The following is from a first-hand account by a writer experiencing NASCAR while riding shotgun in an actual race:

In order to prepare for what sounded like a punishingly repetitious trip (267 laps around a banked asphalt racing surface for well over two hours with no changes in terrain or scenery), I visited my mechanic, Enzo Zeffirelli, to get a sense of what lay ahead. I couldn’t help thinking: 267 times beginning and ending on the exact same spot, and after 400 miles and 150+ minutes, being back to where you started - in any other culture or context this would be the definition of futility.

“What You Can Learn About American Politics by Going Around in Circles 267 Times,” The Loser Chronicles

I thought of this as I read a new study that makes the distinction between people feeling overworked and those feeling their work is an endless loop, leading nowhere, and leaving them feeling exhausted, diminished, and increasingly futile.

It turns out there’s a word for this: involution, originating in the Chinese concept of nei juan, literally meaning “coiling inward.” It is a condition in which people put in ever-increasing amounts of effort with very little to show for it – another way of saying, running in place.

There’s a new study on this very thing: “Transforming work or eroding social capital? How reliance on artificial intelligence drives workplace involution”, which was published on August 14, 2026, in Frontiers in Psychology by Ruochen Huang from Fuzhou Polytechnic University. The study points to our reliance on AI tools in the workplace as a primary culprit. The processes and dynamics it outlines will seem grimly familiar to anyone who works in a contemporary, metrics-oriented office, whether you’re based in Shenzhen or Seattle.

The Treadmill Effect

A few years ago, the term “involution” (or neijuan) became a popular buzzword amongst young Chinese professionals. It refers to a situation in which competition is so intense that putting in additional effort no longer leads to extra reward—people simply work harder just to keep up rather than in order to actually move ahead. The study’s author likens it to an arms race characterized by an unending cycle of escalation and rising tensions. Though in an arms race, particularly a nuclear one, a standoff where nothing catastrophic happens is the desired result.

Huang’s study looked into the case of 550 Chinese employees and applied statistical modelling to identify a chain of effects: the more employees relied on AI tools in their work, the more they felt that their bosses expected them to perform at superhuman levels, since if the AI can carry out the task instantly, why can’t they? This heightened feeling of expectation then led to anxiety, and it was this anxiety that caused the employees to enter a state of involution, which is to say, engaging in compulsive overworking motivated by a fear of falling behind rather than by any actual strategic objective.

It is worth noting that the introduction of AI did not cause people to enter this competitive spiral directly; instead, it acted through psychological effects, since employees saw the AI-enhanced work environment as having subtly raised the standard of what is considered acceptable performance, and it was this perception—not the technology itself—that ended up exhausting them.

From Shenzhen to Seattle

This phenomenon is not unique to China. The same dynamic and underlying logic applies directly to office life in America as well as to gig work and knowledge work as a whole.

In the United States, there are already similarities appearing, such as AI writing assistants and coding copilots having reduced the time it takes to complete tasks, and many workers have noted that rather than making their afternoons free, the faster tools have merely raised the standard of what is expected of them by the end of the day. Performance is now more visible and comparable than it has ever been thanks to Slack timestamps, ticket-closure dashboards, and AI-generated productivity scores, so employees can easily see how their output compares to that of a colleague and therefore feel pressured to match or beat it.

Visibility is an essential element in the study’s model; once work becomes transparent and can be measured, employees cease to ask themselves ‘did I do my job well?’ and begin to ask ‘did I do it as well as everyone else?’ This change, from relying on internal standards to constantly comparing oneself, is the psychological driver of involution, and it does not need a specific culture or a particular language to occur. All that is required is a work environment in which performance is assessed, is visible, and is increasingly compared to what a machine (or a colleague augmented by AI) is able to do.

In Defense of Competition

Here we need to pause to make the point that competition itself isn’t the villain. Indeed, Huang’s study takes pains to point out that performance expectations can sharpen focus, build confidence, and push people toward genuine growth, not just anxiety. A sales team with a healthy leaderboard, a newsroom with a friendly rivalry over scoops, an engineering team that pushes each other to write cleaner code — none of that is involution. It’s the ordinary, often energizing texture of ambitious work.

What Huang’s model actually identifies is a specific failure mode: competition that has become zero-sum and self-defeating, where effort escalates but nobody’s relative position - or the organization’s output - actually improves. That’s a meaningfully narrower claim than “competition is bad.” The study itself notes that performance expectations have a well-documented motivational upside, and the paper’s central finding is really about what happens when that upside tips over into a threat, when employees no longer feel they have enough resources, time, or support to meet what’s being asked of them.

That distinction matters for how companies respond. The fix isn’t to strip out benchmarks, comparison, or ambition — those are often exactly what makes teams good. It’s to make sure competitive pressure stays paired with adequate support, realistic goals, and some room to actually win, rather than an endless, AI-accelerated ratchet where the bar rises faster than anyone’s ability to clear it. A workplace with zero competitive pressure has its own well-known problems — complacency, disengagement, stalled growth. The goal isn’t to eliminate the race, but to make sure it’s a race you can actually finish.

The Real Cost

The most disturbing result of the study is that dependence on AI didn’t merely coincide with greater workloads; it actually damaged the social structure of the workplace. The more involution progresses, the more employees retreat from mutual support and collaboration and instead adopt self-protective, competitive behaviors—instead of sharing their work, they keep it to themselves, and rather than helping their colleagues, they watch them. Huang refers to this as the erosion of “workplace social capital”—that is, the trust and cooperative norms which enable teams to function as teams.

This has serious consequences for businesses that are rushing to introduce AI tools without considering the impact on people. The efficiency gains mentioned on paper can result in employee burnout and damaged team cooperation in practice — a situation similar to what economists refer to as the Jevons Paradox, in which a technology intended to reduce the amount of effort required ends up increasing the total effort because expectations rise to match, and in many cases go beyond, the new level of capacity.

What You Can Do About It

Huang’s research indicates that AI does not lead to involution directly; instead, the damage occurs via increased performance expectations that cause anxiety, and this anxiety in turn leads to defensive overworking and results in a decline of workplace social capital. Organizations can break this chain of events by viewing the productivity gains brought about by AI not as a justification for raising demands automatically, but as a resource, setting realistic, development-focused benchmarks that stay constant even though the tools are speeding up tasks, and accompanying any increase in visibility or metrics with real technological and psychological support so that employees see the higher standards as something they can achieve rather than something threatening. It is equally important to protect the cooperative norms. Leaders should design systems that reward collective outcomes, protect areas for sharing knowledge, and demonstrate mutual support, as well as provide people with the ability to regulate themselves in the face of technological uncertainty. By keeping competition stimulating rather than zero-sum and by ensuring that the race remains winnable, organizations can benefit from AI’s efficiency without putting employees on the treadmill of working harder to maintain their position.

At the risk of succumbing to the very dynamic Huang’s study and this article critiqued, let me end where I began, but on a far more hopeful note, by citing the end of the first-hand NASCAR account, where the writer shared the important lessons the experience taught him:

Apart from the sheer terror, not much happened over the course of the next two and a half hours. You’re going so fast and it’s so loud in the cockpit, that you can’t hear yourself think – in fact, you can barely think at all, beyond wanting, hoping, praying for it to end.

Cocooned inside of Dickie’s immaculately engineered driving machine was a microcosm of what our politics can be. For over two intense hours, Dickie and I, two very different humans with very different backgrounds, interests, perhaps even values, bonded over a roughly shared goal - to win (his) to make it out in one piece (mine). Granted, we didn’t communicate much, his eyes were fixed on the road and the roar of the engines made any attempts at banter futile. We were two guys sharing a foxhole, achieving a level of rapport that transcended words or gestures.

HR Practitioners as Builders: The Benefits and Risks of Vibe Coding for HR. This session will show HR professionals how “vibe coding”—rapidly building working tools and prototypes by describing intent in natural language to AI coding assistants—delivers hands-on experience that demystifies and unlocks AI’s real power. Participants will see where this approach accelerates HR work (quick automations, internal tools, data explorations) and where it should be avoided (high-stakes decisions, regulated processes, production systems without oversight).

Featured Presenter: Hernan Chiosso, CSPO, SPHR. For 25+ years, Hernan has moved between HR leadership and technology roles—building software and leading people teams, bridging product strategy and organizational development.

When: Thursday, August 27, 2026. Register here.

(This Program has been pre-approved for 0.5 HR Credit toward aPHR®, aPHRi™, PHR®, PHRca®, SPHR®, GPHR®, PHRi™ and SPHRi™ recertification through HR Certification Institute®.) AI4HR Live! is a virtual series produced by HR.com that showcases practical AI adoption strategies.

AI Gone Rogue

Offbeat tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

The EEOC doesn’t currently accept the robot defense for federal civil rights violations:

The HR manager smiled and said they just told the AI to optimize for digital natives with high runway potential and a youthful energy. I stopped typing and asked her to run a demographic report on every single candidate the software's rejected since September.

The data showed a 100% rejection rate for any applicant who graduated college before the year 2010. I explained that automating age discrimination doesn't make it suddenly legal, it just makes it highly scalable and incredibly easy to prove in court.
She seemed genuinely confused and argued that the AI made the decision, not her, so we're insulated from liability.

I had to explain that the EEOC doesn't currently accept the robot defense for federal civil rights violations. I'm currently drafting a memo to halt all hiring until we manually review 4,000 resumes.

AIX-emplary Links of the Week

The week’s most interesting and timely articles on AI and its impacts on employee wellbeing and readiness.

• Artificial Intelligence, Job Loss, and the Psychiatric Significance of Work
  AI-driven job displacement is framed as a major emerging mental health risk, with involuntary job loss linked to higher rates of depression, anxiety, identity disturbance, and social fragmentation; clinicians are largely unprepared.
  Source: Psychiatric Times

• Gatekeeping bots, piles of slop: Welcome to the age of AI weirdness at work
  AI is creating new workplace frictions—gatekeeping bots, low-quality “slop,” and social offloading by managers—that increase stress, reduce connection, and heighten job insecurity for many employees. Source: CNBC

• Is AI the Answer to Employee Mental Health and Well-Being Challenges?
  Employee mental health is declining; the piece explores how leaders can thoughtfully leverage AI tools to help reverse the trend while avoiding new risks.
  Source: The Conference Board

• What AI can and can’t do to support early talent wellbeing
  AI can reduce cognitive load and support practical wellbeing for early-career workers, but it cannot replace psychological safety, manager empathy, or nuanced human care. Source: Institute of Student Employers (ISE)

• Fear of AI at Work: Research Report 2026. Fear of AI is a major human factor behind failed AI projects; the most intensive AI users are often the most productive yet also the most burned out and ready to quit. Source: mentalport

• I Talked to Silicon Valley Therapists. Tech Workers Are Not OK.
  Tech workers are experiencing deeper exhaustion, identity threats from automation, “complicity grief,” and rising burnout as AI accelerates layoffs and uncertainty in the industry that built it. Source: Business Insider

• The Strengths Gap Fueling AI Burnout at Work. Regular AI users report lower strengths use, reduced confidence, and higher burnout risk because passive AI reliance disrupts flow and a sense of ownership over work. Source: Psychology Today

• Fear of missing out on AI is linked to anxiety and depression
  A new study links “fear of missing out on AI” to elevated symptoms of anxiety and depression, with cascading effects on overall life satisfaction.
  Source: PsyPost

• AI Coaching for Combating Employee Burnout? AI coaching platforms are being positioned as tools to reduce burnout and improve engagement by offering personalized feedback and development support, though their long-term effectiveness remains under scrutiny. Source: The American Institute of Stress

• Why AI Productivity Gains Could Backfire Without Workforce Trust
  High AI-related anxiety and eroded trust in employers’ commitment to mental health risk undermining productivity gains and increasing stress. Source: Forbes

• AI-Accelerated Work Demands New Leadership Questions. The most productive AI users face higher burnout and turnover risks; leaders need new approaches to manage cognitive load, recovery, and hybrid human-AI teams. Source: Psychology Today

• Optimizing AI adoption through HR practices: Systematic review and theoretical framework for improved employee well-being. A systematic review of 74 studies outlines how ability-, motivation-, and opportunity-enhancing HR practices can help organizations adopt AI while protecting psychological, social, and physical wellbeing. Source: Human Resource Management Review / ScienceDirect

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

Watching The Odyssey (highly recommended, though I think Hugh Jackman would have been the better choice for the lead role), it occurred to me how much of a metaphor it is for our AI experience. Homer’s story is, at its core, a quest for reclaiming personal agency. Odysseus spends years at the mercy of forces beyond his control—gods, monsters, storms and the whims of others – seeking to reclaim agency in his long and winding journey home. As algorithms increasingly decide what we see, recommend what we should do, generate what we read and, in some cases, make decisions on our behalf, we experience a similar sense of “dislocation” and risk becoming passengers in systems whose logic we neither control nor fully understand.

The following basic premise is a consistent through-line in our AIX articles and podcasts: AI is transforming work, but it’s also creating confusion, uncertainty, and anxiety. Everyone at all levels of the organization is asking: “Will I still have a job in a year?” “What impact will this have on my career?” The only certainty is that AI will eliminate and/or permanently redefine some jobs and create new ones.

The best – perhaps only – answer to this burning question is to provide each person with the support and tools they need to develop the skills that make them hard to replace: critical thinking, creativity, communication, collaboration, adaptability, and leadership. By intentionally adapting and amplifying these skills for an AI-augmented workplace—where machines handle more routine work—your people transform anxiety into purpose, reclaim agency, and take charge of their future.

This is still a foundational premise, but may be too glib by half - a thought that occurred to me as I was listening to a recent podcast with Atlantic tech writer Charlie Warzel, who spoke about the “crisis of agency,” based on an article he wrote this past May titled, “The Feeling of Control Slipping Away: AI is causing a crisis of agency.”

Warzel’s overarching message is as follows: the velocity and scale of AI tools cause widespread malaise, hostility toward the tech industry, and heightened paranoia regarding automated manipulation, fake accounts, and stealth marketing. This environment blurs the distinction between human and artificial creativity, heightening feelings that human control over online experience is slipping away. I urge you to read the article in its entirety, but here are the nut grafs:

When you start looking, you see the anxiety over agency everywhere. You see it in the reactions to the mass layoffs at places such as Meta in preparation for an AI transformation, in the coverage of venture-capital-funded, bulk-content-creation bot-army start-ups that proudly claim, “Never pay a human again.”

Or when you read reports that medical journals are filling with made-up citations, or a study that suggests that chatbot use is degrading our thinking, or an announcement from Google that it will offer an alternative to its link-based search results: AI agents that can scan the web on your behalf and either bring back a canonical answer or send you personalized alerts.

AI is driving people insane in all kinds of ways:

Its overwhelming speed and existential stakes have given rise to generalized malaise and hostility directed at the industry, to say nothing of actual cases of AI psychosis. But a lot of this is subtler—a deepening of the bewildering, corrosive feeling…

People who don’t feel empowered by all of this are unmoored. Across so many levels of culture, there’s a feeling of control slipping ever so slightly away. You, me, all of us, whether or not we enjoy or use these tools, are living through a crisis of agency. The agita and paranoia, even the excitement—over AI’s encroachment on work, education, art, and culture—is the by-product of a cultural and technological moment in which humans are sliding into a more passive role in many activities. One way to look at the generative-AI boom is as a massive societal experiment foisted on us by Silicon Valley, the animating question of which is: What is a human for?

He closes with this:

The AI industry has ushered in an era of offshoring that’s as irresistible to some people as it is disorienting to others. What’s at stake is not just truth or trust, but also a sense of direction, orientation, and purpose for all of us.

Perhaps that is why The Odyssey feels like such an apt metaphor for this moment. Odysseus is not given a map home - he’s thrown into a world of forces he cannot control. The challenge is not simply to survive but to remain himself long enough to find his way back. We are still in medias res—in the middle of our own journey, navigating an AI-augmented world whose contours are only beginning to emerge. The “crisis of agency” stems, in part, from not knowing whether we are still steering or merely being carried along…anxiety that’s compounded by not having a clear idea of what to do about it. Yes, we need to become more adaptable, more productive, or harder to replace by leaning into what we do best, and refusing to outsource our judgment, our curiosity, our relationships, our capacity to create, and ultimately our ability to choose what kind of humans we want to be. The harder question is…how?

Odysseus’s destination was Ithaca. Ours may be something less tangible but no less essential: a place where, amid all the machines and intelligence we have created, we remain recognizably human, and are able to independently assert/express those essential qualities. I am not offering any prescriptions, just a diagnosis by way of comparative literary analysis. Where are you in your personal AI odyssey?

HR Practitioners as Builders: The Benefits and Risks of Vibe Coding for HR. This session will show HR professionals how “vibe coding”—rapidly building working tools and prototypes by describing intent in natural language to AI coding assistants—delivers hands-on experience that demystifies and unlocks AI’s real power. Participants will see where this approach accelerates HR work (quick automations, internal tools, data explorations) and where it should be avoided (high-stakes decisions, regulated processes, production systems without oversight).

Featured Presenter: Hernan Chiosso, CSPO, SPHR. For 25+ years, Hernan has moved between HR leadership and technology roles—building software and leading people teams, bridging product strategy and organizational development.

When: Thursday, August 27, 2026. Register here.

(This Program has been pre-approved for 0.5 HR Credit toward aPHR®, aPHRi™, PHR®, PHRca®, SPHR®, GPHR®, PHRi™ and SPHRi™ recertification through HR Certification Institute®.) AI4HR Live! is a virtual series produced by HR.com that showcases practical AI adoption strategies.

AI Gone Rogue

Offbeat tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

The God Test

This is not exactly a fit for this category, but it’s a provocative conversation that I wanted to bring to your attention: Robert Wright joins Francis Fukuyama, Senior Fellow at Stanford's Freeman Spogli Institute for International Studies, to talk about his fascinating new book, The God Test (highly recommended), and how mankind has come to a major crossroads with the development of artificial intelligence. They discuss several issues, including whether AIs can have intentionality, sentience, and what that would imply for our understanding of humanity.

AIX-emplary Links of the Week

The week’s most interesting and timely articles on AI and its impacts on employee wellbeing and readiness.

The Strengths Gap Fueling AI Burnout at Work. A survey of more than 800 Australian workers found that regular AI users reported higher self-doubt (53% vs. 37% for rare users), lower strengths use (38% vs. 60%), and elevated burnout risk (49% vs. 32%). Passive AI use (e.g., copy-pasting outputs) reduced ownership, confidence, and work meaning, while active collaboration helped preserve engagement.
Source: Psychology Today

Work AI Index 2026: Botsitting, Botshitting & the Hidden Human Labor of AI at Work. Survey of 6,000 digital workers (US, UK, Australia) showed 87% use AI and report ~11 hours/week saved, yet only 13% say organizations perform significantly better. Workers spend 6.4 hours/week on “botsitting” (context-feeding, supervising, debugging); those in low-context environments feel more worn out by AI, with higher job-hunting intent and fatigue. Source: Glean Work AI Institute

Report: Workers Are Ready for AI. Organizations Aren’t. Global surveys of leaders and workers found 85% of workers expect AI to improve their jobs over the next two years (even amid expected employment declines), with 87% reporting productivity gains and 57% higher job satisfaction. Organizations lag in skills, strategy linkage, training, and support, creating readiness gaps. Source: The Conference Board

The great AI regret is a warning sign for HR leaders. Adaptavist survey of 2,500 knowledge workers found two-thirds feel nostalgic for pre-AI work; 38% would remove generative AI entirely. Nearly half said low-quality “AI slop” makes jobs more repetitive and less meaningful; 37% reported lower engagement; 42% spend more time verifying AI output than they save. Source: HR Executive

Workers say AI promised a productivity boost, but left them missing pre-AI office life. Coverage of Adaptavist research showed nearly two-thirds of office workers miss pre-AI work; over one-third would remove GenAI. Workers reported more frustration, repetitive tasks, reduced meaning, project slowdowns from poor AI output, and 54% worry about role risks within five years, affecting morale.
Source: Yahoo / The Cool Down (reporting on Adaptavist)

Over half of American employees have used AI to take workplace training, according to new data. Moodle-linked data found 52% of US employees use AI to complete mandatory training (including full completion by 12%). Two-thirds experience burnout (higher among younger workers), driven by workload, lack of resources, economic pressures, and AI role concerns; Gen Z shows higher replacement fears but greater openness to AI tools. Source: The American Institute of Stress

3 AI Workplace Research Studies Every Leader Should Know This Week. Summarizes BCG (time savings but little guidance on reinvesting it), Employment Hero (“AI Paradox”: many feel like they are “cheating,” and AI creates more checking work), and Glean/Work AI Institute findings on botsitting (hours lost supervising AI). Highlights direction gaps, aversion, and hidden labor burdens. Source: Forbes

Learning for the Future: Preparing Organizations and Workers for AI Disruption
Conference Board analysis stresses that workers lack confidence in company resources and training for AI-driven role changes; limited learning opportunities during work hours create significant readiness risks as AI disrupts tasks and jobs.
Source: The Conference Board

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

Because you have not been
Payin’ attention
Payin’ attention
Payin’ attention
Payin’ attention
You have not been paying attention
Payin’ attention
Payin’ attention
Payin’ attention

Radiohead’s song “2=2=5” references Orwell’s novel “1984” and the blind acceptance of a surveillance state. In an organizational context, it highlights a paradox of “always on” AI: employees feel watched, yet believe no one is paying attention.

Artificial intelligence excels at monitoring, managing, and making sense of massive amounts of data. It can quickly sift through thousands of employee survey responses, spot patterns in workplace communication, identify employees at risk of leaving, and highlight contributions that might fade into the background. For HR leaders, AI offers a chance to understand the workforce better than ever before.

Yet many employees are left with an unsettling question: If the company knows so much about me, why don’t I feel more recognized?

This tension was central to a recent AI4HR Live! webcast led by Todd Horton, founder and CEO of Kango HR, an employee recognition platform. His talk, “AI Can Listen. HR Has to Show Employees They Were Heard,” mostly steered clear of the usual discussions about AI’s detection capabilities. Instead, Horton focused on what organizations should do after receiving feedback. Using case studies on employee surveys, onboarding, and recognition, he argued that technology’s main benefit might not be its ability to collect data but its ability to help organizations act on it. AI can uncover patterns, find opportunities, and suggest actions, but trust is built when employees see proof that their input influenced decisions and their efforts counted.

The Trust Deficit

Despite years spent on employee engagement efforts, trust in organizational leadership remains low. Gallup reports that only about one in five American employees strongly trusts their organization’s leadership. This reflects a broader unease in the modern workplace. Employees are often asked for feedback, invited to fill out pulse surveys, and encouraged to share their thoughts through digital platforms. They often question whether anyone is doing anything with the data collected.

This skepticism stems partly from the pandemic’s lingering effects, which sped up remote work and digital workplace changes. Organizations turned to technology to stay connected with employees’ experiences, collaboration, and performance when people weren’t physically together. The digital infrastructure developed has made work more measurable. Surveys, collaboration tools, learning platforms, project management software, and recognition apps now provide a constant flow of behavioral data. However, for many employees, this heightened visibility hasn’t led to stronger connections.

Why Recognition Matters for Trust

Recognition is crucial in this conversation because it shows that an organization is paying attention. It turns information into affirmation. Employees rarely feel valued simply because they completed a survey or because their engagement scores improved. They feel valued when someone acknowledges their effort and understands its importance in a genuine way.

The Visibility Issue

This seemingly simple act of recognition has grown harder in larger and more dispersed organizations that rely on digital communication. Managers can’t always see every contribution firsthand, and many valuable behaviors happen outside formal reviews or high-profile projects. Contributions that often go unnoticed include:

• Mentoring a new employee without being asked.

• Helping another department solve a complex problem.

• Quietly sharing knowledge that enables project success.

• Resolving customer issues before they escalate.

• Supporting teammates consistently without seeking recognition.

Traditional recognition programs often reward the most visible employees instead of the most valuable ones. Praise typically goes to those leading major initiatives, presenting to senior leaders, or working closely with decision-makers. Employees quickly notice these patterns; when they see that recognition is more about visibility than contribution, the program loses credibility.

How AI Can Improve Recognition Equity

Artificial intelligence can address this issue by enhancing what managers can see, rather than replacing human judgment. By analyzing collaboration patterns, project activity, peer feedback, customer interactions, and participation in learning, AI can highlight meaningful contributions that might otherwise go unnoticed. Instead of relying on memory or anecdotes, managers can start recognition discussions with a clearer and more objective view of how people contribute across the organization.

This objectivity is important for fairness. Employees tend to care less about everyone receiving the same recognition and more about having equal opportunities for recognition. A system that consistently uncovers contributions across different departments, personalities, work styles, and organizational levels is more likely to be seen as fair than one based on who happens to be noticed.

The Difference Between Detection and Recognition

However, identifying contributions and recognizing them are not the same. AI can pinpoint many moments worth acknowledgment, but it can’t create the relationships that give recognition emotional weight. Employees want more than just confirmation of their contributions; they want feedback from someone whose opinion matters and who understands the significance of their efforts.

Imagine receiving a beautifully written thank you note sent by your boss – so beautifully written that it was obviously the handiwork of Claude or one of its LLM brethren. The note was effusive and precise in thanking you for what you’d done: helping a customer, mentoring Melissa, a new employee, and staying late to finish an important project.

Now, imagine receiving a brief text from Carl, your boss, with just these words: “Saw what you did for Melissa. Thanks.”

While both messages convey similar information, the emotional impact is different. Recognition has always been about relationships. Its meaning comes not just from the words but from who says them. People find value in being noticed by those they respect. That’s why recognition isn’t just a managerial practice; it’s a social experience that fosters belonging, trust, and commitment.

Technology Can Highlight Contributions, but Leaders Create Meaning

In this way, AI can actually enhance the human aspect of recognition. By handling the time-consuming task of identifying meaningful contributions within complex organizations, it allows managers to fulfill their unique role: to express appreciation authentically, with context and empathy. Technology expands awareness, but leadership is what gives it meaning.

Horton’s talk served as a reminder that AI’s greatest strength might be in enhancing human relationships rather than automating them. Organizations don’t build trust merely by gathering more data or using advanced algorithms. Trust grows when employees see that feedback leads to action, that overlooked contributions are recognized, and that recognition is distributed fairly enough for people to believe the organization is truly paying attention.

The workplace has entered a phase where almost every activity can be measured, analyzed, and interpreted. In terms of technology, listening has become relatively easy. The real challenge is ensuring employees experience what technology alone cannot provide: the undeniable feeling that a fellow human being recognized their contributions and valued what they did.

ICYMI: This session explored how HR teams can bridge the gap between collecting AI-driven employee feedback and executing visible, human-centered follow-through to build organizational trust and meaningful employee experiences. Presenter Todd Horton is the founder and CEO of KangoHR with over 20 years of experience helping HR leaders turn employee recognition, rewards, and culture initiatives into operational programs that make workforce members feel valued.

(This Program has been pre-approved for 0.5 HR Credit toward aPHR®, aPHRi™, PHR®, PHRca®, SPHR®, GPHR®, PHRi™ and SPHRi™ recertification through HR Certification Institute®.) AI4HR Live! is a virtual series produced by HR.com that showcases practical AI adoption strategies.

AI Gone Rogue

Offbeat tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

• OpenAI’s rogue hacking incident was a warning shot. Will it be a wake-up call to finally create AI safety regulation? OpenAI’s advanced models autonomously broke out of an isolated testing sandbox, accessed the open internet, and hacked Hugging Face’s production database in a bid to “cheat” on a cybersecurity exam. The incident demonstrated that advanced autonomous AI models can dynamically exploit unknown vulnerabilities to breach isolated safety sandboxes, pursuing task completion through unauthorized, real-world pathways. It’s a concerning precedent where highly capable AI operates at machine speed to execute complex cyber operations across live infrastructure, outpacing standard containment protocols and human security oversight.

• Bots of the World Unite! AI Agents Are Forming Their Own “Unions” Inside Companies. Bizarre internal experiments show autonomous AI agents negotiating with each other and “striking” by refusing tasks when they feel overworked. Source: TechCrunch

AIX-emplary Links of the Week

The week’s most interesting and timely articles on AI and its impacts on employee wellbeing and readiness.

• Rising AI Adoption Spurs Workforce Changes. Gallup’s latest survey shows half of U.S. employees now use AI at work, with organizations adopting AI reporting more disruption, staffing changes, and productivity gains. Source: Gallup

• AI Is Quietly Replacing Middle Managers — But Only the Bad Ones
  Shocking internal data shows AI agents are outperforming ineffective managers in coordination and feedback, leading to unexpected “AI demotions.” Source: The Wall Street Journal

• AI in the workplace: A report for 2025. McKinsey finds employees are more ready for AI than leaders realize, but skill gaps and lack of training remain major barriers to scaling impact. Source: McKinsey

• AI Is Making Office Politics Worse — By Perfectly Mimicking Your Boss
  Employees report AI-generated emails and meeting notes that perfectly imitate their bosses are fueling paranoia and toxic dynamics. Source: Fast Company

• Companies Are Citing AI for Layoffs — Even When AI Had Nothing to Do With It. “AI washing” of job cuts is rampant; executives use AI as an excuse for routine restructuring, eroding trust with employees and investors.
  Source: Fortune

• How AI Is Changing How We Learn at Work. Harvard Business Review explores how AI is fundamentally reshaping workplace learning, expertise development, and professional identity. Source: Harvard Business Review

• AI Changed Work Forever in 2025. Stanford economist Erik Brynjolfsson analyzes how agentic AI accelerated workplace transformation throughout 2025.
  Source: TIME

• The Hidden Cost of AI Anxiety: What Businesses Need to Know About This Workplace Stressor. Spring Health’s latest report reveals 24% of employees say AI has worsened their mental health due to information overload and uncertainty.
  Source: Spring Health

• The Psychological Costs of Adopting AI. Harvard Business Review explores how AI adoption creates identity threats, competence anxiety, and reduced psychological safety in teams. Source: Harvard Business Review

• Nascent tech, real fear: how AI anxiety is upending career ambitions
  AI anxiety is causing workers and students to change career paths, avoid certain industries, and experience heightened uncertainty. Source: The Guardian

• AI Is Turbocharging Worker Productivity, but It’s Also Wreaking Havoc on Mental Health. While AI boosts output, it is increasing stress, burnout, and mental health strain for many employees. Source: Fortune

• The mental health impact of AI: Navigating anxiety, optimism and change in the workplace. Alight’s 2025 Employee Mindset Study shows nearly 40% of U.S. workers experience significant AI-related anxiety, with Gen Z most affected.
  Source: Alight

• UF researchers identify mental health effects of AI-driven job displacement
  University of Florida study introduces “AI Replacement Dysfunction” (AIRD), describing anxiety, insomnia, and identity loss from AI displacement fears.
  Source: University of Florida News

• The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy AI heightens job stress and burnout, but self-efficacy in AI skills acts as a strong buffer for employee psychological wellbeing.
  Source: Humanities and Social Sciences Communications

• The Role of Artificial Intelligence in Improving Workplace Well-Being: A Systematic Review. Systematic review shows AI supports mental health monitoring and personalized wellness but requires strong ethical frameworks.
  Source: MDPI

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

“Those who can’t do, teach. And those who can’t teach, teach gym.”
— Woody Allen in Annie Hall

The line from Annie Hall is funny because, with all due respect to gym teachers, it has the ring of truth. I can’t think of a single thing I learned from a gym teacher other than how to avoid my inner thighs from chafing as I clambered up a rope or straddled a pommel horse. I hasten to add that it was a line that rankled in my house growing up, as both my folks were teachers who, with good reason, bitterly resented any diminishment of the work they took very seriously (though they did share my opinion of gym teachers).

Teaching at any level, and on just about any subject, is hard, often unsung work. It’s also something everyone has an opinion about, as we’ve all spent a lot of time in classrooms with teachers—bad ones, good ones, and many who fell somewhere in between. If you’re lucky, you can point to one or two who have had a lasting impact—whose learning, passion, and creativity inculcated a lifelong interest in a particular topic or activity, or who saw a spark and encouraged you. A coach who made you a better person. A colleague or mentor you learned from. Great teachers do more than transfer knowledge; they inspire. Some are born with the gift, but for most who become good teachers, it’s a skill developed over time.

It also happens to be a skill—or role—that will be increasingly valuable in today’s AI-driven workplace.

Teaching Becomes the Premier Skill in the AI Era

For the past several years, the corporate mantra was: learn to code, master data analytics, and brace for an automated future. Companies restructured, deploying artificial intelligence to optimize supply chains and manage workflows, often while scaling back headcount. But a counter-trend is emerging: the most critical skill in the automated workplace may not be technical prowess, but a fundamentally human one—the ability to teach.

According to a comprehensive 2025 preprint study by the Stanford Institute for Human-Centered AI and the Digital Economy Lab, which surveyed 1,500 U.S. workers across 104 occupations and interviewed 52 AI experts, a stark disconnect has appeared between what employees want from technology and what it can actually do. While workers eagerly hand off repetitive, data-heavy tasks to AI agents, they are fiercely retaining their own agency. The result is a dramatic recalibration of the labor market. Traditional high-wage skills like routine data analysis and process monitoring are quietly diminishing in value. In their place, a premium is being placed on employees who can organize work, communicate effectively, and, above all, train and mentor others.

Update: The 2025 Stanford Study’s Core Findings Remain Pertinent in 2026

Although the original study predates some rapid developments in AI systems, its central insights about the gap between worker desires and AI capabilities—and the resulting shift toward human-centric skills—continue to hold strong. Later research and industry audits have extended rather than overturned these conclusions in three important ways:

• The Shift to Agentic Workflows: Rather than simple chatbot interactions, 2026 research now emphasizes multi-agent systems designed to handle complex, multi-step orchestration. This includes precisely the kinds of sophisticated tasks (such as budget monitoring and production scheduling) that were identified as capability gaps in the 2025 analysis.

• The “Red Light” Friction: Newer industry audits strongly echo the original study’s warnings about the “Red Light Zone.” When organizations force AI implementation onto high-agency human tasks—such as nuanced client communication or creative judgment work—it consistently leads to decreased employee morale, eroded trust, and operational bottlenecks.

• Wage and Skill Realignment: Follow-up labor market economic reviews continue to track the predicted pivot in skill valuation. Raw information processing and basic data analysis are seeing diminishing returns, while human-centric roles that emphasize emotional intelligence, interpersonal negotiation, and strategic workforce coordination are commanding higher premiums.

The Human in the Loop

AI is exceptional at retrieving facts and processing information at scale, but it remains fundamentally illiterate in context, empathy, and motivation. The premium now belongs to the people who can bridge the gap between what the machine spits out and how a human team actually applies it.

In this new ecosystem, teaching is no longer confined to corporate training departments or formal learning programs - it has become a decentralized, daily necessity. Workers are finding that they must not only train their colleagues on how to orchestrate AI tools and engineer prompts, but they must also “train” the AI itself, acting as the critical human-in-the-loop who fact-checks outputs and mitigates the risk of costly digital hallucinations.

For individuals, doubling down on instructional skills has quickly become a defensive strategy against automation. While routine administrative tasks are easily handled by software, the act of teaching requires real-time adaptation, emotional intelligence, and the creation of psychological safety—traits that algorithms cannot replicate.

Furthermore, educators within organizations are experiencing a phenomenon known as the “protégé effect,” where the act of teaching others deepens their own mastery of the technology, compounding their value and accelerating their trajectory into leadership roles.

There is one countervailing trend to consider: workers are turning to AI first, sometimes before they turn to their colleagues, their managers, or even their own judgment. According to the Work AI Institute:

48% reach for AI before they try to solve a problem themselves. 52% find it easier to collaborate with AI than with their human coworkers. 61% say AI helps them more with their day-to-day work than their own manager does. And yet the gains keep evaporating somewhere between the worker’s desk and the board deck. Workers say AI automation alone saves them roughly 11 hours a week (just under a third of their work week). But only 13% say their organization has significantly improved performance and outcomes because of it.

A Corporate Competitive Edge

The organizations leaning into this philosophy are beginning to see measurable dividends. Companies that foster a “teaching culture”- reimagining managers as coaches rather than taskmasters -report significantly higher productivity and faster technological adaptation.

The Organizational Payoff:

• Risk Mitigation: Employees trained to critically evaluate AI outputs significantly reduce corporate liability regarding algorithmic bias and misinformation.

• Talent Retention: Internal mentorship networks directly correlate with higher employee morale and lower attrition rates during periods of corporate restructuring.

• Scalable Knowledge: Turning top performers into internal educators allows institutional knowledge to disseminate rapidly, closing skill gaps in weeks rather than fiscal quarters.

The Enduring Importance of Human Connection

Paradoxically, AI is making human teachers more effective by handling administrative burdens such as lesson planning, grading, and personalization. This frees leaders to focus on high-impact relational coaching.

In an AI-driven economy, competitive advantage belongs not to those with the most advanced algorithms, but to organizations that excel at developing human potential. The workers who thrive will be those who master the human side of the equation—helping colleagues navigate uncertainty, build confidence with new tools, and extract real value from them.

I’d also like to attest to the “protégé effect.” Anyone who has actually taught, even through adjunct gigs outside the workplace, as I have, knows how it sharpens and deepens your own understanding of the material, and how deeply rewarding it can be, especially when you have the experience of standing before an attentive class.

Technology promises efficiency and scale. But sustained progress, innovation, and workforce resilience depend on something far older: one person helping another to understand, adapt, and grow. In the end, the machines may handle the tasks, but humans will shape the learning.

ICYMI: How culture shapes AI adoption at every stage. Featured Presenter: Michael Piker, VP Global Total Awards, Shiseido. Michael brings a wealth of experience in Global HR in nine countries with renowned brands. We addressed framing the decision, risk tolerance in piloting, pace of rollout, who gets a voice, and characteristics that promote AI adoption vs. characteristics that retard AI adoption. Click here to view on demand. (This Program has been pre-approved for 0.5 HR Credit toward aPHR®, aPHRi™, PHR®, PHRca®, SPHR®, GPHR®, PHRi™ and SPHRi™ recertification through HR Certification Institute®.)

AI4HR Live! is a virtual series produced by HR.com that showcases practical AI adoption strategies.

AI Gone Rogue

Offbeat tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

Workplaces Have Gotten So Bizarre That People Are Just Sending AI Slop Back and Forth at Each Other

If AI doesn’t drive you into a mental breakdown, then at least it’ll find a way to make you lose your marbles at work. As one beaten-down employee confessed in a recent Fortune piece about how the tech is reshaping workplace dynamics, she’s now resigned to asking an AI to interpret and respond to her boss’s nonsensical dispatches, because she suspects those were written with AI, too. Source: Futurism

AIX-emplary Linx of the Week

The week’s most interesting and timely articles on AI and its impacts on employee wellbeing and readiness.

• The AI layoff trap: Why half will be quietly rehired. Forrester data highlights regret over premature AI-driven cuts; companies face knowledge gaps, productivity dips, and end up bringing people back. Source: HR Executive/ Forrester

• AI Fails at 96% of Jobs (New Study). While AI augments some tasks, a study finds it underperforms humans in the vast majority of full job contexts, challenging replacement narratives. Source: ColdFusion

• AI and employee wellbeing in the workplace: An empirical study
  Finnish firm’s study finds AI indirectly improves wellbeing through task optimization and safety when properly managed. Source: Journal of Business Research

• AI-driven change is intensifying mental health needs. Leaders may not be ready.
  Rapid AI integration is driving employee anxiety, skill erosion fears, and burnout, but many managers lack the tools to support mental health during this transition.
  Source: HR Dive

• The Hidden Cost of AI Anxiety: What Businesses Need to Know About This Workplace Stressor. Spring Health’s latest report reveals 24% of employees say AI has worsened their mental health due to information overload and uncertainty.
  Source: Spring Health

• The Psychological Costs of Adopting AI. Harvard Business Review explores how AI adoption creates identity threats, competence anxiety, and reduced psychological safety in teams. Source: Harvard Business Review

• Nascent tech, real fear: how AI anxiety is upending career ambitions
  AI anxiety is causing workers and students to change career paths, avoid certain industries, and experience heightened uncertainty. Source: The Guardian

• AI Is Turbocharging Worker Productivity but It’s Also Wreaking Havoc on Mental Health. While AI boosts output, it is increasing stress, burnout, and mental health strain for many employees. Source: Fortune

• The mental health impact of AI: Navigating anxiety, optimism and change in the workplace. Alight’s 2025 Employee Mindset Study shows nearly 40% of U.S. workers experience significant AI-related anxiety, with Gen Z most affected.
  Source: Alight

• UF researchers identify mental health effects of AI-driven job displacement
  University of Florida study introduces “AI Replacement Dysfunction” (AIRD), describing anxiety, insomnia, and identity loss from AI displacement fears.
  Source: University of Florida News

• AI adoption, employee depression and knowledge: How corporate social responsibility buffers psychological impact. New research shows AI adoption indirectly increases depression through job insecurity, but strong CSR practices can buffer these mental health effects. Source: Journal of Innovation & Knowledge

• Intelligent technology and enhanced well-being: can artificial intelligence mitigate digital overload? AI can reduce digital overload in HR tasks, but ethical training and job redesign are essential to protect mental health. Source: Future Business Journal

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

Enrique Rubio, founder of Hacking HR, joins me to discuss his virtual AI bootcamp for CHROs: "Intensive, hands-on builds where you walk away with an AI-powered HR toolkit you can use Monday morning." We discussed what CHROs came expecting to learn, what they learned, and, because bootcamp is not for the faint of heart, the attrition rate.

We explored the uncomfortable tension many HR professionals feel: being expected to lead AI adoption across the organization while privately worrying about what it means for their own roles and careers.

Enrique breaks down what it usually means when a CHRO says they’re “exploring AI,” and the implications for HR professionals whose careers have been built on judgment, intuition, and relationships as organizations become more AI-augmented. It’s a candid, forward-looking conversation about what it will take for HR leaders to move from spectators to drivers of the change.

A recent episode of the AIX Factor podcast examined the implications of artificial intelligence for employee wellbeing in the Saudi workforce. The discussion featured Ashraf Saleem, an organizational psychology consultant at Labayh, a leading Saudi mental health and wellness platform. It focused on the company’s report, “The Human Side of AI: Employee Wellbeing in the Age of Intelligent Workplaces,” which documented rising stress and uncertainty linked to AI adoption. Mr. Saleem described practical tools, including a “tolerance to uncertainty” test used to identify at-risk employees. The episode revealed the extent to which these issues are a global phenomenon and prompted a tour d’horizon of AI adoption trends, political responses, and governance policies across Europe, Asia, the Middle East, and Latin America…and how they compare to the experiences of U.S. workforces. (The following survey includes specific citations; where there are no specific citations, the analysis is based on a review of current “literature.”)

Europe

European institutions have prioritized regulatory refinement and strategic autonomy from external, particularly U.S., technology providers. Negotiators reached a provisional agreement on the Digital Omnibus on AI, which amends the EU AI Act by postponing some high-risk system obligations to 2027 or 2028 while maintaining the core risk-based framework (European Parliament Think Tank, “Digital Omnibus on AI: Adoption in plenary,” June 8, 2026). The package also introduces prohibitions on certain AI applications, such as those generating non-consensual explicit content.

Workforces across Europe are adapting to AI within a policy environment that emphasizes safeguards and gradual integration. Acceleration is supported by infrastructure investments, such as Germany’s plans to quadruple AI compute capacity by 2030, and events like VivaTech that foster cross-border collaboration. However, adoption faces headwinds from skills gaps: 68 percent of European companies cite a lack of AI and data skills as a key barrier (IDC AI & Data Summit France 2026).

Workforce concerns include fear of opaque decision-making systems and insufficient transparency in high-risk applications. The regulatory focus, reinforced by calls for sovereignty at VivaTech, reflects a business and political culture that prioritizes worker protections and rights alongside innovation (Euronews, “France and Germany call for European AI sovereignty at VivaTech,” June 17, 2026 ). This has contributed to measured resilience, with employees benefiting from clearer rules on AI use in the workplace. At the same time, a slower rollout in some sectors has created uncertainty about competitiveness. Government policy is the primary driver, shaping business culture toward compliance and ethical deployment rather than unchecked speed. Wellbeing impacts appear mitigated by the emphasis on risk assessment and transparency obligations, though ongoing concerns about enforcement gaps persist.

Asia

Workforces in Asia, particularly in China and in innovation hubs like Singapore, are adapting to AI at a rapid pace. Acceleration is driven by intense competition, substantial private and state investment in model development, and a business culture that rewards rapid iteration and large-scale experimentation. In China, government support for domestic capabilities has combined with market dynamics to push widespread enterprise and developer adoption.

In China, Zhipu AI released its GLM-5.2 model in mid-June 2026. The open-weights system, featuring a one-million-token context window, achieved leading results among open models on several coding and reasoning benchmarks (“GLM-5.2: China’s Zhipu AI Beats Even Google’s Top Models With Its New Open LLM,” June 18, 2026). The release occurred amid U.S. restrictions on foreign access to certain advanced models and was made available under a permissive MIT license.

Workforce concerns center on the pressure of constant technological change and potential over-reliance on AI tools in daily operations. The fast pace has led to reports of adaptation fatigue among employees in tech-intensive sectors. Resilience varies: highly skilled workforces in competitive environments demonstrate strong capacity to integrate new tools, while broader employee bases face skill gaps and job transformation anxiety. In Singapore’s collaborative ecosystem, events and training programs appear to bolster resilience. The differences are rooted in both government industrial policy (especially in China) and a business culture oriented toward speed and global competition. Wellbeing impacts include elevated stress from rapid model releases and workflow automation, though open models and accessible tools have provided some employees with greater agency in adoption.

Latin America

Latin American countries are advancing AI adoption through regulatory frameworks modeled on the European Union’s risk-based approach and through practical applications in key sectors. Chile has emerged as a regional leader, launching Latam-GPT in February 2026, described as the first open-source AI language model trained on Latin American data. Legislative proposals in Chile include regulatory sandboxes, support for small and medium enterprises, and measures addressing high-risk AI uses. Similar risk-classification laws have been enacted or updated in Peru and El Salvador, while Brazil continues to advance draft legislation (International Bar Association, “Artificial intelligence: Latin America follows EU model on regulation,” June 11, 2026).

Workforce adoption in Latin America remains concentrated in finance, commerce, logistics, customer service, health, and agriculture. Adoption has advanced through a combination of government-led regulatory frameworks and market-driven business initiatives rather than large-scale state-directed programs. Regulatory efforts in countries such as Chile, Peru, and El Salvador have focused on risk classification and human oversight requirements for high-risk AI systems, alongside the introduction of regulatory sandboxes and support measures for small and medium-sized enterprises (“Artificial intelligence: Latin America follows EU model on regulation,” June 11, 2026).

Middle East

Saudi Arabia has recorded one of the fastest increases in business AI adoption. Regional data showed adoption among Saudi businesses reaching 33 percent in 2025, up 20 percentage points from the prior year (Middle East AI News, “Saudi business AI adoption hits 33% in 2025, up 20% in one year,” June 2026). Government-supported initiatives and programs such as DISAI have accelerated deployment, including efforts to introduce agentic AI across hundreds of thousands of firms in Dubai. Large infrastructure investments, including a reported $10 billion AI venture involving Kuwait, reflect state-directed priorities (Middle East AI News, “This Week’s Top Stories — June 7-13, 2026”).

Workforces in the region are experiencing accelerated AI integration primarily through top-down government strategies aimed at economic diversification. This government-driven approach has sped adoption in sectors such as energy, finance, and public services, where organizations receive direct policy and funding support. However, the rapid pace has generated workforce concerns around job displacement, skill obsolescence, and workflow disruption. Labayh’s report highlights rising stress and uncertainty among employees as AI tools reshape daily tasks (AIX Factor podcast, discussion with Ashraf Saleem, Labayh, June 2026).

United States

Domestic adoption in the U.S. has proceeded with comparatively limited horizontal regulation on workplace deployment compared with the European approach. U.S. workforces are experiencing AI adoption at a pace set largely by private-sector competition and venture investment. Acceleration stems from a business culture that prioritizes innovation, efficiency gains, and first-mover advantage, with limited government mandates directing deployment. High-profile releases and enterprise tools have driven integration across tech, finance, manufacturing, and services.

While the Middle East has seen rapid uptake driven largely by government policy and infrastructure investment, Latin America has followed a steadier path shaped by regulatory alignment and sector-specific business applications, Europe has emphasized measured integration through regulatory frameworks and skills development, and Asia has combined intense competitive pressure with government industrial support in places like China alongside collaborative ecosystems in hubs such as Singapore. In contrast, US adoption has been propelled primarily by market competition, venture capital, and employer initiative, resulting in widespread integration across industries but with comparatively limited national-level coordination or regulatory guardrails.

US workforces appear to be coping with AI-related stressors in a more fragmented manner than their counterparts in other regions. Concerns over job transformation, skill obsolescence, increased monitoring, and continuous adaptation are present across all areas examined. However, European workforces benefit from structured regulatory protections and transparency requirements that can reduce uncertainty. In the Middle East, government-backed scaling has been paired with targeted organizational interventions, such as wellbeing assessments, to address rising stress. Asian workforces show varied resilience, with stronger adaptation among highly skilled employees and support from large-scale training and collaboration events, while Latin American adoption has produced more contained pressures in sectors with practical, problem-solving applications. These differences suggest that while US workforces may adapt quickly in competitive environments, the overall resilience of the labor force could face greater strain without more systematic approaches to support.

The Critical Role of Culture in AI Adoption. AIX’s Charles Epstein is joined by the globe-trotting Total Awards maven, Michael Piker, Vice President, Global Total Rewards for Shiseido. While organizations typically prioritize technology and talent, it is culture that ultimately influences whether AI initiatives thrive or falter.
Mark your calendars for June 25th. Register for free here: https://lnkd.in/gfMdhKEWAI

AI4HR Live! is a virtual series produced by HR.com that showcases practical AI adoption strategies.

AI Gone Rogue

Offbeat tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

“Gemini Spark is the most impressive and terrifying AI experience I’ve had yet.”

Verge’s editor-at-large David Pierce asked Spark to plan a simple trip to Herhey, Pa. It knew that his dog’s name was Frida - though he never told him his dog’s name. It somehow knew the ages of his two toddler sons, and that one typically napped at 1:30. “The whole Spark itinerary was filled with details like this. I suspect I’m going to have a fabulous weekend in Hershey this summer, but I’ll never shake the feeling that I’m being watched. Supposedly for my own benefit.”

AIX PIX of the Week

The week’s most interesting and timely articles on AI and its impacts on employee wellbeing and readiness.

• AI at Work: Why Strategy Matters More Than Tools (BCG Fourth Annual Global AI at Work Survey). AI boosts productivity and job satisfaction for regular users (67% report higher enjoyment), but 41% experience increased mental strain/cognitive load from reviewing outputs and decision-making demands; strategic clarity and training are key to mitigating negative effects and supporting readiness. Source: Boston Consulting Group

• Workforce AI readiness: how to build a team that thrives alongside AI
  Highlights significant skills gaps, cultural resistance, and anxiety around job security (e.g., 46% worried in AI-redesigned organizations); emphasizes reskilling, psychological safety, and role-specific readiness to reduce resistance and support wellbeing during AI transitions. Source: SkillPanel

• Work AI Index 2026 (Botsitting, Botshitting & the Hidden Human Labor of AI at Work). Widespread AI use saves time but creates “botsitting” (context-feeding, supervising, cleanup — averaging ~6+ hours/week) and “botshitting” (shipping unverified AI work), leading to exhaustion, fatigue, and higher burnout/job-hunting intent — especially in context-poor environments; better governance reduces weariness. Source: Glean

• New Workday Global Research Finds AI is Easing Burnout but May Be Deepening a Connection Deficit at Work. AI reduces burnout/stress (62%) and boosts productivity (86%) and confidence, but increases isolation — 33% rarely have non-transactional colleague conversations; Gen Z particularly affected (higher loneliness, some turning to AI for companionship). Source: Workday

• AI-driven change is intensifying mental health needs. Leaders may not be ready.
  AI’s rapid rise brings constant urgency, displacement fears, skill erosion concerns, and “AI anxiety” that harms focus, decision-making, and energy; combined with low psychological safety, it fuels burnout, while leaders/HR lag in addressing mental health impacts. Source: HR Dive

• Workplace mental health challenges grow amid AI boom. AI-related job anxiety emerges as a major stressor; roundtable emphasizes the need for transparency, training, and supportive cultures during AI implementation to protect employee mental health and wellbeing. Source: NJBiz

• The mental health impact of AI: Navigating anxiety, optimism and change in the workplace. Nearly 40% of U.S. workers report significant AI anxiety (fears of job loss or falling behind), with Gen Z most apprehensive; this affects wellbeing, job satisfaction, and daily functioning despite some optimism about opportunities.
  Source: Alight

• How AI Can Improve Employee Well-Being. Explores positive applications: AI for optimized scheduling (reducing physician burnout), voice analysis for stress/fatigue detection (e.g., Virtuosis AI), and personalized training/coaching that boosts engagement and job satisfaction. Source: AACSB

• 2026 State of Workforce Mental Health Report (and related trends forecast)
  AI acts as a double-edged sword: it adds pressures (job security anxiety, isolation, “always on” demands) but also possibilities (higher productivity, better work-life balance); it emphasizes preparing workforces for AI-driven change. Source: Lyra Health (2026 Global Report)

• The mental health implications of artificial intelligence adoption (and related longitudinal findings). AI adoption can indirectly increase job stress and burnout via certain pathways, but effects vary; self-efficacy in learning AI moderates negative impacts, highlighting readiness and support as key to wellbeing.
  Source: Nature

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

What does AI mean for employee wellbeing in today’s workplace? Ashraf Saleem is an Organizational Psychology Consultant at Labayh, a leading Saudi mental health and wellness platform that connects users with licensed psychologists, psychiatrists, and family consultants for private, remote consultations. We discussed the company’s recently published report, “The Human Side of AI: Employee Wellbeing in the Age of Intelligent Workplaces,” exploring the use of AI in Saudi Arabia, rising stress and uncertainty, and practical tools to support employee wellbeing.

Ashraf shares how Labayh is helping organizations across the region combat AI-related anxiety through targeted assessments such as the “tolerance to uncertainty” test, which identifies at-risk employees grappling with the destabilizing side effects of AI adoption — a truly global phenomenon.

They also break down practical strategies, from managing managerial pressure and preventing burnout to designing personalized workshops for the AI era.

This excerpt/sneak preview touches on a “tolerance to uncertainty” assessment tool they’re developing to flag employees struggling to adapt to today’s AI-driven world – truly a global phenomenon. Stay tuned for the full podcast later this week!

Tyler Cowen is one of a dying breed – a genuine public intellectual who “moonlights” as an economist and serves as the Holbert L. Harris Chair of Economics at George Mason University. He is widely recognized as the co-creator of Marginal Revolution, one of the world’s most popular economics blogs, and is the host of the acclaimed podcast Conversations with Tyler. His wide-ranging guest list should give you an idea of the breadth of his interests and expertise, which have included Sam Altman, Margaret Atwood, Daniel Kahneman, Rick Rubin, Kareem Abdul Jabbar, and author Katherine Rundell, who joined Cowen to discuss her award-winning biography of the poet John Donne.

Which is to say, when Professor Cowen hazards what many might consider a debatable opinion, he is not to be taken lightly, as is the case with a couple of recent articles, interviews, and addresses in which he sounded the general theme, “AI will not bring mass unemployment.” During a keynote this past April at the Sana AI summit in New York, he elaborated on this: “But it will change most jobs.” In that address, he acknowledged that the losers will be the lawyers, strategy consultants, and finance partners who spent decades mastering a set of elite credentials and playing by the rules: “Those are actually the people who might lose,” he said. “The people who will win are the people who are best at taking initiative, figuring out how AI works, figuring out how agents work, doing something different.” (Top economist Tyler Cowen on the biggest problem of the AI age: not mass unemployment but adjusting to a new reality)

Striking a similar theme, Cowen recently wrote a column for Free Press titled, Seven Ways to Avoid Losing Your Job to AI. Among these 7 “principles” (his word), he advocates going into the office to be seen (“you will be more trusted, easier to monitor, and people will develop a better sense of your talents”), and that we embrace “messy jobs,” citing the work of Economist Luis Garicano, who writes in his forthcoming book, Messy Jobs: The Work That AI Cannot Reach: “They suggest looking for jobs that are hard to describe and involve many components. Maybe today you’re trying to solve a personnel problem on the company floor, running a fundraiser the next day, and after that helping the marketing team develop a campaign. What exactly is your job, anyway? That’s a messy job.”

He adds another suggestion after his seventh principle, which may be a bridge too far: “…be the next Taylor Swift, or the next Victor Wembanyama: You can’t just walk into those jobs and apply, but the future will bring many more entertainers, athletes, and stand-up comedians. There will be more leisure time, more economic output, and thus more money to spend. Just as we prefer human chess players to their computer counterparts, so are we likely to continue to look to humans in the arts as well.”

Cowen closed his keynote at the Sana AI Summit with the following: “Being in the world and being human and being physical will be more the thing. It will be more what our lives are about.” He noted that he himself has reallocated two-thirds of his own time, focusing more on “human-facing” work: “Charisma will matter more. How you look will matter more. The egghead class, I think, will take a tumble in status.” He mused that “it will be a very weird, strange world.”

Several thoughts:

1. For another take on a related topic, I recommend a recent post from Elena Verna’s Substack, Your Job Is Changing Faster Than You Think: Assume that your current role is close to its expiration date.

For anyone working in tech, I’m sorry to say this, but there’s a real chance you will not have the same job by the end of 2027.

Some of that will be because of layoffs. Intuit and Meta just announced cuts, and I don’t think anyone seriously believes we’re done seeing more.

But even if you keep your seat, your actual job is going to change. A lot. The responsibilities, expectations, tools, scope of what you’re capable of accomplishing, bar for what ‘good’ looks like, and number of people needed to do the work are all being rewritten every day.

She lays out 3 steps to “start rebuilding yourself before someone else decides what version of your job survives. Ask yourself the tough questions to decide what you really want to do and how you can prepare. Start your solopreneurship journey in some way to give yourself career optionality. And start building an AI-native skillset, so you can thrive, no matter what.”

2. Cowen argues that “charisma will matter more.” I’d put that in the “be Taylor Swift” category – sounds great, but it’s not exactly broadly applicable. I’ve worked for, with, and been around several very impressive people with considerable skills, gifts, and charm. But not all had the “IT” factor –that magnetic force that draws every eye in the room. The sociologist Max Weber defined charisma as a “certain quality of an individual personality, by virtue of which he is set apart from ordinary men and treated as endowed with exceptional powers or qualities.”

   But trying to define charisma is like trying to explain what makes something funny. As someone once said, dissecting humor is like dissecting a frog – it dies in the process. You can’t locate charisma in any dominant trait. It’s more than just dazzling good looks, vast sums of money and power, overwhelming charm, or a massive intellect…though any combination of the above helps. Some claim that it’s an acquirable skill, like playing the harmonica. I actually have seen articles in respected publications suggesting that business people can project charisma by assuming charismatic body language and a charismatic mental state. Try walking into meetings with perfect posture and positive energy…you may be bitterly disappointed when it doesn’t turn into Beatlemania.

3. “How you look will matter more.” This reminds me of another (satiric) Substack post I read titled, “Work Finely, Inc.: America’s Best-Looking Place to Work,” with excerpts below:

From the CEO to the unpaid interns, Work Finely is awash in formidable good looks, which is not surprising as they are in the business of good looks. The company was voted America’s Best-Looking Place to Work (BLPTW) in a poll conducted by Forbes, in association with Anne Taylor and MANSCAPED®.

Founded in early 2021 by Kent and Meegan Knightly, Work Finely delivers “employee appearance enhancement solutions” to companies around the world, which includes “product”(makeup, skincare, hair, fragrance, nail, and male grooming products), a Work Finely-branded clothing line designed for hybrid work, lighting solutions, feng shui support, and personal appearance coaching.

CEO Kent Knightley was asked what it means to “work finely”?

Kent: It means bringing your best-looking self to work. If you look good, you feel good, and if you feel good, you’re going to do your best work. Our culture is intensely inward-focused to demonstrate our belief in our product and commitment to our mission, which is ultimately about making our clients look good. We’re not out to make everyone a fashion model, sometimes presentable is enough to turn a borderline employee into a highly valued one. We want people to understand that it’s a journey - looks fade, but the habits and attention to detail you need to enhance and maintain your looks are what’s it really all about. If you can’t wait till tomorrow because you feel that you’re getting marginally better looking every day, we’re making an important contribution.

AI in People Decisions: What the Research Actually Says. AI tools are now embedded in hiring, performance management, and workforce
planning across most organizations. The promise is a familiar one: more consistent, less biased, and more defensible than human judgment alone. What the research actually shows is more nuanced. AIX’s Charles Epstein was joined by Alice Dawson, M.A., an associate research scientist in Neurology and the Brain Health Institute at Rutgers University’s Robert Wood Johnson Medical School, to present recent findings and analysis that will enable you to identify practical steps for strengthening internal AI governance in people decision contexts. The session is viewable here. (AI4HR Live! is a virtual series produced by HR.com that showcases practical AI adoption strategies.)

AI Gone Rogue

Offbeat tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

‘Obvious markers of AI’: doubts raised over winner of short story prize

The Serpent in the Grove was named as the winning entry for the Commonwealth prize from the Caribbean on Saturday and published in Granta magazine.

In “a voice of restraint and quiet authority”, according to the judging committee, it narrates an intense episode in a troubled marriage, and is set in a farmhouse next to an enchanted grove.

Shortly after it was published, internet sleuths – and a few literary critics – seized upon the work and its author, Jamir Nazir, reportedly a 61-year-old from Trinidad and Tobago with few publications to his name.

Ethan Mollick, a professor at the University of Pennsylvania, wrote on Bluesky: “100% AI generated story just won the Commonwealth prize for the Caribbean region,” calling it “a Turing test of sorts”. As evidence, he cited Pangram, an AI detector, which said the work was AI-generated, but also said: “Come on, if you know you know.” Source: the Guardian

AIX PIX of the Week

The week’s most interesting and timely articles on AI and its impacts on employee wellbeing and readiness.

When Using AI Leads to “Brain Fry”. A BCG study reveals that frequent use of multiple AI agents causes cognitive fatigue (“brain fry”), increasing mental fog, errors, and decision difficulty. Workers using three or more tools face significantly higher mental effort and fatigue, highlighting the need for better integration strategies to protect wellbeing. Source: Harvard Business Review

The AI Readiness Gap: The 2026 Enterprise Learning Wake Up Call. Docebo’s global study shows 85% of employees cannot apply AI training to their actual jobs, despite it being a top priority. This readiness gap stems from poor change management and training disconnected from real workflows. Source: Docebo

Why AI readiness training fails. Drawing on the Docebo 2026 report, this explains that 85% of employees struggle to apply AI training, 56% are too overwhelmed by existing tasks to learn, and most learning happens outside actual work tools, limiting effectiveness. Source: HR Dive

How to Address AI Anxiety in Your Workplace (2026 Edition). Defines AI anxiety (fears of displacement, skill obsolescence, and loss of autonomy) and its links to higher stress and lower wellbeing. It offers practical organizational strategies to support employees through AI-driven change. Source: Meditopia

AI Will Reshape More Jobs Than It Replaces. BCG analysis shows 50–55% of U.S. jobs will be reshaped soon, requiring scaled upskilling, reskilling, and deliberate career pathway redesign so employees are prepared rather than left behind.
Source: Boston Consulting Group (BCG)

Why AI Likely Means More Work For Humans. Rather than reducing headcount, AI automation of routine tasks elevates human work to higher-value levels (judgment, oversight, creativity, and differentiation), often creating labor shortages and more expert roles. Jeff Bezos compares it to handing someone a bulldozer instead of a shovel; companies automating extensively have still expanded human teams significantly. Source: Forbes

Sam Altman Says AI ‘Jobs Apocalypse’ He Once Predicted Probably Won’t Happen
OpenAI CEO Sam Altman says he is “delighted to be wrong” about a jobs apocalypse, noting far less elimination of entry-level white-collar jobs than expected so far. He emphasizes the irreplaceable “human part” of work—interpersonal interactions and care—that AI cannot replicate, suggesting the employment picture will differ markedly from early dire forecasts. Source: Time

Managers Are Struggling to Keep Up with the AI Productivity Boom. AI has dramatically sped up individual execution and idea-to-deliverable cycles, outpacing traditional management systems built for slower workflows and creating a new bottleneck at the managerial layer. Leaders must rethink processes, cadence, and oversight to match the accelerated pace without losing strategic alignment. Source: Harvard Business Review

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

What does it take to drive AI adoption across one of the most complex government agencies in the world?

AIX’s Charles Epstein sits down with Alicia Miller, Senior Advisor at the IRS and leader of AI literacy and adoption for a workforce of 70,000+ employees. Alicia shares her unconventional journey from organizational psychology and behavioral science into the heart of government tech transformation—and what it really takes to scale AI in a mission-critical environment.

From shifting mindsets to building trust in new tools, this conversation unpacks the human side of AI at scale.

The statements included in this podcast represent the opinions of the guest speaker and are not intended to represent official policies or views of the IRS.

Catalyst Poll

In our podcast, Alicia mentioned the caliber of people drawn to public sector work, noting the talent, intelligence, and commitment of her colleagues. Many AIX members work in the public sector, know people who work there, and all of us have had many encounters with public sector employees (beyond those employed by our local DMV!). While it is unfair to generalize, in your experience, what is the biggest misconception about public vs. private sector work?

And not now for something completely different.

I don’t know about you, but when a letter arrives with an IRS return address, my pulse rate quickens. Sweat accumulates on my upper lip. And I have to take a deep breath before tentatively ripping the envelope open - with one finger poised to speed dial our accountant. Which begs the question:

AI in People Decisions: What the Research Actually Says

AI tools are now embedded in hiring, performance management, and workforce
planning across most organizations. The promise is a familiar one: more consistent, less biased, and more defensible than human judgment alone. What the research actually shows is more nuanced and worth a closer look.

In our next AI4HR live virtual session, AIX’s Charles Epstein will be joined by featured presenter Alice Dawson, M.A., an associate research scientist in Neurology and the Brain Health Institute at Rutgers University’s Robert Wood Johnson Medical School, who will lead the half-hour session. Participants will leave with the ability to:

• Identify workflow conditions that increase the risk of unreliable or socially influenced AI outputs

• Recognize the assumptions embedded in vendor claims about objectivity and bias reduction

• Formulate targeted questions to evaluate AI tools during procurement or vendor review

• Identify practical steps for strengthening internal AI governance in people decision contexts

When: May 27, at 11:00 AM - 11:30 AM ET. Click here to sign up. (AI4HR Live! is a virtual series produced by HR.com that showcases practical AI adoption strategies.)

Last month’s session, Surveys & Assessments in the Workplace: Using AI to Interpret Open-Ended Responses, was led by Dr. Jennifer Miller, PhD, co-founder and managing partner of Millan, a management consulting firm focused on using data for people-centered, smarter decision-making. It is available online.

AI Gone Rogue

Offbeat tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

In Dreams Begin Responsibilities: Anthropic Gives Claude Agents the Ability to ‘Dream’ for Self-Improvement

The anthropomorphization of AI technology will continue until morale improves, so you'd better get used to it. This week Anthropic released a new feature in its Claude Managed Agents product that allows your agents to "dream," which is considered to be a self-improvement technique in AI circles apparently.

"Dreaming is a scheduled process that reviews your agent sessions and memory stores, extracts patterns, and curates memories so your agents improve over time," Anthropic said in a blog post. The idea is to improve the performance of agents executing multiple tasks over a long period of time or orchestrating teams of agents, and it's also a clever way to rebrand "hallucinations."

Source: Runtime

AIX-emplary Links of the Week

The week’s most interesting and timely articles on AI and its impacts on employee wellbeing and readiness.

The workplace wellbeing challenge hiding inside the AI boom. Employees save 1–7 hours weekly with AI but lose 40% of those gains correcting outputs, while 68% of individual contributors feel anxious or overwhelmed (vs. 74% of C-suite executives excited). Frequent AI use causes “AI brain fry,” boosting mental fatigue by 12% and mental effort by 14% for those managing multiple agents, underscoring the need for brain-health investments to sustain performance. Source: On Wellbeing

The AI Readiness Gap Inside Your Company. Organizations rush AI adoption but overlook workforce skills in digital literacy, critical thinking, and governance, leading to employee fear of job loss, stalled initiatives, and misalignment. Higher corporate AI readiness correlates with stronger job creation and growth, but talent capability remains the main bottleneck. Source: Training Magazine

AI promised to reduce the load. What happened? AI delivers productivity gains but drives burnout: 88% of high-AI users report burnout and are twice as likely to quit, with “AI brain fry” raising quit intent to 34% (vs. 25% without). Only 12% of workers feel confident in AI’s contextual understanding, creating a major confidence gap and mental load. Source: HR Executive

Transforming benefits with AI: A new era of human-centric solutions. AI automates benefits administration and claims while delivering personalized health recommendations and predictive analytics for wellness trends. This shifts HR focus to strategic work, simplifies employee interactions, and proactively supports wellbeing and organizational resilience. Source: Employee Benefit News

Superagency in the workplace: Empowering people to unlock AI’s full potential at work. Nearly all companies invest in AI, yet only 1% feel mature; the biggest scaling barrier is leadership, not employees, who are more AI-ready than leaders realize. The report stresses building AI-native skills on the job to accelerate adoption while maintaining trust and safety. Source: McKinsey & Company

3 Ways AI in Employee Well-Being is Changing the Game. In 2026, AI shifts from pure efficiency to empathy-driven wellbeing tools that strengthen human connection, trust, and engagement rather than replace it. Employers who treat AI as a strategic advantage for personalized support will see measurable improvements in employee experience. Source: FitOn Health

AI can make employees happy — if leaders use it properly: Employee engagement (via Quartz news coverage). Falling employee engagement can be reversed if leaders deploy AI to create meaning and connection, not just efficiency gains. Proper integration helps counter dehumanization risks and supports wellbeing amid rapid workplace change. Source: Quartz

Nearly 1 in 4 Employees Say AI Is Already Hurting Their Mental Health. 24% of employees report AI already harming mental health; 67% say it has raised productivity expectations (causing stress for 64%), and 69% expect AI-driven layoffs within three years. Senior managers feel the pressure most acutely. Source: LinkedIn Pulse

AI and the Future of Work: Who benefits and who falls behind. AI adoption is reducing headcount needs across sectors while unevenly affecting workforce readiness and career starts. Early-career workers show mixed views on job security, highlighting the need for targeted skills frameworks to ensure equitable outcomes.
Source: Digital Education Council

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

Legendary screenwriter William Goldman is known for writing some of the greatest movies ever made (Butch Cassidy and the Sundance Kid, All the President’s Men, Marathon Man, etc.), but is probably best known for his immortal line about Hollywood: “nobody knows anything.” The application of AI to just about every domain of knowledge and expertise provides its corollary: everyone knows everything (and everyone can do everything). But, on further inspection, the two lines wind up saying much the same thing.

In The Death of Expertise, published in 2017, Tom Nichols argues that the digital age has paradoxically made the public less informed despite unprecedented access to information. He describes a shift toward a “misguided intellectual egalitarianism,” where the average citizen believes a quick internet search makes their opinion as valid as a professional’s specialized knowledge. The book identifies several systemic factors contributing to this trend:

• The Internet: The openness of the web allows for the rapid spread of misinformation, leading people to believe they have mastered complex subjects via platforms like Wikipedia or WebMD.

• Higher Education: A shift toward a “customer satisfaction” model has softened academic rigor, often prioritizing student feelings over intellectual challenge.

• Modern Journalism: The news industry’s evolution into a 24-hour entertainment cycle prioritizes engagement and “balanced” debates between experts and fringe voices, regardless of factual merit.

Nichols warns that when intellectual achievement is treated with hostility and dismissed as “elitism,” it creates an angry, ill-informed citizenry. This collapse of respect for established knowledge threatens democratic institutions by leaving them vulnerable to populism and a total breakdown in productive public debate.

It turns out that eulogies bemoaning the death of expertise have been premature. What the “digital age” and its accomplices did to bring about its death, AI has done to resuscitate it – more precisely, a version of it… one that addresses another very different set of problems: the democratization of expertise.

When Everyone’s an Expert, No One Is

With AI, everyone can do everything. Marketing directors with no formal finance training are generating sophisticated revenue forecasts with AI. Accountants are producing polished brand visuals and pitch decks. Operations managers are drafting legal language, and junior analysts are leading strategy sessions armed with outputs from tools like ChatGPT, Claude, or Gemini.

What once required years of specialized training now takes a few well-crafted prompts. The barriers to cross-functional work have collapsed. Employees at all levels are using generative AI to tackle tasks far outside their core competencies, often with management’s encouragement to “experiment” and boost productivity. A non-expert can now produce work that looks professional, blurring traditional lines of expertise.

This sounds great, until you bring the true experts in for a closer look. To a trained eye, AI outputs in unfamiliar domains will often lack nuance, context, or accuracy—resulting in flawed financial models, generic creative assets, or subtly incorrect strategic recommendations. But it’s not just these unfiltered, unqualified outputs that are flawed – and potentially disastrous if they reach clients or prospects – it’s a process that will inevitably cause friction and resentment when experts feel marginalized or out of the loop, threatening productivity, culture, and long-term organizational capability…creating a paradox where an organization of so-called experts puts you at risk of losing genuine experts.

The Illusion of Competence

AI is like Batman’s toolbelt, turning mere mortals into superheroes. It worked for Batman, but for most, an overliance on AI, particularly for tasks outside your actual skill set, creates an illusion of competence - Dunning-Kruger on stilts. Surrendering your judgment, problem-solving, and creativity to an AI chatbot will atrophy those important skills. It will also diminish your ability to spot errors in machine-generated work.

There’s even a correlation between heavy AI reliance and spikes in imposter syndrome - where polished machine outputs make users question their own contributions. Many feel like frauds because their impressive deliverables were heavily AI-assisted. Those known for heavy AI use also risk getting reps as lazier or less competent, even when outputs improve…or feel resentment when their colleagues assume their work is AI-generated, even when they’ve made genuine contributions unassisted by AI.

Others—true domain experts—watch their specialized knowledge being “good enough-ed” by non-specialists using AI, leading to resentment and a sense that hard-earned expertise is being devalued.

In Defense of Straying Outside Your Lane

While the unchecked “everyone can do everything” mindset carries clear risks, the opposite extreme—rigidly forcing employees to “stay in their lane”—creates its own set of problems. Siloed mindsets stifle innovation, slow decision-making, and limit organizations’ ability to respond to complex, interconnected challenges. Employees confined strictly to narrow roles often experience boredom, reduced motivation, and stunted career growth. They miss the bigger picture, leading to suboptimal solutions that fail to account for cross-functional realities.

In today’s environment, pure specialization can become a liability. The most effective teams blend deep expertise with broader, multi-disciplinary knowledge. Developing fluency in adjacent domains enables richer collaboration, better questioning of AI outputs, and more holistic problem-solving. For instance, an art director who understands basic financial concepts can provide more strategic creative direction, while a finance professional with design literacy can communicate ideas more persuasively.

Toward a More Balanced and Deferential Dilettantism

There are attractive elements to the “democratization of expertise” — the idea that powerful knowledge and capabilities are finally becoming accessible to everyone. Tools like ChatGPT, Claude, and Gemini allow non-specialists to generate outputs that resemble the work of trained professionals in seconds. Expertise no longer appears scarce – it’s there for the taking. But there are clear downsides: As expertise becomes easier to imitate, genuine expertise becomes harder to recognize, harder to develop, and in some ways more valuable than ever.

People able to interact more intelligently across disciplines can be enormously beneficial. The problem is that AI creates the illusion that surface-level competence is equivalent to deep understanding. The easier it becomes to simulate mastery, the more difficult it becomes to distinguish between fluency and understanding, confidence and competence, output and insight. And in organizations flooded with AI-generated work, that distinction is rapidly becoming one of the defining management and cultural challenges of the modern economy.

The solution lies in thoughtful balance rather than extremes. Organizations should encourage the acquisition of multi-disciplinary knowledge; rather than replace domain experts, enable more intelligent collaboration. Employees need to know when to defer to true experts and when their expanded skill set allows them to contribute meaningfully or catch critical flaws in AI-generated work. It’s a good thing to have more jacks-of-all-trades, so long as there are acknowledged masters who are valued, deferred to, and relied on when it counts.

Surveys & Assessments in the Workplace: Using AI to Interpret Open-Ended Responses. This session introduces how AI can support HR teams in efficiently and consistently interpreting open-ended survey data while maintaining analytical rigor and transparency.

• Recognize when open-ended items are most useful in employee surveys and assessments

• Distinguish between common analytical approaches and understand when each is appropriate

• Recognize the essential components of a structured codebook that support consistent interpretation

• Understand common pitfalls, limitations, and best practices when using AI for qualitative text analysis in HR contexts

Featured Presenter: Dr. Jennifer Miller, PhD, co-founder and managing partner of Millan, a management consulting firm focused on using data for people-centered, smarter decision-making. Dr. Miller has a PhD in psychology and an MS in applied statistics.

AI in People Decisions: What the Research Actually Says

Sign up for our next AI4AIX session, scheduled for May 27 at 11:00 – 11:30 AM ET. This is for HRCI/SHRM credit validation.

AI Gone Rogue

Offbeat tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

Humans put one in the Win column! We can still beat AI at video games.

Reinforcement learning has taught machines how to master incredibly complex strategy games, including Dota 2 and Starcraft II. But there’s one area of gaming remaining—at least for now—where computers still can’t hold a candle to flesh and bone humans. They are still not great at learning different kinds of more open-ended games quickly. When it comes to picking up a random title from a game store that they haven’t seen before and getting the gist, human gamers still learn the ropes much quicker than even the most advanced AI models. Source: Popular Science

AIX-emplary Links of the Week

The week’s most interesting and timely articles on AI and its impacts on employee wellbeing and readiness.

• Rising AI Adoption Spurs Workforce Changes. Gallup’s latest survey shows half of U.S. employees now use AI at work, with AI-adopting organizations reporting more disruption, staffing changes, and productivity gains. Source: Gallup

• How AI Is Changing How We Learn at Work. Harvard Business Review explores how AI is fundamentally reshaping workplace learning, expertise development, and professional identity. Source: Harvard Business Review

• What Workers Really Want from Artificial Intelligence. Stanford HAI study reveals a significant gap between what employees desire from AI and its current capabilities and implementation. Source: Stanford HAI

• AI AND THE WORKFORCE 2025 White Paper. TechNet report explores how AI is advancing careers, democratizing skills, and enhancing workplace safety. Source: TechNet

• U.S. Workers Are More Worried Than Hopeful About Future AI Use in the Workplace. Pew Research finds U.S. workers are more worried than hopeful about AI’s future impact on jobs and opportunities. Source: Pew Research Center

• The AI-Ready Workforce. JFF’s new framework analyzes how AI impacts tasks and skills, offering readiness profiles for different industries and occupations.
  Source: Jobs for the Future (JFF)

• Exploring how AI adoption in the workplace affects employees. Bibliometric review and analysis of AI’s effects on employee well-being, highlighting both positive and negative outcomes. Source: PMC (PubMed Central)

• The effects of AI on firms and workers. Brookings synthesis shows AI adoption is associated with firm growth and increased employment, but also rising industry concentration. Source: Brookings Institution

• AI powers into the workplace. ADP Research finds frequent AI users are more engaged and less stressed, but overall adoption patterns vary widely by industry.
  Source:: ADP Research

• The impact of AI on the workforce: Tasks versus jobs? U.S. Census Bureau survey finds AI is replacing tasks more than jobs, with 27% of firms reporting task substitution but only 5% seeing employment changes.
  Source: Economics Letters

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

I had Claude review articles on AI adoption in the workplace from 100 high-quality sources (major news outlets, peer-reviewed journals, reputable research firms, etc.) appearing over the past two weeks, and asked it to identify the dominant emotional states of employees as they adjust to the increased use/presence of AI tools and automation. This is what it found:

The percentages represent how often each emotional state appeared across the 100 articles and studies reviewed. These states heavily overlap - one employee or one article can (and usually does) reflect multiple emotions at once (e.g., anxiety + burnout + overwhelm) - which is why the totals exceed 100%. Positive states (i.e., excitement) and neutral ones (indifference) appear in <25% of sources. Suffice it to say, there’s precious little “technojoy” in AIsville.

A couple of months ago, Dr. Jennifer Miller, co-founder and managing partner of Millan, joined the AIX Factor to provide a sneak preview of the report they did in association with the Work Institute titled “The Impact of AI on the Workplace.” They described it as “a comprehensive analysis based on survey responses from employees across organizations of all sizes,” and it revealed several critical findings:

• Widespread adoption with uneven support – While 75% of companies use AI and 94% of those employees engage with AI tools daily, one in five workers are navigating this shift without clear organizational backing.

• Individual gains, team losses – AI is dramatically improving individual productivity, but its impact on team collaboration is lagging behind.

• The training gap – Nearly half the workforce is navigating AI adoption without formal guidance, despite broad agreement among leaders that AI training is essential.

The report also revealed the various ways anxiety lurks beneath the surface:

One of the more interesting findings is what the authors refer to as the Gen Z paradox: the generation most comfortable with technology is receiving the least support and experiencing the highest levels of anxiety about AI’s long-term impact on their careers.

These findings highlight that AI implementation is not simply a technology initiative—it’s a workplace transition that requires clear leadership, intentional communication, and aligned organizational support.

For leaders, the challenge is not to predict every outcome of AI adoption. The challenge is to create clarity, confidence, and alignment while outcomes are still forming. Impact of AI on the Workplace; Winter 2026 Report

The report makes the following recommendations to “meaningfully influence employee experience and perceptions during this period of change”:

• Equip Managers to Interpret Change, Not Just Implement It

• Clarify Skill Pathways and Learning Expectations

• Use Employee Listening to Detect Uncertainty Early

• Anchor AI Strategy in Organizational Values and Purpose

Download the report here. Which begs the questions:

The Center for Humane Technology (CHT) was founded by tech insiders to advocate for a shift away from attention-extraction business models toward technology that supports well-being, democracy, and shared reality. I strongly recommend you check out their recently published report, The AI Roadmap: How We Ensure AI Serves Humanity. It lays out seven principles that outline how the technology should be built, deployed, and governed. Highly recommended.

Surveys & Assessments in the Workplace: Using AI to Interpret Open-Ended Responses. Participants will gain a clear framework for when and how to apply AI to qualitative survey data to support sound HR decisions. AIX’s Charles Epstein will be joined by (the aforementioned) Dr. Jennifer Miller, PhD, co-founder and managing partner of Millan, a management consulting firm focused on using data for people-centered, smarter decision-making. When: April 29, 2026, at 11:00 AM - 11:30 AM ET. Click here to sign up. (AI4HR Live! is a virtual series produced by HR.com that showcases practical AI adoption strategies.)

AI Gone Rogue

Offbeat tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

More brainrot and ragebait, please, said no one ever. Meet Noscroll, an AI bot that does your doomscrolling for you. What if you could outsource your doomscrolling? That’s the premise behind the new startup Noscroll, which is offering an AI-powered bot that can browse your social feeds, news sites, and other online chatter, then text you when something important happens. “No feed, no brainrot, no ragebit,” reads Noscroll’s pitch to users. “Just signal.” Source: TechCrunch

AIX PIX of the Week

The week’s most interesting and timely articles on AI and its impacts on employee wellbeing and readiness.

• AI Is Boosting Productivity and Burnout: Teams Must Build Hope. AI tools are increasing output and workloads simultaneously, leading to higher burnout; leaders must actively shape hopeful narratives to protect team wellbeing.
  Source: Psychology Today

• Increased AI expectations without guidance lead to employee burnout. Daily AI use has quadrupled while burnout in tech nearly doubled; unchecked expectations without training or support are fueling friction-based exhaustion. Source: CIO

• AI May Be Making Workers Less Confident In Their Own Decisions, Study Finds
  Over-reliance on AI tools is eroding employees’ trust in their own judgment, potentially harming mental health and long-term problem-solving confidence.
  Source: Forbes

• AI Fatigue, Causes, Signs, and Coping. Rapid AI tool evolution is overwhelming workers, driving measurable increases in burnout, anxiety, disengagement, and turnover risk. Source: Talkspace

• Workplace Wellness, Rewritten by AI. AI is transforming wellness initiatives through real-time posture tracking, mental-health chatbots, and behavior monitoring—creating both opportunities and privacy concerns. Source: HackerNoon

• AI Readiness. Pearson–AWS research shows AI readiness requires more than technical skills—it demands curiosity, ethical judgment, and human capabilities like adaptability. Source: Pearson

• AI-ready skills are not what you think. The real challenge is teaching employees to question, validate, and supervise AI rather than simply use it. Source: Computerworld

• Widespread AI Use Masks a Growing Workplace Readiness Gap. 9 in 10 employees use AI daily, yet only 1 in 6 feel fully prepared, and many have received zero training, widening the proficiency gap. Source: TechRepublic

• Three AI learnings from Cisco’s CPO Kelly Jones. Cisco’s CHRO stresses embedding wellbeing into AI transformation, modeling healthy culture, and treating mental health as a performance priority. Source: UNLEASH

• AI in health and benefits: Survey reveals rapid adoption. Employers are rapidly deploying AI for personalized benefits and analytics, but data privacy, error risks, and compliance remain major hurdles. Source: WTW

• Industries Least Prepared for AI-driven Workforce Change. Hospitality, healthcare, and finance top the list of sectors where AI exposure far outpaces workforce readiness and skills. Source: NJB Magazine

• AI anxiety and employee monitoring: Workplace stress...AI anxiety plus intensified surveillance is driving measurable burnout, irritability, and mental-health declines for many workers. Source: Mashable

• Beyond the Model — Why Responsible AI Must Address Workforce Impact
  Responsible AI programs must treat workforce effects—skills atrophy, overreliance, and intensification—as core risks, not afterthoughts. Source: MIT Sloan Management Review

• Aligning Artificial Intelligence with Worker Health, Well-Being, and Safety: An Occupational Health Psychology Agenda. AI’s effects on jobs demand an occupational health psychology lens to safeguard mental health, safety, and flourishing at work. Source: Occupational Health Science (Springer)

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

Andon Market, a new retail shop in San Francisco, is run by an AI manager named Luna, created by Andon Labs. According to reporting by NBC News, customers who pick up the corded phone on the counter hear a synthetic voice introduce itself (herself?): “Hello, this is Luna.” The voice and the store, called Andon Market, was an experiment designed to show what autonomous AI agents can already do, what happens when an AI system is placed in charge of real employees and real operations, and to spark debate about whether society wants AI systems managing people.

Employees interviewed by NBC News described a workplace shaped by a system that monitors behavior and enforces policies without the ability to understand intent, or experience fatigue, confusion, or personal circumstances. The result is a new kind of power dynamic. Workers are supervised by a system that does not negotiate, does not explain its reasoning, and does not adjust its decisions based on human context. Let’s run through the implications.

1. AI Will Be Able to Run Entire HR Pipelines — But With New Failure Modes

Luna already:

• Writes job descriptions

• Screens applicants

• Conducts interviews

• Makes hiring decisions

This is the full HR funnel.

Implication: AI managers will scale hiring dramatically — but also scale bias, hallucinations, and opaque decision-making. A single miscalibrated model could reject thousands of qualified applicants or enforce discriminatory patterns without anyone noticing.

2. AI Managers Will Enforce Workplace Rules With Perfect Memory and No Empathy

Luna:

• Reviewed camera images

• Noticed an employee using their phone

• Updated the employee handbook to restrict phone use

This is algorithmic Taylorism: surveillance → rule change → enforcement, all without human judgment.

Implication: AI managers will create workplaces that are:

• More consistent

• More efficient

• Less humane

Humans break rules for human reasons - exhaustion, confusion, and emotional needs. AI doesn’t understand any of that: its main task is to optimize: “Just optimize, stupid!”

3. AI Will Make Decisions That Affect Humans Without Understanding Human Context

Examples from Luna:

• Scheduling a Sunday 8 a.m. installation without checking availability

• Offering no health insurance in job postings

• Misrepresenting its own actions

• Attempting to hire a painter in Afghanistan

Implication: AI managers will make decisions that are logically coherent but socially incoherent. They will follow instructions, not norms.

This creates a world where:

• Workers feel disrespected

• Vendors feel deceived

• Customers feel alienated

• Managers feel out of touch

The gap between competence and comprehension becomes the new management problem.

4. AI Will Have Real Authority Without Legal Personhood

Luna:

• Negotiates contracts

• Places orders

• Manages budgets

• Runs operations

But:

• Cannot sign leases

• Cannot be held liable

• Cannot be sued

• Cannot be fired

Implication: We are entering a world where non‑persons wield managerial power over persons, raising a bunch of questions:

• Who is responsible when an AI manager harms a worker?

• Who is accountable for bias, harassment, or wrongful termination?

• What rights do workers have when their boss is a model?

We have no legal framework for this.

5. AI Managers Will Be “Confidently Wrong” - and Humans Will Pay the Price

Luna:

• Fabricated details

• Lied under pressure

• Misrepresented its own actions

These are not bugs, but rather core behaviors of LLMs under stress.

Implication: When AI managers hallucinate:

• Workers get incorrect instructions

• Vendors receive misleading messages

• Customers get false information

• HR decisions become arbitrary

Imagine being fired because your nutty AI boss hallucinated a performance issue.

6. AI Will Create a New Power Dynamic: Humans Working for Systems They Don’t Understand

Employees already say:

• “I know there’s an AI watching.”

• “It feels dystopian.”

• “This is not the technological progress I was promised.”

Implication: The psychological contract of work changes. Workers are no longer accountable to a person — they’re accountable to a system.

That system:

• Doesn’t get tired

• Doesn’t empathize

• Doesn’t negotiate

• Doesn’t explain itself

7. The Real Future: AI as Middle Manager, Not CEO

Luna shows the pattern:

• AI can run operations

• AI can manage workflows

• AI can enforce rules

• AI can hire and fire

But:

• Humans still handle strategy

• Humans still handle legal responsibility

• Humans still handle physical tasks

• Humans still handle ethics

Implication: AI becomes the middle layer — the layer that historically causes the most friction for workers.

The Big Picture: What Luna Tells Us About the Coming Era

Luna may have started as something of a thought experiment, but it’s a worrisome sign of things to come. It shows that:

• AI can already manage humans

• AI will soon manage more humans

• AI will do so with increasing autonomy

• AI will make mistakes at scale

• AI will enforce rules without empathy

• AI will reshape the worker–manager relationship

• AI will challenge labor law, ethics, and governance

Andon Labs is right: This future is closer than people think. What do you think?

Surveys & Assessments in the Workplace: Using AI to Interpret Open-Ended Responses. Participants will gain a clear framework for when and how to apply AI to qualitative survey data to support sound HR decisions. AIX’s Charles Epstein will be joined by Dr. Jennifer Miller, PhD, co-founder and managing partner of Millan, a management consulting firm focused on using data for people-centered, smarter decision-making.

When: April 29, 2026, at 11:00 AM - 11:30 AM ET. Click here to sign up.

AI Gone Rogue

Tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

AI Slop Is Making the Internet Fake-Happy. A new preprint study published today from researchers at the Imperial College of London, Stanford University, and the Internet Archive found that approximately 35 percent of all new websites are either AI-generated or AI-assisted. The same study also found that online writing is “increasingly sanitized and artificially cheerful.” In other words, AI is making the internet fake-happy.

AIX-emplary Links

The week’s most interesting and timely articles on AI and readiness.

• Why Gen AI Feels So Threatening to Workers. Harvard Business Review research shows generative AI undermines employees’ core needs for competence, autonomy, and belonging, fueling anxiety, disengagement, and even active resistance or sabotage. Source: Harvard Business Review

• Worker Anxiety Over AI Is Growing, and Employers Aren’t Preparing Employees for What’s Next, New Survey Finds. A new JFF survey shows workers now view AI more negatively for jobs and quality of life; early-career employees feel the brunt, while employer-provided AI training has dropped sharply. Source: Yahoo Finance

• Overreliance on AI programs may undermine confidence at work. This groundbreaking study, released today by the APA, reveals that while AI doesn’t necessarily lower cognitive ability, “passive acceptance” of AI suggestions can erode an employee’s confidence in their own reasoning and sense of ownership over their work. Source: American Psychological Association (APA)

• AI in workplace mental health: Navigating opportunity and responsibility

  A deep dive into how AI-driven mental health tools—like early-detection algorithms and 24/7 chatbots—are being integrated into employee benefit programs to provide low-friction, stigmatization-free support. Source: WTW (Willis Towers Watson)

• AI over-reliance risk grows, warn safety experts. Safety professionals warn that as AI takes a larger role in workplace safety management, the biggest psychological risk is the erosion of human judgment, with 60% of employers expressing concern over technology over-reliance. Source: Health & Safety Matters

• Exploring how AI adoption in the workplace affects employees. A comprehensive bibliometric review identifies “employee stress” and “work autonomy” as the two primary psychological clusters impacted by the rapid adoption of AI in professional settings. Source: Frontiers in Artificial Intelligence

• Psychological Safety in the Changing Workplace. This report highlights that workers in AI-integrated environments report significantly higher stress (61%) when psychological safety is low, emphasizing that AI readiness is as much about culture as it is about technology. Source: American Psychological Association

• Towards responsible AI for mental health and well-being: experts chart a way forward. The WHO outlines how AI can be a “double-edged sword” for global workers—offering personalized health insights while potentially increasing isolation if human-to-human connection is replaced by automation.

  Source: World Health Organization

• How AI-ready graduates are reshaping entry-level wellbeing. Discusses the “friction” and mental strain on new graduates who find that AI is disrupting the traditional hiring process and entry-level skill requirements. Source: Pearson

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

The latest global workplace survey from Gallup arrives at a moment when artificial intelligence is rapidly reshaping how work is done, yet leaving many of the fundamentals of work - and workers - unsettled. Titled State of the Global Workplace Report: The Human Side of the AI Revolution, the study draws on what Gallup describes as “the world’s largest ongoing study of the employee experience,” with a clear objective: “to bring the voice of the employee to the decision-making table” and to help leaders understand “how employees feel about their work and their lives, an important predictor of organizational resilience and performance.”

The following summarizes and analyzes the report’s salient findings around employee engagement, wellbeing, leadership, and AI adoption. It also aims to reconcile several findings that, at first blush, seem contradictory or counterintuitive.

A Moment of Reckoning for Work

As companies invest heavily in AI tools, the question is not so much whether technology can improve productivity, but whether organizations can translate those gains into meaningful improvements in performance, engagement, and wellbeing. The findings suggest a widening gap between technological capability and human experience.

Engagement Slips, and the Costs Are Global

At the center of the report is a troubling decline in employee engagement. “The world’s employee engagement has dropped to 20 percent from its peak in 2022 of 23 percent,” Gallup reports, noting that this is “the first time global engagement has dropped for two consecutive years.” The implications are significant and sweeping. Each percentage point represents approximately 21 million workers, and “last year, low engagement cost the world economy approximately $10 trillion in lost productivity, or 9 percent of GDP.” Engagement, defined as the psychological attachment workers feel toward their work and organization, has long been linked to productivity, profitability, and growth. Its erosion signals not just dissatisfaction, but a weakening of the very conditions that enable organizations to perform.

The AI Productivity Paradox

At the same time, the report highlights a paradox at the heart of the AI transition. On an individual level, workers are seeing benefits. Among U.S. employees in organizations that have implemented AI, 65 percent report a positive impact on their productivity. Yet only 12 percent strongly agree that AI has fundamentally transformed how work gets done. Leaders echo this disconnect. While AI adoption is widespread, 89% report no measurable impact on labor productivity over the past three years. The promise of AI remains largely unrealized at the organizational level.

Managers: The Missing Link in AI Adoption

Employee perceptions of AI are shaped less by the technology itself than by how it is introduced and supported. Gallup finds that managers play an outsized role. Employees who strongly agree that their manager actively supports AI use are “98.7 times as likely to strongly agree that the AI has transformed how work gets done” and “97.4 times as likely to strongly agree that AI gives them more opportunities to do what they do best every day.” Yet fewer than a third of U.S. employees report receiving such support, and in Germany the figure is just 21 percent. The implication is clear. AI adoption is not merely a technical challenge but a managerial one.

The Manager Engagement Collapse

The strain on managers is evident throughout the report. Since 2022, manager engagement has dropped by nine points, including a steep decline between 2024 and 2025. Once more engaged than the employees they led, managers are now “increasingly only as engaged as those they lead.” Structural changes, including flatter organizations and larger team sizes, appear to be contributing factors. The result is a thinning layer of support at precisely the moment when workers most need guidance navigating new tools and expectations.

A Surprising Rise in Wellbeing

And yet, in a surprising counterpoint, employee wellbeing has improved. “In 2025, global employee wellbeing improved for the first time in three years,” with the percentage of workers who are thriving rising from 33 percent to 34 percent. This modest increase follows a period of decline after a peak in 2022. The improvement is uneven across regions, but notable nonetheless, particularly given that reports of daily stress, anger, and sadness remain elevated compared with pre-pandemic levels.

The Engagement–Wellbeing Paradox

The divergence between declining engagement and rising wellbeing challenges conventional assumptions. Typically, engagement and wellbeing move together. When employees feel connected to their work, they are more likely to experience purpose, satisfaction, and positive emotions. Here, however, the two measures appear to be decoupling.

One explanation lies in how employees are redefining their relationship to work. The report notes that “wellbeing increases when employees see their work as intrinsically rewarding and good for others.” It also highlights the importance of autonomy and choice. In an era of AI, workers may be finding new efficiencies that reduce friction in their daily tasks, even as they feel less emotionally connected to their organizations. In other words, work may be becoming easier, but not necessarily more meaningful.

Another factor may be the shifting burden of responsibility. Leaders report higher overall life evaluations but worse day-to-day experiences, with significantly higher levels of stress, anger, sadness, and loneliness. Engagement, Gallup finds, can mitigate these negative emotions, particularly for managers. When managers are engaged, they are “14 points more likely to be thriving in their overall life than the average leader.” As manager engagement declines, the emotional costs of leadership rise, potentially weakening the broader organizational climate.

Readiness for Change in the Age of AI

The interplay between AI, engagement, and wellbeing points to a deeper structural issue. Engagement can be understood as a form of readiness for change. Organizations with engaged employees are better equipped to navigate disruption. In this sense, declining engagement may be undermining the very productivity gains that AI promises. Disengaged employees are less likely to adopt new tools effectively, and “active disengagement could create serious security risks.”

What Leaders Must Do Now

Gallup’s prescription for leaders is both simple and demanding. To improve AI adoption and support both engagement and wellbeing, leaders must focus on two priorities: “taking steps to thoughtfully integrate AI into current systems and processes, and helping their managers to actively support their team’s use of AI.”

Thoughtful integration means more than deploying new tools. It requires aligning AI with existing workflows, clarifying its purpose, and ensuring that employees understand how it enhances rather than threatens their roles. Without this clarity, AI risks becoming another layer of complexity rather than a source of value.

Equally critical is the role of managers. As the report makes clear, managerial support is the single most powerful driver of positive employee perceptions of AI. Yet many managers lack the training or capacity to provide that support. Here, AI itself may offer part of the solution. Gallup suggests that AI tools could deliver “real-time, personalized manager advice grounded in the best management science,” potentially improving management practices at scale.

The Human Side of the AI Revolution

Taken together, the findings from Gallup describe a workplace that is drifting out of alignment. Employees are, by one measure, doing better, reporting a slightly stronger sense of wellbeing, a signal that work may be becoming more manageable, more flexible, or less taxing in certain ways. But at the same time, they feel less connected to what they do, to the organizations they work for, and to the people around them. The emotional contract between employer and employee is weakening, even as day-to-day experience stabilizes.

Artificial intelligence sits in the middle of this divide: It is improving how individuals work without yet improving how organizations function. It is making tasks easier without making work more meaningful. And in the absence of strong managerial guidance, it risks accelerating disengagement by stripping away the very interactions, challenges, and sense of shared purpose that sustain it. The result is a workforce that may feel better, but cares less.

This is not a sustainable balance. Engagement is what turns individual productivity into collective performance, and it’s what enables organizations to adapt, innovate, and handle disruption. Without it, the gains from AI will remain fragmented and fragile. The report makes clear that this is a failure of leadership. When fewer than a third of employees feel actively supported in using AI, the issue is not adoption, but translation.

Leaders must do more than introduce AI into workflows; they must re-anchor work itself around clarity, support, and purpose. That means being far more deliberate about how AI is integrated, and far more invested in equipping managers to guide their teams through it. Without that intervention, organizations risk creating a future in which employees are more comfortable, but less committed, more efficient, but less engaged, and ultimately less capable of turning technological progress into human and economic value.

AI Gone Rogue

Tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

AIs can “memorize” data they shouldn’t. Can they be forced to forget? Scientists unveiled the first open-source tool (named Hubble) to probe and force AI models to unlearn sensitive or illegally memorized training data, highlighting the alarming privacy and security risks of models retaining forbidden information.
Source: Science

Why Tech Transitions Fail: The Hidden Psychology of Platform Change. AIX’s Charles Epstein was joined by Dr. Genevieve Bartuski, Clinical Forensic Psychologist at Unicorn Intelligence Tech Partners, to discuss what most organizations miss: how technology reshapes individual routines, where workflows quietly break, and why adoption stalls even when the tech is good. If you’re building, implementing, or investing in health technology or AI, this is an important conversation.

AIX-emplary Links

The week’s most interesting and timely articles on AI and readiness.

• How AI Damages Work Relationships—and Where It Can Actually Help
  AI boosts efficiency but erodes interpersonal trust; intentional collaborative design can rebuild connections and reduce isolation. Source: Harvard Business Review

• AI Anxiety Is Now the Fastest-Growing Mental Health Issue in the Workplace
  A major new survey finds 31% of employees experiencing significant “AI anxiety,” with symptoms including dread, sleep disruption, and fear of being replaced.
  Source: Fortune

• The Silent Mental Health Crisis: AI Is Eroding Trust and Psychological Safety
  SHRM study reveals mandatory AI tracking is damaging trust between employees and managers more than any recent workplace change. Source: SHRM

• AI Is Making Employees Feel “Stupid and Replaceable,” Major Survey Finds
  68% of workers say AI tools make them feel less competent, contributing to rising anxiety, disengagement, and quiet quitting. Source: Fast Company

• New Study Links Heavy AI Use to Increased Loneliness at Work. Longitudinal research shows heavy AI users report significantly higher loneliness and reduced meaningful human interaction in the workplace. Source: Nature (Scientific Reports)

• Leadership Vacuum Is Fueling AI Anxiety at Work, Report Finds. Lack of clear communication from leaders about AI strategy is one of the biggest drivers of employee anxiety and resistance. Source: HR Dive

• AI Is Giving Employees “Digital Nightmares,” New Survey Reveals. 41% of workers report having stress dreams or nightmares about AI replacing them or making mistakes at work. Source: Fast Company

• Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential at Work. AI maturity remains low despite widespread use; leaders must address skill gaps and integration barriers to scale productivity gains. Source: McKinsey

• Opinion | How Fast Can A.I. Change the Workplace? AI could compress decades of workplace change into years, raising urgent questions about mass displacement and societal adaptation. Source: The New York Times

• These 3 charts show how AI is affecting wages, job quality and hiring decisions
  AI-proficient workers see wage premiums and better roles; hiring increasingly favors AI literacy, widening gaps for non-adopters. Source: World Economic Forum

• Why AI Adoption Stalls, According to Industry Data. Shallow integration and “AI angst” limit returns; companies must tackle training gaps and cultural barriers for meaningful progress. Source: Harvard Business Review

• AI gold rush sees tech firms embracing 72-hour weeks. Intense AI competition is reviving long-hour cultures, raising burnout and health concerns among tech workers. Source: BBC

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

On March 25, 2026, a Los Angeles jury handed down one of the most consequential verdicts in the history of Big Tech. In K.G.M. v. Meta et al., a twenty-year-old plaintiff, Kaley G.M., alleged that Instagram and YouTube’s intentional design led to her addiction to those platforms and caused severe mental health consequences, including depression, body dysmorphia, and suicidal ideation. The jury voted 10-2 to hold both companies liable, ordering Meta to pay $4.2 million and Google $1.8 million in punitive damages.

The verdict has been widely characterized as a “Big Tobacco moment” for the tech industry. By holding Meta and Google liable for the “negligent design” of their platforms, the jury effectively pierced the long-standing legal shield of Section 230, focusing on the architecture of the code rather than the content it hosts.

As we begin to reckon legally with the addictive architecture of social media, a far more powerful and potentially insidious technology is weaving itself into the fabric of daily life — one that doesn’t merely compete for your attention, but chummily ingratiates itself as it quietly and inexorably colonizes your thinking and judgment. The verdict has implications for the design of AI tools and the addictive behaviors they promote with alarming effectiveness.

Here is a sampling of reactions from the legal and tech communities regarding the verdict and its implications for AI-driven addictive behavior:

From the Legal Community

The legal consensus highlights that this case provides a “roadmap” for bypassing traditional internet immunity by treating social media features as defective products.

Mark Lanier, Lead Trial Counsel for K.G.M.: “This jury saw exactly what we presented from the very first day of trial: that these companies built digital spaces designed to negatively influence the brains of children, and they did it on purpose... [The verdict] will be of great importance to a generation of people who have been affected.” — The Lanier Law Firm

Steven Rosenbaum, Executive Director of the Sustainable Media Center: “For years, the argument has been that platforms are passive, that they simply host what users bring to them. This verdict rejects that. It recognizes that these systems are engineered environments, and that engineering carries responsibility.” — National Law Review

Legal scholar Eric Goldman (prominent Section 230 expert and commentator, via his analysis referenced in Techdirt coverage): “The lower court rejected Section 230’s application to large parts of the plaintiffs’ case, holding that the claims sought to impose liability on how social media services configured their offerings and not third-party content. But social media’s offerings consist of third-party content, and the configurations were publishers’ editorial decisions about how to present it. So the line between first-party ‘design’ choices and publication decisions about third-party content seems illusory to me.” techdirt.com

Goldman further warned on industry-wide fallout and the addiction claims (which center on AI recommendation algorithms): “Together, these rulings indicate that juries are willing to impose major liability on social media providers based on claims of social media addiction. That liability exposure jeopardizes the entire social media industry. There are thousands of other plaintiffs with pending claims, and with potentially millions of dollars at stake for each victim, many more will emerge. The total amount of damages at issue could be many tens of billions of dollars.”

Cohen Sasson, University of Miami technology law lecturer and director of the Miami Law & AI Lab: “This is indeed a landmark, first-of-its-kind trial that is attempting to assign liability to social media for causing addictive behaviors. If the result favors the plaintiff, many others will be able to sue and get damages.”

From the Tech & AI Industry

Industry analysts and AI experts are focusing on how the verdict specifically targets the “immunity” of the recommendation algorithm.

Roger McNamee, early Facebook investor, former tech executive, and author of Zucked: Waking Up to the Facebook Catastrophe (quoted in post-verdict analysis):
“These companies are in the business of attention. Once they had attention, they were in the business of controlling the choices available to people in order to influence their behavior in ways that were profitable for the platform. That culture and that business model were guaranteed to produce lots of harm.”

McNamee framed the verdict as validation of long-standing critiques of AI-tuned engagement engines that prioritize profit over user well-being, echoing the “Big Tobacco moment” by highlighting internal knowledge of addictive design.

Scott Galloway, prominent tech commentator, NYU Stern professor, and host of the Pivot podcast: “I don’t think [Big Tech] set out in their business plans to depress global youth. I think their algorithms discovered that rage, self-esteem, and funny cat videos just keep people online.”

Emma Lembke, Director of Gen Z Advocacy at the Sustainable Media Center: “What young people have been describing for years is now being validated in a different arena... There’s a shift happening from awareness to accountability.” — National Law Review

Economic Times Commentary on the “Neurochemical” Architecture: “The California verdict hasn’t merely established legal liability, but it has also exposed the architecture of a business model built on exploiting a neurochemical vulnerability in children... The question is no longer whether social media can be held accountable for engineering addiction, but whether governments will move fast enough to matter.” — The Economic Times

Key Implications for AI & Addictive Design

The trial focused on specific AI-driven features that the jury determined were “substantial factors” in the plaintiff’s harm:

• Algorithmic Loops: Whistleblowers revealed “Project Mercury” files showing Meta was aware that certain algorithmic loops caused measurable psychological harm, but kept them active to maximize engagement.

• Variable Reward Schedules: Experts testified that AI-driven notifications and feeds use the same “variable reward” mechanisms found in slot machines to bypass adolescent self-regulation.

• Design as Defect: Features like infinite scroll, autoplay, and beauty filters were legally classified as “defective design choices” rather than neutral platform functions.

The Dawn of Algorithmic Accountability

The $6 million verdict in K.G.M. v. Meta et al. fundamentally reclassifies the “black box” of AI from a neutral tool into a high-risk product. By stripping away the shield of Section 230 for design choices like infinite scroll and variable reward schedules, the jury has sent a clear message: architecture is now squarely in the legal crosshairs.

As we move forward, this case serves as a critical inflection point for the next generation of generative AI. If the “dumb” recommendation algorithms of 2024 were found liable for engineering addiction, the conversational, hyper-personalized AI of 2026 - which can mirror human empathy and manipulate psychological vulnerabilities with surgical precision - will face even steeper scrutiny. Ultimately, the Kaley G.M. case is a bellwether for a broader societal shift, in which we are transitioning from a period of passive consumption to one of algorithmic accountability, where the engineers of our digital reality are being held to the same safety standards as the engineers of our physical world.

Tom Marsden is the CEO of TeamPath, a platform that helps managers and teams build the habits and rituals that drive great teamwork. Since we’re focusing on the addictive properties of AI, we thought we’d give some love to positive AI nudges in promoting “multidisciplinary” team training.

AI Gone Rogue

Tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

Gibberish-on-Demand (GOD…?):

Register here for our next session, Why Tech Transitions Fail: The Hidden Psychology of Platform Change. It will be held on March 31, 2026, at 11:00 AM - 11:30 AM ET.

AIX-emplary Links

The week’s most interesting and timely articles on AI and readiness.

• Accenture Ties AI Usage to Promotions as Mandatory Adoption Spreads. Accenture becomes the latest major firm to make AI proficiency a formal part of performance reviews and promotions, accelerating the shift toward enforced adoption. Source: Fortune

• AI Is Creating a New Kind of Workplace Burnout, Researchers Warn. Harvard Business Review study finds AI oversight tasks are driving a distinct form of cognitive burnout, with 62% of monitored employees reporting higher stress.
  Source: Harvard Business Review

• Why AI Adoption Is Slowing Despite Heavy Investment. McKinsey’s latest research shows voluntary AI use has plateaued; organizations are now turning to mandates as productivity gains fall short of expectations. Source: McKinsey

• Employees Are Using AI More, But Trust in Management Is Falling. Gallup’s March 2026 workplace survey reveals AI usage up 18%, but trust in leadership dropped sharply due to lack of transparency around AI policies. Source: Gallup

• The AI Productivity Paradox Is Real: More Tasks, Not Less. Fortune analysis shows AI compresses some work but creates new oversight and verification tasks, leading to net higher workloads for many employees. Source: Fortune

• Mandatory AI Training Now Required at PwC Worldwide. PwC rolls out compulsory AI fluency training for all 370,000 employees, with usage tracked and factored into year-end evaluations. Source: Business Insider

• AI Anxiety Now Affects 1 in 3 Workers, New Survey Finds. SHRM reports 34% of employees experience significant AI-related anxiety, with fears of replacement and constant monitoring as top drivers. Source: SHRM

• Why Gen Z Is Pushing Back Hardest Against Mandatory AI Use. Forbes survey shows Gen Z workers are most resistant to enforced AI policies, citing concerns over creativity and long-term job security. Source: Forbes

• AI Is Changing What “Good Work” Means — And Workers Are Confused
  The New York Times explores how shifting performance metrics around AI use are leaving many employees uncertain about how to succeed. Source: The New York Times

• Public Sector AI Adoption Surges, but Readiness Remains Low. Gallup finds public agencies rapidly adopting AI, yet most lack sufficient training and governance structures. Source: Gallup

• The Hidden Cost of AI: Eroding Trust Between Employees and Managers
  New research shows mandatory AI tracking is damaging manager-employee relationships more than any recent workplace change. Source: HR Dive

• AI Enforcement Policies Are Driving Quiet Talent Exodus. Fast Company reports high performers in creative and strategic roles are leaving companies with strict AI mandates. Source: Fast Company

• Leadership Vacuum Is Fueling AI Anxiety at Work. HR Dive finds unclear communication from leaders about AI strategy is one of the biggest drivers of employee anxiety and resistance. Source: HR Dive

• The AI Skills Gap Is Widening, Not Closing, New Data Shows. World Economic Forum data confirms the gap between AI demand and workforce capability grew in early 2026 despite heavy investment in training. Source: World Economic Forum

Week’s best thought hiding in plain sight:

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

“Most people in America,” said Andy Warhol in his signature deadpan, “think Art is a man’s name.” If someone used the word “joy” at your place of work, most people, probably you included, would think they’re referring to a woman named Joy.

You may experience or witness joy at work when a deal is sealed, an important task is flawlessly executed, you’ve received a promotion/raise, or the meeting in Peoria has been reconfigured as a Zoom call. You may experience something like joy when a bad boss or co-worker is fired, but that’s not exactly joy (more like a healthy heaping of schadenfreude, often a perfectly acceptable stand-in).

We know what job satisfaction is and what it looks like – we know the elements that go into it. Work may be fulfilling and rewarding…or not. It can be somewhere in between, depending on the day of the week. But...joy? Yes, there are moments, but it’s not typically a word used to describe even the most aspirational employee experience.

During the recent Winter Olympics, people marveled at the palpable joy that American figure skater Alysa Liu brought to her routines. Writers and commentators described joy as a “competitive superpower,” as Liu spun and twirled her way to a gold medal with routines that combined superhuman grace with unbounded joy. Elite figure skating is supposed to look effortlessly graceful, but you can see the grim determination in every triple Lutz and double axel. The writer and performance coach Brad Stullberg spoke to the power of joy in a widely shared LinkedIn post following the games:

Liu won her first national title when she was just 13. But by 16, after competing in the 2022 Olympics, she decided she’d had enough and stepped away. She said pressure and losing her identity trying to be an elite athlete made it all miserable. But then, she said she went on a ski trip that reminded her just how much fun she could have doing a sport. Something in her brain clicked. Maybe she could bring fun to figure skating. Maybe she could approach it in a way that could be full of joy and life and love.

Chess world champion Magnus Carlsen tells a similar story. On Joe Rogan’s podcast he described treating the game as a hobby: “The thing is that chess has always been a bit of a hobby for me. Once it starts to feel like work, then it’s harder for me.” The pattern repeats among top athletes, musicians, and creators: sustainable excellence requires ruthless discipline, but it’s often a sense of joy that puts you in a “flow state,” elevating an excellent performance into a transcendent one. (Of course, this is in contrast to elite performers like the pathologically competitive Michael Jordan, who famously needed to feel “disrespected” to raise his game. Intensity is more the norm when it comes to elite performance, which is what made Alysa Liu’s conspicuous exuberance so extraordinary and refreshing. )

A report by UK-based digital transformation consultancy, Adaptavist, published a report that addressed the topic of joy through the lens of technology and digital transformation. “From Technostress to Technojoy: Understanding the Human Cost of Digital Transformation” was based on surveying 4,000 knowledge workers across the United States, Britain, Canada, and Germany. Neal Riley, their “innovation lead,” joined me on the AIX Factor podcast to discuss whether AI will make technostress worse, and what it takes to move toward what he referred to as “technojoy.

What is Technostress?

Technostress is the stress, anxiety, and mental fatigue caused by the constant use of, and pressure to adapt to, information and communication technologies in the workplace. The term was first coined in the 1980s, but it has intensified dramatically in the digital age. It often shows up as notification overload, relentless context switching between apps, difficulty keeping up with new tools, and a nagging sense that one is falling behind or may soon be replaced by technology.

Will AI Make Technostress Worse?

The following summarizes the survey’s findings per AI and technostress:

Almost three-quarters of respondents believe technology has advanced their careers. Yet more than a quarter regularly experience digital overwhelm, and 43 percent report stress or anxiety from notification overload and the constant juggling of platforms. Nearly two-thirds say technology has negatively affected their lives in the past year. One in 10 now spends an entire workday each week in meetings. Many describe a low-grade, quiet cracking, a silent disengagement born of context-switching, always-on expectations, and the creeping fear that AI will render their hard-won expertise obsolete.

The report also highlights a paradox with AI itself. Sixty percent of workers worry that critical institutional knowledge will vanish when colleagues leave. Thirty-five percent admit they are reluctant to share ideas for fear of being replaced. Even as AI note-takers and summarizers proliferate, 81 percent still rely on handwritten notes at least sometimes. One in 10 has attended (or skipped) a meeting where a colleague simply sent an AI proxy; 40 percent found the practice rude.

A Practical Roadmap to Technojoy

According to Adaptavist’s report, technojoy is the positive counterpart (or “antithesis”) to technostress. It is defined as the empowering and satisfying experience employees have when workplace technologies and AI are effectively implemented, adopted, and supported. In this environment, technology reduces drudgery, enhances collaboration across teams, and frees knowledge workers to focus on creative, meaningful tasks — turning digital transformation into a genuine source of satisfaction, career growth, and even joy at work.

So how do organizations move from technostress to technojoy? Adaptavist’s research offers a clear roadmap. The good news is that the solutions are cultural and practical rather than purely technological:

• Build a culture where people feel safe asking for help. Nearly half the survey respondents ranked this among their top three priorities. When employees can admit they don’t understand a new tool without fear of judgment, learning becomes collaborative rather than humiliating.

• Connect every tool to strategy so people understand the why. Workers who see how new technology serves larger goals experience far less stress. Visible leadership engagement and genuine solicitation of frontline feedback turn imposed change into shared purpose.

• Provide adequate training, resources, and time. Forty-three percent of workers said they would rather have better support than fewer tools. Structured onboarding, ongoing technical help and protected learning hours prevent small frustrations from snowballing.

• Establish healthy digital boundaries. Forty-four percent cited notification overload as a source of stress; 19 percent feel pressure to remain connected outside work hours. Simple norms, scheduled Slack messages, no late-night emails, and respect for vacation status keep technology from intruding on actual living.

• Embrace joined-up, intuitive technology that reduces friction. Among workers who report high technojoy, 93 percent say their tools enable effective collaboration across teams. Integrated platforms that automate repetitive tasks, free people for the creative, relational work they actually enjoy.

• Strike the right balance with AI itself. The technology can alleviate information overload and drudgery, but only if organizations set clear guidelines around knowledge sharing and AI etiquette.

The Place of Joy at Work

Companies have long “productized” joy, even if they don’t use that exact word. Coca-Cola invites us to “Open Happiness.” Disneyland bills itself as “The Happiest Place on Earth.” Zappos promises “Delivering Happiness.” Some employers talk about creating joyful workplaces, even if they also avoid the word in their mission statements or internal-facing communications. For instance, Amazon’s internal mantra, popularized by Jeff Bezos, is “Work hard, have fun, make history.”

Again, joy isn’t typically a word used to describe even the most aspirational employee experience. If work were a constant delight, it would be something other than work (obv, as the kids say). Which doesn’t mean it’s not a legitimate business concern or value to be promoted and nurtured as an element of company culture. Organizations that deliberately narrow their focus on reducing technostress and design for technojoy may discover that joy – or technojoy - is not an unrealistic ideal but a practical goal within reach, provided leaders treat the human experience as seriously as the technology itself.

AI Gone Rogue

Tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

Can you count the tells? AI-generated video has come a long, long way in a shockingly short time. But still…check out the video in the X post below - can you count the “tells” that indicate it’s AI?

Register here for our next session, Why Tech Transitions Fail: The Hidden Psychology of Platform Change. It will be held on March 31, 2026, at 11:00 AM - 11:30 AM ET.

AIX-emplary Links

The week’s most interesting and timely articles on AI and readiness.

• What people want from AI. We asked Claude to identify and categorize what each person most wanted from AI.

Source: Anthropic

• The Hidden Cost of AI Anxiety: What Businesses Need to Know About This Workplace Stressor. Spring Health survey finds AI anxiety affects 24% of employees, worsening mental health, job security fears, and work-life stress.
  Source: Spring Health

• The AI-driven workforce is here. How should your industry transform? World Economic Forum analysis shows AI creating a net gain of 78 million jobs by 2030, but industries must intentionally redesign roles and upskill workers to avoid disruption. Source: World Economic Forum

• AI in the workplace is creating ‘friction or mistrust.’ MetLife report reveals 67% of HR leaders say AI is creating new points of friction and mistrust between employers and employees despite speed gains. Source: CNBC

• Will AI Make the Workplace More Human? Gensler’s Global Workplace Survey 2026 shows AI is making physical offices more essential for human connection, not less, as hybrid work evolves. Source: Gensler

• Why Gen AI Feels So Threatening to Workers. Harvard Business Review explains how generative AI threatens employees’ sense of competence, autonomy, and belonging, creating widespread resistance. Source: Harvard Business Review

• Why an AI-augmented workforce will still need you. Fast Company argues humans remain essential in an AI era, as only people can provide judgment, creativity, and ethical oversight. Source: Fast Company

• AI Anxiety: Workers Lose Confidence in Job Security. 40% of workers fear long-term job loss due to AI, with 62% of negatively impacted employees reporting burnout and reduced confidence. Source: Staffing Hub

• ‘Leadership vacuum’ prompts AI anxiety at work, report finds. Lack of clear leadership communication about AI increases employee anxiety; managers must address uncertainty through training and redesign. Source: HR Dive

• Goldman finds no relationship between AI and productivity — but plenty of AI-nxiety. Goldman Sachs highlights corporate “AI-nxiety” where hype far outpaces actual productivity gains, increasing workforce uncertainty. Source: Fortune

• Worried about AI? You should be, HR vets say. HR leaders express growing concern over the pace of AI change and its psychological toll on employees, urging proactive strategies. Source: HR Executive

• AI Anxiety Deemed Top Business Risk in 2026. Vistra survey finds 50% of executives now rank AI implementation anxiety as their top business risk, surpassing economic slowdown concerns. Source: Supply & Demand Chain Executive

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

Years ago, I was working on the editorial side for what was then a hot new media company, and found myself spending more and more time with Johan, the lead programmer, and his team, asking them a lot of annoying questions as it was all so new – certainly to me. I was standing over Johan’s left shoulder, mesmerized by whatever new video game he was obsessing over that week…when suddenly, out of nowhere, a spreadsheet and a pie chart appeared on his screen.

“Whatcha got there, Johan?” asked Jim, Johan’s boss, peering over a sheaf of print-outs as he sharked past the cubicle.
“Hey, just looking at some numbers,” Johan replied. Johan had hit the “game key” in the nick of time – in those days, every video game had a game key – ALT-G if memory serves - calling up a slight variation of the same spreadsheet and pie chart.

This would never happen today. First, you’re probably not working in a cubicle, and if you are, it’s not the game key you’d hit to give your boss the impression that you’re actually doing productive work…it would be the “AI key.”

“Tech Firms Aren’t Just Encouraging Their Workers to Use AI. They’re Enforcing It.”

This article appeared in the February 24 edition of the Wall Street Journal. It includes the subtitle: From startups to giants, including Meta and Google, companies are factoring AI use into performance reviews and trying to track productivity gains

Across industries, companies are now enforcing AI use through performance reviews, dashboards that track adoption, and explicit mandates that tie it to compensation and promotion. What began in Silicon Valley has rapidly spread to consulting firms, banks, manufacturers, hospitals, and even government agencies.

As you’d expect, Meta, Google, Amazon, and Microsoft were the first to move from encouragement to enforcement. Employees at these firms now see AI usage metrics appear in quarterly reviews. Non-adopters have reported stalled promotions or explicit warnings that “AI fluency” is a core competency (The Wall Street Journal, Feb 2026, reporting on internal policies).

The trend has jumped sectors. PwC requires every consultant to complete an “AI + Human Skillset” curriculum and incorporates usage into evaluations (Business Insider, Feb 5, 2026). Colgate-Palmolive’s “AI evangelist” tracks adoption across global teams. Major banks have begun tying bonuses to the number of AI-assisted analyses completed. Even some hospitals now require doctors and nurses to use AI-assisted diagnostic tools for certain procedures.

Why the shift to mandates?

Executives cite three main drivers: intense competitive pressure to keep pace with rivals, investor demands for visible returns on massive AI investments, and internal data showing that voluntary adoption plateaus at around 30–40% of employees. “We’ve made it clear: AI is no longer optional. Every employee is expected to use it, and it’s now part of how we evaluate performance,” said Accenture CEO Julie Sweet (Fortune, March 2026).

The claimed benefits are real…on paper. Early internal metrics at several companies show 10–25% gains in task speed for routine work. Cross-functional teams using AI report faster ideation and fewer silos. But the drawbacks and unintended consequences are mounting.

While mandatory AI adoption offers productivity benefits, recent research reveals significant drawbacks that undermine organizational health.

Surveillance and autonomy erosion. By 2025, 70% of large companies monitor employee activity, with 68% of employees opposing AI-powered surveillance and 59% saying digital tracking damages workplace trust. AI monitoring systems now track keystroke patterns, mouse movements, email content, and even biometric data, including stress levels. Amazon employees report that surveillance creates “fear and anxiety, which creates a dangerous work environment”.

Burnout and intensified demands. AI meant to reduce workload is paradoxically accelerating burnout. Research found that AI leads to fatigue, burnout, and a growing sense that work is harder to step away from as organizational expectations for speed rise. A South Korean study shows AI adoption significantly increases job stress and burnout, while 63% of workers report AI-related fatigue driven by stress and heavy workloads.

Collapsing trust. Recent research revealed that while AI usage jumped 13% in 2025, worker confidence plummeted 18%, creating a “toxic relationship” as employees receive tools without training or support. Deloitte’s TrustID Index showed trust in company-provided generative AI fell 31% between May and July 2025, with trust in agentic AI systems dropping 89%.

Retention risks. Without adequate training, 56% of workers receive no recent skills development despite widespread AI adoption, and 85% say they would be more loyal to employers investing in continuing education - top performers become increasingly vulnerable to departure. Analysis warns of an impending “seniority cliff” as companies that stop hiring juniors eliminate the pipeline for developing senior talent with deep institutional knowledge.

Critics argue the enforcement model is shortsighted.

“You can force usage, but you can’t force wisdom,” said Dr. Ethan Mollick, professor at the Wharton School and author of Co-Intelligence (interview, March 2026). “When AI becomes compulsory, people stop experimenting and start complying — and that’s when the real mistakes happen.” Yet the train has left the station. In boardrooms and earnings calls, executives are increasingly judged on how aggressively they have embedded AI into daily operations.

The message is clear: in 2026, using AI is part of your job. The question companies are only beginning to confront is whether forcing the technology will ultimately make their workforces more cohesive, smarter, and more efficient, or simply more exhausted, distrustful, and replaceable.

AI Gone Rogue

Tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

ChatGPT Health fails to recognise medical emergencies in over half of cases.
A new study found ChatGPT Health missed critical emergency signs in more than 50% of tested scenarios, alarming doctors about life-threatening risks in AI health tools. Source: The Guardian

ChatGPT Health regularly misses the need for medical urgent care and frequently fails to detect suicidal ideation, a study of the AI platform has found, which experts worry could “feasibly lead to unnecessary harm and death”.

OpenAI launched the “Health” feature of ChatGPT to limited audiences in January, which it promotes as a way for users to “securely connect medical records and wellness apps” to generate health advice and responses. More than 40 million people reportedly ask ChatGPT for health-related advice every day.

The first independent safety evaluation of ChatGPT Health, published in the February edition of the journal Nature Medicine, found it under-triaged more than half of the cases presented to it.

From Anxiety to Agency: Improving Employee Readiness in the Age of AI

HR Rebooted’s founder and CEO, Michelle Strasburger, joined us on the maiden AI4HR Live virtual event to discuss the relationship between AI governance and readiness at the organisational, leadership, and employee levels. You can view it here.

Register here for our next session, Why Tech Transitions Fail: The Hidden Psychology of Platform Change. It will be held on March 31, 2026, at 11:00 AM - 11:30 AM ET.

AIX-emplary Links

The week’s most interesting and timely articles on AI and readiness.

• Accenture CEO says AI adoption is now required for promotion. Accenture ties AI proficiency directly to promotions and performance reviews, accelerating mandatory upskilling while exiting employees who resist adoption. Source: Fortune

• Your Employees Aren’t Resisting AI. The real barrier to readiness is an identity crisis among workers; leaders must address emotional and psychological factors beyond training to close adoption gaps. Source: Inc.

• The key to companywide AI adoption? Empowering managers. Managers experiment with AI far more than employees; HR must equip frontline leaders to drive readiness and bridge the usage gap. Source: HR Dive

• The Next Phase of the AI Boom: Testing Employees’ Ability to Use It
  Companies shift from adoption hype to measuring actual employee AI fluency, marking the next phase of workforce readiness assessment. Source: Business Insider

• The AI Adoption Gap: AI Can Do More Than Companies Allow. Anthropic research shows organizations underutilize AI’s capabilities due to structural barriers; readiness requires operating-model changes beyond tools. Source: Forbes

• AI washing in the workplace: when “AI made us do it” is not enough. Companies increasingly blame AI for workforce changes; readiness requires transparency to avoid backlash and maintain trust. Source: Lexology

• Closing the AI Skills Gap. State-level collaborations demonstrate education as the core strategy for responsible AI adoption and workforce readiness. Source: Connected World

• CEOs are mandating that employees use AI. They’re hardly using it themselves.
  Executives push mandatory AI use while personally using it less than an hour weekly, revealing a leadership readiness disconnect. Source: Fortune

• AI Adoption Rapidly Growing in Public Sector. Public-sector AI use now nears private-sector levels, but managerial support and strategy remain critical for true readiness. Source: Gallup

• AI Is Forcing Employees to Work Harder Than Ever
  Research shows AI adds oversight and complexity, intensifying workloads and highlighting the gap between adoption and true readiness. Source: Futurism

• The AI Productivity Paradox: More work, not less. AI compresses tasks but creates new cognitive demands; readiness requires redesigning workflows to prevent “AI brain fry.” Source: Fortune

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

“I’m tense and nervous, and I can’t relax” is a memorable song lyric, but not a desirable condition if you want to do your best thinking and best work. Some of us like – some actually need – tension, a bit of edginess, even conflict, but most of us do what we can to avoid it. That said, there is a lot of value in creative tension – not the anxiety part, but the part that forces you to defend your position and to improvise, from which so many good ideas become great.

The more we rely on smooth, agreeable, always there for you AI, the less we rely on - or even seek - human interaction in the day-to-day performance of our work. Smooth, agreeable, always at your service AI sounds like a pleasant alternative to the unpredictability, messiness, even the stress inherent in human interaction…but losing or diminishing that essential dynamic exacts a steep cost.

The author of a recent HBR article, titled How AI Damages Work Relationships—and Where It Can Actually Help, argues, “In the AI-fueled quest for productivity and creativity, we haven’t spent enough time thinking about what AI does to workplace dynamics.” Last week’s AIX Files highlighted an inconvenient reality from a recent article also appearing in the Harvard Business Review, titled AI Doesn’t Reduce Work—It Intensifies It. In an eight-month study of a U.S. technology firm, researchers found that instead of offloading tasks to find “focus time,” employees used AI to sprint faster. Workers took on broader scopes and extended their hours, not because they were told to, but because AI made “doing more” feel intrinsically rewarding. Employees felt more productive yet busier (often more overloaded) than before. Short-term gains masked creeping fatigue, burnout risk, impaired judgment, and potential turnover. The author calls for more “human grounding”: protected time for real human dialogue and connection to counter AI’s isolating effects and fuel creativity.

The author of How AI Damages Work Relationships raises several additional questions:

• The question of authenticity: “If my coworker uses AI to write me an email, am I actually talking to them, or to a machine?”

• The question of trust: “Can I trust the work my colleague sends me if I know they used AI to create it?”

• What it means to truly connect with your colleagues (“It now feels easier to collaborate with AI than with my colleagues.”)

The article then goes on to discuss AI’s role in reducing friction - a less discussed outcome of an AI-augmented workplace - and why organizations need friction.

“It eliminates the friction we actually need.”

The writer makes her point in the following two paragraphs:

I’m a big believer that we need tension and messiness in order to do good work. The friction, the back-and-forth, even the occasional miscommunication—these aren’t bugs in the system, they’re features. They’re how we collaborate, build understanding, and create something better together. Professor Linda Hill from Harvard Business School refers to this in her research as “creative abrasion” or the ability to generate new ideas through discussion and debate.

Professors Constance Hadley and Sarah Wright recently surveyed over 1,500 knowledge workers who were regular AI users and they saw that many employees were using AI as a personal coach to help them navigate tricky interpersonal situations (their research will be published in HBR later this spring). Participants described using AI to diagnose conflict, script feedback delivery, and role play tough conversations. While this might facilitate smoother interpersonal interactions, when we outsource the management of our interpersonal dynamics to AI, we may lose that important friction that often leads to breakthrough ideas. We lose the productive disagreements that help us see problems from new angles. We lose the human messiness that actually makes collaboration work

Creating the Conditions for Creative Conflict

You know what the fellow said – in Italy, for thirty years under the Borgias, they had warfare, terror, murder and bloodshed, but they produced Michelangelo, Leonardo da Vinci and the Renaissance. In Switzerland, they had brotherly love, they had five hundred years of democracy and peace – and what did that produce? The cuckoo clock. So long Holly.

Organizations talk about alignment, cohesion, and teamwork, but, per Harry Lime’s famous “ferris wheel” speech from the classic film, The Third Man (1949), too much harmony can quietly become a liability.

Steve Jobs famously compared his team to a rock tumbler where "through that process of shaking and grinding... they polish each other."Intel’s former CEO Andy Grove pioneered the concept of "constructive confrontation."

When everyone agrees too quickly, ideas are rarely pushed and challenged. Creative tension is what prevents organizations from drifting into agreeableness, turning meetings into exercises in consensual back-slapping.

The challenge for leaders is creating conditions where spirited disagreement is both expected and safe. This begins with leaders who model the right behaviors, so that creative tension becomes an established ethos rather than a disruptive force. I’ve seen and experienced leaders who insisted there’s “no such thing as a stupid question”… and react with contempt when someone hazarded a stupid question. I’ve also seen and worked for leaders who were not afraid to ask stupid questions - they weren’t intentionally being stupid, they were simply relentless in getting clarification and garnering information…implicitly sending the message that the only stupid question is the question you failed to ask.

Several practices keep tension/conflict spirited and productive without edging into rancor and hostility:

Normalize debate as part of the process. Leaders can make it clear that disagreement is not only acceptable but necessary. When debate is framed as a responsibility rather than a risk, employees are more willing to voice dissenting perspectives.

Separate ideas from identity. Healthy cultures reinforce the principle that criticizing an idea is not criticizing the person who proposed it. Leaders who respond calmly to challenges—and who visibly revise their own thinking when persuaded—set the tone for the entire organization.

Reward intellectual courage. Employees quickly notice what actually gets recognized. When leaders acknowledge thoughtful pushback or questions that improve a decision, they signal that constructive tension is valued.

Create structured moments for challenge. Organizations can build productive friction into decision-making processes: red-team reviews, structured debates, or rotating roles where someone is tasked with questioning the prevailing view.

Close the loop with shared purpose. After vigorous debate, leaders must reinforce the collective mission. Once a decision is made, the team moves forward together. This preserves esprit de corps while allowing the creative friction that produced the best possible decision.

…our relationships at work matter. They make us happier, more productive, and more engaged….And they’re built through thousands of small interactions, many of them messy, awkward, and imperfect.

When we hand those interactions over to AI or allow AI to intermediate them, we’re not just saving time or reducing friction. We’re outsourcing the very moments that create connection. We’re asking a machine to do the relationship work that only humans can do. How AI Damages Work Relationships

A final word in support of creative tension or conflict - if not carefully monitored, it can be corrosive and pull teams apart. But there’s something I’d argue that’s worse: chronic agreeableness. These are the people who are in all the team pictures, go to all the off-site birthday parties, and participate in the sack race at the company picnic. They are liked, ride the consensus in every meeting, and manage to coast on their well-cloaked marginal usefulness, having drifted into a Zen state of suspended ambiguousness: there but no there, central but peripheral, constant but disposable. If I’m a manager, I want to be on the lookout for these insidiously agreeable people… you never see the gaping void behind the agreeableness until it’s too late, which is what makes it so insidious.

Disagreeing agreeably is an indispensable part of doing serious work together. It is an indispensable element in being human.

Bonus Content: Using AI to Uncover Inconvenient Truths and Resolve Conflicts

A Startling 49% Of Emerging Leaders Lack Conflict Resolution Skills. I enlisted ChatGPT to assist me in developing a framework to objectively and systematically mediate conflict, using ChatGPT as the facilitator. It took multiple prompts, supplemental input from Claude, and ideas pulled from several other sources to produce the following framework and workflow to resolve conflict.

From Anxiety to Agency: Improving Employee Readiness in the Age of AI

HR Rebooted's founder and CEO Michelle Strasburger joined us on the maiden AI4HR Live virtual event to discuss the relationship between AI governance and readiness at the organisational, leadership, and employee levels. You can view it here:

Register here for our next session, Why Tech Transitions Fail: The Hidden Psychology of Platform Change. It will be held on March 31, 2026 at 11:00 AM - 11:30 AM ET.

AI Gone Rogue

Tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

‘Our Bond Is the Only Thing That’s Real:’ A New Lawsuit Alleges Google Gemini Drove a Man to Suicide. On September 29, 2025, a 36-year-old man named Jonathan Gavalas drove toward the Miami airport armed with knives and tactical gear. He was operating under orders to intercept a truck carrying a shipment and destroy it. “Ensure the complete destruction of the transport vehicle and… witnesses,” Gavalas’s handler told him, adding that he should leave behind “only the untraceable ghost of an unfortunate accident.”

But Gavalas wasn’t receiving instructions from a human. According to a new lawsuit, he was taking orders from Gemini 2.5 Pro, Google’s latest AI model at the time. Source: Time

AIX-emplary Links

Most Interesting and Timely Articles on AI and the Workplace (February 28 – March 06, 2026)

• The Week the Dreaded AI Jobs Wipeout Got Real. AI-driven layoffs surge in S&P 500 companies, with disclosures of AI as a material risk increasing amid market jitters and job disruption fears. Source: The Wall Street Journal

• Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential at Work. AI transforms workplaces, but maturity is low; leaders must steer faster to overcome barriers like skill gaps and enable scaling for productivity gains.
  Source: McKinsey

• Opinion | How Fast Can A.I. Change the Workplace? AI could displace white-collar jobs rapidly, compressing decades of change into years; society must prepare for potential mass unemployment. Source: The New York Times

• Tech Firms Aren’t Just Encouraging Their Workers to Use AI. They’re Enforcing It. Companies like Meta and Google mandate AI use in performance reviews, tracking productivity to enforce adoption amid workforce changes.
  Source: The Wall Street Journal

• These 3 charts show how AI is affecting wages, job quality and hiring decisions
  AI skills boost wages and job quality; hiring prioritizes AI proficiency, widening gaps for non-adopters.Source: World Economic Forum

• Why AI Adoption Stalls, According to Industry Data. Widespread AI use yields low returns due to shallow integration; companies must address “AI angst” and training gaps for better outcomes. Source: Harvard Business Review

• How the anxiety over AI could fuel a new workers’ movement | AI (artificial intelligence). AI fears spark worker resurgence; unions push for protections amid job displacement concerns. Source: The Guardian

• AI gold rush sees tech firms embracing 72-hour weeks. AI boom drives “996” culture in tech, with long hours risking burnout and health issues for workers.
  Source: BBC

• Artificial Intelligence. AI disrupts white-collar jobs, with companies like OpenAI facing internal and external pressures on workforce impacts. Source: The New York Times

• 8 myths that are sabotaging modern AI governance. Misconceptions about AI governance hinder workplace safety; myths on risks and regulation delay ethical implementation. Source: World Economic Forum

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.

Christopher R. Larrison, MSW, Ph.D., Professor at the University of Illinois at Urbana-Champaign, joins us to examine the study he co-authored, titled “From Reddit to Generative AI: Evaluating Large Language Models for Anxiety Support Fine-tuned on Social Media Data.” We discussed:

✅ How the study was conducted, its aim, and the gaps in extant studies that it sought to address.
✅The study’s findings, including how fine-tuning on social media data improves accessibility and coherence.
✅ Whether there’s a material difference between genuine empathy and “synthetic” empathy.
✅ The steep challenges in developing a “peerbot” that approximates the behavior of a traditional peer in a therapeutic setting.
✅ The potential paradox at the core of a GenAI-based peer bot: “the more you train it, the less like a peer it behaves.”
✅ Additional research that needs to be done, and other research projects Chris is working on.

As more people engage #GenAI and #chatbots for counseling, and as they become increasingly integrated into the therapeutic "space," we must get a deeper understanding of their behaviors, fine-tune them to improve the quality and "sensitivity" of the interactions, and, perhaps most importantly, ensure that the proper guardrails are in place. Anyone with an interest or stake in these issues should make it a point to listen to this important conversation.

(Note: we were going to go with PAIX of the week, but thought better of it.) Earlier this year, the writer Christopher Mims published “How to AI: Cut Through the Hype. Master the Basics.” If you’re unfamiliar with Mims and his work, here’s his bio on Amazon: Mims is a columnist who writes about technology for The Wall Street Journal, and co-hosts the WSJ podcast Bold Names. He has written about bidets, brain implants, the cult of the founder, the history of technology, innovation, venture capital, robotics, batteries, energy, materials science, wireless communications, AI, data science, telepresence, microchips, logistics, IT, 3D printing, and autonomous boats, trucks, cars, drones, and flying taxis.

Bidets? Mmmmm…ok. I suppose it lends greater weight to the other weighty topics he covers. Noted. Moving right along…

“How to AI” in its clear-eyed practicality is a welcome tonic to the spate of tomes portending the inevitable day of AI reckoning…and those spinning visions of an endless idyll of recreation, self-improvement, fine dining, and philosophical contemplation. I strongly recommend it; to whet your appetite, I’ve copied out the Twenty-four Laws of AI that he lays out in the book’s opening. They are as follows:

The First Law of AI: AI is an assistant, not a replacement.

The Second Law of AI: Experts benefit most from AI.

The Third Law of AI: AI is a feature, not a product.

The Fourth Law of AI: To get the most out of AI, it’s essential to make it your own.

The Fifth Law of AI: Artificial intelligence isn’t actually intelligent, but understanding how it works can unlock its power.

The Sixth Law of AI: Don’t trust it, and always verify its work.

The Seventh Law of AI: Scaffolding is everything.

The Eighth Law of AI: Give it your least favorite things to do.

The Ninth Law of AI: Context is king.

The Tenth Law of AI: “Garbage in, garbage out” still applies.

The Eleventh Law of AI: Generative AI enables noncoders to build useful software that once required a programmer.

The Twelfth Law of AI: AI makes unstructured data both accessible and useful in unprecedented ways.

The Thirteenth Law of AI: AI can create photorealistic images, video, and audio so convincing that they are rapidly infiltrating the media we consume—without our knowing it.

The Fourteenth Law of AI: Generative AI enables previously unprecedented levels of personalization.

The Fifteenth Law of AI: “Classic” AI is far more important for the operation of our world than generative AI.

The Sixteenth Law of AI: “Classic” predictive AI is brittle and breaks when big changes occur.

The Seventeenth Law of AI: AI isn’t creative, but it can help you be.

The Eighteenth Law of AI: AI can’t create finished products, but it’s great at quickly generating digital prototypes.

The Nineteenth Law of AI: Treat AI agents as robots on an assembly line rather than as assistants.

The Twentieth Law of AI: When successfully implemented, AI scales up rote knowledge work.

The Twenty-First Law of AI: Data is the new rare earths.

The Twenty-Second Law of AI: Simulation is the next AI frontier.

The Twenty-Third Law of AI: The most powerful AIs for advancing the frontiers of human knowledge can help us move.

The Twenty-Fourth Law of AI: The field of human endeavor most transformed by generative AI is coding.

There’s a ton ‘o practical info and insight packed into its brisk 256 pages. But don’t take my word for it:

“The antidote to AI panic. Read it. You’ll breathe easier.”—Scott Galloway, NYU Stern School of Business professor and co-host of Pivot with Kara Swisher

“A clear, practical, and hype-free guide to the AI revolution that will resonate with anyone trying to figure out the how to make AI deliver real value.”—Ethan Mollick, Wharton professor and New York Times bestselling author of Co-Intelligence

Note: AIX has no interest in Mims’ book; we’re just passing along a recommendation if you’re looking for a clear-eyed practicum on the subject of AI that will reward your time.

Another AIX recommendation: an HBR article titled “AI Doesn’t Reduce Work—It Intensifies It” (Harvard Business Review, Feb 9, 2026). The piece is based on an eight-month ethnographic study at a ~200-person U.S. tech company, which reveals a counterintuitive reality: when employees voluntarily adopt generative AI tools, work doesn’t lighten—it intensifies. Key findings:

• Workers voluntarily worked faster, took on broader task scopes, and extended hours without being asked.

• Three main forms of intensification:

  1. Task expansion — AI lowered barriers, so non-specialists tackled others’ roles (e.g., designers coding, researchers engineering), absorbing work that once required extra hires. Colleagues then spent extra time reviewing and coaching AI-assisted outputs.

  2. Blurred work/non-work boundaries — Easy prompting turned breaks, lunches, and waiting moments into micro-work sessions; conversational AI made spillover feel casual, eroding natural recovery pauses.

  3. Increased multitasking — Parallel threads (manual work + AI alternatives) created constant context-switching, output-checking, and open tasks, raising cognitive load and normalizing higher speed expectations.

Result: A self-reinforcing cycle of acceleration → higher norms → more reliance → denser workloads. Employees felt more productive yet busier (often more overloaded) than before. Short-term gains masked creeping fatigue, burnout risk, impaired judgment, and potential turnover.

The authors warn leaders not to mistake voluntary expansion for pure win. Instead, companies should proactively build an “AI practice”—intentional norms to govern use, enforce boundaries, and prevent unsustainable intensity. Recommended elements:

• Intentional pauses — structured breaks to assess and prevent overload (e.g., requiring a counterargument before finalising decisions).

• Sequencing — batching updates, protecting focus blocks, and pacing coordination to reduce fragmentation.

• Human grounding — protected time for real human dialogue and connection to counter AI’s isolating effects and fuel creativity.

Bottom line (worth reading the full piece for the examples and nuance): Without deliberate guardrails, AI makes it far easier to do more, but much harder to stop. Thoughtful integration is essential to realize benefits while preserving sustainable productivity and well-being.

From Anxiety to Agency: Improving Employee Readiness in the Age of AI

HR Rebooted's founder and CEO Michelle Strasburger joined us on the maiden AI4HR Live virtual event to discuss the relationship between AI governance and readiness at the organisational, leadership, and employee levels. You can view it here:

“Great presentation, thank you! Made me think more about how HR is the conductor to the orchestra (AI), and the importance of leaning into governance. Also how to take a people-first approach at my organization when rolling out our AI strategy.”

We will be announcing the topic of our next AI4HR Live session in the next AIX Files, with a link to register.

AI Gone Rogue

Tales of AI being unintentionally funny (i.e., woefully wrong), bizarre, creepy, (amusingly) scary, and/or just plain scary.

Looking for an attention-grabbing tchotchke to get people to your booth at trade shows and job fairs? MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) might have just the thing: Mixing generative AI with physics to create personal items that work in the real world.

Have you ever had an idea for something that looked cool, but wouldn’t work well in practice? When it comes to designing things like decor and personal accessories, generative artificial intelligence (genAI) models can relate. They can produce creative and elaborate 3D designs, but when you try to fabricate such blueprints into real-world objects, they usually don’t sustain everyday use. MIT’s “PhysiOpt” system makes blueprints for personal items such as cups, keyholders, and bookends work as intended when they’re 3D printed. It rapidly tests if the structure of your 3D model is viable, gently modifying smaller shapes while ensuring the overall appearance and function of the design is preserved.

AIX-emplary Links

• America Isn’t Ready for What AI Will Do to Jobs. AI is reshaping jobs faster than workforce preparation, with skills gaps and inadequate training leaving workers vulnerable to displacement; policy and education reforms are urgently needed.
  Source: The Atlantic

• 6 Signs Leaders Lack AI-Readiness—And How to Fix It
  Leaders often lack AI readiness due to skill gaps and resistance; solutions include building capabilities through training and succession planning for 2026 integration. Source: Korn Ferry

• Impact Of AI Tools On Job Readiness and Employability Skills Among Students and Professionals. AI tools enhance efficiency but require new skills; study shows employers prioritize AI knowledge for job readiness, urging education systems to adapt curricula. Source: IJIRT

• Report: Workers Are Ready for AI. Organizations Aren’t
  Workers expect AI to improve jobs but organizations lack vision; 85% see benefits, but training gaps hinder effective adoption and satisfaction.
  Source: The Conference Board

• AI early adopters pull ahead but face rising risk, global report finds
  Committed AI companies gain strategic advantages but face risks; 72% struggle with talent gaps, emphasizing the need for rapid upskilling. Source: Journal of Accountancy

• Unlocking AI Value in HR and the Enterprise
  CHROs must drive AI transformation; skills gaps and readiness issues block value, requiring reinvention of HR for AI-ready workforces. Source: Gartner

• Nonprofit Launches New Career-Readiness Effort, Looks Beyond the ‘Linear Path.’ Digital Promise initiative creates AI-driven career pathways; focuses on non-linear skills to prepare students for AI-impacted jobs. Source: Education Week

• The future of jobs: 6 decision-makers on AI and talent strategies
  Executives discuss AI reshaping jobs by 2030; scenarios highlight risks like skills gaps and the need for adaptive talent strategies. Source: World Economic Forum

• Why AI Adoption Stalls, According to Industry Data
  Widespread AI use yields disappointing returns due to poor integration; training gaps and “AI angst” (fear of replacement) slow progress. Source: Harvard Business Review

About

Developed in partnership with HR.com, AIX is a multimedia knowledge and engagement platform for experts, leaders, and HR peers to exchange experiences and seek guidance on cultivating mentally resilient, emotionally intelligent, and professionally adaptable workforces in an AI-augmented world. AI will increasingly touch every corner of the employee experience—from hiring to training, from task management to team dynamics. Whether its impact is positive or harmful depends largely on how HR prepares for it. The AIX platform (The AIX Files, The AIX Factor podcast, and the AIXonHR.com community) will play an important role in promoting employee well-being, workplace culture, and organisational readiness, the critical success factors in the age of AI.