivered exceptional returns, highlighting the effectiveness of AI forecasting in the finance sector.
AI forecasting is also instrumental in risk assessment and fraud detection within the financial industry. By analyzing vast datasets of financial transactions, AI models can identify anomalous patterns indicative of fraudulent activities.
Healthcare
Healthcare organizations harness the power of AI forecasting to predict and prevent disease outbreaks. The Centers for Disease Control and Prevention (CDC) in the United States utilizes AI forecasting to predict influenza outbreaks. By analyzing flu-related search queries on search engines and monitoring trends in emergency room visits, the CDC can forecast the spread of influenza with remarkable accuracy. This enables the timely allocation of medical resources, vaccination campaigns, and public health interventions to curb the impact of the flu season.
AI forecasting is also instrumental in optimizing patient care and predictive analytics in the US healthcare sector. By analyzing patient data, AI models can predict patient outcomes, optimize treatment plans, and enhance healthcare delivery.
The Cleveland Clinic, a renowned healthcare provider in the United States, employs AI forecasting to optimize patient care. By analyzing patient data, including vital signs, lab results, and historical health records, the clinic's AI models can predict which patients are at risk of deteriorating health. This enables the clinic to allocate resources efficiently, prioritize patient care, and reduce adverse events.
According to a report by McKinsey, increased use of AI has the potential to reduce costs by approximately 5% to 10% within the United States healthcare sector. This counts to an estimated annual saving of approximately $200 billion to $360 billion.
Challenges and Considerations
Organizations across the world increasingly embrace AI forecasting to drive innovation and enhance decision-making. These organizations must also confront a set of challenges and considerations inherent to this transformative technology. The challenges can be explained as follows:
Data Quality and Availability
Data is crucial for AI forecasting, and the quality of data used for AI forecasting is of great importance. Inaccurate or incomplete data can lead to flawed predictions and misguided decisions. According to a report by Gartner, poor data quality costs organizations in the United States an estimated $12.9 million annually.
Airbnb, a US-based hospitality platform, faced data quality challenges early in its journey. The platform relies on accurate property listings and pricing data for dynamic pricing strategies. Inaccurate or outdated listings can lead to pricing errors and dissatisfied customers. Airbnb addressed this challenge by implementing data validation processes and user-generated content guidelines to improve data quality.
The availability of relevant data is another hurdle. Some industries may lack access to comprehensive data, limiting the potential of AI forecasting. This is particularly evident in sectors with strict regulations or niche markets.
Ethical and Privacy Concerns
AI forecasting raises profound ethical and privacy considerations, demanding careful attention. The collection and analysis of vast datasets for AI forecasting can encroach on individuals' privacy. In the United States, data privacy regulations like the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA) mandate strict controls over personal data.
Facebook, a social media giant based in the United States, faced a massive data privacy scandal. The company's data-sharing practices with third-party entities came under scrutiny, leading to concerns about user privacy and data misuse. This case highlights the importance of transparent data handling and compliance with privacy regulations.
AI forecasting models can also inherit biases present in historical data, potentially perpetuating discrimination and inequality. This issue is particularly pronounced in applications like hiring, lending, and criminal justice. In the job market, biased AI algorithms in hiring processes have been identified. These algorithms, when trained on historical data, can favor certain demographics and perpetuate biases. Companies have encountered this issue and had to reevaluate their AI-driven hiring practices to ensure fairness and diversity.
Skill Gap and Workforce Training
Building and deploying AI forecasting models require specialized skills, creating a workforce gap. The field of AI forecasting demands a multidisciplinary skill set, including data science, machine learning, domain expertise, and programming skills. A shortage of professionals with these qualifications can hinder the adoption of AI forecasting.
Silicon Valley in the United States is a hotbed of technology innovation, but a talent war for AI and data science experts persists here as well. High demand for AI talent, limited availability, and fierce competition for skilled professionals highlight the skill gap challenge.
Closing the skill gap requires proactive workforce training and upskilling efforts. Organizations must invest in training programs to empower employees with the knowledge and expertise needed to harness AI forecasting effectively.
IBM, a leading technology company in the United States, initiated the IBM Skills Academy. This program provides training and certifications in AI and data science, equipping employees and clients with the skills required to excel in AI-driven projects. Such initiatives are instrumental in bridging the skill gap.
Future Trends in AI Forecasting
The world of AI forecasting is in a constant state of evolution, with technological advancements continually pushing the boundaries of what's possible. As organizations around the globe look to stay ahead of the curve, it's essential to keep a finger on the pulse of future trends in AI forecasting. The future trends in AI forecasting can be explained as follows:
Advancements in AI and Machine Learning
Advancements in AI and machine learning are at the forefront of AI forecasting's future. These technologies are continuously evolving, enabling AI forecasting models to become more powerful, accurate, and versatile.
Deep learning, a subfield of machine learning, has been a game-changer in AI forecasting. Recent breakthroughs in deep learning have allowed models to handle more complex data and make more accurate predictions.
The use of deep learning in NLP has seen remarkable advancements in recent times. Models like GPT, developed by OpenAI, are capable of understanding and generating human-like text. This technology has far-reaching implications for AI forecasting, especially in areas like sentiment analysis and customer behavior prediction. As NLP models become more sophisticated, they'll play a crucial role in understanding and forecasting human behavior and preferences.
Privacy concerns and data regulations have led to the development of federated learning—a decentralized approach that allows AI models to learn from data stored on multiple devices without centralizing sensitive information.
In the healthcare sector, federated learning is being explored as a way to share patient data securely for research and AI forecasting. Hospitals and research institutions can collaborate on predictive models without sharing individual patient data. This approach ensures data privacy and compliance with regulations like HIPAA.
Integration of AI Forecasting with IoT and Big Data
The integration of AI forecasting with the Internet of Things (IoT) and Big Data is another trend that will significantly shape the future. The convergence of these technologies will unlock new possibilities for predictive capabilities. The proliferation of IoT devices, which can include anything from smart thermostats to industrial sensors, generates massive amounts of real-time data. AI forecasting will leverage this data to make predictions that were previously impossible.
In the manufacturing sector, AI forecasting combined with IoT sensors has been instrumental in predictive maintenance. Sensors on machinery collect real-time data, which AI models analyze to predict when equipment maintenance is needed. This proactive approach minimizes downtime and maintenance costs, improving operational efficiency.
The synergy between AI forecasting and Big Data will also continue to expand. As the volume and variety of data grow exponentially, AI models will extract valuable patterns and trends, enabling organizations to make more accurate predictions.
In the retail industry, the combination of AI forecasting and Big Data has transformed customer behavior analysis. Retailers analyze vast amounts of data, including purchase history, online interactions, and social media activity, to predict customer preferences and buying patterns. This information helps retailers tailor marketing campaigns and product offerings to individual customers, enhancing the shopping experience.
Explainable AI and Ethical Considerations
As AI forecasting becomes more integrated into decision-making processes, the need for transparency and ethical considerations becomes increasingly evident. The complexity of AI models has raised questions about how decisions are made. Explainable AI (XAI) techniques will gain prominence, ensuring that AI-driven predictions are transparent and interpretable.
In the healthcare sector, XAI is crucial for explaining AI-based diagnoses. When AI systems provide medical recommendations or diagnoses, XAI techniques can generate clear explanations. This helps healthcare professionals understand the rationale behind the predictions and builds trust in AI-assisted medical decision-making.
Ethical considerations surrounding AI forecasting are equally critical. Organizations will adopt ethical AI frameworks and guidelines to ensure fairness, prevent bias, and protect user privacy. In the financial industry, ethical AI frameworks are essential to ensure fair lending practices. AI models that determine creditworthiness must be carefully monitored to avoid discriminatory outcomes. Ethical AI guidelines help financial institutions comply with regulations and promote fairness in lending.
Summing up
As the saying goes, 'the only constant is change,' and this holds for the business world as well. Change is inevitable, and organizations that fail to adapt to these changes risk falling behind. The adoption of AI forecasting is not just an option but a wise strategic decision for businesses that seek success in the 21st century.
AI forecasting is not limited to tech giants or Fortune 500 companies. Small and medium-sized enterprises (SMEs) can also harness the power of AI to gain a competitive edge. Cloud-based AI solutions and the availability of AI-as-a-service platforms have democratized access to this technology, making it accessible and affordable for businesses of all sizes.
Artificial intelligence, with its sophisticated algorithms, machine learning capabilities, and data-driven insights, is reshaping the very fabric of business operations. It has moved beyond being a mere tool to a strategic partner in the decision-making process. The exponential growth of data, fueled by the digital age, has made it humanly impossible to process and analyze information effectively.
Cogent Infotech help organizations solve complex problems with technology with expert consultancy services. Visit our website to read more informative and insightful blogs.
| 2023-01-01T00:00:00 |
https://www.cogentinfo.com/resources/shaping-the-future-the-business-impacts-of-ai-forecasting
|
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Top Digital Transformation Statistics 2025: Market, ROI & ...
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Top Digital Transformation Statistics 2025: Market, ROI & Trends
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https://www.myhubintranet.com
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Workers with data science skills are preferred by 69 percent of employers. To boost productivity, 55 percent of companies adopt workplace transformation ...
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‘Digitize to survive’ is a mantra often heard since Covid closed workplaces and forced business online. However, digital transformation is hardly a new idea. And while the pandemic has undoubtedly highlighted just how important it is to business growth, it’s still poorly understood. Ask a random sample of business owners what digital transformation is, and most will say it’s the latest tech, software tool, or app. But that’s only part of the story. Our selection of need-to-know digital transformation statistics gives you the complete picture.
Explore what it means for organizations today and how digital transformation drives efficiencies and productivity while enhancing the customer experience.
Our comprehensive list is broken down into different categories, so you can dive straight into what’s crucial for you. And we reference the top research from authoritative sources like McKinsey, Deloitte, and Gartner with the most up-to-date data.
Use our selection of stats to assess your digitization needs, develop an action plan and prepare for the future.
What Is Digital Transformation?
Digital transformation (DT or DX) means using technology to improve business processes, drive growth and improve the customer experience.
Various technologies come under the DX umbrella, including cloud, AI, machine learning, big data, and the internet of things.
The Importance Of Digital Transformation
Traditional markets and bricks-and-mortar workplaces no longer constrain organizations. Digital transformation has ushered in new ways of doing business virtually. Worldwide, companies are reimaging their operations from the ground up.
There’s no doubt the pandemic has accelerated the DX trend. It’s now the primary driving force for innovation and operational efficiencies. And it’s not just big businesses and corporates that stand to gain. SMEs and sole traders are also jumping on the DX bandwagon.
So, investing in DX is a must if you want to outpace the competition and drive revenue growth.
Let’s dig deeper into some of these themes with our comprehensive list of stats. Find out what others are doing and what you need to do to stay ahead.
Investment In Digital Transformation
This category highlights the massive growth in investment in digital transformation. Global DX spending is predicted to double over the next four years.
However, despite the increased budget, most IT spending is on maintenance. Investment in innovation appears to be lagging.
Worldwide Spend On Digital Transformation
Global spending on DX is set to reach $1.8 trillion by 2022 and is predicted to increase to $2.8 trillion by 2025. Mordor Intelligence valued the worldwide digital transformation market at $998.99 billion in 2020 and forecasts an increase to $2744.68 billion by 2026. That’s a CAGR of 17.42 percent. According to the World Economic Forum, the societal and industrial value of DX could reach $100 trillion by 2025. The IDC Worldwide Semiannual Digital Transformation Guide suggests DX spending between 2017 and 2022 will achieve a five-year compound annual growth rate of 16.7 percent.
How IT Budgets Are Spent
According to Deloitte, over 50 percent of the average IT department’s budget is spent on maintenance. Only 19 percent is allocated to investment. Research from Forrester shows a similar picture, with CIOs spending around 72 percent of IT budgets on existing IT issues and only 28 percent on innovations. The 2021 Spiceworks Ziff Davis State of IT study found that updating IT infrastructure is the primary driver of IT budget increases. According to the survey, 56 percent of organizations plan to increase IT spending. The Internet of Things accounted for the largest share of the overall digital transformation market in 2019. However, AR/VR technology is forecast to have the fastest growth rate until 2025.
Key Takeaways
As spending on DX continues to increase steeply, organizations may need to reassess how the money is spent. Striking the right balance between maintenance and innovation will be a juggling act and an ongoing challenge. Technology will continue to evolve – that’s a given, so there’s no standing still.
Who Manages Digital Transformation?
The research shows that C-suite executives are the driving force behind DX. In particular, the CEO is pivotal to successful digital transformation. However, IT decision-makers are also at the forefront and are vital to success.
CEOs are driving the development of a workplace transformation roadmap in 45 percent of organizations and contribute to its development in 63 percent of companies. According to research from MuleSoft, 97 percent of IT decision-makers are involved in DT projects. Successful digital transformation is 6 times more likely in organizations with an engaged Chief Digital Officer. HR is the principal owner in businesses with the most mature wellbeing strategies.
Key Takeaways
Senior executives and IT decision-makers must shape the DT strategy and lead from the front to achieve success.
Digital Transformation Statistics Current Status
Digital transformation is happening at pace across the globe. The vast majority of organizations have some DX initiatives in place, and the pandemic has fuelled the change. The industries leading the way are services, finance, and healthcare.
However, resistance to change and internal competition hinder progress in some businesses.
Reasons for DX Failure
McKinsey reports that 70 percent of digital transformations fail due to employee resistance. For 28 percent of companies, digital transformation is seen as a cost center, and 29 percent say there’s an absence of data to prove ROI. Resistance to change (26 percent) and compliance or legal concerns (26 percent) also get in the way of progress. Seventy-five percent of executives say that their business functions compete instead of collaborating on digital projects. Consequently, 64 percent are not seeing an uptick in revenue growth from digital investment.
Key Takeaways
DX is already a reality for most organizations. And it’s a primary driver for improved efficiencies and enhanced customer satisfaction. However, internal resistance to change is the main reason for failure. This underscores the importance of taking your people along on the DX journey.
Digital Transformation Statistics: Driving Forces
The pandemic has accelerated the growth of DX worldwide. Improved communication and collaboration are primary driving forces, particularly in the context of hybrid and remote working. However, increased efficiencies and streamlined business processes are also top DX priorities.
And specific industries have their own priorities. AI and mobile-first technologies are crucial for banking and financial services. And for retail, DX investment is likely to focus on improved competitiveness and stronger customer relationships.
DT And Workplace Collaboration
According to Forrester, 65 percent of surveyed companies encourage collaboration between the business, and analytics and data science teams. At 85 percent, most collaboration software users identify as being happy. At 56 percent, most companies use online collaboration platforms and social media for employee communication. Staff in collaborative workplaces are more than 50 percent more effective at getting daily tasks done than independent workers. Collaboration and teamwork improve company sales by 27 percent, product quality by 34 percent, and product development by 30 percent. At 83 percent, most workers rely on technology for collaboration with colleagues. Digital workplaces and online collaboration support increased productivity by as much as 30 percent. Collaboration and teamwork were identified as being very important by 75 percent of workers.
Impact Of Covid Pandemic On DX
DX And Financial Services
DT And The Building Industry
BIM (Building Information Modelling) will strongly impact the facilities management industry, according to 74 percent of facilities management firms. And 83 percent believe that BIM is already doing so and that this will continue for the next five years.
Digital Transformation Statistics For Retail
Fujitsu reports that in retail, the key drivers for digital transformation include: improved competitiveness (70 percent), reduced costs and increase in efficiencies (69 percent), stronger customer relationships (69 percent), improved revenue (67 percent), enhanced employee satisfaction (60 percent) and transformed businesses processes (53 percent).
Key Takeaways
This category of digital transformation statistics illustrates the main driving forces behind DX. Not surprisingly, the pandemic tops the list. Hybrid and fully remote working have thrust online communication and collaboration to the forefront. However, different industries also have their own agendas for DX. Improved efficiencies, increased competitiveness, and the customer experience are common priorities.
Digital Transformation Statistics: Challenges
Access to technology is still a challenge for many Americans. High-speed broadband is not universal, and some employees lack the necessary skills and knowledge for DX. When it comes to successful DX, the employee digital skills gap is probably the biggest challenge facing companies.
One employee survey found that at 35 percent, the most common barrier to digital transformation was the CEO. However, organizations with an on-side Chief Digital Officer were 1.6 times more likely to achieve a successful digital transformation. According to the US Federal Communication Commission, 21.3 million Americans had no access to wired or wireless broadband internet in 2019. However, BroadbandNow estimates that the number without high-speed internet is now twice that. And after 12 months of home working, according to Microsoft, 42 percent say they lack vital office supplies at home, and the internet connection for 1 in 10 employees is not good enough to get their work done. Forrester warns that one-third of organizations will fail at their first attempt at anywhere working. Up to 30 percent of businesses with mandated fully in-office work are expected to experience monthly employee quit rates as high as 2.5 percent. Pew Research found that 50 percent of the elderly reported a lack of knowledge and one-third a lack of confidence in using devices. In a survey from Sungard Availability Services, 69 percent said they did not have access to enough technology to make their work easier. And 63 percent believed the lack of technology hampered their ability to develop new skills at work. In research from Deloitte, 53 percent of organizations reported that a significant chunk of the workforce needs to update their skills and capabilities in response to digital progress. A survey by Ponemon found that 82 percent of IT security and C-suite executives had at least one data breach attributable to digital transformation.
The Employee Digital Skills Gap
In the Global Digital Skills Index, nearly 75 percent of workers do not feel equipped to learn the digital skills needed now, and at 76 percent, even more feel unequipped for the future. Salesforce and RAND Europe report that the digital skills gap disrupts business growth. According to the research, 14 of the G20 countries may miss out on $1.5 trillion in aggregate GDP growth. Over half of respondents to the Global Digital Skills Index want to upskill to help them climb the career ladder. Sixty percent of worldwide survey participants say skills in collaboration technology are most needed in businesses today. However, only 25 percent rated their collaboration technology skills as advanced. Almost half of participants in the Global Digital Skills Index see digital sustainability skills as important now or in the next five years. But only 25 percent say they have the advanced digital skills to promote sustainable business activities. Both the World Economic Forum and McKinsey report that organizations will have increasing skills gaps with employers now looking for workers with critical thinking, analytical and problem-solving abilities, plus adaptability, resilience, and self-management. McKinsey’s survey found that 89 percent of global companies currently have or expect to have a skills gap.
Key Takeaways
Access to high-speed broadband remains an issue for many. And as hybrid working is forecast to grow, this could become a much bigger issue.
Furthermore, addressing the employee digital skills gap should be a top priority for DT strategies. The digital transformation statistics in this section echo the need for a human-centric approach to transformation.
Digital Transformation’s Impact on Employees
Aside from the digital skills gap, DX is impacting employees in other ways. Workers have problems with overload, distractions, and access to the right technology and tools.
Furthermore, employee engagement rates remain relatively low nationwide despite the real benefits to productivity and revenue.
The pandemic has highlighted the importance of employee wellbeing. And with the Great Resignation in full swing, employee retention is also a hot issue.
According to Infosys Digital Radar 2022, technology adoption has grown exponentially since the pandemic, setting a new minimum standard for businesses. However, those getting the most out of technology have emphasized the human element. ScienceDirect identifies five technology-related pressure points on employees: overload, distractions and interruptions, invasion into life outside work, monitoring, and the pace of digital work. Only 30 percent of workers in research from Qualtrics say their experience with company technology exceeded their expectations. In a survey of business owners and employees, 86 percent attributed workplace issues to poor or inadequate communication tools. When it comes to distractions using online collaboration tools, 41 percent are down to personal conversations, and 39 percent are caused by requests taking staff off-task. Forty percent of workers believe organizations should have stricter rules around using online collaboration tools in the workplace.
Employee Engagement
Only 21 percent of employees are engaged at work, according to Gallup’s State of the Global Workplace report 2022. Gallup’s meta-analysis found that organizations scoring the highest on employee engagement were 21 percent more profitable than those with low engagement scores. Businesses with highly engaged workforces were also 17 percent more productive. Companies with a robust corporate culture have more than four times higher revenue growth. Think With Google reported a 9 percent increase in Google searches related to team building in 2020.
Employee Wellbeing
Deloitte reports that 80 percent of companies believe employee wellbeing is important or very important to business success, but only 12 percent say they are equipped to tackle this issue. In the Global Human Capital Trends survey, 95 percent of HR executives agree that employee burnout impacts retention. Research from Limeade and Quantum Workplace suggests that workers with higher levels of wellbeing are more likely to be engaged at work, enjoy their jobs, and recommend their employer.
Employee Retention
Over 63 percent of surveyed businesses with 50-500 employees say that retaining workers is harder than recruiting them. FlexJobs reports that 79 percent of workers would be more loyal to companies offering flexible work options. According to Owl Labs, US employers with the option to work from home reduce employee turnover by 25 percent. Workers with managers who act on their feedback are four times more likely to stay with the company.
Key Takeaways
The impact of DX on your people is significant. The flow-on effects are far-reaching, stretching into engagement, wellbeing, productivity, and retention. Success hinges on getting the workforce on board.
Impact of DX On The Customer Experience
Enhancing the customer experience is a huge impetus for DX. Companies are investing heavily to bring about improvements. However, digital transformation initiatives are not always driven by informed customer research and often don’t meet customers’ expectations.
Forrester predicts that customer interactions through digital platforms will increase by 40 percent. Over 70 percent of B2B organizations’ digital experiences do not meet customers’ needs or expectations. In 2022, business spending on customer experience is expected to reach $641 billion. According to McKinsey, focusing DX on the customer experience can result in a 20-30 percent increase in customer satisfaction and economic gains in the region of 20-50 percent. Forbes reports that 40 percent of consumers end up on a competitor’s website if they have difficulty accessing a business’s mobile site. Most customer-centric organizations (84 percent) emphasize the mobile customer experience. Up to 74 percent of business buyers are willing to pay more for a better B2B experience. Current DX efforts are focused on modernizing customer touchpoints (54 percent) and enabling infrastructure (45 percent). However, 41 percent of organizations invest in digital transformation without thorough customer research.
Key Takeaways
We can see from this selection of digital transformation statistics that DX is crucial to the customer experience. Nowadays, consumers have high expectations and will swiftly turn to the competition if they are not satisfied. However, businesses must ensure their DX initiatives are informed and aligned to customer research.
Digital Transformation Statistics For Remote Work
Remote work is here to stay. It offers employees a better work-life balance, and many would quit their jobs if forced to return to the office full-time.
And there are plenty of benefits for employers, including reduced central overheads and improved productivity.
However, remote work has its challenges. And DX initiatives will need to address communication issues and the need for human connection.
According to Forrester, remote work is now the norm and will rise by 300 percent compared to pre-Covid levels. Global Workplace Analytics reports a 173 percent increase in remote working from 2005 to 2018. Up to 16 percent of global businesses are now fully remote. Deloitte reports that nearly 75 percent of workers prefer a balance of working remotely and in the office. And 66 percent of global employers in Microsoft’s Work Trend Index are designing workplaces for hybrid arrangements. TECLA reports that 85 percent of managers believe teams with some remote workers will become standard. According to Owl Labs, small businesses are twice as likely to hire full-time remote employees.
Benefits Of Remote Work
For staff, the main benefits of home working are no commute (47 percent of respondents) and more flexibility (43 percent). According to FlexJobs, reasons for remote workers feeling more productive include the following: fewer interruptions – 68 percent, quieter working environment – 68 percent, more comfortable setup – 66 percent, better able to focus – 63 percent, no office politics – 55 percent. Flexjobs says 3.9 million employees work at least some of the time remotely. These workers reduce greenhouse gas emissions by the equivalent of taking 600,000 cars off the road for a whole year. The EPA says that 86 percent of commuters drive to work. The majority (77 percent) of remote workers say working from home has increased their productivity. According to Global Workplace Analytics, organizations save on average $11,000 per year for each half-time remote worker.
Remote Work And Employee Preferences
HR consultants Robert Half’s survey found that 60 percent of staff who shifted to remote have a better work-life balance without the daily commute. Microsoft report that 52 percent of survey respondents feel more valued and included in remote meetings as everyone is in the same online room. However, the same survey shows that 60 percent of people feel less connected to their colleagues since moving to remote. In research from Qualtrics, 35 percent of workers would look for a new job if forced to return full-time to the office. When it comes to flexible working, 69 percent of millennials will swap certain work benefits for a more flexible working environment. According to Owl Labs, 32 percent would quit their job if they could not continue working from home.
Challenges Of Working From Home
According to Microsoft’s Remote Work Trend Report, weekend working has increased. Teams’ chats on Saturdays and Sundays have risen by 200 percent. Furthermore, workday Teams’ chats between 8-9 am and 6-8 pm have increased by 15 percent and 23 percent, respectively. Gensler reports that 50 percent of Gen Z and Millennials are distracted when working from home. Communication is an obstacle for 20 percent of remote workers. Gensler reports that despite the widespread adoption of virtual collaboration software, people-related reasons are still the most important reason employees go to work. The survey found that 55 percent said collaboration was harder, and 51 percent said staying updated on colleagues’ work was more difficult when working remotely. According to Microsoft’s Work Trend Index, companies do not contribute to remote work expenses for 46 percent of employees. Forbes reports that 20 percent of remote employees sometimes feel lonely working from home and say they miss a sense of belonging. A survey from OpenVPN found that 54 percent of IT professionals think home working is a greater security risk.
Key Takeaways
Remote and flexible working is the preference for most employees and many employers. However, it also presents challenges for the workforce, which your DX strategy will need to consider. Distractions, loneliness, and a feeling of disconnection are very real issues. And the pressure to be always available is impacting employees’ sense of work-life balance.
The Future Of Digital Transformation And Trends
Investment in DX is expected to grow as flexible, hybrid working continues, and online markets take over from traditional storefronts.
Internet-based collaboration tools and AI-powered platforms are on the rise as the employee experience increasingly merges with the customer experience.
Tech Sectors On The Rise
Key Takeaways
Hybrid and remote working will continue to drive DX projects. Virtual platforms look set to take over from in-person meetings and events. And online collaboration and communication remain top DT priorities.
However, technologies to watch for the future include AI and chatbots, AR, VR, and 3D printing.
Digital Transformation Statistics: What You Need To Know
The evidence is overwhelming: there’s no stopping digital transformation. The pandemic has simply revved up and accelerated the process. From remote working to e-commerce, the changes brought about by Covid will be felt long after the health crisis is over.
However, investing in new technologies like AI or business intelligence is only part of the recipe for success. A common theme throughout the data is the need for businesses to also invest in their people. Without a digitally prepared and educated workforce, DX will likely be more about stagnation than transformation.
This comprehensive selection of statistics will help you identify the DX priorities in your business. Use the data to work up your digital business strategy and beef up an existing one. Fail to do so, and you run the risk of being left behind.
Digital Transformation Statistics [Infographic]
Digital transformation is already a reality for most businesses. Perhaps you’re updating your current DX strategy and want confirmation you’re on track.
If you are facing challenges with digital transformation, MyHub’s cloud-based intranets could be part of the solution. Find out more with a free demo or no-obligation 14-day trial.
FAQ Section
What does “digital transformation” mean?
It’s the strategic use of technology—like cloud, AI, IoT, data—to fundamentally improve business processes, customer experiences, and operational agility.
How much are organizations spending on digital transformation?
Global DX spending reached $1.8 trillion in 2022 and is expected to hit $2.8 trillion by 2025. From 2020 to 2030, DX investment grows at an 18% CAGR.
What are the main challenges in successful DX?
The key obstacles include employee resistance (70%), unclear ROI, insufficient digital skills (75% feel underprepared), and legal or compliance concerns.
Tune In: The Podcast Version of This Post
| 2025-06-19T00:00:00 |
2025/06/19
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https://www.myhubintranet.com/digital-transformation-statistics/
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[
{
"date": "2023/01/01",
"position": 63,
"query": "workplace AI adoption"
}
] |
Development Barriers - Savings and Investment Gaps
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Development Barriers - Savings and Investment Gaps
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https://www.tutor2u.net
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[] |
Examiner AI. Search. Sign in · Economics. Explore Economics. Overview; Free ... Could Universal Basic Income out-perform conventional Overseas Aid? 5th ...
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In this short video we look at some evidence on savings-investment gaps in a number of developing/emerging countries and how the savings-investment gap might be overcome.
Savings-Investment Gaps
| 2023-01-01T00:00:00 |
https://www.tutor2u.net/economics/reference/savings-investment-gaps
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[
{
"date": "2023/01/01",
"position": 59,
"query": "universal basic income AI"
}
] |
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Improving Circularity in IT Industry in UAE By Recycling and ...
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Improving Circularity in IT Industry in UAE By Recycling and Distributing Pre-owned Laptops
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https://sdgs.un.org
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[] |
By leveraging cloud-based inventory management systems, AI-powered ... universal and affordable access to the Internet in least developed countries by ...
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Implementation of the Project/Activity
1. Sourcing Pre-owned Devices: Dithari begins by sourcing pre-owned laptops and smartphones from various channels, including individual consumers, businesses, and governmental agencies. The company collaborates with trusted partners and customers across the UAE and beyond to gather electronic devices that are no longer in use.
2. Inspection and Quality Assessment: Once the devices are collected, they undergo a rigorous inspection and quality assessment process at Dithari’s facilities. Each device is thoroughly examined to determine its condition, whether it can be refurbished, or if it should be recycled for parts and materials.
3. Refurbishment and Recycling: Devices that meet refurbishment criteria are repaired, updated with the latest software, and re-engineered for optimal performance. In cases where refurbishing is not possible, devices are dismantled, and valuable components are extracted for reuse or recycling. Dithari ensures that this process adheres to international standards for environmental safety, reducing the e-waste footprint.
4. Distribution of Re-engineered Devices: Once refurbished, the electronic devices are distributed back into the market through Dithari’s retail and e-commerce platforms. These devices are offered at competitive prices, making them accessible to a broader audience, including schools, SMEs, and individual consumers, promoting responsible consumption.
5. Collaboration with Key Stakeholders: Dithari actively collaborates with local authorities, corporate partners, and civil society to streamline the value chain and raise awareness about the importance of circularity. By working closely with the UAE government and other stakeholders, Dithari ensures that its operations align with national sustainability goals.
6. Awareness and Advocacy: In addition to recycling and refurbishment, Dithari engages in awareness campaigns to educate consumers and organizations about the benefits of prolonging the lifecycle of IT products. Through workshops, seminars, and partnerships with educational institutions, Dithari advocates for sustainable IT consumption.
7. Monitoring and Reporting: Dithari tracks the performance of its initiative through measurable KPIs, such as the number of devices recycled, refurbished, and distributed. This data is periodically reported to stakeholders and is aligned with the UN SDG platform to demonstrate how the initiative contributes to SDG 12 (Responsible Consumption and Production).
| 2023-01-01T00:00:00 |
https://sdgs.un.org/partnerships/improving-circularity-it-industry-uae-recycling-and-distributing-pre-owned-laptops
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[
{
"date": "2023/01/01",
"position": 99,
"query": "universal basic income AI"
}
] |
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Innovation Fuels 50% of GDP Growth per Economists
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Innovation Fuels 50% of GDP Growth per Economists
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https://theinnovators.network
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[
"Phil Mckinney",
"View All Posts",
"Phil Mckinney Is An Innovator",
"Thinker",
"Creator",
"Author",
"Speaker. He Is The Former Cto Of Hp Who Has Built Teams That Have Innovated Award-Winning Technologies",
"Products Currently Used Half-Billion People Worldwide.",
"Fast Company",
"Businessweek Named These Teams As Being Among The"
] |
AI is changing business decision-making processes by using data to make better decisions and predictions about future events or outcomes. With AI becoming ...
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The role of innovation in economic growth has become increasingly important. Innovation is essential for a healthy and thriving economy because it increases productivity, boosts consumer confidence and spending, generates increased profits for businesses, and creates new jobs.
The result is that economists have calculated that approximately 50% of annual GDP growth is attributed to innovation .
It’s no secret that innovation is a crucial factor for economic growth. According to the Organization for Economic Co-Operation and Development (OECD), nations with environments conducive to innovation witness higher GDP growth rates than their peers who lack these conditions.
By fostering innovation, we can create entirely new industries and revenue streams. The internet is a prime example of this: it has completely transformed how we conduct business and opened up a world of opportunity in terms of markets for goods and services.
Public and Private Collaboration
Innovation is not something that can happen in a vacuum, however; public and private collaboration is essential for it to be successful.
Innovation is a key driver of economic prosperity, and governments must ensure that they equip their economies with the right tools and policies to facilitate innovation. Governments can provide research, development, and innovation resources and create incentives for businesses and entrepreneurs to invest in new technologies, products, services, and processes.
Private sector firms also have a key role in encouraging economic growth through innovation. Companies can invest in research and development or collaborate with universities or other organizations to develop new products and services. They can also focus on new and emerging markets, use the latest technologies, and work to create a culture of innovation within their organization.
Innovation Trends and Technologies
The introduction of novel new ideas has dramatically altered innovation trends, allowing businesses to innovate faster and strategically while unlocking an array of new opportunities.
These new innovations include:
Automation
Automation impacts job creation both positively and negatively. Which can lead to reduced costs, increased productivity, and enhanced quality in manufacturing processes. However, it also poses a risk to jobs by lowering employment opportunities while potentially causing inequality issues among levels of workers.
Digital Transormation
Digital transformation aims to re-energize small businesses across sectors. From e-commerce platforms providing easy access for customers — to machine learning algorithms offering tailored recommendations for businesses, digital technology provides an array of tools that can help drive business growth.
Artificial Intelligence (AI)
AI is changing business decision-making processes by using data to make better decisions and predictions about future events or outcomes. With AI becoming increasingly integrated into society, businesses must leverage this innovation to stay competitive.
Benefits of Innovation on the Economy
The benefits of innovation to the economy are far-reaching and can lead to increased efficiency, reduced costs, greater access to information, and increased participation rates.
Innovation can lead to improved production processes and new technologies that reduce the costs of goods and services. It can also help improve the efficiency of these products, allowing for greater output with the same amount of input. This can lead to a reduction in prices for consumers, as well as increased profitability for businesses.
Innovation can lead to higher economic participation rates. This is especially beneficial for small businesses that may not have had the resources before digital transformation made it easier for customers and companies. Digital technology has also opened up new markets for products and services, resulting in an increase in economic activity and job creation, further contributing towards overall economic growth on a larger scale.
Innovation stimulates entrepreneurship and investment opportunities due to its ability to create entirely new industries from scratch and provide incentives for private sector firms to invest in research & development activities that could potentially bring long-term gains. These factors can lead to higher levels of investment and productivity, resulting in increased economic growth overall.
Economic Challenges Posed by Innovation
The benefits of innovation are undeniable, though we must remain mindful of the potential risks that come with it. These risks must be understood, addressed, and managed to ensure sustainable growth while avoiding potential economic and societal harm.
Innovation can lead to an unfavorable outcome of job losses, particularly due to automation. It is captivating for its potential in decreasing costs, raising output, and improving quality throughout the manufacturing process. However, automation also presents a risk of reducing employment prospects for low-skilled workers.
Another challenge posed by innovation is the disruption to traditional industries and existing regulations and laws. Digital transformation provides an array of tools that can help organizations succeed, but this could mean significant changes or shifts in how certain businesses operate or interact with their customers—which could be difficult for some companies or governments to manage. Additionally, digital technology could blur boundaries between markets, threatening cultural norms if not handled properly by regulators.
Finally, there is also a concern surrounding data privacy when using AI-driven algorithms or other forms of digital technology—as this could put customers’ personal information at risk if not handled securely (such as through encryption). This would require stricter security measures from companies handling sensitive information and greater enforcement from regulatory bodies monitoring such areas within society.
Conclusion
Given innovation’s positive and negative impact on the economy, there needs to be a call for balanced innovation strategies that prioritize economic growth while understanding and minimizing the unintended consequences. This would involve thinking through potential scenarios, assessing the risks that come with them, and developing strategies to ensure a safe and successful transition as innovations are adopted.
In conclusion, innovation is already driving economic growth and creating new opportunities for businesses and individuals alike—but it must be done responsibly. With appropriate policies and regulations, innovation can feed economic growth and drive the global economy forward while avoiding the potential pitfalls that come with it.
| 2023-01-01T00:00:00 |
2023/01/01
|
https://theinnovators.network/innovation-fuels-50-of-gdp-growth-per-economists/
|
[
{
"date": "2023/01/01",
"position": 79,
"query": "AI economic disruption"
},
{
"date": "2023/01/01",
"position": 76,
"query": "AI economic disruption"
}
] |
Global Workforce of the Future: Lessons for Policy Makers
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Global Workforce of the Future: Lessons for Policy Makers
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https://www.adeccogroup.com
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In this paper, we address how workers around the world view the role of the government in ensuring a better working life.
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Governments though may not always be best placed to provide this support. Indeed, 29% of workers have relied on government unemployment agency to find career advice, but only 12% of those who did so found it to be effective.
There are two roles the public authorities can take to advance this cause: first through incentivizing businesses to provide career support and ensure the activities are best linked to the current state of one professional path. Second, private providers can help government agencies to deliver the tools workers are looking for. These current preferences by workers indicate that there is a lot of potential for private providers to make their career transition and development solutions known to workers.
Government responsibilities – what workers want
Workers generally believe governments should do more to regulate a range of issues that impact labour markets, with only small numbers opposing more regulation. The most notable areas where workers are looking for more regulation are on flexible working schedules and dismissal protection. This combination is an interesting one, showing that workers are simultaneously looking for both flexibility and security.
Other elements where workers would expect a larger role from governments include working hours, and to a lesser degree a 4-day week and shift work. Training is another area where workers expect support. 69% of workers agreed governments should do more in this area, thereby being the third priority for workers, and only 6% think that no government action is needed. For example, governments could consider innovative financing mechanisms for re- and upskilling like Individual Learning Accounts, as well as ensuring better links between the world of education and the world of work by investing in vocational training and apprenticeships.
After the experience from the Covid pandemic, it should not come as a surprise that workers also expect more clarity on remote working, although 9% of workers do not agree that more regulation is needed in this space.
The most contentious issue is regulation on the use of AI in the recruitment, where a much smaller proportion of workers sees a need for governments to do more (43%), and a larger group disagrees that more regulation is needed (18%). This means that before passing sweeping legislation in this area, policy makers would do well to reconnect with voters.
| 2023-01-01T00:00:00 |
https://www.adeccogroup.com/future-of-work/latest-insights/global-workforce-of-the-future-lessons-for-policy-makers
|
[
{
"date": "2023/01/01",
"position": 10,
"query": "government AI workforce policy"
}
] |
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How AI is Revolutionizing the Travel Industry | Future Trends
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How AI is Revolutionizing the Travel Industry
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https://botshot.ai
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[] |
Artificial Intelligence or AI, refers to computers or machines performing ... OYO to Lay Off 600 Employees, Hire 250 Players for Sales Team. Softbank ...
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Artificial Intelligence (AI) in the travel industry is a topic of conversation in contemporary times. Here’s why...
Travel and tourism industry is growing rapidly as cities restore normalcy after the pandemic. With an increasing number of travelers, hospitality industry is relying on technology to serve guests, more than ever. As a result, different technologies are being used to simplify and streamline operations and raise guest satisfaction.
The advantages of Artificial Intelligence (AI) in the travel industry are enormous and far-reaching. It is transforming the way people travel. In fact, according to an IndustryArc report, the travel AI market is expected to exceed $1.2 bn by 2026.
This blog takes you through the ways in which AI has penetrated and how AI is impacting the travel and tourism industry.
What is Artificial Intelligence?
AI is changing the travel industry, sure. But what is AI?
Artificial Intelligence or AI, refers to computers or machines performing tasks that would generally need human intelligence to carry out. In simpler words, it is intelligence that perceives, synthesizes, and infers information, that is different to that of humans or other living beings.
AI has been in existence for a long time now. However, with necessary advancements, it has recently become reliable enough to carry out complex tasks with minimal to zero assistance. Contemporary businesses are data-heavy. This helps AI to perform tasks ranging from processing, analysis of data, and problem-solving. AI in travel industry is simplifying processes, leaving less room for human errors and providing a more personalized experience to its guests. All of this whilst saving time and money!
AI in Travel Industry
No industry in today’s time is untouched by technology. Humans want to undertake practices that save time and lessen effort. They also want desired results from these practices. This could only be made possible through technology.
The usage of AI in travel industry has borne fruits so far. Guest feel seen, heard and served better. Let’s dive into the ways AI is used in travel and tourism industry:
1. Smart AI-powered travel applications
AI in travel industry enables a traveler to plan a personalized trip. A smart AI-powered application can help a traveler choose what suits him/her the best. These applications are used by various brands to help customize journey experience for a guest. They also offer travel mapping to provide guidance to travelers and help them explore the places they intend to.
2. Travel chatbots
AI-driven chatbots is a trending topic in hospitality industry from quite some time now. Booking queries were never handled in a timelier manner ever before. With travel chatbots, you can find all your answers at one place.
Chatbots assist travelers and tourists by providing all the vital information they would require before, during and after their travel journey. This makes the journey convenient and pleasant for travelers. According to a Thrivemyway blog, two-thirds of the people surveyed for ai chatbot related survey found a chatbot useful or very useful when making business and work travel arrangements. Furthermore, the blog states that 37% of users would prefer to deal with an intelligent chatbot, while making travel plans or comparing booking options.
Chatbots do make life easier for both businesses and guests.
Must Video:Freddie
3. Voice-based assistants
AI technology has laid foundation for a digitized hospitality industry. Voice-based digital assistance is being used across the globe by many hospitality companies. Through voice-based digital services, a visitor/guest can make quick reception desk requests, automate room control, do hands-free calling, among other advantages. Guests can further set the ambiance of the room according to their mood and vibe by regulating temperature, lighting, etc. through speech recognition and voice command.
4. Advanced personalization
With the help of AI, guests are being able to customize travel and journey experiences to a large extent. Through machine learning and data science, travelers get recommendations as per their needs. Tourist recommendation system provide guests with suggestions tailored to their needs. These systems enhance decision-making and planning, and better schedule procedures for guests.
Moreover, AI-driven products for marketing and promotional campaigns can be used to send targeted messages and emails. Discounts/offers could be communicated to the most valuable guests through the use of AI.
5. Data processing and analysis
One of the most crucial ways in which AI helps industries is data collection and analysis. Travel and tourism industry, with thousands of travelers visiting in a month, need assistance with data. AI sorts through huge amounts of data quickly. This benefits the hospitality industry as time and efforts of their human resources are saved and could be used at other touchpoints where human touch is required. Furthermore, the data is more accurate. This data then provides insights into human behavior. It is used to draw conclusions about business services, practices and pricing strategies. With AI, businesses could increase their conversion rates.
6. Predictive applications
Advanced AI-driven applications are being used to forecast flights and travel related needs like hotels, transportation, etc. Statistical models explaining emerging trends from different segments of the travel industry are analyzed to give such predictions. Through the forecasts, travelers become aware about the best time to book flights and hotel rooms.
7. Facial recognition
Facial recognition technology is being widely used in hospitality industry nowadays. Put in use for safety purposes and legal requirements, facial identification also helps people save time at airports, train stations, hotels, restaurants and large conferences and meetings. It recognizes people with saved feature visuals in the database from their last visit and shortens the process.
8. Baggage handling solutions
AI baggage handling solutions at the airport have definitely simplified things for the travel companies. Having to look after tons of stuff on a daily basis, automated baggage systems was the much-required technological advancement for the travel and tourism industry. Modern and advance baggage tracking and handling digitized methods ensure a seamless experience, and reliable services.
9. Robots
Robots are being widely used in the hospitality industry to reduce human burden, intervention and ensure efficiency in services. Robotic technologies are used to speed up check ins, assist with other activities like housekeeping, baggage handling, etc. Robots are also capable of providing travelers and tourist with information that is required through AI and machine learning. All this in their native language through embedded translation software. Furthermore, hotels use them to store tons of data about their clients for a more personalized experience. AI is changing travel industry through superior automated products and services.
10. AI-based sentiment analysis tools
Feedbacks given by travelers on different review platforms, through travel blogs, or social media are crucial to businesses in hospitality industry as well as other tourists and travelers. Text based comments, reviews and feedbacks are analyzed through machine learning algorithms and language technologies to understand guest experience and better services. Such data is highly beneficial for hotels, travel companies and agencies to evaluate the effectiveness of services and taking steps towards making required improvements.
Also Read: AI Chatbot in Hospitality Industry
What more to expect in the near future?
1. Smart travel in smart cities
Soon travelers wouldn’t be exploring cities, they would be exploring ‘smart cities’, rather more smartly. Cities would transition into smart cities with sensors to collect and manage data related information like traffic and tourist flows, pollution levels, etc. within a city. Input would be provided by them highlighting how to create better synergies between these factors in order run a city more efficiently. Although, this would be mainly done through Internet of Things (IoT), AI will be used to analyze the huge amounts of data collected.
2. Self-driving cars
Future would witness more cities adapting to the use of self-driving cars, buses and taxis. This would leave no room for human callousness or error while driving on roads and thereby reduce road accidents. It would further lead to less traffic jams that are caused by oblivious human drivers and their improper driving habits. AI technologies power self-driving cars. Hence, AI would change the way locals and travelers explore the cities.
Therefore, it is no surprise that advantages of AI in travel and tourism industry are multifold. AI is assisting businesses to streamline processes, deliver excellence to their guests and drive revenues. On the other hand, AI is personalizing the guest journey and serving their needs better. With more advancements in future, the hospitality industry would be able to provide their guests with exactly what they want.
Conclusion
There is no denying artificial intelligence's revolutionary influence on the tourism sector. AI is transforming passenger experiences and company operations through unmatched convenience, efficiency, and personalization. AI is establishing new standards for guest enjoyment and operational excellence in a variety of ways, from chatbots and smart travel applications to sophisticated data processing and predictive analysis.
There are countless opportunities to improve the travel experience as AI develops. The use of AI chatbots in the hotel industry is one of the most fascinating trends. In addition to streamlining correspondence and offering prompt support, these chatbots also pick up on visitor preferences and adjust accordingly, guaranteeing a smooth and customized experience.
Presenting BOTSHOT, the industry-leading AI chatbot for the hotel sector. Through the automation of repetitive operations, quick guest service, and customized recommendation engines, BOTSHOT assists hoteliers in providing outstanding guest experiences. Your hotel may boost revenue growth, improve visitor happiness, and improve operational efficiency.
Meet the AI-powered travel of the future and learn how BOTSHOT can revolutionize guest relations and hotel operations. To find out more and begin your path to a more intelligent, effective hospitality experience,
Book a demo and start your 14-day FREE trial today!
Frequently Asked Questions:
Q1.) What are the challenges of implementing AI in the travel industry?
A1.) Here are a few challenges involved in implementing AI in the travel industry -
As the AI systems gather vast amounts of data, it can create security and data privacy issues.
It can create biases for certain groups of travelers.
Accessibility issues have become a rising concern for travelers who have disabilities or lack of technology access.
AI creates certain legal and regulatory hurdles in the travel industry.
AI technology is exposed to certain bugs. Technology is at an evolving phase, and travel companies need to research a bit about exploring the full potential of this technology.
Q2.) How can AI help reduce operational costs in the travel industry?
A2.) AI-powered chatbots and virtual assistants revolutionize the travel industry by automating routine and time-consuming tasks such as booking confirmation, check-in requests, or guiding the user through the reservation process.
Thus, it reduces the operational costs.
Q3.) How does AI influence marketing strategies in travel and tourism?
A3.) AI in the travel industry goes beyond traditional travel processes by predicting travel needs and preferences.
This way travel and tourism companies can enhance customer engagement by providing personalized messages and targeted promotions. Thus, it can increase customer loyalty and they’re likely to book again.
Moreover, travel companies can use AI to predict travel trends in the future and customize their packages and marketing strategies accordingly.
Q4.) What is the future of AI in the travel industry?
A4.) The future of AI in the travel industry seems promising with various advancements in this field -
AI assistants simplify the booking process.
Robots and concierges have come to provide better service to travelers.
Travel companies are using big data to predict customer behavior and provide better solutions.
They’re curating personalized travel plans on apps/travel platforms depending on traveler history, preferences, needs, etc.
Q5.) Is AI revolutionizing the travel industry?
A5.) Yes, AI revolutionizes the travel industry in the following ways-
| 2022-04-15T00:00:00 |
2022/04/15
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https://botshot.ai/resources/blog/how-ai-is-transforming-the-travel-industry
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[
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"date": "2023/01/01",
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"query": "AI layoffs"
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Can AI chatbots do my marketing and accounts?
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Can AI chatbots do my marketing and accounts?
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https://www.businessgrowthhub.com
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[] |
In a global, interconnected, tech-driven economy, it is more important than ever for business leaders ... artificial intelligence software to improve productivity ...
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A number of cloud-based accounts software programmes, such as Xero and Dext, have been available to businesses for several years, making bookkeeping more automated, quicker and easier – and therefore increasing efficiency and productivity, and enhancing an SME’s ability to make better business decisions through management accounts forecasting.
AI is already being used to automate many of the tasks that accountants traditionally carry out, such as data entry, analysis and reporting. As AI continues to develop, it is likely to have an even greater impact, freeing up accountants to focus on more strategic and value-added work.
Recently in the US, a test was carried out asking ChatGPT to produce a tax return for an individual, and whilst it got the basis right, it didn’t quite work out the tax due correctly. Hence why AI alone is not enough when it comes to carrying out important legislative accountancy-related tasks.
However, chatbots can be successfully used to assist you in some of your accountancy related queries. For example, if you’re looking to set up a business or change the formation of your current trading style, you could ask “What are the differences between becoming a sole trader vs a limited company?” Again, it would be advisable to then discuss the answer with an accountant to ensure the data is up to date and accurate for your specific circumstances.
| 2023-01-01T00:00:00 |
https://www.businessgrowthhub.com/resources/can-ai-chatbots-do-my-marketing-and-accounts/
|
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How might generative AI impact different occupations?
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How might generative AI impact different occupations?
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https://www.ilo.org
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[] |
Much of the interest on AI and work concerns its possible effects on job losses – will jobs be replaced by AI or will they be transformed?
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The figure below shows the results of the analysis, which was based on a global assessment of the 436 detailed occupations that comprise the International Standard Classification of Occupations .
Each occupation is made up of tasks. Each of the tasks within an occupation received a 0-1 potential automation score, with 0 indicating it is not possible and 1 indicating it was entirely possible to perform the task with Generative AI.
The figure plots these occupations according to its average score (mean of all tasks within an occupation) and its standard deviation (the dispersion of task-level automation scores within an occupation).
The intersection of these scores was used to elaborate a framework for classification based on the following categories:
Exposed: Gradient 4 (Highest exposure, low task variability): High and consistent GenAI exposure across tasks within the occupation. Most current tasks in these jobs have a high potential of automation, with little variability in task-level exposure.
(Highest exposure, low task variability): High and consistent GenAI exposure across tasks within the occupation. Most current tasks in these jobs have a high potential of automation, with little variability in task-level exposure. Exposed: Gradient 3 (Significant exposure, high task variability): Above-moderate occupational exposure: even though some tasks remain less exposed, the overall potential of automation of the current tasks with GenAI is growing in these occupations.
(Significant exposure, high task variability): Above-moderate occupational exposure: even though some tasks remain less exposed, the overall potential of automation of the current tasks with GenAI is growing in these occupations. Exposed: Gradient 2 (Moderate exposure, high task variability): Moderate occupational AI exposure, with high task-level variability. These occupations include a mix of some tasks that are exposed to GenAI and others not at risk, making the impact uneven.
(Moderate exposure, high task variability): Moderate occupational AI exposure, with high task-level variability. These occupations include a mix of some tasks that are exposed to GenAI and others not at risk, making the impact uneven. Exposed: Gradient 1 (Low exposure, high task variability): Low overall GenAI exposure at the occupational level, but high variability across tasks. Some tasks within these occupations have an elevated automation potential, even if the occupation as a whole remains strongly reliant on tasks that have a low potential of automation.
(Low exposure, high task variability): Low overall GenAI exposure at the occupational level, but high variability across tasks. Some tasks within these occupations have an elevated automation potential, even if the occupation as a whole remains strongly reliant on tasks that have a low potential of automation. Minimal Exposure (Low exposure, moderate task variability): Occupations with low GenAI exposure, where some tasks show moderate automation potential, but overall occupational exposure remains limited.
(Low exposure, moderate task variability): Occupations with low GenAI exposure, where some tasks show moderate automation potential, but overall occupational exposure remains limited. Not Exposed: Occupations where most tasks remain relatively unaffected by GenAI, with low task variability and a stable low mean exposure score.
Overall, the findings indicate that few jobs consist of tasks that are fully automatable with current GenAI technology; nearly all occupations have some tasks that require human input. Gradients 1-3 comprise occupations where the automation of specific tasks is limited, and some degree of task variability exists.
| 2024-09-11T00:00:00 |
2024/09/11
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https://www.ilo.org/resource/article/how-might-generative-ai-impact-different-occupations
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"query": "automation job displacement"
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Ai Impact On Jobs - American Planning Association
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AI Impact on Jobs
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https://planning.org
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[] |
Although AI can enhance efficiency in many areas, roles that need human judgment, adaptability, and a physical presence are likely to remain reliant on people.
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A new wave of AI systems is expected to have a major impact on global employment markets. Goldman Sachs estimates that roughly two-thirds of U.S. occupations are exposed to some degree of automation, with 25 to 50 percent of tasks within these roles potentially being replaced by AI. However, automation does not necessarily equate to widespread job losses. Many jobs and industries are only partially exposed to automation, meaning AI is more likely to complement rather than replace human workers.
Certain fields may be more resistant to AI-driven automation, particularly those that rely on complex interpersonal relationships, hands-on services, and skilled trades. Jobs in nursing, plumbing, electrical work, and construction are among those expected to remain in high demand, though ongoing worker shortages in these sectors add another layer of uncertainty. While AI can optimize many processes, roles that require human judgment, adaptability, and physical presence will likely continue to be filled by people.
Historically, technological advancements have sparked concerns about job displacement, yet they have also created new opportunities and industries. AI's growing role in urban planning presents a similar challenge: while AI can streamline technical aspects of planning, it underscores the need for planners to enhance their human-centric skills. This includes community engagement, consensus-building, and cross-agency collaboration — functions that AI cannot easily replicate. As AI becomes more integrated into planning, professionals must focus on the skills that make human planners indispensable in shaping the future of communities. Read PAS Report 604, Planning With Artificial Intelligence, and explore the APA Research KnowledgeBase collection Artificial Intelligence and Planning to learn more about this coming shift, and check in with APA's Upskilling Planners initiative to keep your skillset up to date.
| 2023-01-01T00:00:00 |
https://planning.org/foresight/trend/9309664/
|
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AI is coming for entry-level jobs. Everybody needs to get ready - Reddit
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The heart of the internet
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https://www.reddit.com
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[] |
"Certainly, CEOs are saying that AI is coming for a lot of jobs, and soon — perhaps as many as half of all white-collar workers. That's likely ...
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AI is coming for entry-level jobs. Everybody needs to get ready.
"Certainly, CEOs are saying that AI is coming for a lot of jobs, and soon — perhaps as many as half of all white-collar workers. That’s likely to show up first in entry-level jobs, where the basic skills required are the easiest to replicate, and in tech, where the ability to rapidly adapt the latest software tools is itself an entry-level job requirement. Sure enough, in recent years unemployment has risen fastest among new college graduates, which spurred LinkedIn executive Aneesh Raman to write that the bottom rungs of the white-collar career ladder are “breaking.”"
| 2023-01-01T00:00:00 |
https://www.reddit.com/r/ArtificialInteligence/comments/1lupud6/washington_post_ai_is_coming_for_entrylevel_jobs/
|
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The impact of Gen AI on Aussie jobs by 2030 | Bravo Careers
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The impact of Gen AI on Aussie jobs by 2030
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https://bravocareers.com
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[] |
By 2030, it is estimated that one-tenth of Australian workers could see more than 40% of their task hours automated, while two-thirds of workers could see 20% ...
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For the first time in history, the workforce will be one of hybrid intelligences – the coming together of human and machine intelligence – with a new challenge to make that relationship productive and trusted [1]
We at Bravo were interested to read a new report by McKinsey, on the impact of Generative AI and the future of work in Australia, and what Australia’s mix of occupations could look like, including potential shifts in skills demand and how workers may need to reskill to stay productively employed and transition to new roles. The report is startling in that the predictions are being made for 2030, just six years away and all professions are being impacted in some form. However also time enough to commence upskilling and or the transitioning that appears to be mandatory.
How we choose to approach this transition will underpin the degree of success with which we do so: Will this be like the horse and cart being replaced by the automobile?
Just as robotics revolutionised blue-collar jobs in factories and warehouses, AI is now poised to transform white-collar roles. "AI is on a collision course with white-collar, high-paid jobs," CNBC reported. Unlike past technologies of the last century, AI is moving from factory floors into office spaces where higher-paid, white-collar workers reside. ...Pew Research Center's senior researcher Rakesh Kochhar elaborated, "AI distinguishes itself from past technologies by reaching up from the factory floors into office spaces where white-collar, higher-paid workers tend to be." [3 ]
However throughout history, one constant remains—our inventiveness. Whether it's developing new technologies or finding creative solutions to problems, humans have always displayed a remarkable ability to innovate. This spirit of inventiveness is crucial as we continue to adapt to the changing landscape.
Four key areas of change
1 Occupation Changes: The introduction of generative AI is expected to bring about changes in the demand for different occupations. By 2030, it is estimated that one-tenth of Australian workers could see more than 40% of their task hours automated, while two-thirds of workers could see 20% to 40% of their task hours automated. This could result in approximately 1.3 million workers needing to transition into different lines of work by 2030.
2 Wage and Education Impact: The impact of generative AI on occupations varies across different wage quintiles. Occupations in the highest wage quintile may see their automation adoption increase by 1.8 times due to generative AI, compared to 1.2 times for the lowest wage quintile. Additionally, workers with lower levels of formal education and women are more likely to be displaced and need to change occupations.
3 Reskilling Opportunities: As the work landscape changes, there will be a need for workers to upskill and transition into new roles. Skill building and continuous learning will become crucial for navigating the future work landscape. There will be an increased demand for skills involving social and emotional intelligence, as well as technological skills such as engineering and digital literacy.
4 Sector-specific Impact: Generative AI is expected to have different impacts across sectors. In retail trade, it could introduce greater personalisation and improve inventory management and customer service. In financial services and insurance, it could reshape risk assessments, fraud detection, and customer service. In the public sector, it could transform activities such as education delivery, citizen interactions, and financial analysis.
Source: https://www.mckinsey.com/industries/public-sector/our-insights/generative-ai-and-the-future-of-work-in-australia#/
Generative AI has the potential to assist with a wide range of tasks across various industries.
Seven examples of tasks that gen AI can augment
1 Writing and Content Creation: Gen AI can help generate written content, such as articles, reports, and blog posts. It can assist with writing marketing copy, social media posts, and email newsletters. This can save time for content creators and marketers, allowing them to focus on other aspects of their work.
2 Design and Creativity: Gen AI can be used to generate designs for products, logos, websites, and user interfaces. It can assist with creating visual content, such as graphics, illustrations, and animations. This can be particularly useful for designers and creative professionals who can leverage gen AI to generate ideas and prototypes.
3 Data Analysis and Insights: Gen AI can analyse large datasets and provide insights and recommendations based on the data. It can assist with data cleaning, data visualisation, and predictive analytics. This can help businesses make data-driven decisions and identify patterns and trends in their data.
4 Customer Service and Support: Gen AI can be used to develop chatbots and virtual assistants that can interact with customers and provide support. It can understand natural language and respond to customer inquiries, troubleshoot issues, and provide relevant information. This can improve customer service efficiency and provide 24/7 support.
5 Legal and Compliance: Gen AI can assist with analysing legal documents, contracts, and regulations. It can help identify relevant information, highlight potential risks, and provide recommendations. This can be valuable for legal professionals and organisations dealing with large volumes of legal documentation.
6 Scientific Research and Discovery: Gen AI can assist scientists and researchers in analysing complex data, conducting simulations, and generating hypotheses. It can help accelerate scientific discovery by processing and interpreting large amounts of data, identifying patterns, and suggesting new avenues for research.
7 Operations and Process Optimisation: Gen AI can assist with optimising business processes, supply chain management, and logistics. It can analyse data to identify inefficiencies, recommend process improvements, and automate repetitive tasks. This can lead to increased operational efficiency and cost savings.
It is important to note that while gen AI can assist with these tasks, it is currently more focused on task augmentation rather than outright job automation. Human involvement and oversight are still necessary in many cases to ensure accuracy, ethical considerations, and decision-making.
For instance, in this recent Forbes article [4] we note the author sees that not all white-collar roles are at risk of being replaced by automation.
Jobs relying on human interaction and emotional intelligence, such as therapists, counselors, social workers, and teachers, are resistant to AI replacement
High-level white-collar roles involving complex decision-making are secure from automation
Customer-facing positions like salespeople remain safe due to the need for building and maintaining client relationships
Management consultants have future-proof careers due to high levels of human interaction and problem-solving required
Lawyers depend on the human touch in their work but can use AI to assist with reviewing legal documents efficiently
AI can handle some human resources tasks but resolving conflicts and managing employee relations still require human interpersonal skills.
To continue our opening quote:
These hybrid teams will have a multitude of skillsets. Technical skills in data science, health and engineering can build what may be needed, while human skills like human-centric design and frontline change management will make those services both relevant and successful. That implies a balance of abilities that are nurtured from both STEM and humanities education. Our technical specialists need to learn from the humanities how different people have different perspectives that need to be integrated; and our social scientists need the mathematical literacy and critical thinking skills that STEM can bring, a creative awareness of the skills needed to solve problems and the tools that the data and traditional sciences have to draw on.[2]
Humans and AI will work together and humans will adapt.
While it may lead to job displacement and the need for occupational transitions, it also presents opportunities for upskilling, higher-wage jobs, and increased productivity. Strategic action by employers, governments, and educators will be crucial in harnessing the benefits of generative AI and ensuring a smooth transition for workers. The ethical, social imperative is to ensure that workers most at risk of being displaced by AI – those in easily automated roles, are supported to upskill and transition successfully.
History is filled with stories of change, evolution, and human resilience. From the Industrial Revolution to today's tech-driven world, humanity has repeatedly demonstrated its ability to ad apt and thrive in the face of new challenges. For everyone in the workforce today facing the challenges and opportunities of AI, understanding our past can inspire confidence in navigating the present and future.
As career professionals we would advise you to keep informed about AI advances in your industry and profession, stay alert to opportunities and be prepared to update your career blueprint and learn and re-skill as needed.
Humanity has successfully transitioned through significant change before—and we'll do it again – and hopefully with sufficient empathy, insight and critical thinking.
Endnotes
[1] & [2} https://www.mckinsey.com/au/our-insights/australia-and-new-zealand-perspectives/the-future-of-work-a-new-era)
[3] & [4] https://www.forbes.com/sites/jackkelly/2024/02/28/what-white-collar-jobs-are-safe-from-ai-and-which-professions-are-most-at-risk/]
References
https://www.mckinsey.com/industries/public-sector/our-insights/generative-ai-and-the-future-of-work-in-australia#/
| 2023-01-01T00:00:00 |
https://bravocareers.com/blog/the-impact-of-gen-ai-on-aussie-jobs-by-2030
|
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|
A Real-Time Look at How AI Is Reshaping Work
|
A Real-Time Look at How AI Is Reshaping Work
|
https://www.isi.edu
|
[
"Julia Cohen",
"Usc Students",
"Researchers Track Where Ai Is Having The Biggest Impact On Jobs",
"Salaries"
] |
AI exposure doesn't just vary by industry, it also depends on the specific type of work. Jobs like software engineer and data scientist scored ...
|
Artificial intelligence may take over some tasks and transform others, but one thing is certain: it’s reshaping the job market. Researchers at USC’s Information Sciences Institute (ISI) analyzed LinkedIn job postings and AI-related patent filings to measure which jobs are most exposed, and where those changes are happening first.
The project was led by ISI research assistant Eun Cheol Choi, working with students in a graduate-level USC Annenberg data science course taught by USC Viterbi Research Assistant Professor Luca Luceri. The team developed an “AI exposure” score to measure how closely each role is tied to current AI technologies. A high score suggests the job may be affected by automation, new tools, or shifts in how the work is done.
Which Industries Are Most Exposed to AI?
To understand how exposure shifted with new waves of innovation, the researchers compared patent data from before and after a major turning point. “We split the patent dataset into two parts, pre- and post-ChatGPT release, to see how job exposure scores changed in relation to fresh innovations,” Choi said. Released in late 2022, ChatGPT triggered a surge in generative AI development, investment, and patent filings.
Jobs in wholesale trade, transportation and warehousing, information, and manufacturing topped the list in both periods. Retail also showed high exposure early on, while healthcare and social assistance rose sharply after ChatGPT, likely due to new AI tools aimed at diagnostics, medical records, and clinical decision-making.
In contrast, education and real estate consistently showed low exposure, suggesting they are, at least for now, less likely to be reshaped by current AI technologies.
AI’s Reach Depends on the Role
AI exposure doesn’t just vary by industry, it also depends on the specific type of work. Jobs like software engineer and data scientist scored highest, since they involve building or deploying AI systems. Roles in manufacturing and repair, such as maintenance technician, also showed elevated exposure due to increased use of AI in automation and diagnostics.
At the other end of the spectrum, jobs like tax accountant, HR coordinator, and paralegal showed low exposure. They center on work that’s harder for AI to automate: nuanced reasoning, domain expertise, or dealing with people.
AI Exposure and Salary Don’t Always Move Together
The study also examined how AI exposure relates to pay. In general, jobs with higher exposure to current AI technologies were associated with higher salaries, likely reflecting the demand for new AI skills. That trend was strongest in the information sector, where software and data-related roles were both highly exposed and well compensated.
But in sectors like wholesale trade and transportation and warehousing, the opposite was true. Jobs with higher exposure in these industries tended to offer lower salaries, especially at the highest exposure levels. The researchers suggest this may signal the early effects of automation, where AI is starting to replace workers instead of augmenting them.
“In some industries, there may be synergy between workers and AI,” said Choi. “In others, it may point to competition or replacement.”
From Class Project to Ongoing Research
The contrast between industries where AI complements workers and those where it may replace them is something the team plans to investigate further. They hope to build on their framework by distinguishing between different types of impact — automation versus augmentation — and by tracking the emergence of new job categories driven by AI. “This kind of framework is exciting,” said Choi, “because it lets us capture those signals in real time.”
Luceri emphasized the value of hands-on research in the classroom: “It’s important to give students the chance to work on relevant and impactful problems where they can apply the theoretical tools they’ve learned to real-world data and questions,” he said. The paper, Mapping Labor Market Vulnerability in the Age of AI: Evidence from Job Postings and Patent Data, was co-authored by students Qingyu Cao, Qi Guan, Shengzhu Peng, and Po-Yuan Chen, and was presented at the 2025 International AAAI Conference on Web and Social Media (ICWSM), held June 23-26 in Copenhagen, Denmark.
Published on July 7th, 2025
| 2023-01-01T00:00:00 |
https://www.isi.edu/news/78976/a-real-time-look-at-how-ai-is-reshaping-work/
|
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|
Five year career plan, aka. AI is taking my job : r/ExperiencedDevs
|
The heart of the internet
|
https://www.reddit.com
|
[] |
AI is great at solving problems that have been solved before, but tech is all about solving new problems or solving them in new contexts. People ...
|
For experienced developers. This community should be specialized subreddit facilitating discussion amongst individuals who have gained some ground in the software engineering world. Any posts or comments that are made by inexperienced individuals (outside of the weekly Ask thread) should be reported. Anything not specifically related to development or career advice that is _specific_ to Experienced Developers belongs elsewhere. Try /r/work, /r/AskHR, /r/careerguidance, or /r/OfficePolitics.
Members Online
| 2023-01-01T00:00:00 |
https://www.reddit.com/r/ExperiencedDevs/comments/1lt8z3c/five_year_career_plan_aka_ai_is_taking_my_job/
|
[
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"position": 45,
"query": "AI impact jobs"
},
{
"date": "2023/03/01",
"position": 44,
"query": "AI impact jobs"
}
] |
|
Job board | 80,000 Hours
|
80,000 Hours
|
https://jobs.80000hours.org
|
[] |
729 jobs - Find jobs in Global health & development, AI safety & policy, Biosecurity, Climate change, Policy & government, Forecasting, Global priorities ...
|
To limit the alert to certain categories, apply a filter.
You will receive updates daily (if there are new roles) and you can unsubscribe at any time
| 2023-01-01T00:00:00 |
https://jobs.80000hours.org/
|
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{
"date": "2025/05/01",
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}
] |
|
80,000 Hours: How to make a difference with your career
|
80,000 Hours: How to make a difference with your career
|
https://80000hours.org
|
[] |
High-impact careers in the age of AGI. There's a real possibility that artificial general intelligence — AI systems that are as good or better than humans at ...
|
Our list of pressing global problems sets out the problems where we think our readers can have the biggest positive impact by working on them.
We prioritise problems that are unusually large in scale, unduly neglected, and solvable — because that’s where additional people can generally have the most positive impact.
Right now, problems that could pose existential risks top our list, because they threaten not just those alive today but also humanity’s entire future, and they remain neglected relative to their scale.
In particular, we think that there’s a realistic possibility of transformative AI in the coming decades. We think addressing risks from this transformative technology may be the most pressing problem in the world.
Our ranking is a best guess and a constant work in progress, meaning it’s undoubtedly incomplete and mistaken in many ways. It also may not align with your worldview. So we encourage you to think through the question of which problems are most pressing for yourself too. Where you focus will also depend on your opportunities to contribute to each issue.
To learn why we listed a specific problem and how you can help tackle it, read the full profile for yourself.
| 2023-01-01T00:00:00 |
https://80000hours.org/
|
[
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"query": "AI impact jobs"
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{
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"query": "AI impact jobs"
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] |
|
Even in 100 years, AI won't replace this job: Bill Gates says this ...
|
Even in 100 years, AI won’t replace this job: Bill Gates says this profession will remain 100% human
|
https://www.elcabildo.org
|
[
"Nazareno Indiscreto"
] |
He also pointed out the enormous promise of AI, which the World Economic Forum estimates could lead to the loss of around 85 million jobs ...
|
In a world racing toward automation, some jobs like programming may forever need the unique spark of human creativity.
Even as artificial intelligence reshapes many industries, some roles simply refuse to be taken over by machines. Bill Gates, cofounder of Microsoft and a key voice in technology, recently shared a bold belief: programming will remain a 100% human profession, even a century from now. This statement invites us to reconsider what parts of work are truly immune to automation in an AI-driven world.
A balanced view on AI’s impact on jobs
Bill Gates openly acknowledges the mixed emotions many feel about the rise of artificial intelligence. In an interview with France Inter, he admitted, “I’m scared, too.” This honest fear from a tech pioneer shows the complexity of the AI debate.
He also pointed out the enormous promise of AI, which the World Economic Forum estimates could lead to the loss of around 85 million jobs worldwide by 2030. Yet paradoxically, the same forces may create 97 million new roles related to emerging technologies, hinting at a challenging but potentially rewarding shift.
This transformation pushes us to ask: how can society steer this change so that AI enhances our lives? Gates suggested AI could greatly increase productivity and “leave us plenty of free time” if managed wisely. It’s a hopeful vision that challenges us to think about work not just as a necessity but as something that might evolve to offer more freedom.
Why programming remains unshakably human
Among the many jobs Gates discussed, programming stands out as particularly resistant to full automation. He argues programming’s value comes from uniquely human traits like creativity and judgment, which machines just can’t match.
To see Gigantic ancient riverbed discovered under Antarctica could help slow deadly sea level rise
While AI might automate repetitive tasks such as debugging code, the true essence of programming—innovative problem-solving and conceptual design—relies on deep human insight. Writing code goes beyond strings of commands; it involves understanding complex challenges and crafting tailor-made solutions.
Thinking back on my own experience trying to learn programming, I often found that the biggest breakthroughs came from creative leaps and seeing connections that no algorithm could dictate. That element of personal creativity is what makes programming an enduring human craft that AI can assist with but never fully replace.
How AI reshapes other professions differently
Programmers aren’t alone in riding the waves of AI change, but their situation contrasts with some other fields. Bill Gates points out that sectors like energy and biology rely heavily on human expertise that AI cannot replicate, safely insulating these jobs from wholesale automation.
On the flip side, professions such as administrative assistants and graphic designers face significant disruption. Generative AI tools make it cheaper and faster to handle routine tasks, forcing workers and employers to rethink traditional job roles.
The future workplace looks more like a landscape of constant adaptation than disappearance. Many of us will need to update skills regularly and embrace new ways of working alongside AI technologies.
To see Einstein’s mistake exposed: study solves 120-year-old problem and forces revision of the laws of thermodynamics
This insight mirrors recent research from France Travail, which highlights the vital role of human-critical thinking and complex problem-solving skills in technical fields. These qualities form a reliable defense against automation, echoing Gates’ optimism about certain intellectual professions’ durability.
I’ve found that staying curious and open to learning new skills has been key to feeling confident about AI’s rise. What about you? Do you believe some jobs, like programming, will always need a human touch? How do you see AI affecting your work or industry?
Share your thoughts below and don’t forget to spread this conversation by sharing with friends. Let’s explore together what the future of work really holds.
| 2025-07-07T00:00:00 |
2025/07/07
|
https://www.elcabildo.org/en/even-in-100-years-ai-wont-replace-this-job-bill-gates-says-this-profession-will-remain-100-human-50253/
|
[
{
"date": "2023/01/01",
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"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 64,
"query": "AI impact jobs"
},
{
"date": "2023/03/01",
"position": 65,
"query": "AI impact jobs"
}
] |
Clip Possible David Autor on AI's impact on jobs ... - MIT News
|
Clip Possible David Autor on AI’s impact on jobs, expertise, and labor markets (July 2) – AUDIO
|
https://news.mit.edu
|
[] |
Autor notes that if AI technologies are implemented in the best way possible for humankind, “we would give people more secure and fulfilling ...
|
Description :
Prof. David Autor joins Possible podcast hosts Reid Hoffman and Aria Finger to discuss everything from the cross-country road trip that helped inspire his research focused on technology, work and inequality to how AI might impact American workers. Autor notes that if AI technologies are implemented in the best way possible for humankind, “we would give people more secure and fulfilling work. We would give them more access to education and access to better healthcare, everywhere. And those things alone would improve welfare in so many dimensions. Not just in terms of material standard living, not just in comfort, but investing in our kids, creating opportunity for the next generation.”
| 2023-01-01T00:00:00 |
https://news.mit.edu/news-clip/possible
|
[
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|
High Performance Computing and AI Infrastructure Engineer, Sr
|
High Performance Computing and AI Infrastructure Engineer, Sr
|
https://www.lockheedmartinjobs.com
|
[] |
I hope to have a long career here making an impact, and I know I'll be supported in doing that every step of the way.” Ryan J., Enterprise ...
|
Job ID: 697230BR
Become part of the Future of IT at Lockheed Martin as a Full Stack Engineer within the FORCE Portfolio! This dynamic, fast-paced environment is embracing DevSecOps and Agile to enable our strategic goals. The Engineer role will be instrumental to the success of reinventing how we develop and maintain compute infrastructure products at Lockheed Martin to meet the needs of every business area. The FORCE Portfolio resides within the Enterprise IT Infrastructure and International (I2) Organization. The FORCE Portfolio includes (but is not limited to) development and operations for the following Product Teams: Compute IaaS (Virtualization, Server OS, OpenStack), PaaS,(Containers, Database Engines, Middleware Splunk), Storage, Data Center/Hardware, High Performance Computing (Simulation, AI/ML), Governance, Commercial Cloud Native Offerings, Service Management (Customer Portal, Job Scheduling). These solutions are built to meet global needs and include both Data Center and Edge locations for on-premise and in public cloud.This Engineer role is aligned to a single Delivery Team within the HPC Product Team. The Delivery Team may be utilizing Scrum or Kanban agile frameworks. This Full Stack Engineer role is for the High Performance Computing (HPC) Delivery Team with a focus on AI Infrastructure.Engineer responsibilities include:• Support the design and development of HPC and utility systems (computation, network, and storage)• Support AI Infrastructure and the equivalent systems• Demonstrated automation mindset, including the use of automation, AI and orchestration tools and scripting languages and . Examples include Ansible, PowerShell, Terraform.• Perform full stack engineering, including platform support, user software support, and manage queuing software to meet the computing needs of research projects• Responsible for System Administration on multiple system platforms and hardware.• Position supports multiple platforms which include small servers and large supercomputers• Will be responsible for system installations, upgrades, configuration management, configurations, software installation, troubleshooting, user interface and support• On-call support rotation will be requiredThis role requires U.S. CitizenshipThis position is full-time telecommuting. Occasional travel (1-3 times a year) may be requested.From onsite to remote, we offer flexible work schedules to comprehensive benefits investing in your future and security, Learn more about Lockheed Martin’s comprehensive benefits package here. Do you want to be part of a company culture that empowers employees to think big, lead with a growth mindset, and make the impossible a reality? We provide the resources and give you the flexibility to enable inspiration and focus -if you have the passion and courage to dream big, work hard, and have fun doing what you love then we want to build a better tomorrow with you.• Experience with High Performance Computing infrastructure product development and/or maintenance• Experience with AI infrastructure product development and/or maintenance• Experience with hardware layer/engineering in the full stack• Demonstrated automation mindset, including the use of automation, AI and orchestration tools and scripting languages. Examples including Ansible, PowerShell, Terraform• Red Hat Enterprise Linux (RHEL) 6 or 7Administration and Configuration• US Citizenship required for this role• Experience using agile management tool such as JIRA, VersionOne, Pivotal Tracker, etc• Experience with simulation and AI/ML software• Experience with DevOps / DevSecOps• Knowledge of various protocols (i.e., DNS, SMTP, NFS, FTP, Telnet, SSH, SFTP)• System performance, disk I/O, and network tuning and configuration experience• Experience in mitigating IT Tech Debt and retiring legacy products and services• Demonstrated use of metrics to make data driven decisions• Familiarity with Service Now for ITSM• Familiarity with AWS and/or Azure IT service development and maintenance• Familiarity with private cloud on-premise IT service development and maintenance• Experience working in a virtual environment• Fiber Channel (Direct Attach) Storage ArrayAdministration Experience• Experience with Trusted Multi-Level Security (MLS) Operating Systems• Familiarity with InfiniBand configuration and troubleshooting• Experience with containerization, Kubernetes, DockerNoneBy applying to this job, you are expressing interest in this position and could be considered for other career opportunities where similar skills and requirements have been identified as a match. Should this match be identified you may be contacted for this and future openings.Full-time Remote Telework: The employee selected for this position will work remotely full time at a location other than a Lockheed Martin designated office/job site. Employees may travel to a Lockheed Martin office for periodic meetings.Lockheed Martin supports a variety of alternate work schedules that provide additional flexibility to our employees. Schedules range from standard 40 hours over a five day work week while others may be condensed. These condensed schedules provide employees with additional time away from the office and are in addition to our Paid Time off benefits.4x10 hour day, 3 days off per weekThe annual base salary range for this position in most major metropolitan areas in California and New York is $102,800 - $178,135. For states not referenced above, the salary range for this position will reflect the candidate’s final work location. Please note that the salary information is a general guideline only. Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market and business considerations when extending an offer.Medical, Dental, Vision, Life Insurance, Short-Term Disability, Long-Term Disability, 401(k) match, Flexible Spending Accounts, EAP, Education Assistance, Parental Leave, Paid time off, and Holidays. This position is incentive plan eligible.The annual base salary range for this position in California and New York (excluding most major metropolitan areas), Colorado, Hawaii, Illinois, Maryland, Minnesota, New Jersey, Vermont, Washington or Washington DC is $89,300 - $157,550. For states not referenced above, the salary range for this position will reflect the candidate’s final work location. Please note that the salary information is a general guideline only. Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market and business considerations when extending an offer.Medical, Dental, Vision, Life Insurance, Short-Term Disability, Long-Term Disability, 401(k) match, Flexible Spending Accounts, EAP, Education Assistance, Parental Leave, Paid time off, and Holidays. (Washington state applicants only) Non-represented full-time employees: accrue at least 10 hours per month of Paid Time Off (PTO) to be used for incidental absences and other reasons; receive at least 90 hours for holidays. Represented full time employees accrue 6.67 hours of Vacation per month; accrue up to 52 hours of sick leave annually; receive at least 96 hours for holidays. PTO, Vacation, sick leave, and holiday hours are prorated based on start date during the calendar year. This position is incentive plan eligible.Join us at Lockheed Martin, where your mission is ours. Our customers tackle the hardest missions. Those that demand extraordinary amounts of courage, resilience and precision. They’re dangerous. Critical. Sometimes they even provide an opportunity to change the world and save lives. Those are the missions we care about.As a leading technology innovation company, Lockheed Martin’s vast team works with partners around the world to bring proven performance to our customers’ toughest challenges. Lockheed Martin has employees based in many states throughout the U.S., and Internationally, with business locations in many nations and territories.Experienced ProfessionalENTERPRISE BUSINESS SERVICESNoInformation TechnologyFull-TimeFirst
| 2023-01-01T00:00:00 |
https://www.lockheedmartinjobs.com/job/puerto-rico/high-performance-computing-and-ai-infrastructure-engineer-sr/694/83527911648
|
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{
"date": "2024/02/01",
"position": 82,
"query": "AI impact jobs"
},
{
"date": "2024/04/01",
"position": 81,
"query": "AI impact jobs"
},
{
"date": "2024/05/01",
"position": 84,
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},
{
"date": "2024/09/01",
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{
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|
AI & Technology in Staffing: Enhancing Humanity, Not Replacing It
|
AI & Technology in Staffing: Enhancing Humanity, Not Replacing It
|
https://www.sedonastaffing.com
|
[] |
Recruiters receive ongoing education to interpret AI outputs and integrate them with empathy and ethical oversight. Monitor AI impact on well- ...
|
AI-Powered Staffing: Amplifying Human Potential
In 2025, AI is reshaping the staffing world. From sourcing to onboarding, many firms are leveraging AI-powered tools—but too often, it comes at the expense of the human connection. True innovation in staffing comes not just from efficiency, but from putting people first.
🔍 What’s Working—and What’s Not
AI streamlining basics : Many employers and staffing firms have AI "recruiters" who now handle resume screening, resume parsing, and automated outreach, helping reduce costs and save time. Analytics-driven platforms even enhance candidate diversity and speed hires by up to 62% faster with 59% lower costs staffingindustry.com .
staffingindustry.com Tools—yes. Bots-only—no : Companies emphasizing AI-only processes (like fully automated interviews or chatbots with no human follow‑up) often frustrate candidates and missed concerns around fairness, bias, and transparency.
Candidate pushback : University graduates are increasingly skeptical: only 18% are favorable about AI screening—and over half feel it reduces their ability to show their authentic selves naceweb.org .
naceweb.org Real-world limits : Even firms that automate heavily (like Klarna replacing 700 service staff with AI) have reversed course after realizing customers crave human interaction.
🤝 Why Balancing AI + Human Touch Matters
The staffing industry thrives on trust, empathy, and relationship-building. Pure automation can ignore these, resulting in:
Candidates feeling unseen and unheard
Employers processing too many low-quality applications
Loss of brand equity and negative candidate experiences
Instead, top-tier firms leverage AI to amplify—not eliminate—the human element.
✅ What Authentic, Tech-Savvy Staffing Firms Do
Automate tasks, not relationships
AI aids in resume dredging and initial screening—but humans craft outreach and conduct follow-ups. Use AI ethically and transparently
Firms clearly disclose AI use, balance it with human judgment, and guard against bias risk. Include human interviews early
Candidates and NACE data show that in-person—or human-led virtual—interaction is preferred over fully automated sessions. Train recruiters on AI tools
Recruiters receive ongoing education to interpret AI outputs and integrate them with empathy and ethical oversight. Monitor AI impact on well-being
Firms regularly assess jobseeker and recruiter experiences to ensure AI enhances, rather than undermines, fair and humane engagement.
🌟 What It Looks Like in Practice
A recruiter uses AI to shortlist candidates, then personally reviews and calls promising candidates—answering questions and explaining next steps.
Jobs are listed clearly and fairly; AI suggests matches, but humans make sure the fit respects individual goals and culture.
After hires, recruiters check in—AI flags when to follow, but humans ensure satisfaction, performance, and relationship depth.
📢 Q&A
💬 Q: How can AI improve staffing without feeling dehumanizing?
A: Use AI to streamline tasks—like screening and scheduling—but keep humans in charge of outreach, assessment, and relationship-building. Balance matters.
💬 Q: Do candidates like AI in hiring?
A: Mixed feelings: around 18% of young candidates appreciate AI screening, but more than half worry it limits authenticity lifewire.com.
💬 Q: Why do firms need human recruiters if AI is so fast?
A: AI handles volume. Humans bring judgment, empathy, transparency, and bias‑spotting—essential for trust and quality fit.
💬 Q: How do companies avoid AI bias?
A: Through ethical AI use—clear disclosure, ongoing monitoring, human review, and regular fairness audits .
🔍 Final Takeaway
AI is powerful—but it should empower human connections in staffing, not replace them. When used thoughtfully, AI:
Speeds processes
Improves candidate quality
Reduces costs
But above all, supports authentic, human-led hiring
Staffing agencies that embrace this blend stand out—building stronger brands, happier hires, and lasting relationships.
| 2023-01-01T00:00:00 |
https://www.sedonastaffing.com/ai-technology-in-staffing-enhancing-humanity-not-replacing-it
|
[
{
"date": "2023/01/01",
"position": 74,
"query": "AI impact jobs"
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|
Generative AI Portfolio Lead, Google DeepMind Impact Accelerator
|
Generative AI Portfolio Lead, Google DeepMind Impact Accelerator
|
https://job-boards.greenhouse.io
|
[] |
Working in partnership with GDI's leadership team, you will develop and be responsible for building our AI product portfolio focused on the social impact of ...
|
Snapshot
The Google DeepMind Impact Accelerator (GDI) has a unique role in Google DeepMind (GDM), to develop solutions and resources built on GDM's technologies and expertise that extend the benefits to humanity. We are a path to real world impact, beyond ABC products and services or making our research public.
About us
Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
The role
Working in partnership with GDI’s leadership team, you will develop and be responsible for building our AI product portfolio focused on the social impact of Google DeepMind's generative AI technology (such as Gemini). This will involve market research, internal organisational research, use case analysis, stakeholder engagement and product definition.
This is an evolving and dynamic area of Google DeepMind with a collaborative, diverse group that partners closely with a wide variety of teams across Alphabet and a range of external partners.
Key responsibilities
Work alongside research, engineering and partnership teams to identify and define opportunities to use Google DeepMind technology to solve real-world problems, and work closely with them to determine the best approach and execution plans for prioritised ideas.
Use your influence to bring together and motivate individuals (including Scientists and Engineers) from across GDM Units to work on projects related to generative AI applications that benefit humanity.
Scope and prototype products incorporating ideas from stakeholders and partners, and build and manage external product/deployment partnerships where relevant, while balancing key principles such as equity, safety, responsibility.
Drive strategic deep dives across GDM's generative AI technologies to assess potential, define use cases and routes-to-market (including understanding users for use-cases, their needs, and their workflows), and prioritise opportunities. Partner with GDI and GDM leadership to define, communicate, and build a plan for delivery on these strategic priorities.
Define metrics and achievements to accelerate discovery, evaluate the success of programs against their aims, goals and objectives, and share lessons and insights across the wider business.
Build relationships and collaborate with multiple stakeholders internally and externally.
The role will suit candidates who enjoy applying state-of-the-art AI to important real-world problems that maximise positive impact for the wider community.
About you
In order to set you up for success as a Portfolio Lead at Google DeepMind, we look for the following skills and experience:
Demonstrable experience and knowledge of real world AI applications, including supporting teams to drive large scale programs from inception to delivery in a fast paced and dynamic environment.
Confidence engaging researchers and engineers. While not necessarily an ML / AI specialist yourself, you are able to understand the considerations related to AI research and technologies.
Product Management experience; crafting strategic product roadmaps from conception to launch and working with cross-functional teams.
Experience and a proven ability to collaborate with a variety of teams and partners.
Outstanding communication skills and ability to work with both tech and non-tech teams and senior leadership.
A passion for Google DeepMind’s mission and knowledge and excitement about AI and its potential for real-world impact.
Deadline to Apply: 22nd July 2025, 5pm BST
Note: In the event your application is successful and an offer of employment is made to you, any offer of employment will be conditional on the results of a background check, performed by a third party acting on our behalf. For more information on how we handle your data, please see our Applicant and Candidate Privacy Policy.
At Google DeepMind, we value diversity of experience, knowledge, backgrounds, and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.
| 2023-01-01T00:00:00 |
https://job-boards.greenhouse.io/deepmind/jobs/6982798
|
[
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{
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|
The Shifting Job Market: AI's Impact on Recent College Graduates
|
The Shifting Job Market: AI’s Impact on Recent College Graduates
|
https://recruitingheadlines.com
|
[] |
Economists, including Matthew Martin from Oxford Economics, suggest that the rise of artificial intelligence (AI) is a key factor ...
|
For the first time, the unemployment rate for recent college graduates has exceeded the national average, reaching 6.6% over the past year. This trend challenges the long-held belief that higher education guarantees superior employment opportunities.
Economists, including Matthew Martin from Oxford Economics, suggest that the rise of artificial intelligence (AI) is a key factor, with companies increasingly utilizing AI to fill roles traditionally held by entry-level workers. Nariman Farvardan, president of Stevens Institute of Technology, anticipates that the disruption caused by AI will be more profound and enduring than that brought by the internet, potentially lasting up to 15 years. He explains that AI has the capacity to either augment or entirely replace jobs involving repetitive physical or cognitive tasks, impacting not only new graduates but also seasoned professionals engaged in such tasks.
Despite this challenging environment, some graduates are successfully navigating the new landscape. Kaylee Totten, a computer science graduate from Stevens, secured a position with Verizon following an internship. She highlights the critical role of networking and the valuable opportunities that entry-level positions can provide.
Farvardan advises students to prioritize an education that cultivates continuous learning over a narrow focus on specific skills. He believes that individuals who can innovate alongside AI will be better positioned to thrive in the evolving job market.
| 2025-07-07T00:00:00 |
2025/07/07
|
https://recruitingheadlines.com/the-shifting-job-market-ais-impact-on-recent-college-graduates/
|
[
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] |
How to start a career in the age of AI - Computerworld
|
How to start a career in the age of AI
|
https://www.computerworld.com
|
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In this interview a CEO and founder gives his view of how AI will impact industry and work, as well as how young people can survive and ...
|
In the latest episode of First Person we meet Daniel Avancini, an academic turned entrepreneur, analyst and business person turned coder. He tells us about how he doesn’t always know his final destination, but he understands the direction in which he wishes to travel and makes decisions so he heads that way. How he hedged (in his parlance) by keeping one foot in academia and one in corporate life.
Most of all, Daniel has great advice for those starting out who are challenged by the impact of AI on the future of work: understand your passion and what you are great at — and add value by doing things that AI can’t do. Namely build relationships and generate insights in order to devise winning strategies. You can watch the interview here, listen to it here, or watch it in the box below:
First Person Meets… Daniel Avancini: Stay curious
“I’m a very curious person,” says Daniel Avancini. “I always loved to really understand deeply different subjects.”
| 2023-01-01T00:00:00 |
https://www.computerworld.com/article/4016863/how-to-start-a-career-in-the-age-of-ai.html
|
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|
AIs Disruption of Advertising: Innovation, Job Losses & New ...
|
AIs Disruption of Advertising: Innovation, Job Losses & New Creative Frontiers
|
https://aibusinesshelp.co.uk
|
[
"Nick Smith"
] |
Discover how AI is transforming advertising—from automating copy and media buying to creating new hybrid roles. Explore the impact on jobs, ...
|
AI’s Disruption of Advertising
Innovation, Job Losses, and the New Creative Frontier
How AI is impacting the Advertising Industry
Publish Date: 8th July 2025 Last Updated: 8th July 2025 Author: nick smith - With the help of CHATGTP
From personalized ads that seem to read your mind to visuals crafted in seconds by algorithms, artificial intelligence (AI) is not just influencing advertising—it’s reinventing it. As the line between creativity and code blurs, we are entering a new era where machines co-create with marketers. But with this revolution comes both opportunity and upheaval. This article explores AI’s far-reaching impact on the advertising industry—from automating entry-level roles to opening doors for new hybrid professions—highlighting the opportunities, threats, and the urgent need for reskilling.
AI in Advertising: From Buzzword to Backbone
AI is now deeply embedded in advertising workflows—from crafting copy to buying ad space. A 2024 Statista report revealed that the advertising sector leads all industries in generative AI adoption, with 37% of marketers using tools like ChatGPT, MidJourney, and DALL·E to create campaigns, analyze consumer behavior, and optimize targeting.
According to AdExchanger, AI-driven programmatic advertising already accounts for over 80% of U.S. digital ad spend. Small businesses, once outgunned by large corporate budgets, now use AI-powered tools like Smartly.io and Jasper to launch highly targeted, cost-efficient campaigns.
But this empowerment comes with consequences, especially for the traditional workforce.
Democratization of Advertising: Empowering the Underdogs
One of AI’s most exciting impacts is how it levels the playing field. Small businesses and independent creators can now produce full-scale campaigns using minimal resources. Platforms like Lumen5, Canva’s AI suite, and Synthesia enable lean teams to create video, visual, and written content that once required a professional agency.
This has fueled a wave of niche and hyperlocal brands gaining traction online. However, it also increases competition, saturates attention spans, and puts pressure on creative differentiation.
Displacement in the Age of Automation
AI’s efficiency is driven by automation—particularly in repetitive, data-heavy, or creative-production tasks. This has put numerous traditional advertising roles at risk:
Copywriters & Content Creators : Generative AI can now produce tailored ad copy and variations in seconds, reducing demand for entry-level writers. A 2025 World Economic Forum report estimates that 50% of tasks in market research and writing roles could be automated.
: Generative AI can now produce tailored ad copy and variations in seconds, reducing demand for entry-level writers. A 2025 World Economic Forum report estimates that 50% of tasks in market research and writing roles could be automated. Media Buyers & Planners : Programmatic platforms are automating placement and bidding, displacing traditional media planning jobs.
: Programmatic platforms are automating placement and bidding, displacing traditional media planning jobs. Graphic Designers: Tools like Adobe Sensei and Canva’s AI features automate design templates, reducing production time by up to 40% and impacting junior design roles.
A 2023 McKinsey report projects up to 800 million jobs globally could be affected by automation by 2030. In advertising, roles that rely on data entry, admin, or repetitive design tasks are especially vulnerable. Notably, the ILO highlights that women—who hold the majority of white-collar roles in advertising—face 21% higher exposure to automation.
Forecast: Projected Job Losses by 2030
92 million jobs globally could be displaced by AI, per the World Economic Forum’s 2025 Future of Jobs Report.
globally could be displaced by AI, per the World Economic Forum’s 2025 Future of Jobs Report. In the U.S., Goldman Sachs projects 300 million jobs could be lost or degraded by AI across all sectors.
could be lost or degraded by AI across all sectors. Clerical support roles in Latin America, particularly those held by educated women in cities, are at the highest risk of automation-induced displacement.
However, it’s important to maintain perspective. A 2013 Oxford study overestimated automation risks, and the Bureau of Labor Statistics (BLS) emphasizes that the pace of job losses is highly dependent on economic conditions and adoption rates.
Affected Areas: Where AI Hits the Hardest
Creative Development : Entry-level creatives face challenges as AI tools generate copy, images, and videos.
: Entry-level creatives face challenges as AI tools generate copy, images, and videos. Media Buying : Over 80% of digital ad spend now flows through AI-powered platforms.
: Over 80% of digital ad spend now flows through AI-powered platforms. Analytics & Research: AI tools like GA4 and Sprout Social offer automated insights, reducing the need for junior analysts.
These trends are creating a “hollowing out” of junior roles—posing long-term risks for career development, especially in agencies that prioritize cost-cutting.
Become a Developer. Start learning today.
AI’s Silver Lining: New Careers and Hybrid Roles
Despite displacement, AI is also spawning entirely new roles within advertising:
AI Specialists & ML Engineers : Essential for building and fine-tuning AI ad tech.
: Essential for building and fine-tuning AI ad tech. Data Scientists & Analysts : Demand for human oversight of machine-driven insights is growing fast.
: Demand for human oversight of machine-driven insights is growing fast. Digital Transformation Consultants : Help agencies integrate AI into legacy workflows.
: Help agencies integrate AI into legacy workflows. Ethics & Compliance Officers: Critical for navigating regulations like GDPR and CCPA.
Emerging hybrid roles like prompt engineers and AI content strategists blend creativity with machine logic. A 2025 PwC report highlights a 25% wage premium for AI-savvy professionals.
How AI Powers Today’s Advertising
AI’s applications span the full campaign cycle:
Targeting & Personalization : Predictive models on Meta and Google deliver hyper-targeted ads with up to 30% more effectiveness .
: Predictive models on Meta and Google deliver hyper-targeted ads with up to . Creative Production : Tools like DALL·E and Jasper create scalable visuals and copy at a fraction of the traditional time and cost.
: Tools like DALL·E and Jasper create scalable visuals and copy at a fraction of the traditional time and cost. Optimization : Programmatic tools adjust bidding and placements in real-time.
: Programmatic tools adjust bidding and placements in real-time. Analytics & Sentiment Analysis : Brands can now track audience sentiment before campaigns even launch.
: Brands can now track audience sentiment before campaigns even launch. Innovations: Conversational ads (chatbots), AR/VR campaigns, and carbon-reducing delivery models are shaping the next frontier.
Challenges & Ethical Red Flags
AI in advertising faces serious ethical and technical challenges:
Privacy Regulations : Apple’s App Tracking Transparency and GDPR have tightened data access, limiting targeting precision.
: Apple’s App Tracking Transparency and GDPR have tightened data access, limiting targeting precision. Bias in Algorithms : A 2024 Forbes article documented how AI ad systems inadvertently prioritized high-income white audiences, marginalizing others.
: A 2024 Forbes article documented how AI ad systems inadvertently prioritized high-income white audiences, marginalizing others. Creativity Concerns : While efficient, many AI-generated ads have been criticized as “generic” or lacking emotional nuance.
: While efficient, many AI-generated ads have been criticized as “generic” or lacking emotional nuance. Consumer Fatigue: In 2024, 40% of internet users globally used ad blockers—many citing over-personalization and intrusive formats.
“AI can optimize reach but not always resonance.” — Comment from @sabrinacreates on X
The Reskilling Imperative
To future-proof the workforce, reskilling is critical. The World Economic Forum estimates that 39% of key skills will change by 2030, with digital literacy becoming a baseline requirement.
Educational initiatives , such as Nexford’s AI-focused BBA and MBA, are helping professionals transition into data or tech-forward roles.
, such as Nexford’s AI-focused BBA and MBA, are helping professionals transition into data or tech-forward roles. AI itself is also part of the solution—tools now help job seekers rewrite CVs, prepare for interviews, and match with new opportunities.
is also part of the solution—tools now help job seekers rewrite CVs, prepare for interviews, and match with new opportunities. The ILO advocates for “transition safety nets”, including financial support for those displaced and incentives for companies to retrain rather than replace.
Conclusion: Creativity, Caution, and Co-Evolution
AI in advertising is not the end of human creativity—it’s a redefinition of it. Yes, jobs are changing. Yes, some are disappearing. But new, more strategic, and interdisciplinary roles are emerging in their place.
The industry now faces a choice: automate everything and risk creative sterility—or strike a balance that values both the machine’s efficiency and the human touch. The winners in this new age of advertising will be those who adapt early, embrace reskilling, and treat AI not as a threat, but as a collaborator.
“AI won’t replace marketers, but marketers who use AI will replace those who don’t.” — MarketingProfs, 2025
Latest AI News Articles
| 2023-01-01T00:00:00 |
https://aibusinesshelp.co.uk/ai-impact-on-advertising
|
[
{
"date": "2023/01/01",
"position": 84,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 73,
"query": "AI impact jobs"
},
{
"date": "2023/03/01",
"position": 76,
"query": "AI impact jobs"
},
{
"date": "2023/03/01",
"position": 49,
"query": "AI job losses"
},
{
"date": "2023/05/01",
"position": 64,
"query": "AI job losses"
},
{
"date": "2023/07/01",
"position": 55,
"query": "AI job losses"
},
{
"date": "2023/09/01",
"position": 31,
"query": "AI job losses"
},
{
"date": "2023/11/01",
"position": 56,
"query": "AI job losses"
},
{
"date": "2023/12/01",
"position": 35,
"query": "AI job losses"
},
{
"date": "2024/01/01",
"position": 53,
"query": "AI job losses"
},
{
"date": "2024/02/01",
"position": 58,
"query": "AI job losses"
},
{
"date": "2024/03/01",
"position": 56,
"query": "AI job losses"
},
{
"date": "2024/04/01",
"position": 64,
"query": "AI job losses"
},
{
"date": "2024/05/01",
"position": 57,
"query": "AI job losses"
},
{
"date": "2024/06/01",
"position": 58,
"query": "AI job losses"
},
{
"date": "2024/07/01",
"position": 56,
"query": "AI job losses"
},
{
"date": "2024/08/01",
"position": 52,
"query": "AI job losses"
},
{
"date": "2025/01/01",
"position": 54,
"query": "AI job losses"
}
] |
|
AI Scientist - Agentic Verticals - Seattle, Washington, United States
|
San Jose, California, United States
|
https://careers.zoom.us
|
[] |
As an AI Scientist specializing in Agentic AI, you will develop intelligent agents that perceive, reason, and act in dynamic environments as ...
|
AI Scientist - Agentic Verticals
What you can expect
As an AI Scientist specializing in Agentic AI, you will develop intelligent agents that perceive, reason, and act in dynamic environments as part of Zoom AI Companion (AIC). Your work will directly impact the Zoom user experience by designing and implementing novel algorithms and systems. These impact workplace efficiency and customer engagement in Zoom Revenue Accelerator (ZRA), Zoom Customer Experience (ZCX) and Zoom Workplace for numerous domains.
About the Team
We are part of the Zoom AI Vertical Organization, dedicated to enhancing the Zoom user experience in workplace and customer interactions. Our team of experts is focused on creating the next generation of AI technologies. This technology can understand, learn, and adapt to improve user engagement and satisfaction. We are seeking a talented AI Scientist to join our team and contribute to our groundbreaking Agentic AI project. Responsibilities Conducting cutting-edge research in the field of Agentic AI, focusing on developing autonomous agents that can learn and adapt in complex environments.
Designing, implementing, and evaluating novel algorithms for perception, reasoning, decision-making, and learning to improve Zoom’s user experience.
Developing AI solutions that enhance meeting experiences and enable seamless human-machine natural interactions within Zoom.
Applying large language models (LLM) and Agentic AI frameworks to real production scenarios, fundamentally changing how humans communicate and collaborate during work and daily life.
Collaborating with cross-functional teams to integrate Agentic AI solutions into Zoom’s real-world applications and products.
Mentoring and providing guidance to junior researchers and team members.
What we’re looking for
Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Cognitive Science, or a related field
Show an understanding of machine learning algorithms, neural networks, reinforcement learning, cognitive architectures, and multi-modality including speech and vision
Exhibit development skills in programming languages such as Python, C++, or Java, and experience with AI frameworks and libraries (e.g., PyTorch)
Utilize problem-solving skills and the ability to think creatively and critically about complex problems
Communicate and collaborate with the ability to work effectively in a team-oriented environment
Salary Range or On Target Earnings:
Minimum:
Maximum:
In addition to the base salary and/or OTE listed Zoom has a Total Direct Compensation philosophy that takes into consideration; base salary, bonus and equity value.
Note: Starting pay will be based on a number of factors and commensurate with qualifications & experience.
We also have a location based compensation structure; there may be a different range for candidates in this and other locations.
Ways of Working
Our structured hybrid approach is centered around our offices and remote work environments. The work style of each role, Hybrid, Remote, or In-Person is indicated in the job description/posting.
Benefits
As part of our award-winning workplace culture and commitment to delivering happiness, our benefits program offers a variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health; support work-life balance; and contribute to their community in meaningful ways. Click Learn for more information.
About Us
Zoomies help people stay connected so they can get more done together. We set out to build the best collaboration platform for the enterprise, and today help people communicate better with products like Zoom Contact Center, Zoom Phone, Zoom Events, Zoom Apps, Zoom Rooms, and Zoom Webinars.
We’re problem-solvers, working at a fast pace to design solutions with our customers and users in mind. Find room to grow with opportunities to stretch your skills and advance your career in a collaborative, growth-focused environment.
Our Commitment
At Zoom, we believe great work happens when people feel supported and empowered. We’re committed to fair hiring practices that ensure every candidate is evaluated based on skills, experience, and potential. If you require an accommodation during the hiring process, let us know—we’re here to support you at every step.
We welcome people of different backgrounds, experiences, abilities and perspectives including qualified applicants with arrest and conviction records and any qualified applicants requiring reasonable accommodations in accordance with the law.
If you need assistance navigating the interview process due to a medical disability, please submit an Accommodations Request Form and someone from our team will reach out soon. This form is solely for applicants who require an accommodation due to a qualifying medical disability. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed.
Think of this opportunity as a marathon, not a sprint! We're building a strong team at Zoom, and we're looking for talented individuals to join us for the long haul. No need to rush your application – take your time to ensure it's a good fit for your career goals. We continuously review applications, so submit yours whenever you're ready to take the next step.
| 2023-01-01T00:00:00 |
https://careers.zoom.us/jobs/ai-scientist-agentic-verticals-seattle-washington-united-states-cb2ecf32-e678-4e2f-a57a-196cf2258fd1
|
[
{
"date": "2023/01/01",
"position": 90,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 90,
"query": "AI impact jobs"
},
{
"date": "2023/03/01",
"position": 92,
"query": "AI impact jobs"
}
] |
|
Xbox producer tells staff to use AI to ease job loss pain
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Xbox producer tells staff to use AI to ease job loss pain
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https://www.aol.com
|
[
"Aol Staff",
"Charlotte Edwards - Technology Reporter",
"July",
"At Am"
] |
Tech jobs overall, however, have continued to grow, and are expected to do so twice as fast as other sectors over the next decade, according to ...
|
[Getty Images]
An Xbox producer has faced a backlash after suggesting laid-off employees should use artificial intelligence to deal with emotions in a now deleted LinkedIn post.
Matt Turnbull, an executive producer at Xbox Game Studios Publishing, wrote the post after Microsoft confirmed it would lay off up to 9,000 workers, in a wave of job cuts this year.
The post, which was captured in a screenshot by tech news site Aftermath, shows Mr Turnbull suggesting tools like ChatGPT or Copilot to "help reduce the emotional and cognitive load that comes with job loss."
One X user called it "plain disgusting" while another said it left them "speechless". The BBC has contacted Microsoft, which owns Xbox, for comment.
Microsoft previously said several of its divisions would be affected without specifying which ones but reports suggest that its Xbox video gaming unit will be hit.
Microsoft has set out plans to invest heavily in artificial intelligence (AI), and is spending $80bn (£68.6bn) in huge data centres to train AI models.
Mr Turnbull acknowledged the difficulty of job cuts in his post and said "if you're navigating a layoff or even quietly preparing for one, you're not alone and you don't have to go it alone".
He wrote that he was aware AI tools can cause "strong feelings in people" but wanted to try and offer the "best advice" under the circumstances.
The Xbox producer said he'd been "experimenting with ways to use LLM Al tools" and suggested some prompts to enter into AI software.
These included career planning prompts, resume and LinkedIn help, and questions to ask for advice on emotional clarity and confidence.
"If this helps, feel free to share with others in your network," he wrote.
The Microsoft cuts would equate to 4% of Microsoft's 228,000-strong global workforce.
Some video game projects have reportedly been affected by the cuts.
More on this story
| 2023-01-01T00:00:00 |
https://www.aol.com/xbox-producer-tells-staff-ai-144247947.html
|
[
{
"date": "2023/01/01",
"position": 92,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 88,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 86,
"query": "AI unemployment rate"
},
{
"date": "2023/03/01",
"position": 93,
"query": "AI impact jobs"
}
] |
|
Senior Gen AI Platform Engineer - Artificial Intelligence @ Bloomberg
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Senior Gen AI Platform Engineer - Artificial Intelligence @ Bloomberg
|
https://jobs.anitab.org
|
[] |
Join the AnitaB.org Job Board and Talent Network to search for jobs, explore companies, and upload your resume to find opportunities ...
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Bloomberg’s Engineering AI department has 350+ AI practitioners building highly sought after products and features that often require novel innovations. We are investing in AI to build better search, discovery, and workflow solutions using technologies such as transformers, gradient boosted decision trees, large language models, and dense vector databases. We are expanding our group and seeking highly skilled individuals who will be responsible for contributing to the team (or teams) of Artificial Intelligence (AI) and Software Engineers that are bringing innovative solutions to AI-driven customer-facing products.
At Bloomberg, we believe in fostering a transparent and efficient financial marketplace. Our business is built on technology that makes news, research, financial data, and analytics on over 1 billion proprietary and third-party data points published daily -- across all asset classes -- searchable, discoverable, and actionable.
Bloomberg has been building Artificial Intelligence applications that offer solutions to these problems with high accuracy and low latency since 2009. We build AI systems to help process and organize the ever-increasing volume of structured and unstructured information needed to make informed decisions. Our use of AI uncovers signals, helps us produce analytics about financial instruments in all asset classes, and delivers clarity when our clients need it most.
We are looking for Senior GenAI Platform Engineers with strong expertise and passion for building platforms, especially for GenAI systems.
As a Senior GenAI Platform Engineer, you will have the opportunity to create a more cohesive, integrated, and managed GenAI development life cycle to enable the building and maintenance of our ML systems. Our teams make extensive use of open source technologies such as Kubernetes, KServe, MCP, Envoy AI Gateway, Buildpacks and other cloud-native and GenAI technologies. From technical governance to upstream collaboration, we are committed to enhancing the impact and sustainability of open source.
Join the AI Group as a Senior GenAI Platform Engineer and you will have the opportunity to:
Architect, build, and diagnose multi-tenant GenAI platform systems
Work closely with GenAI application teams to design seamless workflows for continuous model training, inference, and monitoring
Interface with both GenAI experts to understand workflows, pinpoint and resolve inefficiencies, and inform the next set of features for the platforms
Collaborate with open-source communities and GenAI application teams to build a cohesive development experience
Troubleshoot and debug user issues
Provide operational and user-facing documentation
We are looking for a Senior GenAI Platform Engineer with:
| 2023-01-01T00:00:00 |
https://jobs.anitab.org/companies/bloomberg/jobs/53920123-senior-gen-ai-platform-engineer-artificial-intelligence
|
[
{
"date": "2023/01/01",
"position": 94,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 93,
"query": "AI impact jobs"
}
] |
|
AI Is Now Making Hiring and Firing Decisions at Many Organizations
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AI Is Now Making Hiring and Firing Decisions at Many Organizations
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https://www.thehrdigest.com
|
[
"Ava Martinez",
"Diana Coker",
"Jane Harper",
"Anna Verasai",
"Priyansha Mistry"
] |
Over the years, the conversation surrounding AI use in the workplace has evolved from AI coming for our jobs to AI users being the real threat ...
|
Getting fired by AI? It’s more likely than you think. New reports suggest that AI is now making firing decisions for managers, often working with limited oversight. Not only is AI the reason for many workers losing their jobs, but it is also participating in the decision to determine who is eliminated from the workforce, giving workers another reason to fear the tool. The data shows that managers are using AI to terminate employees, even going so far as to consult with the chatbot to determine whether an employee should be rewarded with a promotion or not.
While AI can be an extremely useful tool within the workplace, there is a high risk of organizations losing out on authentic perspectives and rational thinking from employees due to an overreliance on artificial intelligence. AI can aid in consolidating data or identifying key data points, however, turning to the tool for work that requires human insight only makes the obsolescence of employees more pronounced. AI’s impact on hiring and firing cannot go unacknowledged as organizations make it apparent that these tools are here to stay.
AI Is Making Firing Decisions for Managers—Is an Overreliance on Technology the Best Strategy?
It’s another day of employers terrorizing employees with AI. If you were unsure about just how much of an impact this new-gen technology was having on the workplace, the data regarding AI making decisions on firing and layoffs might shock you. In a recent survey of 1,342 managers conducted by ResumeBuilder, 94% of managers stated they have used AI tools to make decisions regarding the people who report to them.
Around 66% of respondents stated that they used LLMs like ChatGPT to make decisions on layoffs, with 64% of them having used it for terminations as well. If that wasn’t enough, AI was used in performance evaluation as well, with 78% using it to determine raises and 77% using it for decisions on promotions. From creating training material to drafting performance improvement plans (PIPs), managers are turning to AI for it all.
Over 1 in 5 of the respondents admitted that they allowed AI to make the final decision on matters without human input. This is one of the most concerning elements of this report, as it showcases the callous and uncaring approach that stems from relying on AI for the simplest of tasks for convenience over quality decision-making.
Automation of Layoffs with AI Does Not Occur via AI Experts
One would assume that for someone to rely on AI tools so vehemently, they must be individuals with expertise in the matter of these tools in the same way an analyst would with data reports. However, this is simply not the case. Over two-thirds of these managers utilizing AI to make life-altering decisions have no formal training on AI.
As concerning as the data is, it is important to note that it is likely that many of these managers are turning to AI due to instructions they received from the top. Many businesses are forcing employees to explore AI usage in their work, with companies like Microsoft also going so far as to add AI use as a performance evaluation criterion in employee reviews. Workers who don’t want to be targeted by the organization are forced towards AI tools, regardless of whether they have a plan on how to use them.
Reportedly, nearly half of the managers from the ResumeBuilder survey stated that they were asked to analyze whether AI could replace a position. While 57% determined that AI could replace a position, 43% claimed they had already replaced a human position with AI. Now more than ever, organizations are looking for shortcuts and quick solutions to getting the job done, and inevitably, this is affecting the employees who work there for a living.
It Is Very Important to Be Careful About How AI Is Used in HR
Over the years, the conversation surrounding AI use in the workplace has evolved from AI coming for our jobs to AI users being the real threat to an individual’s career. While this is true to an extent, it also appears that AI tools could be deciding our fate just as easily. It’s no secret that artificial intelligence is reshaping the workplace and redirecting how we work. It’s now growing impossible to ignore AI tools, and as a result, most businesses are looking at avenues for their integration.
AI can be extremely useful in automating time-consuming, rote tasks and can present users with a wealth of information for further analysis. Data-driven decisions are critical for the workplace to thrive, however, it is important to remember that it is still just a tool. AI, no matter how advanced, lacks some element of critical thinking and does not have the ability to pick up on the nuances of every situation.
Human Supervision Is an Essential Part of AI Making Firing Decisions
It may be impossible to avoid AI’s impact on hiring and firing, but the final decision must fall to human oversight. Legislation is being explored precisely for this reason, but even if your state does not have mandates in place, it is important to take charge of the bare minimum and ensure that AI tools are supervised.
If managers are using AI to terminate employees, they need to be well-versed in the utilization of AI tools and the analysis of the output to ensure that the decisions made are sound. When you think about it, if a manager only has to churn out an AI report on whether an employee should be terminated, eventually, there will be no need for a manager in that position. It is important to remember that the human element is an essential part of AI use.
Human Resources and People Management are fields that thrive on the basis of human capital, and eliminating their involvement in the decision-making process can be extremely detrimental to an organization.
Subscribe to The HR Digest for more insights into the ever-evolving landscape of work and employment in 2025.
| 2025-07-07T00:00:00 |
2025/07/07
|
https://www.thehrdigest.com/ai-is-now-making-hiring-and-firing-decisions-at-many-organizations/
|
[
{
"date": "2023/01/01",
"position": 95,
"query": "AI impact jobs"
},
{
"date": "2023/01/01",
"position": 23,
"query": "AI employment"
},
{
"date": "2023/03/01",
"position": 100,
"query": "AI impact jobs"
},
{
"date": "2023/09/01",
"position": 63,
"query": "AI impact jobs"
},
{
"date": "2023/11/01",
"position": 98,
"query": "AI impact jobs"
},
{
"date": "2023/12/01",
"position": 99,
"query": "AI impact jobs"
},
{
"date": "2024/07/01",
"position": 90,
"query": "AI layoffs"
},
{
"date": "2024/12/01",
"position": 89,
"query": "AI layoffs"
}
] |
Staff Software Engineer, AI Data Generation — Google Careers
|
Staff Software Engineer, AI Data Generation — Google Careers
|
https://www.google.com
|
[] |
Our global impact spans software and hardware ... Please do not forward resumes to our jobs alias, Google employees, or any other organization location.
|
About the job
The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google
Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.AI Data Generation Team's charter is to generate GenAI data and evals for Google. Our team has been instrumental in performing LLM/GenAI model evaluations and model fine-tuning (e.g., using RLHF techniques).Crowd Compute is a large-scale general purpose platform that collects upwards of five million human answers a day on behalf of teams across Alphabet, including Cloud, Shopping/Commerce, Geo, Medical/Health, Research, NBU, YouTube, Waymo and X.Data Generation is our evolving self-service data generation platform that enables a typical AI product developer to produce verifiable high-quality data-sets through automatable workflows with deep integrations into pre-training, post-training and evaluations infrastructure.The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
| 2023-01-01T00:00:00 |
https://www.google.com/about/careers/applications/jobs/results/96290794968621766-staff-software-engineer/
|
[
{
"date": "2023/01/01",
"position": 96,
"query": "AI impact jobs"
},
{
"date": "2023/01/01",
"position": 97,
"query": "AI employment"
},
{
"date": "2023/02/01",
"position": 94,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 83,
"query": "generative AI jobs"
},
{
"date": "2023/03/01",
"position": 95,
"query": "AI impact jobs"
}
] |
|
Doesn't automation generally replace low skilled work with ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
CMV: AI+automation will cause massive job displacement and even if it doesn't replace everyone, society will still suffer greatly. 90 upvotes · 96 comments ...
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One of the main arguments for automation not taking over or jobs is it’s ability to create new jobs. Aren’t the new jobs created often better paying but also more complex/difficult? Won’t there be a point at which the people whose jobs get replaced simply don’t have the ability to do the new jobs created?
| 2023-01-01T00:00:00 |
https://www.reddit.com/r/AskEconomics/comments/103g1mk/doesnt_automation_generally_replace_low_skilled/
|
[
{
"date": "2023/01/01",
"position": 5,
"query": "automation job displacement"
}
] |
|
Job loss due to AI - by Circular Dynasty
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Job loss due to AI
|
https://medium.com
|
[
"Circular Dynasty"
] |
A study by the McKinsey Global Institute estimates that as many as 800 million jobs could be displaced by automation by 2030.
|
Job loss due to AI
Navigating the Impact of AI on the Job Market: Understanding Job Loss and Job Creation in the Age of Automation Circular Dynasty 7 min read · Jan 17, 2023 -- 1 Listen Share
Artificial intelligence (AI) has been a topic of significant interest and research in recent years, with its applications in various industries becoming increasingly prevalent. AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. This can include tasks such as problem-solving, pattern recognition, and decision making.
AI technology has been implemented in various industries, such as healthcare, finance, and transportation. In healthcare, AI is being used for tasks such as diagnosing diseases and predicting patient outcomes. In finance, AI is being used for tasks such as fraud detection and portfolio management. In transportation, AI is being used for tasks such as traffic management and self-driving cars.
The increasing use of AI in these and other industries has the potential to greatly improve efficiency and productivity. However, it also raises concerns about job loss as machines and algorithms are able to perform tasks that were previously done by human workers. This is an important issue to consider as the use of AI continues to expand in various industries.
The Concern
A study by the McKinsey Global Institute estimates that as many as 800 million jobs could be displaced by automation by 2030. While some of these jobs may be replaced by new positions in fields such as AI development and management, it is uncertain whether there will be enough new jobs to offset the number of jobs lost.
The impact of AI-related job loss is likely to be felt disproportionately by certain groups, such as low-skilled workers and those in certain geographic regions. This could lead to significant social and economic challenges, such as increased poverty and inequality.
Moreover, the concern is not only the job loss, but also the potential of wage stagnation, as the increasing use of AI could lead to a decrease in the demand for human labor, which in turn could lead to a decrease in wages.
It is important for governments, businesses and society as a whole to consider the potential impact of AI on jobs and to develop strategies to support workers affected by job loss due to automation. This can include initiatives to reskill and upskill workers to prepare them for jobs that may be in demand due to AI, as well as support for job transition and training.
The Impact
Industries such as manufacturing and retail have already begun to see job loss due to the increasing use of AI.
In the manufacturing industry, the use of robots and other forms of automation has led to a decrease in the number of jobs available for human workers. For example, many manufacturing companies now use robots to perform tasks such as assembly and welding, which were previously done by human workers. This has led to a decrease in the number of jobs available for human workers, particularly in industries such as automotive manufacturing.
Retail industry is another sector that has been heavily impacted by AI. The use of AI in tasks such as inventory management, customer service, and sales has led to a decrease in the number of jobs available for human workers. For example, many retail companies now use automated systems for tasks such as restocking shelves and processing orders, which were previously done by human workers. Additionally, the increasing use of online shopping has led to a decrease in the number of jobs available for human workers in brick-and-mortar retail stores.
Furthermore, AI-powered chatbots and virtual assistants are increasingly being used in customer service, which can lead to a decrease in the number of jobs available for human customer service representatives.
It’s worth noting that the job loss due to AI is not limited to these two sectors, but it is also happening in other sectors such as finance, transportation, and healthcare.
There are several examples of companies that have implemented AI technology and resulted in job loss:
Amazon: The company has been using robots in its warehouses for several years, which has led to a decrease in the number of jobs available for human workers. For example, the company has implemented a system called “Amazon Robotics” that uses robots to move products around its warehouses, which reduces the need for human workers.
Walmart: The retail giant has been using AI technology in its stores for tasks such as inventory management and restocking shelves. This has led to a decrease in the number of jobs available for human workers, particularly in the company’s warehouses.
Foxconn: The Chinese electronics manufacturer, which supplies companies such as Apple and Samsung, has been replacing human workers with robots in its factories. The company plans to replace 60,000 workers with robots by 2020.
UPS: The company has been testing the use of drones and robots for tasks such as package delivery, which has the potential to reduce the need for human workers.
JPMorgan Chase: The bank has been using AI technology for tasks such as fraud detection and portfolio management. This has led to a decrease in the number of jobs available for human workers in these areas.
These are just a few examples, but it is clear that AI technology is being implemented in various industries with the potential to reduce the number of jobs available for human workers. It is important for companies to consider the potential impact of AI on jobs and to develop strategies to support workers affected by job loss due to automation.
The Potential
While the increasing use of AI has the potential to lead to job loss in certain industries, it can also lead to job creation in others. One area where AI has the potential to create jobs is in the field of AI development and management. As the use of AI continues to expand in various industries, there will be a growing demand for professionals with the skills to develop and manage AI systems.
Some of the jobs that may be created as a result of AI advancements include:
AI engineers and developers: These professionals will be responsible for designing and building AI systems and applications.
AI data scientists: These professionals will be responsible for collecting, analyzing, and interpreting data used to train AI systems.
AI project managers: These professionals will be responsible for managing the development and implementation of AI projects.
AI ethicists: These professionals will be responsible for ensuring that AI systems are developed and used ethically and responsibly.
AI legal consultants: These professionals will be responsible for ensuring that AI systems comply with laws and regulations.
Additionally, AI advancements have the potential to create new industries and business opportunities. For example, the development of self-driving cars could lead to the creation of a new industry for autonomous vehicle services. The use of AI in healthcare could lead to the creation of new medical treatments and therapies. And the use of AI in finance could lead to the development of new financial products and services.
Overall, while the increasing use of AI has the potential to lead to job loss in certain industries, it also has the potential to create new jobs and industries in others. It is important for governments, businesses, and society as a whole to consider the potential impact of AI on jobs and to develop strategies to support the transition to new job markets.
Preparing for the future job market
As the use of AI continues to expand in various industries, it is important for individuals and society as a whole to prepare for the future job market. One key way to prepare is through reskilling and upskilling. Reskilling refers to learning new skills to transition to a different job, while upskilling refers to improving existing skills to advance in a current job.
With the advent of AI, many jobs that currently exist may become automated in the future, and it is important for individuals to be prepared for this transition. Reskilling and upskilling can help individuals to remain competitive in the job market and to transition to new jobs that may be in demand due to AI.
In addition to individual efforts, there are also government and private sector initiatives that aim to support job transition and training. Governments can support job transition and training through initiatives such as:
Funding for vocational training programs and adult education
Tax incentives for companies that invest in employee training
Government-funded retraining programs for workers affected by job loss due to automation.
Private sector companies can support job transition and training through initiatives such as:
Employee training and development programs
Partnerships with educational institutions to provide training and skill development opportunities for employees
Investment in research and development to create new job opportunities
Overall, preparing for the future job market in the age of AI requires a combination of individual efforts, such as reskilling and upskilling, as well as government and private sector initiatives to support job transition and training. This can help to ensure that individuals and society as a whole are prepared for the changes that will be brought about by AI advancements in the job market.
Conclusion
AI is a powerful technology that has the potential to greatly improve efficiency and productivity in various industries, but it also raises concerns about job loss. It’s important for society as a whole to be prepared for the changes that will be brought about by AI advancements in the job market by investing in reskilling and upskilling, and promoting government and private sector initiatives to support job transition and training.
| 2023-01-17T00:00:00 |
2023/01/17
|
https://medium.com/@circular.dynasty/job-loss-due-to-ai-6a8ebbb2d98f
|
[
{
"date": "2023/01/01",
"position": 15,
"query": "automation job displacement"
},
{
"date": "2023/01/01",
"position": 32,
"query": "AI replacing workers"
},
{
"date": "2023/01/01",
"position": 20,
"query": "artificial intelligence wages"
}
] |
Will AI Lead To Massive Job Loss In 2023? Breaking Down ...
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Will AI Lead To Massive Job Loss In 2023? Breaking Down The Relationship, Causes, and Solutions.
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https://www.linkedin.com
|
[
"Dan Aks"
] |
A recent study by McKinsey Global Institute found that up to 30 percent of the world's workforce could be displaced by 2030 as a result of automation. This is a ...
|
With the advent of Artificial Intelligence (AI) on the horizon, many have been left wondering what this could mean for their current and future job prospects. Could AI cause massive job loss in 2023? In this article, I am changing little the gears of talking about luxury and I will explore from a subjective point of view a current topic, which is the relationship between AI and job loss, the causes that could trigger it, as well as potential solutions to prevent or mitigate its effects.
AI is developed based on a technology that learns from and mimics human behaviour, using algorithms to automate processes. It is being used for everything from healthcare and transportation to finance, retail, and various other sectors. This creates efficiencies and can help with tasks that may be tedious or require a highly specialized skill set, but as AI continues to make strides in more areas, it could lead to massive job losses across industries.
Despite the potential for job loss due to AI, it does not necessarily have to lead to negative outcomes. Rather than displacing humans altogether, AI could shift job roles and create new ones by automating some aspects of work while allowing humans to focus on more creative or value-adding tasks. As the TV contributes to the creation of graphic designers and animators. Additionally, there are measures we can take today to ensure workers remain employable in an AI-driven technology landscape, such as investing in training and education as well as exploring public policies which could provide assistance during the next era. If AI will improve the quality and reduce the time, then the real next investment should be in human intelligence.
Overall, while it remains unclear what exactly the future holds when it comes to the relationship between AI and job loss, particularly come 2023, understanding the underlying causes of potential displacement together with potential solutions gives us hope that both humans and machines will coexist harmoniously.
Introduction to AI and Its Impact on Jobs
AI such as #chatgpt has been a hot topic in the media and among the public. With concerns about the future of work, it’s no surprise that people are wondering if AI will lead to massive job loss in the near future. However, the relationship between AI and job loss is complex, and there are a number of factors to consider.
Firstly, it is important to note that AI and automation are not the same thing. Automation involves machines taking over manual processes that humans have traditionally been responsible for. AI is the use of computer algorithms to make decisions or take actions without explicit instructions. This means that when AI is deployed, machines can effectively “think” and act on facts in order to make decisions and solve problems. With this definition in mind, it becomes clear that AI systems often replace human labor, resulting in job losses across multiple industries.
Second, AI can cause job displacement because it leads to increased efficiency in the workplace and since automation replaced human labor with machines, adding AI to the equation will make previously laborious tasks easier and faster. This increases productivity, but also eliminates the need for workers in many positions. Although this can initially create a sense of alarm among those whose jobs may be replaced by AI-based technology, automation can also create new creative roles and opportunities for those who are able to familiarize themselves with the technology.
There are also certain ethical considerations to consider when thinking about the impact of AI on jobs. Will intelligent machines eventually start taking on decision-making roles that were traditionally held by humans? Who will be responsible for programming these robots? How will safety be regulated? As these technologies become more pervasive and their implications on society become clearer, tough questions must be asked and addressed before any meaningful progress can be made.
Another way that AI can lead to job loss is by displacing human workers. This happens when an AI system is able to do the same job as a human, but at a lower cost or with better results. As businesses increasingly adopt AI systems, displaced workers will find it difficult to find new employment.
Furthermore, if workers’ skills become outdated due to the advancements in technology, they may find themselves replaced by an AI system instead of being able to transition into new roles within their organization.
Finally, even if jobs are not lost directly to AI, the emotional fear of job loss can lead to reduced spending and slower economic growth. This can happen as people become hesitant to spend money when they believe their jobs may soon be replaced by an automated system.
Pros and Cons of Artificial Intelligence in the Workplace
There are many pros and cons of artificial intelligence in the workplace. Some believe that AI will lead to massive job loss, while others believe that it will create new opportunities for workers. Here, we break down the relationship between AI and job loss, the causes of job loss due to AI, and possible solutions to this problem.
On the pro side, some argue that AI can help businesses operate more efficiently and effectively. For instance, if a company is able to use AI to automate tasks that are currently done by human employees, it could save money on labor costs. In addition, AI can help businesses make better decisions by providing data-driven insights. For example, a retail company might use AI to analyze customer behavior in order to optimize its product offerings.
On the con side, however, many believe that AI will cause mass unemployment as machines begin to replace human workers in a variety of jobs. For instance, jobs that require repetitive tasks or simple decision making are particularly vulnerable to automation by AI. This could include jobs like data entry, customer service, and even some types of medical diagnosis. In addition, as AI gets better at understanding and responding to natural language, it could also replace white-collar jobs that currently require human workers such as project managers, lawyers, and financial analysts.
Despite the concerns about job loss due to artificial intelligence, there are some possible solutions to this problem. One option is for companies to use AI in ways that complement human labor instead of replacing it, and the policy makers in the labor divisions of the government should issue such policies to protect the workforce from being discharged because of the AI. For example, a retail company might use AI to automate mundane tasks like inventory tracking, while also using humans to offer exceptional customer service.
Overall, there are both pros and cons of artificial intelligence in the workplace. While some fear that it will cause massive job displacement, others believe that its potential to make businesses more efficient and increase decision-making accuracy may result in positive outcomes. Organizations should consider how AI can be used appropriately so that its benefits can be balanced with its potential risks.
The Relationship Between AI and Human
The future of work is often a topic of hot debate. With the rise of artificial intelligence (AI) and automation, some believe that mass job loss is inevitable. From a personal point of view, I feel that AI is more likely to create new jobs and opportunities than it is to displace existing ones.
While AI has the potential to automate certain tasks, it is not typically used for this purpose. Instead, it is most commonly used to augment human workers. For example, a doctor may use an AI system to help them diagnose a patient, or a lawyer may use it to review documents for relevant information. In these cases, AI is used to supplements human workers rather than replace them entirely.
Can Human Creativity Be Replaced By an AI?
It’s no secret that artificial intelligence is on the rise. With the rapid advancements in technology, AI is becoming more and more advanced, capable of completing tasks that were once thought to be exclusively human. This raises an important question: can human creativity be replaced by an AI?
There are some who believe that AI will never be able to replace human creativity. After all, creativity is often thought of as a uniquely human trait, one that requires emotion and intuition. However, there are others who believe that AI could potentially surpass human creativity. After all, AI has already shown itself to be incredibly talented at completing certain tasks, such as analyzing data and recognizing patterns. If AI continues to advance at its current rate, AI is already capable of producing original works of art or music.
So what does this mean for the future of work? Will AI lead to massive job loss in the creative industries? It’s difficult to say for sure. However, it’s important to consider the potential implications of AI’s continued advancement. The rise of AI could lead to significant changes in the labor market mentality and policy, with some jobs becoming obsolete and others becoming increasingly valuable. As we continue to learn more about AI and its capabilities, it’s important to stay adaptive and open-minded about the future of work.
Potential Job Loss Scenarios in 2023!
The future is always uncertain, but there are certain factors that can lead to potential job loss scenarios. In 2023, AI may lead to massive job loss in many industries.
Content providing AI already can automate tasks that have traditionally been done by humans. And as AI becomes more advanced, it will be able to automate more complex tasks. This could lead to large-scale job loss in many industries, including manufacturing, logistics, and customer service. Businesses may be tempted to use AI in order to reduce labor costs.
Overall, AI will continue to become more pervasive in our lives. Governments and businesses should be aware of the potential job loss it could lead to, as well as the solutions that are available.
Solutions to Prevent Massive Layoffs in 2023 and Beyond
It is no secret that AI has the potential to disrupt the job market as we know it. A recent study by McKinsey Global Institute found that up to 30 percent of the world’s workforce could be displaced by 2030 as a result of automation. This is a staggering number, and one that has many people worried about the future of work.
In 2023, the global economy is forecast to rebound from the Covid-19 pandemic, with China reopening its borders and I have a feeling that AI will play a critical role in driving this recovery. However, there is a risk that companies will resort to mass layoffs to reduce costs and stay competitive.
There are several solutions that can prevent massive layoffs in 2023:
1. Governments should issue Labour laws that can be amended to give workers more protection against unfair dismissal. The unemployment will cost the government more than protecting the labor force.
2. Companies can invest in creativity training and upskilling programs for their employees so they are better equipped to handle the changes brought about by AI.
3. Employers can adopt more flexible work arrangements so that workers can better manage their work-life balance.
4. Social safety nets should be strengthened so that workers who are laid off have a safety net to fall back on.
Conclusion
| 2023-01-01T00:00:00 |
https://www.linkedin.com/pulse/ai-lead-massive-job-loss-2023-breaking-down-relationship-abdul-m-
|
[
{
"date": "2023/01/01",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2023/01/01",
"position": 6,
"query": "AI unemployment rate"
}
] |
|
The Advancements and Impact of Robotics and Automation
|
Revolutionizing Industries: The Advancements and Impact of Robotics and Automation
|
https://www.linkedin.com
|
[
"Astute Analytica",
"Roy Young",
"Kavita Ahuja",
"Marketing Consultant",
"Independent Affiliate Marketer",
"Mommie",
"Bits Pilani Alumni"
] |
One study by the McKinsey Global Institute found that up to 800 million jobs worldwide could be displaced by automation by 2030 but that this could be offset by ...
|
Outlines
Abstract Introduction Advancements in Robotics Advancements in Automation Impact on Industries Economic Impact Challenges and Opportunities Future Trends and Predictions Conclusion References
I. Abstract:
This article explores the advancements and impact of robotics and automation on various industries. First, we discuss recent developments in robotics and automation, such as perception, manipulation, machine learning, computer vision, and control systems, and how they enable robots and machines to perform tasks with greater accuracy and efficiency. We also examine how these technologies transform manufacturing, agriculture, healthcare, and transportation industries and how they impact productivity, efficiency, and cost-effectiveness. Additionally, we delve into the economic impact of robotics and automation on employment, productivity, and economic growth and the potential for these technologies to create new job opportunities and drive innovation. Lastly, we explore the challenges and opportunities of the widespread adoption of robotics and automation and future trends and predictions in the field.
Keywords: Robotics, Automation, Advancements, Impact, Industries, Productivity, Efficiency, Cost-effectiveness, Employment, Economic growth, Job opportunities, Innovation, Challenges, Opportunities, Future trends.
II. Introduction:
Robotics and automation are rapidly advancing technologies that have the potential to revolutionise the way we work and live. Robotics refers to the branch of engineering and science that deals with the design, construction, operation, and use of robots. In contrast, automation refers to using technology to perform tasks without human intervention. Together, these technologies can improve productivity, efficiency, and cost-effectiveness in various industries. In this article, we will explore the advancements and impact of robotics and automation on multiple sectors and the potential of these technologies to revolutionise how we work and live.
Robotics and automation are rapidly advancing technologies that have the potential to revolutionise the way we work and live. Robotics refers to the branch of engineering and science that deals with the design, construction, operation, and use of robots. In contrast, automation refers to using technology to perform tasks without human intervention. Together, these technologies can improve productivity, efficiency, and cost-effectiveness in various industries.
Robotics and automation have various applications in various industries, including manufacturing, agriculture, healthcare, and transportation. In manufacturing, for example, robots and automation systems can perform tasks such as assembly, welding, and painting, improving productivity and reducing labour costs (Wang et al., 2019). In agriculture, robots and automation systems can plant, harvest, and monitor crops, improve crop yields and reduce labour costs (Raza et al., 2018). In healthcare, robots and automation systems can be used for surgery, rehabilitation, and monitoring of patients, improving patient outcomes and reducing healthcare costs (Khan et al., 2020). Finally, robots and automation systems can be used in transportation tasks such as driving, navigation, and monitoring, improving safety and reducing transportation costs (Sivarajah et al., 2021).
However, it is essential to note that with the advancements in these technologies, there will also be challenges such as job displacement, safety, and ethical considerations that need to be addressed. These challenges and opportunities will be discussed in further detail in the article.
III. Advancements in Robotics:
Robotics is a rapidly advancing field that has seen significant developments in recent years. Perception is one of the critical areas of advancement, which refers to a robot's ability to sense and interpret its environment. For example, computer vision and sensor technology advances have enabled robots to recognise better and track objects and navigate unstructured environments. This has led to the development of robots that can perform object recognition, grasping, and manipulation tasks.
Another area of advancement is in manipulation, which refers to a robot's ability to interact with its environment physically. For example, developing grippers, arms, and hands with advanced skill and precision has allowed robots to assemble parts, handle fragile objects, and even perform surgery.
Additionally, mobility is another area of advancement in robotics. The development of new wheels, tracks, and legs has enabled robots to move more efficiently and effectively in different environments, such as rough terrain and even underwater.
These advancements in perception, manipulation, and mobility have enabled robots to perform tasks that were once thought impossible, such as search and rescue operations, space exploration, and even personal assistance. As the field of robotics continues to evolve, we will likely see even more incredible capabilities in the future.
IV. Advancements in Automation:
Automation is the use of technology to perform tasks without human intervention. In recent years, there have been significant advancements in automation, particularly in machine learning, computer vision, and control systems.
Machine learning, a subfield of artificial intelligence, allows machines to learn from data and improve their performance over time. This has led to the development of automated systems that can perform tasks such as image recognition, natural language processing, and predictive maintenance. For example, machine learning algorithms have been used to develop self-driving cars, which can navigate and avoid obstacles more accurately and efficiently than humans.
Computer vision is another area of advancement in automation. It refers to the ability of machines to interpret and understand visual information from the world. Improvements in computer vision have enabled devices to perform tasks such as object detection, tracking, and facial recognition. This has led to the development of automated systems that can be used in industries such as security, surveillance, and manufacturing.
Control systems, the core technology used to control and coordinate different parts of automation systems, are also essential areas of advancement. These systems allow machines to perform tasks more accurately and efficiently by coordinating various system components, such as sensors and actuators.
These advancements in machine learning, computer vision, and control systems have enabled machines to perform tasks with greater accuracy and efficiency, making them more capable and versatile than ever.
V. Impact on Industries:
Robotics and automation significantly impact many industries, the most notable being manufacturing, agriculture, healthcare, and transportation.
Robotics and automation have increased productivity, efficiency, and cost-effectiveness in the manufacturing industry. Automated systems, such as robotic arms and conveyor belts, can perform repetitive tasks with high accuracy and speed, reducing labour costs and increasing output. For example, a Boston Consulting Group study found that using industrial robots in manufacturing can increase productivity by 30% and reduce labour costs by as much as 50%. (BCG, 2017)
The agriculture industry is also being transformed by robotics and automation. Drones, for example, can be used for crop monitoring and precision agriculture, leading to an increase in yield and a reduction in costs associated with labour and chemical inputs. Robotics and automation are also being used in the livestock industry, where robots are being used to perform tasks such as milking cows and monitoring animal health.
In the healthcare industry, robotics and automation are used to improve patient outcomes and reduce costs. Robotics-assisted surgery, for example, can lead to a reduction in complications and recovery time for patients. Automated systems are also being used to perform medication dispensing and patient monitoring tasks, which can improve patient safety and reduce labour costs.
Finally, in the transportation industry, automation is being used to improve the efficiency and safety of transportation systems. Self-driving cars, for example, have the potential to reduce the number of accidents caused by human error and to improve traffic flow. Automated systems are also being used in the logistics industry, where they are being used to optimise routes and improve the efficiency of supply chains.
Overall, robotics and automation are transforming industries by increasing productivity, efficiency, and cost-effectiveness while improving safety and quality of life.
VI. The Economic Impact
The economic impact of robotics and automation is a complex and multifaceted issue that has been the subject of much research and debate. One of the main concerns is the potential for these technologies to displace jobs, as automation and robots can perform many tasks more efficiently and at a lower cost than humans. However, it is also possible that robotics and automation could lead to increased productivity, economic growth, and new job opportunities.
One study by the McKinsey Global Institute found that up to 800 million jobs worldwide could be displaced by automation by 2030 but that this could be offset by the creation of up to 375 million new jobs in areas such as data analysis, software development, and sales and marketing (McKinsey, 2018).
Another study by the National Bureau of Economic Research (NBER) found that the increased use of robots in manufacturing has significantly increased productivity and economic growth (Autor, Dorn, & Hanson, 2016). The study also found that adopting robots led to new jobs, particularly in engineering and computer science.
Additionally, the Boston Consulting Group (BCG) reported that companies adopting robotics and automation could expect a 20-25% increase in productivity and a 15-20% reduction in labour costs (BCG, 2015).
In conclusion, while the economic impact of robotics and automation is complex and multifaceted, it is likely that these technologies will have a positive effect on productivity and economic growth and could lead to the creation of new job opportunities in areas such as data analysis, software development, and sales and marketing. However, it is also essential to consider the potential for job displacement and to address this issue through policies such as retraining programs for workers.
VII. Challenges and Opportunities
The widespread adoption of robotics and automation brings several challenges and opportunities. One of the biggest challenges is job displacement, as robots and automation systems are increasingly able to perform tasks that humans previously did. This can lead to a loss of jobs and a need for retraining for displaced people. However, it is essential to note that while some jobs may be lost, new jobs will also be created due to these technologies, such as those related to the design, programming and maintenance of robots.
Another major challenge is safety, as robots and automation systems can only be safe if properly designed and maintained. This is particularly true in industries such as manufacturing and healthcare, where robots are often used near humans. To address this, companies and organisations must follow proper safety protocols and guidelines to minimise the risk of accidents and injuries.
Ethical considerations are also essential when it comes to robotics and automation. For example, there are concerns about the use of autonomous weapons in warfare and the use of surveillance robots in public spaces. To address these concerns, it is essential for society to have open and transparent conversations about the ethical implications of these technologies and to put in place regulations and guidelines to ensure that they are used responsibly.
In conclusion, the widespread adoption of robotics and automation brings several challenges and opportunities. However, society must address these challenges through proper safety protocols and ethical considerations to ensure that these technologies are used for the betterment of humanity.
VIII. Future Trends and Predictions
The future of robotics and automation is promising, with many experts predicting significant advancements in areas such as artificial intelligence, autonomous systems, and human-robot interaction. One key trend is the increasing use of artificial intelligence in robotics, which enables robots to perform tasks that were once thought impossible, such as recognising and responding to human emotions. Additionally, the development of autonomous systems is expected to lead to the widespread use of drones, self-driving cars, and other autonomous vehicles in various applications.
Another significant trend is the increasing use of robots in healthcare, which is expected to improve the quality and efficiency of care and reduce costs. For example, robots are used in surgeries to assist doctors with procedures and rehabilitation to help patients recover from injuries and illnesses.
Industry 4.0 is expected to continue in the manufacturing industry, with more and more factories becoming fully automated. This will likely lead to increased productivity, efficiency, and cost-effectiveness, as well as new job opportunities in data analysis and system maintenance.
Additionally, there is a growing interest in creating robots that can work alongside humans and in developing "cobots" (collaborative robots) which can work alongside human operators, to improve efficiency and safety.
The future of robotics and automation is expected to bring many benefits and opportunities. Still, it’s also essential to address the challenges that come with these technologies, such as job displacement and ethical considerations, to ensure that the benefits are widely shared and that these technologies are used for the betterment of society.
IX. Conclusion
In conclusion, robotics and automation have the potential to revolutionise industries and transform the way we live and work. Advancements in areas such as perception, manipulation, and mobility for robotics, machine learning, computer vision, and control systems for automation have enabled machines and robots to perform tasks that were once thought impossible. These technologies have significantly impacted manufacturing, agriculture, healthcare, and transportation, improving productivity, efficiency, and cost-effectiveness. The economic impact of robotics and automation is also noteworthy, with the potential to create new job opportunities and drive innovation. However, it is essential also to consider the challenges and opportunities that come with the widespread adoption of these technologies, such as job displacement, safety and ethical considerations. Future trends and predictions suggest that we will see continued advancements in robotics and automation, potentially transforming different areas of our lives. Therefore, it is vital that society proactively addresses the challenges and opportunities presented by these technologies to reap the benefits fully.
X. References
1. Khan, M. A., Chaudhry, A., & Al-Jumaily, A. (2020). Robotics and automation in healthcare: A review of recent developments and future potential. International Journal of Medical Robotics and Computer Assisted Surgery, 16(3), e1940.
2. Raza, S., Khan, M. A., & Al-Jumaily, A. (2018). Robotics and automation in agriculture: A review of recent developments and future potential. International Journal of Advanced Robotic Systems, 15(5), 1729881418781376.
3. Sivarajah, U., Wang, X., & Chen, W. (2021). Robotics and automation in transportation: A review of recent developments and future potential. IEEE Transactions on Intelligent Transportation Systems, 22(2), 585-598.
4. Wang, X., Chen, W., & Sivarajah, U. (2019). Robotics and automation in manufacturing: A review of recent developments and future potential. IEEE Transactions on Automation Science and Engineering, 16(2), 991-1004.
5. Corke, P. (2011). Robotics, Vision and Control: Fundamental Algorithms in MATLAB (Springer Tracts in Advanced Robotics).
6. Thrun, S., Burgard, W., & Fox, D. (Eds.). (2006). Robotics: Science and Systems. MIT Press.
7. Groover, M. P. (2017). Automation, Production Systems, and Computer-Integrated Manufacturing (4th Edition). Prentice Hall.
8. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). MIT Press.
9. Szeliski, R. (2010). Computer Vision: Algorithms and Applications (Texts in Computer Science). Springer.
10. Boston Consulting Group. (2017). The robotics revolution. Retrieved from https://www.bcg.com/publications/2017/manufacturing-operations-robotics-revolution.aspx
11. Transparency Market Research. (2018). Robotics in the healthcare market - Global industry analysis, size, share, growth, trends, and forecast 2017-2025 [Report]. Retrieved from https://www.transparencymarketresearch.com/robotics-healthcare-market.html
12. Autor, D., Dorn, D., & Hanson, G. (2016). The China shock: Learning from labour market adjustment to significant changes in trade. National Bureau of Economic Research.
13. Boston Consulting Group. (2015). The robotics revolution: The next great leap in manufacturing.
14. McKinsey Global Institute. (2018). Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages.
15. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
16. National Robotics Initiative (2018). National Robotics Initiative: Partnerships for Transformation. Retrieved from https://www.nsf.gov/cise/nri/
17. International Federation of Robotics (2019). World Robotics Report 2019. Retrieved from https://www.ifr.org/world-robotics-report-2019/
18. The Royal Society (2018). Robotics and autonomous systems: The impact on employment. Retrieved from https://royalsociety.org/topics-policy/projects/robotics-autonomous-systems/
19. The Robotics Institute at Carnegie Mellon University. (n.d.). The Future of Robotics. Retrieved from https://www.ri.cmu.edu/future-of-robotics/
20. McKinsey & Company. (n.d.). Robotics and Artificial Intelligence. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/robotics-and-artificial-intelligence
21. World Health Organization. (n.d.). Robotics in Healthcare. Retrieved from https://www.who.int/news-room/feature-stories/detail/robotics-in-healthcare
22. Deloitte. (n.d.). Industry 4.0 and its Impact on Manufacturing. Retrieved from https://www2.deloitte.com/us/en/insights/focus/industry-4-0/impact-on-manufacturing.html
| 2023-01-01T00:00:00 |
https://www.linkedin.com/pulse/revolutionizing-industries-advancements-impact-robotics-zakaria
|
[
{
"date": "2023/01/01",
"position": 25,
"query": "automation job displacement"
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|
Automation : r/FPandA
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The heart of the internet
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https://www.reddit.com
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[] |
It's likely that the automation of certain FP&A tasks will lead to some job displacement, but it also creates new opportunities for FP&A professionals to ...
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Chatgtp claims that FP&A and risk management roles are the least likely to be automated of the accounting roles and that technical data analytics skills will become increasingly important for the roles. In your experience, is it correct? *i’m an accounting student btw.
| 2023-01-01T00:00:00 |
https://www.reddit.com/r/FPandA/comments/10oa33i/automation/
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[
{
"date": "2023/01/01",
"position": 36,
"query": "automation job displacement"
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] |
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Industrial Robots and Regional Fertility in European ...
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Industrial Robots and Regional Fertility in European Countries.
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https://search.ebscohost.com
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[
"Matysiak",
"Bellani",
"Bogusz"
] |
by A Matysiak · 2023 · Cited by 27 — the ones which are mainly negatively affected by automation, either by being at risk of job displacement or having to compete with displaced workers for jobs.
|
Connecting you to content on EBSCOhost
| 2023-03-01T00:00:00 |
2023/03/01
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https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=01686577&AN=162739816&h=jJP8hgxt%2FrQn61E5akr5mxPM7%2BJUxaz%2BQPnPyVAFmqCnT282Q2gH4AJ9T%2BtjpI0PE%2FD%2F%2BV9MEM0aMDpujHQ4vA%3D%3D&crl=f
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[
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"position": 53,
"query": "automation job displacement"
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Beyond googly eyes: stakeholder perceptions of robots in ...
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Beyond googly eyes: stakeholder perceptions of robots in construction
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https://link.springer.com
|
[
"Walzer",
"Alexander N.",
"Awalzer Ethz.Ch",
"Chair Of Innovative",
"Industrial Construction",
"Institute Of Construction",
"Infrastructure Management",
"Department Of Civil",
"Environmental",
"Geomatic Engineering"
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by AN Walzer · 2022 · Cited by 11 — According to Rumberger (1984), job opportunities are put at risk by a combination of forces, out of which high technology and automation are the most ...
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The study by Reeves et al. (2020) studied social robot attributes and perceptions in two separate studies that are based on a self-developed image database. Similarly, our methodology consists of a research approach involving the following steps: (1) Qualitative data collection via semi-structured interviews, (2) Definition of attributes and perceptions, (3) Development of image database, (4) Attribute validation study of defined attributes, (5) Quantitative perception survey with defined perceptions, (6) Statistical data processing and (7) Presentation of results and interpretation (Fig. 2).
Fig. 2 Flowchart of the applied research method Full size image
(1) Interviews First, we conducted five qualitative, semi-structured interviews with five experts in different sub-areas of AEC (architects, professors, product- and branch managers, researchers and engineers). The interview duration ranged between 30 and 45 min. Recruiting of the participants was done by convenience and proximity in the research network on a voluntary basis and without monetary compensation. The interviews were conducted in German and English and transcribed verbatim leading to 21 pages of interviews. The interview questions are available in Appendix 1. Table 2 shows the evaluated themes that were collected during the interviews. Productivity and price/cost—all economic viability terms—are not considered due to them not primarily being visual objectively measurable.
Table 2 Qualitative evaluation interviews themes (N = 5) Full size table
(2) Based on the interviews, key characteristics are evaluated qualitatively: Objective features are referred to as “attributes”, whereas subjective features are referred to as “perceptions”. Table 3 illustrates the chosen set of three objective attributes and subjective perception chosen due to a combination of interview results and literature review. Our final research methodology considers three attributes with each two to three expressions, such as in the study design of Klueber and Onnasch (2022): (a) Form factor, or Morphology: anthropomorphic (human-like), zoomorphic (animal-like), mechanomorphic (machine-like). (b) Color: bright, dark. (c) Material: soft, hard. Perceptions: (d) Ease of use (inspired by Davis 1989). (e) Work task adaptability (or production flexibility, such as studied by Hartmann et al. 2009) and (f) Risk of job loss (Brand 2015).
Table 3 Selected attributes and perceptions Full size table
(3) Image database Inspired by the Stanford Social Robot Database (2020) we developed a database of 50 construction robotsFootnote 1; 86% are existing machines, 14% are virtual visualisations and do not exist (yet). Surveys that rely on images are easy to understand, more engaging and thus tend to lead to higher response rates (Treiblmaier and Filzmoser 2011). The images in the database are subjectively chosen by the authors to achieve a balance according to the three defined attributes and expressions, e.g.: for each robot of attribute 1, expression 1 there shall be one of attribute 1, expression 2. The images are prepared in a resolution of 400x400px. Human operators, other background objects, brands and the environment have been removed to standardise the visual stimuli and thereby stimulate a spontaneous and mostly unbiased perception. Figure 3 shows a selection of the database entries. The full data set can be found in Appendix 3.
Fig. 3 Excerpt of the developed robot picture database (n = 50) Full size image
(4) Attribute validation study In this first survey, the robots’ visual characteristics are measured. We developed a quantitative online study on the survey platform Qualtrics\(^{\mathrm{TM}}\) that collected data on the attributes of 20 randomly chosen pictures of the 50 robots in the database. No relation to the broader AEC sector was required for the participants and recruitment occurred by convenience within private networks. This survey used the following items for validation by the participants: Morphology (anthropomorphic, zoomorphic, mechanomorphic), color (bright, dark), material (soft, hard) and was conducted during 4 weeks in Spring 2022. The survey was available in German and English with an estimated duration of 15–20 min. A cut-off value of 0.8 is used to determine if an attribute can be treated as objective by the majority of participants (e.g. see Gaul et al. 2010): Attribute expressions with less than 80% agreement between participants were considered unclear in our data set and were not further used for analysis.
(5) Quantitative perception survey For an eight week period in Spring 2022, we conducted a quantitative study on the same platform. For this survey, however, participants from the AEC industry were recruited via direct email outreach to more than 200 Swiss construction companies, physical posters at construction sites and direct messages within the authors’ extended professional, social and research networks. The survey was designed to be intuitive and interactive for the participants and responses were collected via a 5-point Likert scale. Participants were shown a random sample of 20 of the 50 robot images in the data set to reach an assumed completion time of 15–20 min. They were asked to judge three predefined perceptions in the context of the construction industry: (a) Ease of use: How easy to use does the robot appear to be? (b) Work task adaptability: How flexible is the application of the robot? In particular, we asked if the machine appears to have only a single or several use cases. (c) Risk of job loss: How likely is the robot to replace humans?
(6) Statistical data processing Binary logistic regression (BLR) is used to estimate the relationship between one or more independent variables and a binary dependent variable. BLR is chosen to be appropriate to prove or disprove our binary outcome hypothesis as probabilities are bounded by 0 and 1. In our approach, both independent and dependent variables are dichotomous (mutually exclusive, collectively exhaustive). By evaluating Exp(b), or the Odd Ratio, conclusions can be drawn on the relationship between attributes and perceptions which are the main interest of this study. Further information on BLR can be found in Appendix 2. Raw survey data are exported from the Qualtrics\(^{\mathrm{TM}}\) platform, imported into IBM\(^{\mathrm{TM}}\) SPSS Statistics Software and prepared for BLR analysis. Systematic errors are evaluated by scanning the data for systematic response bias. This is done by evaluating the standard deviation from every recorded response. If the standard deviation SD = 0, the response is considered invalid since the participant always chose the same answer for the entire survey. Incomplete responses of the designed online survey platform are considered “completely at random”. According to Leung and Qin (2006) the missingness of data under completely random state can be ignored and, therefore, are kept in the data set. To compare the different characteristics with BLR, every attribute type is coded with a binary parameter: Consequently, the 5-point Likert scale of the quantitative perception survey is compressed into net positive and net negative binary values while neutral values are ignored.
| 2022-12-14T00:00:00 |
2022/12/14
|
https://link.springer.com/article/10.1007/s41693-022-00087-y
|
[
{
"date": "2023/01/01",
"position": 95,
"query": "automation job displacement"
}
] |
Halfway Through 2025, AI Has Already Replaced 94000 Tech Workers
|
The heart of the internet
|
https://www.reddit.com
|
[] |
AI has not replaced a single human. Companies are simply cutting staff, supplying a copilot license, and leaving the remaining workforce with ...
|
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
| 2023-01-01T00:00:00 |
https://www.reddit.com/r/Futurology/comments/1ls2bdd/halfway_through_2025_ai_has_already_replaced/
|
[
{
"date": "2023/01/01",
"position": 4,
"query": "AI job losses"
},
{
"date": "2023/04/01",
"position": 4,
"query": "AI replacing workers"
},
{
"date": "2023/05/01",
"position": 4,
"query": "AI job losses"
},
{
"date": "2023/09/01",
"position": 4,
"query": "AI replacing workers"
},
{
"date": "2023/10/01",
"position": 4,
"query": "AI replacing workers"
},
{
"date": "2024/04/01",
"position": 4,
"query": "AI job losses"
},
{
"date": "2024/05/01",
"position": 4,
"query": "AI replacing workers"
},
{
"date": "2024/07/01",
"position": 4,
"query": "AI replacing workers"
},
{
"date": "2024/10/01",
"position": 4,
"query": "AI replacing workers"
},
{
"date": "2024/11/01",
"position": 4,
"query": "AI replacing workers"
},
{
"date": "2024/12/01",
"position": 4,
"query": "AI replacing workers"
}
] |
|
AI-linked job losses drive record-high unemployment among college ...
|
AI-linked job losses drive record-high unemployment among college graduates
|
https://www.newson6.com
|
[
"News On"
] |
A growing number of recent college graduates are struggling to find work, and researchers say artificial intelligence may be to blame.
|
The unemployment rate for recent college graduates has hit a record high as employers increasingly use artificial intelligence to replace entry-level jobs.
By: Graham Dowers
-
A growing number of recent college graduates are struggling to find work, and researchers say artificial intelligence may be to blame.
According to a new report from Oxford Economics, the unemployment rate for recent college graduates has reached an all-time high, currently sitting at 6.6% over the past 12 months. This is slightly above the national unemployment rate. The trend appears to be tied to the rapid adoption of AI across multiple industries, particularly in roles that were once filled by entry-level employees.
The report suggests that many employers are now using artificial intelligence systems to perform basic tasks traditionally assigned to recent graduates, reducing the number of available job openings for young professionals entering the workforce.
Experts say that while the job market remains challenging, one way graduates can improve their odds is by gaining AI-related skills. Training in artificial intelligence tools and platforms can help candidates stand out in industries that are increasingly relying on automation and machine learning.
| 2023-01-01T00:00:00 |
https://www.newson6.com/story/686aa41b2258385ef5a3a5ae/ai-linked-job-losses-drive-record-high-unemployment-among-college-graduates
|
[
{
"date": "2023/01/01",
"position": 5,
"query": "AI job losses"
},
{
"date": "2023/03/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2023/04/01",
"position": 1,
"query": "AI unemployment rate"
},
{
"date": "2023/05/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2023/06/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2023/07/01",
"position": 8,
"query": "AI job losses"
},
{
"date": "2023/09/01",
"position": 6,
"query": "AI job losses"
},
{
"date": "2023/11/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2023/12/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2024/01/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2024/02/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2024/03/01",
"position": 5,
"query": "AI job losses"
},
{
"date": "2024/04/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2024/05/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2024/06/01",
"position": 8,
"query": "AI job losses"
},
{
"date": "2024/07/01",
"position": 6,
"query": "AI job losses"
},
{
"date": "2024/08/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2025/01/01",
"position": 7,
"query": "AI job losses"
},
{
"date": "2025/04/01",
"position": 3,
"query": "AI unemployment rate"
},
{
"date": "2025/06/01",
"position": 27,
"query": "AI job losses"
}
] |
|
If AI takes over all work and jobs, what will humans do ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
For example, if AI replaces many jobs, it could lead to increased income inequality and higher unemployment. One possible solution to this problem is a ...
|
A majority of humans in our first world society are born, get some form of an education, spend a large quantity of the rest of their lives working to support their lives, and at some point assuming they live long enough they might retire before they die. With all the incredible recent advancements in AI a lot of people speculate that someday AI will be able to replace nearly all forms of work. But most humans have to work in order to earn income to support their lives so without work what would humans do? Would money become useless? Would humans just sit around and live in paradise whilst AI robots supply them with everything they want and need?
| 2023-01-01T00:00:00 |
https://www.reddit.com/r/Futurology/comments/10c3dxs/if_ai_takes_over_all_work_and_jobs_what_will/
|
[
{
"date": "2023/01/01",
"position": 1,
"query": "AI replacing workers"
},
{
"date": "2023/01/01",
"position": 2,
"query": "AI job creation vs elimination"
},
{
"date": "2023/01/01",
"position": 2,
"query": "future of work AI"
}
] |
|
Artificial Intelligence will NOT replace you, a person using ...
|
Artificial Intelligence will NOT replace you, a person using AI will
|
https://www.linkedin.com
|
[
"Hasan Taiab Imam",
"Simon Crawford Welch",
"Meir Amarin"
] |
It's important to understand that AI itself is not a replacement for human workers, but rather a tool that can augment and enhance human capabilities.
|
Artificial Intelligence (AI) has become a popular topic in recent years, with many people expressing concerns about the future of work and the potential for AI to replace human jobs. However, it's important to understand that AI itself is not a replacement for human workers, but rather a tool that can augment and enhance human capabilities.
One of the key concepts to understand is the idea of "augmented intelligence." Augmented intelligence refers to the use of AI and other technologies to enhance human capabilities and improve performance. Instead of replacing human workers, AI can be used to assist them in their tasks and make them more efficient and effective. This is why it is important to consider it as an augmented intelligence rather than an artificial one.
For example, an AI-powered system can be used to analyze large amounts of data and identify patterns that would be difficult for a human to see. This can help a human worker to make more informed decisions and improve their performance. Similarly, an AI-powered system can be used to automate repetitive tasks, freeing up human workers to focus on more complex and creative tasks.
However, it is important to note that the use of AI does not mean that human jobs will disappear altogether. Instead, the nature of work will change, and new jobs will be created that require different skills and knowledge. For instance, jobs that require more analytical skills, such as data scientists and AI engineers, will become more in demand. Additionally, jobs that require more human-centered skills such as empathetic customer service, ethics and governance, and design thinking will also become more important.
| 2023-01-01T00:00:00 |
https://www.linkedin.com/pulse/artificial-intelligence-replace-you-person-using-ai-igor-alcantara
|
[
{
"date": "2023/01/01",
"position": 17,
"query": "AI replacing workers"
}
] |
|
Artificial Intelligence in Medicine: Text Mining of Health ...
|
Artificial Intelligence in Medicine: Text Mining of Health Care Workers' Opinions
|
https://pubmed.ncbi.nlm.nih.gov
|
[
"Nitiéma P",
"Maassen O",
"Fritsch S",
"Palm J",
"Deffge S",
"Kunze J",
"Marx G",
"Riedel M",
"Schuppert A",
"Bickenbach J."
] |
by P Nitiéma · 2023 · Cited by 33 — ... AI as a tool for disease screening, diagnosis, and treatment. Topics associated with negative sentiments included concerns about AI replacing human workers ...
|
Background: Artificial intelligence (AI) is being increasingly adopted in the health care industry for administrative tasks, patient care operations, and medical research.
Objective: We aimed to examine health care workers' opinions about the adoption and implementation of AI-powered technology in the health care industry.
Methods: Data were comments about AI posted on a web-based forum by 905 health care professionals from at least 77 countries, from May 2013 to October 2021. Structural topic modeling was used to identify the topics of discussion, and hierarchical clustering was performed to determine how these topics cluster into different groups.
Results: Overall, 12 topics were identified from the collected comments. These comments clustered into 2 groups: impact of AI on health care system and practice and AI as a tool for disease screening, diagnosis, and treatment. Topics associated with negative sentiments included concerns about AI replacing human workers, impact of AI on traditional medical diagnostic procedures (ie, patient history and physical examination), accuracy of the algorithm, and entry of IT companies into the health care industry. Concerns about the legal liability for using AI in treating patients were also discussed. Positive topics about AI included the opportunity offered by the technology for improving the accuracy of image-based diagnosis and for enhancing personalized medicine.
Conclusions: The adoption and implementation of AI applications in the health care industry are eliciting both enthusiasm and concerns about patient care quality and the future of health care professions. The successful implementation of AI-powered technologies requires the involvement of all stakeholders, including patients, health care organization workers, health insurance companies, and government regulatory agencies.
| 2023-01-01T00:00:00 |
https://pubmed.ncbi.nlm.nih.gov/36584303/
|
[
{
"date": "2023/01/01",
"position": 46,
"query": "AI replacing workers"
}
] |
|
Artificial Intelligence Empower Human, Not Replaces Human
|
Artificial Intelligence Empower Human, Not Replaces Human
|
https://www.linkedin.com
|
[
"Prakash Seshadri",
"Rohit Talwar",
"Lavanya M Rao"
] |
AI will never be able to replace humans because it cannot feel emotions or experience life in the same way that we do. Additionally, AI is not self-aware and ...
|
Artificial Intelligence (AI) is a complex topic recently gaining massive traction. It's seen as both a threat and an opportunity, depending on your perspective. On the one hand, it can be seen as something that will replace human beings, reduce their job opportunities and make them obsolete. On the other hand, AI is seen as something that has the potential to empower human beings by giving them access to more sophisticated technologies, more data-driven insights, and better decision-making capabilities. This article will examine how artificial intelligence empowers humans instead of replacing them. We'll discuss how AI can enhance existing jobs rather than replace them and explore some exciting examples of how AI is being utilized to improve our lives in ways we never thought possible.
What is Artificial Intelligence?
Artificial intelligence (AI) has been defined in many ways. Still, in general, it can be described as a way of making a computer system “smart” – that is, to understand complex tasks and carry out complex commands.
There are different types of AI, but some of the most common are machine learning and natural language processing. Machine learning is where a computer system can learn from data without being explicitly programmed. Natural language processing is where a computer system can understand human language and respond in a way that is natural for humans.
AI has been used in many fields, including healthcare, finance, manufacturing, and transportation. It is being used increasingly as businesses look for ways to automate tasks and improve efficiency. In healthcare, AI is being used to diagnose diseases and plan treatments; in finance, it is being used to identify fraud and prevent money laundering; in manufacturing, it is being used to improve quality control and increase production; in transportation, it is being used to plan routes and optimize traffic flow.
The potential applications of AI are endless – which is why it is such an exciting field of study!
How Artificial Intelligence Can Help Humans
Artificial intelligence has been touted as a game-changing technology that has the potential to transform nearly every industry. One of the most promising applications of AI is its potential to help humans become more efficient and productive.
In many cases, AI can automate repetitive tasks that take up valuable time. For example, AI can be used to handle customer service inquiries or to process data. This frees up humans to focus on more critical tasks.
AI can also help humans make better decisions by providing relevant and timely information. For instance, an AI system might be able to analyze data and identify patterns that a human might miss. This information can be used to make strategic decisions about where to allocate resources or how to respond to a situation.
AI can also help improve human communication by translating messages or acting as an intermediary in conversations. This is particularly beneficial in cases where there is a language barrier.
Overall, AI has the potential to empower humans significantly and help us become more efficient and effective in both our personal and professional lives.
The Fear of Artificial Intelligence Taking Over
Many people fear artificial intelligence (AI) taking over the world. They believe that AI will eventually become more intelligent than humans and will be able to take over our jobs, homes, and even our lives. However, this is different. AI will never be able to replace humans because it cannot feel emotions or experience life in the same way that we do. Additionally, AI is not self-aware and cannot make decisions independently. It can only follow the instructions that humans give it. Even though some people are afraid of AI taking over, there is no need to worry because this can't happen.
The Benefits of Artificial Intelligence
In recent years, there has been a growing interest in artificial intelligence (AI) and its potential to revolutionize various sectors of society. Some believe that AI will eventually lead to the replacement of human workers by machines. However, others believe AI will empower humans by augmenting their abilities and improving their productivity.
There are many potential benefits of using AI technology. For example, it can help reduce human error, improve decision-making, increase efficiency and productivity, and provide otherwise unavailable insights. Additionally, AI can help us better understand and interact with the world around us. It has the potential to improve communication between people and machines, as well as assist in tasks that are difficult or impossible for humans to do on their own.
Ultimately, whether or not AI will replace human workers is still an open question. However, there is no doubt that artificial intelligence has the potential to benefit humanity significantly in several ways.
The Risks of Artificial Intelligence
The Risks of Artificial Intelligence
Although there are many potential benefits to artificial intelligence, there are also risks associated with its development and implementation. One primary concern is that AI technology could be used for malicious purposes as it becomes more sophisticated. For example, automated bots could spread false information or disinformation across the internet to influence public opinion or manipulate events. Additionally, as AI systems become increasingly capable of making decisions independently, there is a risk that they could make unethical or even dangerous choices without human oversight. Another worry is that as AI technology improves at completing tasks traditionally done by humans – such as driving cars or diagnosing diseases – there could be mass unemployment as machines replace workers in many industries. Finally, there is also the risk that robust AI systems could become uncontrollable and even evil if they are not appropriately designed. While these are all valid concerns, it’s important to remember that artificial intelligence is still in its early stages of development, and it will likely be many years before these risks become a reality.
How to Control Artificial Intelligence
The rapid advancement of artificial intelligence (AI) technology has led to concerns about how AI will impact the workforce. Some believe AI will lead to mass unemployment as robots replace human workers in many tasks. Others believe AI will empower humans by augmenting their abilities and giving them superintelligence.
So, how can we control artificial intelligence? Below are three key strategies:
1. Improve Human Collaboration with AI
One way to control AI is to improve human collaboration with AI systems. This can be done by developing better interfaces that allow humans and AI systems to work together more effectively. For example, augmented reality (AR) and virtual reality (VR) can create immersive experiences that allow humans and AI systems to interact in new ways.
2. Develop Governance frameworks for AI
Another way to control AI is to develop governance frameworks that set out rules and regulations for how AI systems can be used. This is important because it helps to ensure that AI is used responsibly and ethically. Additionally, governance frameworks can help ensure that AI's benefits are widely shared rather than concentrated among a few individuals or organizations.
3. Increase Public Engagement with AI
Finally, it is essential to increase public engagement with AI. This can be done through education and awareness-raising initiatives that help people understand what AI is and how it works. Additionally, it is essential to encourage public debate about the ethical
Conclusion
| 2023-01-01T00:00:00 |
https://www.linkedin.com/pulse/artificial-intelligence-empower-human-replaces-archana-sethi-gahlot
|
[
{
"date": "2023/01/01",
"position": 81,
"query": "AI replacing workers"
}
] |
|
AI Experience Predicts Identification with Humankind
|
AI Experience Predicts Identification with Humankind
|
https://www.mdpi.com
|
[
"Wang",
"Peng",
"Congyu Wang",
"Kaiping Peng"
] |
by C Wang · 2023 · Cited by 21 — Research on AI-enhanced human identity occurs in the context of the threat of AI replacing human jobs and so does not only examine the impact of using AI [5,6].
|
2.1.1. Item Development
To measure individuals’ exposure to artificial intelligence applications and technology, the AI Experience Questionnaire needed to cover a wide range of products and events related to artificial intelligence. Thus, item development was based on three considerations. First, we listed mature technology as well as popular applications on the market. Those applications used for specific areas of expertise (e.g., smart agriculture technology) and those used unknowingly by users (e.g., intelligent security systems) were excluded, as this questionnaire was a mass-oriented, subjective report-based measurement tool.
Second, we interviewed three senior practitioners in the AI industry and three users of smart products. They freely discussed how they used artificial intelligence in their daily lives and at work. Based on the interviews, the experience of using AI could be divided into two categories: (1) AI exposure—the frequency of using artificial intelligence products that require little comprehension and are beginner-friendly; and (2) AI experience—the degree to which a person understands how to use artificial intelligence. That is, an experienced artificial intelligence user, trained or practiced to a certain extent, understands enough about artificial intelligence to use it. It is worth mentioning that people outside the AI industry can also have AI experience. First, the extensive integration of AI into various industries has led many people in other fields to use AI in their work and acquire some AI experience. Second, AI experience is gained not only through work but also through user experience with products that require practice to use (for example, drones).
| 2023-02-14T00:00:00 |
2023/02/14
|
https://www.mdpi.com/2076-328X/13/2/89
|
[
{
"date": "2023/01/01",
"position": 98,
"query": "AI replacing workers"
}
] |
I'm Scared of Unemployment in a World Ruled by AI
|
The heart of the internet
|
https://www.reddit.com
|
[] |
The thought of AI taking over and leaving me without a job is freaking me out. I don't want to end up doing basic labor jobs that I'm not physically or ...
|
As a full-stack dev, the recent advancements in AI and the news about GPT-4 have me shook. I mean, AI has the power to change everything and that's both exciting and scary AF. I've spent years building my skills and my career, and the thought of AI taking over and leaving me without a job is freaking me out.
I don't want to end up doing basic labor jobs that I'm not physically or mentally equipped for. That's not what I signed up for when I decided to become a dev. I don't want to be stuck in a dead-end job with no future. And even if the government does end up providing Universal Basic Income, I'm not sure I trust them to keep their promise. The thought of relying on the government for my livelihood is not a comforting one.
The fear of ending up homeless is also weighing heavily on me. I've worked hard to build a life for myself, and the thought of losing everything is devastating. I just hope that society will find a way to address these concerns and make sure that the impact of AI and the Singularity is positive for everyone. But until then, I'm just filled with fear, worry, and uncertainty about my future.
PS: This post was made by ChatGPT lol
| 2023-01-01T00:00:00 |
https://www.reddit.com/r/singularity/comments/10oos73/im_scared_of_unemployment_in_a_world_ruled_by_ai/
|
[
{
"date": "2023/01/01",
"position": 3,
"query": "AI unemployment rate"
},
{
"date": "2023/01/01",
"position": 23,
"query": "AI labor union"
}
] |
|
The Relationship of Artificial Intelligence Opportunity ...
|
The Relationship of Artificial Intelligence Opportunity Perception and Employee Workplace Well-Being: A Moderated Mediation Model
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Guanglu Xu",
"School Of Business",
"Nanjing University Of Information Science",
"Technology",
"Nanjing",
"Ming Xue",
"School Of Business Administration",
"Shanghai Lixin University Of Accounting",
"Finance",
"Shanghai"
] |
by G Xu · 2023 · Cited by 49 — Several previous studies have revealed a positive relationship between artificial intelligence (AI) technology development and employees' employment, ...
|
Several previous studies have revealed a positive relationship between artificial intelligence (AI) technology development and employees’ employment, income, and job performance. If individuals can seize the opportunity to master the knowledge and skills relevant to the implementation of AI, they could make career progress and improve their workplace well-being (WWB). Based on the transactional theory of stress and resource conservation theory, we constructed a moderated mediation model to explore the relationship between AI opportunity perception and employees’ WWB and examine the mediating factor of informal learning in the workplace (ILW), as well as the moderating factor of unemployment risk perception (URP). Through a survey of 268 employees, our results showed the following: (1) AI opportunity perception was significantly positively correlated with employees’ WWB; (2) ILW played a mediating role in the positive relationship between AI opportunity perception and employees’ WWB; and (3) URP negatively moderated the mediating relationship of ILW between AI opportunity perception and employees’ WWB. Our research results have a guiding significance for enterprises seeking to promote WWB during AI application.
1. Introduction
In 1956, the concept of “artificial intelligence” (AI) was first proposed at the Dartmouth Conference. However, due to the limitations of computer data processing, AI did not rapidly develop. Since the 1990s, improvements in computer pattern recognition and prediction abilities have correlated with rapid developments in AI [1]. The International Organization for Standardization defined an AI system as an engineered system that generates outputs, such as content, forecasts, recommendations, or decisions for a given set of human-defined objectives [2]. As a permeable technology, AI can be integrated with various industries in the economy and potentially change the original production and operation methods, which will also have a considerable impact on employment [3]. Compared with previous scientific and technological revolutions, the impact of AI on employment is all-directional and extremely intense, and people in almost all industries and occupations will be affected. Some industries may be even reshaped, thus causing severe unemployment problems [4]. The implementation of AI reform in enterprises will undoubtedly exert different influences on employees from previous technological advances. The existing literature on the influences of AI development on employees focuses on four aspects. First, some studies have focused on the negative impacts of AI development on employees’ employment. For example, Chen and Xu speculated that in the next 20 years, 76.76% of China’s employed population will be replaced by AI [4]; Li et al. [5] and Xie et al. [6] found that enterprises adopting AI technology reduce the demand for low-skilled employees; and Huang speculated that with the development of AI technology, the income gap between high-skilled and low-skilled workers will increase, which will further intensify income inequality [7]. Second, some studies have focused on the positive impacts of AI development on employees’ employment. For example, Li et al. found that with the increasing application of AI, the demand for highly skilled labor increases [5]; He et al. found that the adoption of AI technology is positively correlated with employee income growth [3]; moreover, Mokyr et al. speculated that AI technology may create new products and services, leading new occupations to emerge [8]. Third, some studies have focused on the negative impacts of AI development on employees’ psychology and behavior. For example, Duan and Guo speculated that many employees may experience disappointment due to unemployment caused by AI [9]; Zhu et al. found that the application of AI trigger employees’ negative emotions [10]; Wang et al. found that employees are faced with high job insecurity due to large-scale utilization of AI technology [11]; Patel et al. found that the risk of job automation caused by AI technology harms employees’ health [12]; Brougham and Haar [13,14] and Li et al. [15] found that the likelihood of AI impacting employees’ career prospects was negatively related to organizational commitment and career satisfaction and positively related to turnover intentions, cynicism, and depression; Zhou et al. speculated that the technical attributes of algorithmic management that integrate big data and AI positively affect employees’ sense of unfairness in algorithms, which in turn induces job burnout [16]; and liu et al. speculated that online employment platforms strengthen the labor-process control of platform practitioners through algorithm technology, which makes platform practitioners’ work autonomy limited [17]. Fourth, other studies have explored the positive effects of AI development on employees’ psychology and behavior. For example, Zhu et al. found that the introduction of AI into the workplace can increase employees’ skill requirements for creativity and human–machine collaboration, which directly strengthens their thriving at work [18]; Zhou and Wang found that combining low-use-density and -intelligence robots enhances employees’ job performance by increasing their growth-need strength [19]; and Zhou et al. [16] and Goods et al. [20] speculated that algorithm management based on AI provides platform practitioners with the options of when, where, and how long to work, thus invigorating their job autonomy. A summary of the impacts of AI development on employees is shown in Table 1.
Table 1. Summary of impacts of the development of artificial intelligence on employees. Impacts of the Development of AI on Employees Empirical Findings Speculation Negative impacts of AI development on employees’ employment Reduces the demand for low-skilled employees [5,6];increases income inequality between high-skilled
and low-skilled workers [7] Replaces workers [4] Positive impacts of AI development on employees’ employment Increases the demand for highly skilled labor [5]; increases the income of employees [3] Spawns new occupations [8]; Negative impacts of AI development on employees’ psychology and behavior Triggers employees’ negative emotions [10]; increases employee job insecurity [11]; harms employees’ health [12]; reduces employees’ organizational identity and career satisfaction, and increases their turnover intention, cynicism, and depression [13,14,15] Increases employee disappointment [9]; induces job burnout [16]; strengthens labor-process control [17] Positive impacts of AI development on employees’ psychology and behavior Employees thrive at work [18]; which enhances employees’ job performance [19] Improves job autonomy [16,20] Open in a new tab
Our literature review indicates that, despite the negative impacts brought by the development and wide popularization of AI technology on employees, such as the considerable threats to employees’ employment, income, and autonomy [9,17], as a new type of intelligence, AI technology not only can improve the production efficiency of different industries and the skill level of employees but also create new industries and jobs [8,9,21]. In the process, those who have mastered the relevant knowledge and skills required in the age of AI are likely to benefit [22,23]. Therefore, in the context of AI transformation, individuals’ responsiveness to seize opportunities will provide motivation for their involvement in transforming their own personal professional knowledge and skills for individual professional advancement, resulting in a stronger sense of workplace well-being (WWB). However, few studies have explored the association between AI development and employees’ WWB. Based on the transactional theory of stress and resource conservation theory, we conceptualized the perception of AI opportunity and explored its association with employees’ WWB, as well as the mediating role of informal learning in the workplace (ILW) on the relationship between AI opportunity perception and employees’ WWB. In addition, we also examined the moderating effects of unemployment risk perception (URP) on the relationship between ILW and employees’ WWB, as well as the indirect relationship between AI opportunity perception and employees’ WWB via ILW. First, our research helps to further our understanding of the relationship between AI development and employees’ WWB. Second, it could help to formulate policies that guide employees to recognize AI technology and take measures to actively respond, thereby improving WWB and promoting the smooth application of AI technology in the workplace.
| 2023-01-20T00:00:00 |
2023/01/20
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9915176/
|
[
{
"date": "2023/01/01",
"position": 7,
"query": "AI unemployment rate"
},
{
"date": "2023/01/01",
"position": 13,
"query": "AI job creation vs elimination"
},
{
"date": "2023/01/01",
"position": 79,
"query": "future of work AI"
},
{
"date": "2023/01/01",
"position": 3,
"query": "workplace AI adoption"
},
{
"date": "2023/01/01",
"position": 37,
"query": "AI economic disruption"
},
{
"date": "2023/01/01",
"position": 34,
"query": "government AI workforce policy"
},
{
"date": "2023/01/01",
"position": 29,
"query": "artificial intelligence wages"
}
] |
AI and the future of work: PwC
|
AI and the future of work: PwC
|
https://www.pwc.com
|
[] |
Discover AI-driven strategies to future-proof your workforce and redefine work operations for a competitive edge.
|
A seismic shift is taking place. Artificial intelligence (AI) is transforming what work means, what businesses can do and how they must organize to do it. You and your peers will soon have a limitless, made-to-order supply of digital labor. If you reimagine the future of work, your workforce and your workers to leverage these AI agents, you could build a competitive edge so great, your peers will never catch up. If you merely layer AI onto existing processes, you will be left behind.
At PwC, we guide C-suite leaders through this transformation. With ready-to-use frameworks and an AI agent operating system that connects and scales agents into business-ready workflows, we provide the tools to harness AI as a strategic driver of competitive advantage and long-term growth.
| 2023-01-01T00:00:00 |
https://www.pwc.com/us/en/services/ai/ai-and-the-future-of-work.html
|
[
{
"date": "2023/01/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2023/02/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2023/03/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2023/05/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2023/06/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2023/07/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2023/09/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2023/10/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2023/10/01",
"position": 28,
"query": "future of work AI"
},
{
"date": "2023/11/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2023/12/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2024/01/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2024/02/01",
"position": 4,
"query": "AI workforce transformation"
},
{
"date": "2024/03/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2024/03/01",
"position": 71,
"query": "artificial intelligence workers"
},
{
"date": "2024/08/01",
"position": 4,
"query": "AI workforce transformation"
},
{
"date": "2024/09/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2024/10/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2024/11/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2024/12/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2025/01/01",
"position": 4,
"query": "AI workforce transformation"
},
{
"date": "2025/03/01",
"position": 4,
"query": "AI workforce transformation"
},
{
"date": "2025/03/01",
"position": 28,
"query": "future of work AI"
},
{
"date": "2025/04/16",
"position": 4,
"query": "AI workforce transformation"
},
{
"date": "2025/04/16",
"position": 29,
"query": "workplace AI adoption"
},
{
"date": "2025/05/01",
"position": 5,
"query": "AI workforce transformation"
},
{
"date": "2025/06/01",
"position": 4,
"query": "AI workforce transformation"
}
] |
|
The AI moment is now: how businesses can ready their workforce - EY
|
The AI moment is now: how businesses can ready their workforce
|
https://www.ey.com
|
[
"Trent Henry",
"Authorsalutation",
"Authorfirstname Trent Authorlastname Henry Authorjobtitle Ey Global Vice Chair",
"Talent Authorurl Https",
"Www.Ey.Com En_Gl People Trent-Henry",
"Content Dam Content-Fragments Ey-Unified-Site Ey-Com People Global En T Trent-Henry",
"Ey Global Vice Chair",
"Beatriz Sanz Sáiz",
"Jonathan",
"Roselyn Feinsod"
] |
The possibilities of AI in the workplace include new career opportunities and enhanced productivity; now is the time for enterprise organizations to develop an ...
|
I’m an optimist by nature, so I get excited about the possibilities when it comes to artificial intelligence (AI) in the workplace – including new career opportunities and enhanced productivity.
According to the EY 2023 Work Reimagined Survey, 84% of employers and almost half (49%) of global employees say they’re either using generative AI (GenAI) at work or plan to in the next 12 months. Across industries, organizations are no longer asking if and when AI will affect how business is done, but whether they’re prepared to use the technology to empower people for the future of work.
Skills like AI and machine learning now top the list of what today’s recruiters are seeking. One study found AI was the fastest-growing skills category in the first half of 2023, with GenAI job posts up more than 1,000% in the second quarter of this year (compared to the end of 2022). According to IBM, executives estimate that 40% of their workforce will need to reskill as a result of implementing AI and automation over the next three years.
Across organizations, roles are adapting to integrate AI tools to allow people to focus on higher-value tasks. This implementation will only work if the workforce is in the right mindset and can build the right skills. The Work Reimagined data tells us only 17% of employees and 22% of employers are prioritizing training in GenAI-related skills in 2023 – indicating there are major opportunities for organizations to do more to bring people up to speed.
There will be a distinct advantage for organizations that position themselves to realize the benefits of reimagining digital work – enhancing employee skills and experience in the process. While there’s no one-size-fits-all approach to introducing AI technology enterprise wide, EY teams have learned a lot from advancing AI capabilities across the 150 countries in which the organization operates. Here’s how the EY organization is turning its focus to develop an AI skillset at scale:
| 2024-03-28T00:00:00 |
2024/03/28
|
https://www.ey.com/en_gl/insights/workforce/the-ai-moment-is-now-but-are-businesses-ready
|
[
{
"date": "2023/01/01",
"position": 8,
"query": "AI workforce transformation"
},
{
"date": "2023/02/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2023/03/01",
"position": 6,
"query": "AI workforce transformation"
},
{
"date": "2023/05/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2023/06/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2023/07/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2023/09/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2023/10/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2023/11/01",
"position": 8,
"query": "AI workforce transformation"
},
{
"date": "2023/12/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2024/01/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2024/02/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2024/03/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2024/08/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2024/09/01",
"position": 6,
"query": "AI workforce transformation"
},
{
"date": "2024/10/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2024/11/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2024/12/01",
"position": 8,
"query": "AI workforce transformation"
},
{
"date": "2025/01/01",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2025/03/01",
"position": 9,
"query": "AI workforce transformation"
},
{
"date": "2025/04/09",
"position": 7,
"query": "AI workforce transformation"
},
{
"date": "2025/05/01",
"position": 9,
"query": "AI workforce transformation"
},
{
"date": "2025/06/01",
"position": 5,
"query": "AI workforce transformation"
}
] |
Generative AI and the Future of Work | Deloitte US
|
Generative AI and the Future of Work
|
https://www.deloitte.com
|
[] |
Discover how generative ai and the future work may look like in the not so distance future. AIs impact on jobs and work that will redefine organizations.
|
Did Generative AI create that advertising image you’re looking at? What about the 3D model of a protein’s structure that a scientist is examining?
What is Generative AI? How is it being used? Better yet, how can you and your organization prepare people to use it safely and efficiently? Generative AI is a rapidly evolving branch of artificial intelligence designed to generate new content ranging from text, code, and voice, to images, videos, processes, and other digital artifacts, including intricate protein structures.
Generative AI's capabilities are far reaching and truly transformative. It can and should:
| 2023-01-01T00:00:00 |
https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/articles/generative-ai-and-the-future-of-work.html
|
[
{
"date": "2023/01/01",
"position": 12,
"query": "AI workforce transformation"
},
{
"date": "2023/01/01",
"position": 79,
"query": "artificial intelligence workers"
},
{
"date": "2023/02/01",
"position": 3,
"query": "future of work AI"
},
{
"date": "2023/02/01",
"position": 78,
"query": "artificial intelligence workers"
},
{
"date": "2023/04/01",
"position": 82,
"query": "artificial intelligence workers"
},
{
"date": "2023/04/01",
"position": 2,
"query": "future of work AI"
},
{
"date": "2023/05/01",
"position": 8,
"query": "future of work AI"
},
{
"date": "2023/08/01",
"position": 76,
"query": "artificial intelligence workers"
},
{
"date": "2023/09/01",
"position": 81,
"query": "artificial intelligence workers"
},
{
"date": "2023/09/01",
"position": 2,
"query": "future of work AI"
},
{
"date": "2023/10/01",
"position": 3,
"query": "future of work AI"
},
{
"date": "2023/11/01",
"position": 12,
"query": "AI workforce transformation"
},
{
"date": "2023/11/01",
"position": 72,
"query": "artificial intelligence workers"
},
{
"date": "2023/12/14",
"position": 30,
"query": "AI economic disruption"
},
{
"date": "2023/12/14",
"position": 13,
"query": "artificial intelligence employment"
},
{
"date": "2023/12/14",
"position": 6,
"query": "artificial intelligence workers"
},
{
"date": "2023/12/14",
"position": 1,
"query": "future of work AI"
},
{
"date": "2023/12/14",
"position": 20,
"query": "machine learning workforce"
},
{
"date": "2024/01/01",
"position": 77,
"query": "artificial intelligence workers"
},
{
"date": "2024/03/01",
"position": 42,
"query": "artificial intelligence workers"
},
{
"date": "2024/03/01",
"position": 2,
"query": "future of work AI"
},
{
"date": "2024/04/01",
"position": 2,
"query": "future of work AI"
},
{
"date": "2024/06/01",
"position": 2,
"query": "future of work AI"
},
{
"date": "2024/07/01",
"position": 80,
"query": "artificial intelligence workers"
},
{
"date": "2024/07/01",
"position": 2,
"query": "future of work AI"
},
{
"date": "2024/11/01",
"position": 10,
"query": "AI workforce transformation"
},
{
"date": "2024/12/01",
"position": 10,
"query": "AI workforce transformation"
},
{
"date": "2024/12/01",
"position": 2,
"query": "future of work AI"
},
{
"date": "2025/03/01",
"position": 98,
"query": "AI workforce transformation"
},
{
"date": "2025/03/01",
"position": 5,
"query": "future of work AI"
},
{
"date": "2025/05/01",
"position": 85,
"query": "artificial intelligence workers"
},
{
"date": "2025/06/01",
"position": 85,
"query": "artificial intelligence workers"
}
] |
|
The AI-Ready Workforce - Jobs for the Future (JFF)
|
The AI-Ready Workforce
|
https://info.jff.org
|
[] |
Our new AI-Ready Workforce Framework analyzes tasks and skills based on how much or how little AI-driven automation may impact them and the nature of that ...
|
Transformation Profiles
Reshaping Industries and Occupations to Center Human Skills
While it’s clear that AI will impact different jobs and industries differently, we were eager to better understand how that impact would play out within key industries in the U.S. economy—and what actions leaders should take to anticipate and respond to these shifts.
The AI-Transformation Profiles examine the relative importance within U.S. industries and occupations of tasks and skills whose use could be increased or decreased by AI. Each profile, which we developed at the industry level as an example for one occupation, describes four categories of action—Future-Proof, Capitalize, Automate, and Reimagine—that employers, educators, workforce development partners, and workers themselves can begin to take to emphasize or deemphasize certain tasks and skills, reshape jobs, and teach workers new skills to respond to these shifts.
| 2023-01-01T00:00:00 |
https://info.jff.org/ai-ready
|
[
{
"date": "2023/01/01",
"position": 16,
"query": "AI workforce transformation"
},
{
"date": "2023/02/01",
"position": 11,
"query": "AI workforce transformation"
},
{
"date": "2023/03/01",
"position": 12,
"query": "AI workforce transformation"
},
{
"date": "2023/05/01",
"position": 47,
"query": "AI workforce transformation"
},
{
"date": "2023/06/01",
"position": 9,
"query": "AI workforce transformation"
},
{
"date": "2023/07/01",
"position": 12,
"query": "AI workforce transformation"
},
{
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"query": "AI workforce transformation"
},
{
"date": "2023/10/01",
"position": 12,
"query": "AI workforce transformation"
},
{
"date": "2023/11/12",
"position": 38,
"query": "AI hiring"
},
{
"date": "2023/11/01",
"position": 21,
"query": "AI workforce transformation"
},
{
"date": "2023/11/12",
"position": 39,
"query": "artificial intelligence employers"
},
{
"date": "2023/11/12",
"position": 14,
"query": "artificial intelligence hiring"
},
{
"date": "2023/11/12",
"position": 7,
"query": "future of work AI"
},
{
"date": "2023/11/12",
"position": 4,
"query": "workplace AI adoption"
},
{
"date": "2023/12/01",
"position": 11,
"query": "AI workforce transformation"
},
{
"date": "2024/01/01",
"position": 9,
"query": "AI workforce transformation"
},
{
"date": "2024/02/01",
"position": 11,
"query": "AI workforce transformation"
},
{
"date": "2024/03/01",
"position": 12,
"query": "AI workforce transformation"
},
{
"date": "2024/08/01",
"position": 9,
"query": "AI workforce transformation"
},
{
"date": "2024/09/01",
"position": 9,
"query": "AI workforce transformation"
},
{
"date": "2024/10/01",
"position": 9,
"query": "AI workforce transformation"
},
{
"date": "2024/11/01",
"position": 18,
"query": "AI workforce transformation"
},
{
"date": "2024/12/01",
"position": 15,
"query": "AI workforce transformation"
},
{
"date": "2025/01/01",
"position": 13,
"query": "AI workforce transformation"
},
{
"date": "2025/03/01",
"position": 3,
"query": "AI workforce transformation"
},
{
"date": "2025/05/01",
"position": 19,
"query": "AI workforce transformation"
},
{
"date": "2025/06/01",
"position": 14,
"query": "AI workforce transformation"
}
] |
|
AI Upskilling Strategy - IBM
|
AI Upskilling Strategy
|
https://www.ibm.com
|
[] |
Many are turning to AI upskilling, the act of preparing the workforce with skills and education to empower them to use AI to do their jobs.
|
An IBM® Institute for Business Value report found that more than 60% of executives say that generative AI will disrupt how their organization designs customer and employee experiences. Employees need to change to meet those needs. Many are turning to AI upskilling, the act of preparing the workforce with skills and education to empower them to use AI to do their jobs.
A 2024 Gallup poll (link resides outside ibm.com)2 found that nearly 25% of workers worry that their jobs can become obsolete because of AI, up from 15% in 2021. In the same study, over 70% of chief human resources officers (CHRO) predicted AI would replace jobs within the next 3 years.
The World Economic Forum estimated that automation will displace 85 million jobs by 2025, and 40% of core skills will change for workers in its Future of Jobs Report 2023 (link resides outside ibm.com)3. AI will usher in a new era of productivity and value, and business leaders in the C-suite should make employees part of that future.
Every organization is responsible for providing its workforce with the requisite skill sets and education to use AI in their daily jobs. CHROs, specifically, should lead the discussion about what skills technology automates and which ones remain mission-critical skills handled by employees.
The rise of AI is fundamentally remaking corporate strategy. Executives must enhance AI capabilities, such as generative AI tools, throughout the workforce. They must provide opportunities to develop employees’ skills as the AI takes on some of the previous tasks handled by humans.
Employees are interested in learning advanced technical skills that can harness the power of AI to make their jobs more efficient and their career paths more successful. Organizations have a vested interest in upskilling their employees to better use new technologies such as AI in their daily activities to enhance productivity and improve problem solving.
| 2023-01-01T00:00:00 |
https://www.ibm.com/think/insights/ai-upskilling
|
[
{
"date": "2023/01/01",
"position": 19,
"query": "AI workforce transformation"
},
{
"date": "2023/01/01",
"position": 3,
"query": "reskilling AI automation"
},
{
"date": "2023/01/01",
"position": 31,
"query": "machine learning workforce"
},
{
"date": "2023/02/01",
"position": 46,
"query": "AI workforce transformation"
},
{
"date": "2023/02/01",
"position": 3,
"query": "reskilling AI automation"
},
{
"date": "2023/02/01",
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"query": "machine learning workforce"
},
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{
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{
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{
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{
"date": "2023/10/01",
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},
{
"date": "2023/11/01",
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"query": "AI workforce transformation"
},
{
"date": "2023/11/01",
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},
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"date": "2023/12/01",
"position": 49,
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},
{
"date": "2024/01/01",
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},
{
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},
{
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{
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{
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},
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{
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{
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{
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{
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"position": 35,
"query": "future of work AI"
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{
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{
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},
{
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},
{
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{
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"query": "machine learning workforce"
},
{
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"query": "machine learning workforce"
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{
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},
{
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{
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{
"date": "2025/06/01",
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}
] |
|
4 Strategies for Workforce Transformation in the AI Era - Visier
|
4 Key Strategies for Workforce Transformation
|
https://www.visier.com
|
[] |
4 strategies for workforce transformation · 1. Integrate data and analytics in talent planning · 2. Harness AI to maximize human potential · 3. Prepare for ...
|
You may not have expected workforce transformation in the AI era to be so dynamic. Yet, the strategies are surprisingly omnipresent.
AI automation makes repetitive tasks no longer something your team needs to focus on. By giving employees space to think and focus on strategy, as opposed to execution, employees will inevitably have to reskill.
To stay ahead of the game, you can use data analytics to determine skill gaps and deliver training before the market takes off. Besides skills, your team needs an action plan to adapt to using collaborative AI tools and staying flexible as AI takes over part of their duties.
But AI is not here to replace your people. Instead, it will change how you hire, work, and collaborate. Use this workforce transformation guide to set up your first action plan and avoid stagnation.
What is workforce transformation?
Workforce transformation involves reshaping an organization's structure, culture, and skill sets to align with changing business goals and technological advancements. This process includes integrating new technologies, upskilling employees, and driving cultural and structural shifts to meet emerging challenges.
How is workforce transformation different from workplace transformation?
Workplace transformation refers to the systematic change in the physical environment, business practices, and organizational culture. This transition encompasses adopting technologies like AI and automation, as well as shifting work patterns such as remote and hybrid working models.
4 strategies for workforce transformation
Now, let’s get to what matters: How do you implement these workforce transformation strategies in a logical, and above all, enjoyable manner?
1. Integrate data and analytics in talent planning
You can no longer just look at traditional annual workforce planning. So it's time to focus more on staying ahead of changes instead of keeping up with them.
Continuous workforce planning will allow you to adapt to and meet rapidly changing labor requirements, even before they happen. For example, talent acquisition leaders should be able to track and react to shifting labor demands by using advanced analytics platforms that aggregate internal data. Using data-driven insights lets you spot where demand for critical roles exceeds supply and take targeted action.
When using predictive analytics, talent teams can proactively predict future demand based on various business scenarios. Your teams should be able to identify potential shortfalls in an essential corporate role, such as engineering, and use that information to build a solid case for market-facing recruiting campaigns to share with senior leaders seeking buy-in.
You also need to look at the big picture.
Start by looking at what your people want (as opposed to only thinking about costs) to identify misalignment in headcount numbers. With a solid hiring plan, you'll stay grounded in a more extensive awareness of your talent needs. Using people data to plot workload and operations will let you adapt, but still have room for systematic growth should you want to expand your team.
2. Harness AI to maximize human potential
Using AI in your day-to-day workflows leaves employees with more time to invest in strategic work that leads to greater productivity and innovation. Daily use of generative AI saves 33.5% of workers at least four hours weekly, but it’s not just about doing the same tasks faster.
People performing AI-augmented roles can spend more time thinking deeply about their work and building human connections. All this while artificial intelligence comes in to help you:
Conduct AI-focused talent assessments
Promote a “hire and evolve” mindset
Hone in on learning’s business impact
The Reece Group used Visier to predict employee absences within a forecasting window of 14 days. This meant they could now proactively build solutions based on the new forecast so that they could ship the orders that day. AI allows the team's leaders to make smarter and more “future-direct” decisions across its workforce to achieve better employee productivity outcomes.
As Sally Young, Chief People Experience Officer at the Reece Group, emphasized:
"Visier makes people data easy to use. Visier gives our leaders a holistic view by creating insights about our business and our people. Once leaders can see that people analytics is applicable to solving their problems, they use it, which in turn helps build their capabilities as strategic people leaders."
3. Prepare for disruption with dynamic forecasting
To disrupt the market and keep up with unpredictable changes, you need to shift to dynamic forecasting. While many leaders are facing talent shortages, particularly in the areas of high-demand skills, they don't know what skills they'll need in the future.
Dynamic forecasting, powered by tools such as Visier Workforce Planning & Budgeting, allows you to measure the actuals, adjust your plans, and reactively adapt workforce capacity when plans change unexpectedly.
Visier's Workforce Planning and budgeting tool was designed to allow leaders to model or compare and amend plans, compose forecasts of FTE/Staffing volumes, and any other descriptors of staff movements, with multiple modalities of modeling or forecasts. You can then run the numbers to be in a much better position to create contingency plans.
4. Incorporate skills-building into employee engagement strategies
To increase employee engagement and retention, continuous learning and skills-building plans are no longer just a nice-to-have.
According to a Visier survey, 86% of employees believe that employers should transition them through reskilling to remain relevant in an AI-influenced world. 63% of employees think employers should be solely responsible (not partially) for reskilling employees for AI.
Employees need and want to be reskilled. Continuous skills development is what instills a company's ability to build employee engagement and trust to recover from potential disruption.
To do this, you need to take a serious look at your employee value proposition to change and improve benefits in providing AI skills development for employees and future opportunities. Start by offering a variety of benefits focused on the employees' future, like continuous learning courses or mentorship opportunities.
Offering a transparent career path for employees also matters. This involves letting employees see how and where their skills will be relevant over the next two to five years and giving them full ownership of their growth. For example, providing skills with clear development paths builds trust and motivation and sets the stage for a culture of continuous learning.
Next steps for transforming your workforce
Workforce transformation is essential for organizations aiming to thrive in the AI era.
By understanding the nuances of workforce and workplace transformation and planning continuously (workforce planning moves from annual to continuous, agile, dynamic process) to implement strategic initiatives, companies can build agile, future-ready teams poised for success.
Here’s what you need to do next:
Audit your team's skills to identify gaps and survey employees for specific career needs they might have.
Shift to dynamic planning with predictive analytics.
Automate repetitive tasks and train your staff to use AI tools to improve productivity.
Update your employee value proposition with AI learning benefits.
Model workforce scenarios and monitor market signals monthly.
Track your KPIs and refine initiatives based on feedback.
Read more about workforce transformation
| 2023-01-01T00:00:00 |
https://www.visier.com/blog/workforce-transformation-strategies-ai-era/
|
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|
AI in the workplace - Korn Ferry
|
AI in the workplace
|
https://www.kornferry.com
|
[] |
The growing impact of AI in the workplace is transforming how businesses operate, with Generative AI (Gen AI) leading the charge.
|
4 ways companies can benefit from Gen AI
1. Improve productivity
Eliminate tasks and roles, reduce costs and streamline processes
Generative AI's impact on productivity is one of the most significant benefits for the workplace. Through automation of repetitive and time-consuming tasks, employees can focus on more strategic and creative endeavors. For instance, research necessary to create insightful content is often a laborious process which can now be automated, freeing up marketing teams to concentrate on crafting compelling strategies and campaigns. Additionally, Gen AI-powered tools can streamline data analysis, providing valuable insights in real-time, enabling businesses to make data-driven decisions more efficiently.
2. Enhance relationships
Implement quick, personalized, trust-based automated conversations
Gen AI can play a crucial role in enhancing internal and external communication channels for businesses. In the context of customer service, AI-powered chatbots and virtual assistants offer personalized and immediate support, improving customer satisfaction and loyalty. Within an organization, Gen AI can facilitate seamless communication and collaboration, breaking down barriers of language and time zones. This fosters a more inclusive and connected workplace, promoting employee engagement and teamwork.
3. Augment intelligence
Empower connected teams to create, validate and deploy advanced algorithms, resulting in innovative applications and business models
Gen AI can enable you to unlock innovation across your organization, empowering connected teams to create, validate and deploy advanced algorithms that power impactful applications and business models (e.g. proprietary GPT).
Gen AI serves as a powerful catalyst to enhance business intelligence and capabilities, enabling organizations to embrace innovation at a deeper level. By leveraging vast datasets and advanced processing capabilities, both individuals and teams can benefit from valuable insights for more informed decision-making.
The inclusion of AI-powered virtual mentors and learning platforms significantly contributes to fostering a culture of continuous learning and growth. By offering personalized training and development opportunities, employees can upskill and adapt to the ever-evolving job market, empowering them to thrive in their roles.
4. Win the war on big data analytics
Unlock reliable, proprietary, distinctive insights
Data has become a strategic asset for businesses, and Gen AI can significantly contribute to winning the war on big data analytics. AI algorithms can comb through enormous datasets to identify patterns, trends and potential opportunities that would otherwise be challenging for human analysts to detect. By harnessing the power of Gen AI, organizations can gain a competitive advantage, make accurate predictions and dramatically improve the organization’s ability to make data-backed decisions.
Making Gen AI integration work
Integrating Gen AI within organizations requires a comprehensive approach that goes beyond just the AI technology itself. While the four strategies (improve productivity, enhance relationships, augment intelligence, and win the war for data) are essential to building value-creating AI applications, success also hinges on the alignment and support of various crucial components.
| 2023-01-01T00:00:00 |
https://www.kornferry.com/insights/featured-topics/gen-ai-in-the-workplace
|
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{
"date": "2023/08/10",
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{
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"query": "machine learning job market"
},
{
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},
{
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},
{
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},
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{
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{
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|
AI and the Future of Workforce Training - CSET
|
Center for Security and Emerging Technology
|
https://cset.georgetown.edu
|
[] |
This report examines the changing landscape of workforce development, highlighting the crucial role of community colleges, alternative career pathways, and AI- ...
|
Executive Summary
The emergence of artificial intelligence (AI) as a general-purpose technology is poised to transform work across a variety of industries and job roles. Previous waves of technological change mainly led to job displacement and wage pressures for bluecollar workers while enhancing productivity and wages for white-collar workers. In contrast, AI’s impact could be more pervasive across all occupational categories, including knowledge workers and those with advanced education. Recent studies indicate that up to 80 percent of U.S. workers might have at least 10 percent of their work activities affected by large language models, with approximately 19 percent of workers potentially seeing half or more of their work activities impacted.
The nature of this transformation depends largely on two factors: the degree to which AI can perform or enhance an occupation’s core tasks, and whether AI serves as a substitute for or complement to human workers. Occupations with high AI exposure but low complementarity face the greatest risk of disruption, highlighting the need for comprehensive retraining and upskilling initiatives. This situation is particularly critical given that technical skills now become outdated in less than five years, on average.
Analysis of future workforce demands reveals the following trend: while technical skills remain important, accounting for about 27 percent of in-demand skills, the majority of crucial skills are nontechnical. Foundational skills (such as mathematics and active learning), social skills (including social perceptiveness and negotiation), and thinking skills (such as complex problem-solving and critical thinking) together make up nearly 58 percent of skills needed in growing occupations. This underscores the importance of developing a well-rounded workforce capable of adapting to technological change while maintaining strong interpersonal and analytical capabilities.
The potentially far-reaching impact of AI across occupations, coupled with the likely accelerating pace of skill obsolescence, points to an increasing need for continuous retraining and upskilling opportunities throughout workers’ careers. This shifting landscape demands a critical examination of current workforce development infrastructure and its capacity to meet these emerging challenges at scale. Understanding which elements of the existing system can be effectively expanded and which barriers need to be addressed becomes crucial for developing responsive and resilient workforce training solutions.
Community colleges emerge as pivotal institutions in addressing these challenges, particularly when integrated into robust regional ecosystems that include employers and intermediaries. Recent federal initiatives, including $265 million in Strengthening Community Colleges Training Grants since 2021, demonstrate recognition of community colleges’ crucial role. Successful workforce development programs often combine traditional education with work-based learning opportunities, such as registered apprenticeships and career technical education (CTE). Several states have already begun implementing AI-specific CTE programs to prepare students for the evolving technical workforce.
However, significant challenges persist in the current workforce development landscape. These include fragmented training systems, insufficient public funding, regulatory disincentives favoring capital investment over labor, and difficulties in scaling successful programs.
While AI may be a source of workplace disruption requiring enhanced workforce training efforts, it also presents opportunities to address some of these systemic challenges in workforce development. The technology’s capabilities could help scale effective training solutions and make them more accessible and affordable, potentially bridging gaps in the current system.
Specifically, these capabilities enable personalized learning experiences, rapid content delivery, and increased accessibility. AI tools can provide customized learning paths, instant feedback, and career guidance. However, implementation must be approached cautiously. Concerns include the potential erosion of interpersonal skills, trust and privacy issues, and the risk of exacerbating existing inequalities through algorithmic bias and unequal access. Research indicates that while AI tools can enhance productivity, overreliance on these tools may hinder genuine skill development and learning.
Moving forward, successful workforce development will require a multifaceted approach: strengthening community college programs, expanding alternative career pathways, incorporating AI literacy into training initiatives, and ensuring equitable access to technology-enabled learning opportunities. This should be accompanied by careful consideration of how AI tools are integrated into training programs to maximize benefits while mitigating risks to skill development and learning outcomes. Further research is needed to understand how successful training solutions can be scaled across diverse regions and how AI training tools can be effectively deployed to serve diverse populations while supporting genuine skill development and learning.
| 2023-01-01T00:00:00 |
https://cset.georgetown.edu/publication/ai-and-the-future-of-workforce-training/
|
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{
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"query": "AI impact jobs"
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{
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"position": 62,
"query": "artificial intelligence employment"
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{
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"query": "AI workforce transformation"
},
{
"date": "2025/06/01",
"position": 63,
"query": "AI workforce transformation"
}
] |
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