Employers nationally have quickly adopted AI products throughout the hiring process. It is easy to see why: A recruiter facing thousands of applications for a single posting cannot meaningfully review every single one. AI tools can sort, rank, and surface the strongest candidates in seconds, all before any recruiter or human resources representative reviews an application.

The same technology can also widen a candidate pool. AI can surface qualified applicants who may have been missed by a keyword search and reduce the inconsistency created when different hiring managers apply their own unwritten standards to different stacks of resumes. AI does not simply make hiring faster; it can make hiring more consistent and predictable. On its face, this can look fairer than a purely human process riddled with its own unexamined biases.

AI is, however, only as smart as the rules a human builds into it. The uncomfortable truth about AI and its integration within the employment relationship is that employment law risk does not generally flow from a rogue algorithm taking a step an employer never intended. Instead, the risk comes from a hiring team using an AI system as part of its decision-making process but not completely understanding it.

AI hiring: A popular litigation target

AI hiring tools create the opportunity for expensive and damaging class actions. One such high-profile case, Mobley v. Workday, Inc., tests whether HR technology vendor Workday violated state and federal anti-discrimination law by creating an AI-powered applicant screening tool that discriminated on the basis of age and disability. The case remains in discovery after the Court granted preliminary collective certification, paving the way for potentially millions to join the collective action.

Similarly, in Kistler v. Eightfold AI Inc., another HR vendor is currently facing suit for allegedly acting as an unregistered consumer reporting agency, scraping data on more than 1 billion workers, and scoring applicants on a 0-to-5 “likelihood of success” scale, without making the requisite disclosures under the Fair Credit Reporting Act.

While the third-party vendor may create these screening tools, employers utilizing biased tools may also face liability. In Harper v. Sirius XM Radio, LLC, an employer faces multiple discrimination claims premised on its use of an AI system that allegedly evaluated candidates using data points functioning as unlawful proxies for race. If the Harper case teaches employers anything, it should be that using third-party vendor tools will not insulate them from litigation and, ultimately, liability.

AI does not eliminate accommodation needs

Overreliance on AI tools without human involvement creates particular exposure to claims of disability discrimination. This reality led the DOJ and EEOC to issue guidance in 2022 on how employers’ use of hiring technologies may violate the ADA. For example, a hiring tool built to predict “who will be a good employee” by comparing candidates to current successful staff can exclude people with disabilities, simply because these individuals were underrepresented in the “good employee” comparison pool.

Likewise, tools like facial or voice analysis assessments can screen out applicants with certain disabilities like autism or speech impairments without providing them the opportunity to request an accommodation. Information about an assessment and how to request an accommodation should be visible before a candidate starts the evaluation. A human-reviewed alternative can serve as a reasonable accommodation, allowing employers to evaluate these candidates fairly. Without a meaningful accommodation process, employers risk unnecessarily, and potentially unlawfully, excluding disabled but qualified applicants.

The shifting regulatory framework

Governmental actors have also addressed the rising use of AI in hiring. As referenced earlier, Biden-era DOJ-EEOC guidance highlighted how hiring technology may violate the ADA. A subsequent executive order has since withdrawn this guidance, but a shift in administration may also reignite federal interest in the topic.

In recent years, cities and states have acted where the federal government has not. These efforts have focused on disclosing AI use, mandating internal audits and ensuring meaningful human involvement in decisions. For example, New York City requires a bias audit before an employer can use an “automated employment decision tool,” plus public disclosure and candidate notice. Illinois is taking multiple actions, one for AI video interview analysis and another for discriminatory impact generally. California, Colorado, New Jersey and Oregon are among the states taking legislative or administrative action in light of the federal absence. Short of significant congressional action, employers should prepare to face a patchwork of state and local regulation on this topic.

Candidates are adapting too

The AI arms race runs in both directions. Candidates increasingly prepare their submissions with AI screeners in mind. For example, many applicants are adding hidden white-on-white text meant to manipulate AI screening tools. While invisible to the human eye, these so-called prompt injections surreptitiously direct the AI screener to give the candidate a favorable ranking regardless of the content of the resume. A recent Duke University study discovered that 1% of roughly 200,000 resumes within the dataset contained prompt injections.

Candidates are also using AI to draft everything from cover letters to coding samples. Employers seeking to discover and exclude AI-drafted materials should do so cautiously. Technologies that attempt to catch AI-drafted materials are flawed and may create their own exposure. Stanford research found leading AI detectors misclassified more than 60% of essays written by non-native English speakers as AI-generated, because the parameters reward linguistic sophistication that native speakers produce more easily. Future tools may close that gap, but today’s tools are unreliable enough that using them may cause more harm than good.

Steps to reduce risk and improve outcomes

Given the rise of litigation around AI-powered hiring, employers should inventory every tool that screens, ranks or recommends. Yes, that includes the ones you’ve used for years but never labeled as “AI.” From there, leaders should ask difficult questions of their HR technology teams, both internal and external, to understand the parameters of their AI-powered decision-making processes.

Once leadership understands the parameters built into the system, employers should ensure that the system works as desired. Employers benefit from building tools that periodically audit the system’s results to identify potential bias. Given the prompt injection risk, employers should safeguard their systems by stripping formatting before any application document goes through an AI reviewer.

Beyond the internal technological solution, employers also benefit from disclosing the use of AI after the initial screening of application materials. Employers that inform applicants what AI tools are in use and how to request an accommodation, if needed, allow qualified candidates with disabilities to fully and meaningfully participate in the hiring process.

Taking these steps will reduce litigation risk and prepare employers for the increasingly complex patchwork of state and local regulation. Moreover, these steps will both improve efficiency and expand the number of qualified candidates in an employer’s applicant pool.

The post Recruiting and hiring: Where AI delegation meets exposure and exclusion appeared first on HR Executive.

“AI adoption most often breaks down at the onset of change: the point where operating reality, data context and human behavior meet,” she says. Capable platforms are not in short supply, she observes, but adoption becomes meaningful only when people understand the problem being solved, trust the knowledge behind the tool and know how to apply its output in their day-to-day work.

That is why she considers adoption rarely a technology problem. What makes AI different from previous waves of workplace technology, in her view, is that it exposes organizational gaps faster—because it changes not only the system people use, but how they ask questions, interpret answers and make decisions. The shift asked of employees is considerable: from searching manually to prompting effectively, from relying on static documents to validating generated responses, and from treating technology as a separate system to embedding it into the operating rhythm. “That transition requires confidence, ownership and repeated practice,” she says.

See also: Is prompt engineering dead? One expert describes what HR should focus on instead

What a pilot revealed

Tay draws on a generative AI chatbot pilot she supported for a finance function. The technology itself was already available and could be scaled by onboarding the relevant knowledge base. She found the real work lay in making that knowledge usable.

Reviewing the pilot’s queries, she observed that how closely a user’s prompt matched the language of the underlying documents was a key determinant of answer quality, and she converted knowledge files into more reliably readable formats to serve a wider audience. The pilot ultimately performed at 89.8%, clearing the recommended 80% threshold, but that was not the lesson she took away. “Adoption depended on knowledge, design, access, governance and user readiness, not just the AI engine,” she says.

The pilot also surfaced what she describes as a translation gap—an early warning sign that a workforce is not yet ready for the tools being rolled out. A user might ask about “milestones” while the underlying document describes an “operating cycle.” The answer suffers, but not because the technology has failed. “That is not a technology failure; it is a translation gap between business language, documentation structure and AI retrieval behavior,” she explains.

Other warning signs follow a pattern: employees with access to a tool who revert to old ways of working because they are unsure when to trust it; low-quality prompts; inconsistent interpretation of outputs; over-reliance without validation; or outright avoidance because users cannot see how the tool fits their responsibilities. In finance, she notes, the stakes are higher because outputs often inform reporting, control reviews and stakeholder decisions.

In her view, the cause is underinvestment in change management. Organizations concentrate on the rollout while spending less time on use-case design, prompt guidance, documentation quality, training and governance. Her response is to treat AI adoption as an operating model change: clarifying what the tool should and should not be used for, who owns the knowledge, how quality is reviewed and how users build confidence over time.

She is careful, though, not to frame imperfect starts as failure. “Most products and tools are not released in a perfect or ideal state. From a progress standpoint, starting, failing safely and learning quickly are far better than avoidance or remaining with the status quo,” she says.

Accountability stays human

If one principle anchors Tay’s view of responsible adoption, it is accountability. “When AI fails, creates risks or leads to a poor decision, humans remain accountable. You cannot sue an AI in the courtroom,” she says. “AI may support judgement, but it does not replace responsibility.”

This, she argues, is where domain professionals earn their place in AI adoption. Technologists build the platform and enable the capability; domain experts determine whether the output is meaningful, the interpretation sound and the recommendation appropriate in context. In the pilot, her role was not to tune a model technically but to assess whether responses made sense to enterprise users asking real questions—gathering user champions, reviewing both acceptable and weaker responses, and identifying where document structure and prompt wording needed better alignment. “That is the contribution of a domain expert: knowing what ‘good’ looks like in practice,” she says.

The organizations that succeed at scale, she believes, will combine technology expertise with strong domain ownership—generating not just answers, but trusted, decision-useful ones.

The post ‘You cannot sue an AI’: Why adoption comes down to people, not platforms appeared first on HR Executive.

Deputy’s 2026 Shift Pulse Report, which analyzed 731,805 anonymous in-app responses from shift workers, found that millennials and Gen X felt the least positive after their shifts. Millennials came in at 74.52%, followed by Gen X at 75.92%. By comparison, Gen Alpha reported the highest score at 88.88%, while Gen Z came in at 78.42%.

How do shift workers feel when they clock out?

Deputy’s Shift Pulse tool asks workers at the end of a shift to choose one of five responses—Stressed, Frustrated, Okay, Good or Amazing. The company classifies “Good” and “Amazing” responses as positive sentiment and “Stressed” and “Frustrated” responses as negative sentiment. The data closes in on a realtime view of how workers feel immediately after work.

Feeling the least positive are Gen X and millennials, cohorts Deputy characterizes as the “mid-career” core of the workforce. The report points to a “squeeze” on these workers, with family, caregiving and financial commitments weighing on daily work experiences. Deputy also identifies cost-of-living pressure, the need to balance multiple jobs and demand for stable income and flexible schedules as triggers for these generations.

Gen Z presents a contradiction because these workers ranked second in positive sentiment but also had the highest negative-sentiment rate of any generation, at 8.04%. “This combination of high highs and high lows points to a cohort that is emotionally invested in work but also more exposed to its frustrations, frontline customer roles, poly-employment and pressure around hours and pay,” write the report authors.

Read more: Employee engagement sinks as workers struggle with digital overload

Does positivity tie to retention?

Gallup’s U.S. workforce studies found that 61% of Gen Z employees said they were either actively looking for another job or watching for opportunities, compared with 57% of millennials. Taken together with Deputy’s findings, this may hint that feeling good after a shift and feeling committed to an employer are distinct outcomes.

Gallup found that just 31% of U.S. employees were engaged at work in 2025, the same as in 2024 and well below the 2020 high. Engagement dropped the most among Gen Z and millennials. Gen X also saw a drop while engagement among Baby Boomers held steady. Gallup tied the decline to some basic workplace issues. Researchers believe that employees were less clear about what was expected of them, less likely to feel that someone at work cared about them and less likely to see opportunities to learn and grow.

“Actionable drivers are at the heart of this divide,” according to the Deputy report. Positive sentiment is linked to “autonomy, predictability, robust team cohesion and genuine interpersonal connections,” while understaffing, physical exhaustion, low-control work and high emotional demands can weigh on morale.

The post Gen X and millennials feel least positive after work, new research finds appeared first on HR Executive.

“It was a little rough in the beginning. No one likes an org change, especially when you’re losing great people,” says Williams, chief people officer at Match Group, the parent company of Tinder, Hinge, Match.com and more than a dozen other dating brands. “But what we found is people adapted very quickly once they saw the value in it.”

In an exclusive interview with HR Executive, Williams describes the flattening as an effort to align “the doers” with the people making decisions, so employees closer to the work would have a voice in shaping it. Additionally, as title and tenure become less reliable indicators of authority, Match needed clearer ways to measure employee growth.

‘Skill over tenure’

Williams believes in focusing on talent and skills instead of how long an employee has stuck around with the org. “We’re moving away from tenure and title being our proxies for value, and we’re focused on outcomes,” he says. “It doesn’t have a title attached to it.”

That idea now runs through nearly every part of how Match Group develops people, from how it pays them to how it mentors them to how it brings them in the door.

Match Group’s compensation bands, already in place before the restructuring, allow employees to grow their pay through skill acquisition and performance rather than waiting for a promotion. Williams says that structure has become more important now that there are fewer management roles to advance into.

This pay structure allows for compensation growth through skill acquisition, outcomes and performance, and “actually brings us closer to pay for performance than a title change would, or how long you’ve been sitting in a role,” he says.

Williams is also direct about why that matters for people who have no interest in managing others. “A lot of people don’t want to be managers of people and teams. They want to remain individual contributors,” Williams says. “This allows for that.”

Match Group’s flexible mentoring program

Match Group still runs its original mentoring model, a six-month pairing built around a specific skill someone wants to develop. But Williams’ team is now building what he calls a flexible mentoring program, designed around skill rather than schedule or hierarchy.

“It is not structured or time-bound. It is not title-bound,” he says. “You have the ability to go into a system and choose someone who has a higher title, a lower title. They could be across the other end of the world, and that engagement could be two weeks or two years, where the focus is you building that skill set, and you get to decide.”

He expects it to become one of the company’s defining offerings. “We truly believe that’s going to be one of our signature programs, as people want to acquire skills,” he says. “I already know that there’s going to be a lot of folks looking for AI training, from engineers as well, just so they can apply it to their daily jobs.”

The idea for turning that flexible program into an actual internal tool originated at the top. “Moving the mentorship flex program to a card stack that looks like our app came out of vibe coding from our CEO,” Williams says. “My team took it over. It was like, we’re gonna put this in production. Let’s go build this out.”

Rotations built for context

Match Group’s Corporate Rotational Analyst Program differs from a traditional rotational track, Williams says. Rather than moving participants among roles, the program is designed to give them a multidisciplinary understanding of the company’s work. “Having a broader understanding of how what you do, what you specialize in, impacts other areas of the business will help you move faster, will help you think with an understanding lens, so everyone’s rolling in the same direction.”

The program started with a single cohort inside Tinder and is already expanding to other brands for next year. Williams has a personal stake in the underlying idea, having left HR early in his career for two years to work in sales. “It was not popular that I took time out of HR to go do a sales job,” Williams says. “My CHRO said, I don’t want you to go, because you’re not gonna come back, you’re gonna go over there and make a ton of money and not come back.”

He did come back. “When I did get back to HR, I wanted to make sure everything we did succeeded, even more so than before I left. And it was because of that support.”

Thoughtful employee development

Match Group’s internship program had required at least two years of prior work experience for most roles, effectively locking out candidates straight out of school. “We had to shift our organizational model to meet where the next generation talent is, and they’re coming right off campus,” he says. The company dropped the requirement and reopened the internship program. Roughly 20,000 people applied for about 27 slots. “It’s definitely working,” Williams says.

Asked whether the development programs were designed before the restructuring or in response to it, Williams didn’t claim a strategy that predated the disruption. “It was a little bit of chicken and egg,” he says. “A lot of it was being driven by employees, them saying, hey, if this happens, this is what the need now looks like.”

That feedback runs through a few specific channels: a twice-yearly engagement survey cycle, listening circles, and a direct line to the CEO called DM Me. “He reads every one of them, and you can categorize what your feedback is,” Williams says.

Williams runs his own version internally, sessions called Life at Match, focused entirely on employee experience.

“There was no master plan in the background,” he says. “Those who will do well are taking a step back and saying, we actually need to start with a blank sheet of paper and do some really critical understanding, some research, some listening, to understand what our next phase is.”

AI treated as a skill to build

Match also gives people room to build AI skills. The company ran a global AI Day where employees set aside their regular work entirely. “Everyone [went] pencils down on any work that was being done, and everyone just focused on any project that did not require an outcome,” he says. “We had over 400 submissions come out of AI Day, globally.”

He’s explicit about the promise attached to that experimentation. “We were very clear that the goal was not to reduce headcount as a result of AI,” Williams says. “There has to be a human at the center of the work that we do. AI is going to help us propel the work, but it should not be doing the work for us.”

His own HR team put that to use directly, building a workaround for a data security bottleneck using Claude. “This saved many people many hours of cobbling different spreadsheets together,” he says. “At the end of the day, it’s a human making the decision. They’re bringing the data and we’re verifying, and it’s worked out really well.”

Williams sums up his philosophy with a phrase he returns to more than once: talent over tenure. “That is the wave that most people will need to move to in order to be competitive in the marketplace,” he says. “It’s something I’m extremely passionate about.”

The post How Match Group’s CPO rebuilt talent growth after flattening the org appeared first on HR Executive.

The system has more than 240 physicians who serve half a million patients in California, Rhode Island and New York. Since joining the organization three years ago, McGee has overseen its transformation into the first AI-native health system in the country through its tool ScopeAI, which performs a clinical investigation prior to a physician connecting with the patient, offering recommended diagnoses and care plans.

“We’ve learned a lot about what it takes to lead this kind of change,” McGee says. Its work that has been rooted in listening and emphasizing shared purpose—discovering the most significant challenges facing clinicians and leveraging AI to address them.

“Critical to our approach is that we are not looking to replace doctors, but to use AI to reduce the cognitive load and give physicians better information to make better decisions,” McGee says. “Ultimately, AI should make healthcare more human, not less. If we can take work and cognitive burden away from our clinicians, they can spend more time doing what only they can do—listening, exercising judgment and caring for the person in front of them.”

See also: AI adoption is a people challenge before it’s a technology one

Building a strategic foundation

Any successful transformation, McGee says, needs to start with a common purpose. His team had to ensure they saw that AI transformation could ultimately help them deliver better care to patients.

“Expanding access to high-quality care is our north star,” he says. “AI is not the goal; it’s a tool to help us get there.”

Breaking down resistance to AI adoption was furthered by several strategies. For instance, super users—early-adopter physicians—were integral to helping leadership design workflows, piloting the tool and becoming an advocate for it among their colleagues. That work was complemented by product and engineering teams working “at the elbow” with physicians.

“These working relationships build trust, and trust allows us to have honest conversations when things aren’t working,” McGee says.

The organization also recently launched its Clinical AI Advisory Council, comprised of Akido medical directors and outside clinical experts, who together can help the organization “challenge assumptions and stay grounded in what actually matters in patient care.”

Measuring the success of ScopeAI involves both quantitative and qualitative efforts. Are physicians using optional features and finding new ways to use it? And do they communicate that they both understand and believe in the product roadmap?

“The ultimate measure,” however, McGee says, is patient care. “Are we improving access and quality? Are we delivering better outcomes? Ultimately, AI adoption only matters if it translates to better patient care.”

AI adoption across industries

Akido’s experience integrating technology highlights a reality that translates across sectors: AI adoption isn’t just a technology challenge; it’s a leadership challenge.

The technology certainly matters, but so does change management. And leadership is the connection between the two, McGee says.

“For the C-suite, this needs to be viewed as a business transformation, not a technology implementation,” he says. “The opportunity isn’t to add AI to how we work today, but to use AI to fundamentally rethink our business and how the work gets done.”

McGee envisions a parallel between healthcare and broader organizational AI adoption: Patients want more quality time with their doctors, just like employees want more quality time with their leaders.

Yet too often, that time is muddied by “lower-value work.”

“If AI can take away some of that burden, it creates more capacity for what matters, like coaching, feedback, development and helping the business navigate change. If we get this right,” he says, “AI should make work more human, not less.”

Learn more about leading through AI transformation during a keynote at HR Tech from Kelly Jones, chief people officer at Cisco. In her Oct. 20 address, Jones will speak to how HR can lead with clarity and resilience amid ongoing transformation. Click here to register.

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These questions matter, but they overlook a more fundamental issue. AI is not simply changing the work we do; it is changing the way we think while we do it. As psychologist B.F. Skinner suggested, the real problem is not whether machines think, but whether people do. Skinner’s theories of operant conditioning and environmental control explain how modern AI platforms act as advanced behavioral modification engines. As AI automates tasks, it changes the stimulus-response loops for human workers, leading to unforeseen behavioural changes.

Every major technological revolution has altered human behavior. Email changed our expectations of responsiveness, smartphones reshaped our attention and social media influenced identity and belonging. AI is beginning to influence something even more fundamental: judgment.

As a psychologist, I believe this cognitive atrophy, where over time workers lose their critical thinking and basic problem-solving skills, represents one of the most important organizational challenges of the next decade. Not because AI will replace human thinking, but because if we are not intentional, people may slowly choose to do less of it.

See also: How HR can spot—and solve—your team’s cognitive overload

Most organizations are investing in AI and digital literacy. Few are investing in behavioral AI literacy, which is the ability to recognize how AI influences human cognition, decision-making, motivation and relationships and to consciously design work that strengthens rather than diminishes these capabilities. The technology itself is rarely the greatest organizational risk. Our psychological response to it is.

Cognitive offloading: When convenience becomes dependence

Psychologists have long understood that humans naturally conserve cognitive effort—we use calculators instead of mental arithmetic and satnav instead of mental maps. Whether drafting reports, writing emails, summarizing meetings or generating strategy, none of these activities are problematic in themselves. To develop expertise, people need to regularly practice cognitive processes, and learning is not simply acquiring answers; it is exercising judgment. The challenge is in ensuring AI removes unnecessary effort without removing opportunities to think.

The AI confidence illusion

AI communicates with extraordinary confidence, but confidence and accuracy are not the same thing. Psychologists have repeatedly demonstrated that people mistake fluency for expertise. Information delivered clearly often feels more believable, regardless of whether it is correct. AI exploits this cognitive shortcut, not intentionally, but inevitably. When outputs appear polished, employees become less likely to question them.

When looking for differentiated talent in the future, those who (in addition to being AI users) are AI challengers will be in high demand.

The erosion of productive struggle

“Education is not the learning of facts, but the training of the mind to think.”—Albert Einstein

One of psychology’s most robust findings is that learning requires effort. The moments we struggle are often the moments our brains are building new capability. If every difficult task becomes an AI task, organizations will need to maintain opportunities for employees to develop resilience, creativity and critical thinking.

Identity in an AI workplace

For many professionals, competence forms part of identity. When AI performs tasks once associated with expertise, employees may begin questioning their own value. The challenge for organizations extends beyond reskilling; it includes helping people redefine professional identity around judgment, curiosity, ethics and creativity, qualities that remain profoundly human.

Impact on relationships

Psychology suggests that relationships are critical to sustained motivation, building trust, collegiate teams and performance. Behavior creates culture, and where employees increasingly seek knowledge from AI rather than colleagues, to maintain relationships, organizations should actively create opportunities for mentoring, collaborative problem solving and the formation of connections.

The new competitive advantage

Often considered to be one of the leading philosophers of the late 19th century, William James theorized that “it is not intelligence that counts most, but the ability to direct it.” That becomes relevant when applied to AI.

For decades, organizations rewarded knowledge, and in a future where knowledge is more abundant, we may see a move toward rewarding judgment, which remains scarce. Organizations that thrive will likely be those that deliberately cultivate independent thinking alongside AI adoption and track human behavior over time.

What organizations should do next

Behavioral AI literacy is not another technology program; it is a people strategy. It means asking different questions, such as whether employees are still developing judgment, where AI should accelerate work and deliberately slow thinking while being cognizant of cognitive debt—the long-term cost of outsourcing too much thinking. We can then move to consider the human skills paradox, how to reward critical evaluation rather than rapid acceptance, how people leaders can model healthy challenge of AI-generated outputs, preserve learning and teach people to think with AI rather than simply how to use it.

With the AI revolution transforming cognition, we have an opportunity and responsibility to ensure that, in becoming more AI-enabled, organizations do not become psychologically less capable. This may be the real future of work.

Introducing the “P-R-I-M-E” framework

Alongside plans for AI adoption, this roadmap provides five adjacencies:

1. Personal impact on relationships: How to enable continued collaborative thinking and trust

Providing guidance on when AI should be used first and when people should think independently or ask colleagues, e.g., “Human-first” should be encouraged for brainstorming and complex decision-making, while offering opportunities for mentoring, connection, cross-team collaboration and knowledge sharing. Reverse mentoring is helpful to have AI super-users (likely AI natives who are earlier in their careers) lead “what I learned from AI this month” sessions, explaining what AI generated, how they critically evaluated it and what they learned from it, particularly what they would have missed without using their own judgement. This shared reasoning can build collective intelligence while also raising the profile of junior staff.

2. Reward: How to recognize reflection and judgement

Helping managers recognize those who ask questions to improve decisions and treat AI as a tool to interrogate rather than always rely on it for the final answer. Employees could be asked to share examples of having identified AI errors, improved recommendations or challenged AI-generated outputs to reinforce the expectation of using independent evaluation. Recognizing efforts to collect appropriate evidence, consider alternatives, involve stakeholders and explain reasoning will be a subtle move toward assessing decision-making as well as outcomes. It will shift language such as “that paper was excellent” to “your judgment interpreting the information and identifying the implications added value for the client.”

3. Insight: How to assess critical thinking/judgement capability in those who become over-reliant on AI

Creating “find the flaw” training could provide an AI-generated document for people to identify inaccuracies and provide evidence of having used independent verification and judgement. Seeking explanation for why the reasoning presented is weak or incomplete, rating confidence in each AI-generated conclusion and justifying it, and correcting the work using reliable sources can also help. Similarly, introducing a “think before prompt” guide, providing examples to encourage employees before using AI to spend a few minutes summarizing their own assessment, possible solutions and assumptions used, before then testing those assumptions using AI.

4. Measurement: How to measure employee capability to improve assumptions generated by AI

Testing genuine understanding as well as AI-assisted drafting, by creating a measurement or marking system to recognize improved structure, reliability of sources, analysis, reflection on AI limitations and clearer advice. The key will be maintaining opportunities to practice writing, analysis, problem-solving and collaboration. Some organizations track prompt to acceptance time as an indicator of critical scrutiny applied. This could be taken one step further by looking for evidence of verification and edits that demonstrate critical evaluation.

5. Ethical reflection: How to ensure employees remain cognizant of ethics

Creating ethical training that enables employees to identify risks in AI-drafted material associated with confidentiality, data protection and potential bias, to increase focus on professional responsibility.

Creating a framework in this way is important to preserve organizational reputation often built on the foundations of sound judgement and trust. This is not about slowing AI adoption; it is about ensuring it is even more valuable by remaining intentional about it enhancing judgement rather than diluting it.

The post What happens when people stop thinking: The case for behavioral AI literacy appeared first on HR Executive.

A recent study by Robert Walters found that nearly 60% of Gen Z employees surveyed prefer to stay as individual contributors rather than moving into middle management. Similarly, Robert Half research reports that 40% of Gen Zers would only take a promotion if it meant they didn’t have to manage a team.

Why are workers so resistant to managing today? Technology is one factor, says Ryan Starks, vice president of workplace and AI & transformation at AI-powered team performance platform Rising Team.

“Gen Z grew up with tech that lets them multiply their own output solo, so building and leading a team isn’t the default instinct it used to be,” he says.

Equally influential is the burnout individual contributors are witnessing among middle management, particularly as organizations slash middle-management layers to operate more leanly, leaving those who are left worried about job security while struggling to keep up with bigger workloads. One recent study found that more half of managers report experiencing burnout.

“ICs are watching senior leaders get buried in constant org change and some are deciding people leadership just looks like a headache,” Starks says.

In the age of AI, manager support is needed

Another factor weighing heavily on the shoulders of today’s managers is AI.

Unlike previous tech transformations, AI is integrating in workplaces from the bottom-up, instead of top-down, putting more pressure on managers to redesign work and build automation into everyday processes.

“Employees aren’t experiencing AI transformation from the C-suite; they’re experiencing it through their manager,” Starks says.

Too often, however, managers don’t get the support they need. Many organizations are throwing new managers into their roles with “little more than a generic classroom-like experience.”

Adding risk to that reality is that the entry level is shrinking at many organizations, creating a smaller pool of qualified talent to move into leadership. What used to be a traditional career pyramid now looks more like a diamond, with AI eliminating swaths of entry-level jobs, which leads to managers being promoted with less experience and preparation.

In such an environment, Starks says managers need the opportunity to practice with tailored guidance for the moments that matter. Without it, managers will likely fail real-time tests of their ability to lead, further deepening the management crisis and emphasizing to ICs that management isn’t an appealing path. Starks points to Rising Teams’ AI leadership personal trainer, named Arti. Leaders are able to leverage insights from the tool to improve manager and team capabilities.

“That’s how AI transformation actually gets unlocked,” Starks says, “through managers, not in spite of them.”

The post Why more young workers are saying ‘no thanks’ to becoming managers appeared first on HR Executive.

The survey found that 53% of the 1,022 U.S.-based Gen Z knowledge workers asked said they would choose to work for “the world’s best employer over America’s best” when salary, title, benefits and location were equal. Among respondents in data sciences, 66% chose the global employer, the highest share among the functions included in the report.

Fifty-nine percent of respondents said a lack of opportunities to work abroad could affect whether they accepted a job offer, while 64% said they were extremely or very interested in temporary work abroad.

Read more: New research highlights 3 fast-moving global HR trends

The realities of working abroad

It’s important to keep in mind that the vendor-sponsored survey measures respondents’ impressions of employer readiness, not the actual availability of global-career programs.

BCG found that international movement among highly skilled workers fell 8.5% year over year through August 2025. However, this doesn’t suggest that these opportunities have dried up, as BCG also estimated that 2.4 million highly skilled workers moved internationally during the prior 12 months.

Only 19% gave U.S. employers an A for preparing young professionals for global careers, while 15% named corporate America as the best source of international exposure, trailing universities and study-abroad programs, “travel / digital nomad / freelance opportunities” and military or government experience, 16%.

Read more | Digital nomad visas: global mobility trends employers aren’t prepared for

Costs, skills and balancing global mobility

Though Gen Z may want mobility, Mercer has identified high costs as a continuing barrier to employee relocation, and KPMG’s mobility benchmarking found that demonstrating program ROI is a top challenge for corporate mobility leaders. Gen Z respondents also realize that global mobility doesn’t come cheap. Cost of living and financial concerns were respondents’ most-cited barriers to global work, at 59%. Foreign-language skills followed at 43%, family or personal commitments at 42% and visa or work-authorization requirements at 40%.

The report also found that Gen Z respondents believe human capabilities, not AI skills, drive long-term growth. Eleven percent ranked AI skills first, compared with 28% who chose professional networks and 26% who chose leadership or management experience. Pebl said communication, relationship-building and adaptability were among top capabilities respondents expected to matter most over the next decade.

Four in 10 respondents said they see their careers spanning two or three countries. Their preferred destinations are U.K., Western Europe and Canada. And get this: 5% of Gen Z respondents, concentrated in construction and hospitality, said they’d want work experience in outer space. It’s a small number, but perhaps it’s a sign of how far this generation is willing to stretch the definition of a workplace.

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Glassdoor’s economic research team reported that AI mentions in U.S. employee reviews increased 240% from May 2025 to May 2026. The tone of those references has deteriorated just as quickly. In 2019, 81% of reviews mentioning AI were positive. By 2026, just 43% were positive, while 53% were negative or listed AI as a con.

Trust and AI at work

Meanwhile, buzzing in the background, trust in AI leadership has become a defining storyline this year, from tech executives writing manifestos to a CNBC Generation Lab survey showing broad skepticism toward the people leading the sector. According to the study, around 75% of U.S. adults ages 18 to 34 said they did not trust Anthropic CEO Dario Amodei to act responsibly with AI. Roughly 70% said the same of OpenAI CEO Sam Altman and Meta CEO Mark Zuckerberg. Amodei has described the broader reaction as “fundamentally a crisis of trust.”

The Wall Street Journal has argued that the AI backlash holds a leadership lesson. Executives at major technology companies appeared to miss the extent of public resistance to AI, especially opposition to the data-center buildout supporting it. The Glassdoor data suggests a parallel risk inside organizations, where AI adoption can compound employee concerns about job security, surveillance and leadership credibility.

Glassdoor’s findings do not measure public trust in AI CEOs directly. But they hint that employee confidence in AI appears tied to whether workers see a tangible benefit for the business and receive adequate support to use the technology.

Positive and negative views on AI

The analysis identified two leading themes among positive AI comments. Employees cited their company’s commercial benefit from AI in 44% of positive comments, while 41% pointed to helpful tools, training or other forms of employer support. The negative comments skated around different angles, but fear of replacement was the most common concern, appearing in 20% of critical AI comments. Other recurring complaints included being required to use AI tools, concern that AI was distracting the company from its core business and workplace-surveillance concerns.

The data also links AI criticism to broader workforce strain. Employees raising negative AI concerns in reviews were 3.7 times more likely to mention burnout or job insecurity and six times more likely to mention layoffs than other reviewers. One-quarter of reviews that listed AI as a con also referenced layoffs directly.

Employees who discussed AI positively were 47% less likely to apply for another job in the month after posting their review, according to Glassdoor. The findings suggest that AI sentiment may also reflect whether employees feel their employer has a solid strategy and considers the consequences of AI implementation.

The post Workers are turning more critical of AI, and leadership is part of the problem appeared first on HR Executive.

Cornerstone Chief People Officer Carina Cortez explains how workforce readiness is more than a skills problem. It’s a leadership challenge that includes employee experience, organizational change, tech adoption, and aligning people with business strategy.

Workforce readiness is often viewed through the lens of the skills problem organizations face.

In reality, it’s a leadership challenge that also involves employee experience, organizational change, technology adoption, and aligning people with the overall business strategy.

In the second installment of the three-part podcast series, Ready or Not: The Skills Strategies and Stakes of the AI Workforce, HR Executive contributor Michael Krieger speaks with Cornerstone Chief People Officer Carina Cortez to discuss workforce readiness and how AI is influencing the way HR teams approach this issue.

In their discussion, Krieger and Cortez delve into:

• How leaders create trust, bring people along, and build confidence when employees are understandably anxious about what AI means for their roles.
• What it looks like to candidly assess readiness in a rapidly evolving business.
• Where organizations are struggling most as their workforces adapt to working with AI tools.
• How HR and IT can work together more effectively to ensure workforce readiness doesn’t feel like a technology rollout or a compliance exercise.

To hear the entire episode, click here. Then, continue following the Ready or Not: The Skills Strategies and Stakes of the AI Workforce podcast series for more on how AI is changing HR and the workforce.

Listen to episode #1: Ready or Not? The Workforce AI Wake-up Call

Learn more about Cornerstone Workforce AI, click here.

The post Ready or Not: The Skills Strategies and Stakes of the AI Workforce series: Episode #2 appeared first on HR Executive.