Date & Time: Wednesday, September 9, 2026 at 3:00 pm ET

Speakers:

Will Guidara, Author, Producer, Unreasonable Hospitality

Description:

As the former co-owner of Eleven Madison Park in New York City, Will Guidara led the restaurant to the top of the World’s 50 Best Restaurants list in 2017. His impact was so undeniable, he serves as a co-producer of The Bear, an Emmy-winning series. How? Guidara built his career on an approach to customer service that goes beyond the basics, pushing creativity and innovation to deliver an experience for guests that isn’t just great, but unforgettable. This is the philosophy behind “unreasonable hospitality.”

In this webinar, Guidara breaks down how:

• to transform ordinary service into extraordinary experiences in any industry
• leaders can create a people-first culture built on feedback
• automated, easy-to-use tech empowers leaders to focus on their people — not HR processes

If you’ve ever wanted to add an exciting new dimension to your organizational culture, this webinar is one you won’t want to miss.

Register here!

The post Unreasonable Hospitality Author Will Guidara on Transformative Leadership and People-First Culture appeared first on HR Executive.

With AI reshaping work, Cornerstone Senior Vice President Eric Lee talks about why traditional workforce development approaches are falling behind and how HR leaders must prepare employees to be ready for continuous change. 

AI is changing the nature of work at a pace that is challenging traditional approaches to workforce development. While 94% of leaders say their workforce investments have a clear, demonstrated impact, only 17% of employees feel completely prepared for how their roles will change over the next year or two.  

Part of AI Week, the first installment of this three-part podcast series called Ready or Not: The Skills Strategies and Stakes of the AI Workforce sees HR Executive contributor Michael Krieger talk with Eric Lee, Senior Vice President of Global Support Services and Learning at Cornerstone, about this growing workforce readiness gap and what it means for HR leaders navigating rapid AI-driven change. 

In this episode, called “Ready or Not? The Workforce AI Wake-up Call,” Lee and Krieger discuss: 

• The workforce readiness gap: Why traditional planning and training models are struggling to keep pace as AI reshapes roles in real time 

• Warning signs organizations are falling behind: From shadow AI and reactive skilling to an overreliance on external hiring 

• Rethinking learning and skills: Why development needs to become part of employees’ everyday work rather than something that happens separately 

• Building continuous readiness: How greater visibility into skills and closer collaboration between HR and IT can help organizations prepare their people for what comes next 

To hear the entire episode, click here:

Continue following the Ready or Not: The Skills Strategies and Stakes of the AI Workforce series for more on how AI is changing HR and the workforce.

To learn more about Cornerstone Workforce AI, click here. 

The post Ready or Not: The Skills Strategies and Stakes of the AI Workforce series: Episode #1 appeared first on HR Executive.

As the first major technological threat to white-collar work emerges, the evidence of employer adaptation is striking. My co-authors and I analyzed job-posting data from more than 60,000 employers, tracking hiring patterns at S&P 1500 firms across 13 years. This included data from before and after ChatGPT’s release in 2022. At firms with the highest concentrations of white-collar workers, weekly job postings fell by as much as 18.9% after the release, a contraction comparable to hiring declines during the early months of the COVID-19 pandemic.

But the decline was selective. Firms with stronger financial positions, greater digital readiness and more technically skilled workforces adapted most aggressively, cutting lower-level white-collar roles, such as administrative positions, junior analysts and entry-level office jobs, while accelerating demand for higher-level technical, managerial and AI-related talent.

See also: Why HR needs a ‘recovery layer’ for real AI transformation

Companies and workers are dealing with gen AI’s disruption differently

Organizations are responding decisively and strategically to technological disruption. But inside those same firms, employees are reacting very differently. Glassdoor data from the same period reveals significant declines in CEO approval ratings and business outlook scores. The sharpest drops came from junior employees whose roles were most directly in the path of the hiring cuts. However, senior-level employees reported feelings of uneasiness as well, suggesting the distrust runs deep.

When we examined the language employees used in their reviews, two concerns surfaced repeatedly: job insecurity and a lack of transparency from management. In the absence of clear direction, many workers acted on their own. Among more than 15.8 million workers who changed jobs after ChatGPT’s release, those in vulnerable roles were more likely to pivot toward AI-related work or shift into less exposed occupations. Employees weren’t resisting change; they were navigating it alone.

The issue is a fundamental corporate mismatch: Employers are executing a deliberate workforce strategy, while employees experience it as growing anxiety, uncertainty and declining trust in leadership.

Whether the gap reflects intentional withholding or poor communication, the consequences are the same. AI transformation requires workers to learn new tools, take on new responsibilities, and often redefine their roles. That kind of buy-in necessitates trust in leadership and confidence in where the organization is headed.

Adaptation is uneven: leaders’ response will determine if gap grows

The data also captured uneven adaptation. Women were significantly less likely to pivot to AI-related work, and R&D firms, which might be expected to lead adaptation, cut hiring across levels even as they increased demand for AI skills.

The research, detailed in a new brief from Columbia Business School’s Reuben Mark Initiative for Organizational Character and Leadership, offers a clear diagnosis. How leaders respond will determine whether this gap widens or closes. Transparent communication about which skills will be valued, how responsibilities will evolve and what the organization is working toward may help close the gap revealed in the data. So, too, might framing generative AI as a tool that expands what employees can do, rather than one that determines how many of them are needed.

Generative AI will reshape the white-collar workforce regardless of how any one company responds. What remains within leaders’ control is whether employees experience that transformation as an opportunity or as something being done to them. The difference will be determined not by technology, but by humans themselves.

This article was co-authored by Philip G. Berger of the University of Chicago, Lin Qiu of Purdue University and Cindy Xinyi Shen of Northwestern University.

The post Employees are navigating AI disruption in the dark appeared first on HR Executive.

The bias catch

Researchers tested GPT, Claude, Gemini and other models in a simulated hiring task adapted from psychology research. In each of 40 rounds, a model acting as a consultant selected one applicant from one of four fictional demographic groups for a job such as doctor, lawyer, janitor or childcare aide. Every group-job pairing had the same independently sampled 90% probability of success, meaning the model had no real performance differences to detect. Each model and prompt condition was tested across 30 runs.

Despite those equal odds, the models developed unequal patterns, repeatedly putting different fictional groups into different categories of jobs. The authors link the result to an early, random success or failure can lead a decision-maker to repeat apparently successful job pairings rather than test alternatives. The same dynamic has been documented in the human experiments that inspired the task

That finding discounts what most HR technology buyers have been told to expect. Standard fairness benchmarks, the kind vendors cite when they say a model has been tested for bias, generally show newer models performing better over time. This research measured whether a model builds a new stereotype from its own decision history.

Read more: AI works better when HR helps lead it, new research finds

What can be done?

The researchers tested several fixes. Prompting the model to reason step by step barely helped. Neither did increasing randomness in its outputs or shortening how much decision history it had to reference. Removing the game’s point system made no difference, either. What did work, at least in the researchers’ synthetic setting, was giving the model an explicit, measurable diversity objective instead of a general instruction to be fair. Models told to optimize for varied outcomes produced allocations close to random assignment.

When the researchers ran a version of the test where groups genuinely did perform differently, forcing a diversity objective into the mix lowered overall success rates. The intervention only helps when the underlying population truly is equivalent, and confirming that requires a judgment call.

The paper, presented at the International Conference on Machine Learning in 2026, tested a synthetic hiring scenario with invented demographic labels, not a live enterprise system. Its authors are careful to note the mechanism, not the specific numbers, as the transferable finding. Still, as more HR tools move from single-decision scoring toward agentic systems that learn and adapt across a series of choices, the question the study raises deserves a place in procurement conversations.

“This raises a central tension in alignment: How do we limit generalization in sensitive cases without suppressing reasoning as a whole?” wrote the authors. “The challenge ahead is to design interventions that selectively discourage harmful pattern-matching while preserving the constructive forms of abstraction that make LLMs powerful.”

The post AI hiring tools can invent their own bias, research finds appeared first on HR Executive.

Date & Time: Thursday, September 24, 2026 at 2:00 pm ET

Speaker:

Kylene Zenk, Senior Principal Human Insights Program Manager, UKG

Description:

Frontline employees make up nearly 80% of the global workforce, yet they remain one of the most essential and complex populations to attract, engage, and retain.

In this session, we’ll share insights from UKG research capturing the voices of thousands of frontline employees around the world. Discover what matters most to today’s frontline workforce, how AI and workplace technologies are shaping their day-to-day experiences, and where expectations are evolving across pay, scheduling, well-being, recognition, and flexibility.

We’ll translate these findings into practical, research-backed actions HR and operations leaders can take to strengthen the frontline employee experience. You’ll also learn how technology can help address critical workforce needs and create a more connected, people-centered workplace.

Session Takeaways:

• Understand the realities of the frontline employee experience and how AI and workplace technology are shaping the way work gets done.
• Identify practical, high-impact actions to strengthen engagement, retention, and well-being.
• Explore how technology can help address key frontline workforce needs and support a more connected, people-centered strategy.

Register here!

The post Frontline Perspectives: Turning Workforce Insights into Action appeared first on HR Executive.

Date & Time: Wednesday, September 16, 2026 at 2:00 pm ET

Speakers:

Cam Torre, VP, GTM Strategy and Operations, Workhuman

Niamh Hoey, Senior Product Manager, Workhuman

Description:

Every service milestone is an opportunity to make employees feel genuinely valued – yet many programs still treat them as generic transactions, rather than meaningful moments. Six in ten employees say they’ve never had a milestone that felt like a real celebration of their work, and this traditional approach to service awards is costing organizations in engagement and retention.

Hear from Workhuman’s own Cam Moore, VP of Strategy & Operations, and Niamh Hoey, Senior Product Manager, to learn how organizations can rethink work anniversaries as strategic opportunities to strengthen belonging and reinforce culture. We’ll examine where traditional service award programs fall short, the elements that make milestones memorable, and how Workhuman’s Service Milestone technology combines recognition, storytelling, and peer participation to bring those experiences to life consistently and at scale.

You’ll walk away with: 

• A research-backed diagnostic for why service awards fail to land, so you can pinpoint the gaps in your own strategy.
• A practical starting point for creating more impactful celebrations, whether you’re refreshing an existing program or building one from scratch.
• Real examples of how Service Milestone recognition technology simplifies the administrative load, so your team can focus on elevating the human experience.

Register here!

The post Making Service Milestones Matter: Transforming Work Anniversaries into Meaningful Employee Experiences appeared first on HR Executive.

According to an internal document viewed by Bloomberg, members of Google DeepMind’s AGI Safety and Alignment Team advised internal candidates on their team to fill out a second form alongside their application to increase their chances of having their application viewed by a human.

“We have an applications system with a non-trivial probability your CV will be screened out incorrectly or take too long to reach us,” the team said in the document. “Filling out this form makes sure that a real human on the team will get to see your application.”

Pointedly, the language suggests candidates could be improperly screened out, raising doubt about the dependability of how Google is using AI in its hiring.

Trust issues on both sides of the hiring equation

The Google situation highlights the ongoing thorniness of building trust with job candidates in the age of AI.

According to recent research from Gartner, only about one-quarter of job candidates think AI tools will fairly evaluate their application, and nearly one-third are concerned the tech will screen them out of the hiring process. More than one-quarter said they trust a potential employer less if the organization uses AI in the job application process.

At the same time, employer use of AI of hiring is exploding: Resume Genius research finds that about 87% of U.S. hiring managers say they use AI, while nearly 60% say the tech helps them screen resumes. However, not all are forthcoming about how the tech is being deployed: About 20% don’t tell candidates how they’re using AI to make hiring decisions.

Mistrust related to AI use appears on both sides of the hiring equation: Gartner found that nearly 40% of job candidates say they used AI during the application process, most commonly to build their resume.

It’s a reality that the Google DeepMind researchers are aware of. According to Bloomberg, the secondary form the team sent to candidates warned them that “A real human will read” the application, and they should present the materials with limited AI usage. “These humans get really tired of reading LLM answers, because they all sound very same-y.”

Learn more about how organizations are navigating the rise of AI screening tools at HR Tech, Oct. 20-22 in Las Vegas. Click here to register.

The post Google insiders warn job candidates about its AI hiring filters appeared first on HR Executive.

A growing body of research suggests that most generative AI initiatives don’t fail because the technology doesn’t work. They fail because people don’t use it effectively, consistently or with confidence. In some studies, that number is as high as 95% when adoption breaks down at the human level.

By the time an AI tool is deployed, the technical decision has already been made. What determines whether it delivers value is whether employees trust it, whether managers know how to guide its use and whether teams understand when human judgment is expected to override it. Those are all culture questions.

See also: From fear to flourishing: How HR can help teams adopt AI

From my vantage point as a chief people officer at the center of AI change, one thing is clear: Scaling AI is less about deploying tools and more about building the conditions for people to use them well.

This requires that leaders empower people to experiment, learn, unlearn and relearn. It means admitting that you don’t have all the answers, and providing psychological safety and clarity about where human judgment still matters. So-called “soft skills” are the difference between AI pilots that stall and adoption that sticks and drives value. This environment matters just as much as the technology itself.

Here’s what I’ve learned as the chief people and AI enablement officer at an enterprise tech company:

Start with the middle of the organization

The clearest signal of whether AI is taking hold is usually not at the top, but in the middle.

Senior leaders often see the strategic upside and are eager to move quickly, but frontline managers are the ones translating that ambition into actual work. They’re answering questions in real time: Can we use AI for a first draft? Should it be used for analysis? How much editing is expected before something goes out the door?

If managers don’t have clear answers, teams tend to default in one of two directions. Some avoid the tool entirely because they’re unsure where it’s appropriate. Others over-rely on it because there are no clear guardrails. Neither behavior leads to meaningful adoption.

That’s why frontline managers are one of the most important feedback loops in any AI rollout. They see where workflows break down, where employees need more guidance and where well-intentioned policies create confusion. They can also tell you the difference between thoughtful usage and performative usage.

If you want to understand whether AI is actually changing how work gets done, listen to your managers.

Be explicit about where human judgment still matters

Mandating that employees use AI isn’t the same as helping them use it well. Adoption improves when people clearly understand how to use AI and what its limits are. Employees need both access to tools and clarity on the judgment they must apply to their work.

A team using AI to summarize research still needs someone to confirm that nuance hasn’t been lost. A recruiter using AI to generate a job description still needs to decide whether it reflects the reality of the role and the team. A communicator leveraging AI to draft an internal memo outline still needs to shape language that is clear, empathetic and, above all, human. In each of these cases, AI can assist the work but can’t own the decision or final output.

When leaders don’t define that line clearly, they create risk in both directions. Some employees become overly cautious because they don’t know when they can rely on the tool. Others blindly trust it because no one has outlined where human oversight is required, oftentimes resulting in “workslop”—what researchers at BetterUp Labs and the Stanford Social Media Lab describe as “low-effort, passable-looking work that ends up creating more work for … co-workers.”  These inconsistencies erode both credibility and professional relationships.

Leaders cannot eliminate the uncertainty defining the AI era, but they can provide clarity around how it should influence and affect everyday work. Trust in AI grows when employees understand both its value and its limitations, and when accountability remains firmly with people.

Cultivate an adoption mindset based in shared learning

Another common mistake is treating AI implementation as if it were a finished plan: Pilot, train, deploy, optimize. In reality, adoption is more dynamic—and, let’s face it, messier— than that.

Teams uncover unexpected use cases. Some workflows improve quickly, while others introduce more friction than value. Employees find better prompts than leadership anticipated. A guideline that works in theory needs to be adjusted in practice.

That’s why the organizations making the most progress are the ones building strong feedback loops and adapting as they go. They create space for teams to experiment and to say when something isn’t working. They treat course correction as part of the process, not a failure of it. They make it clear that employees’ real-life experiences with AI matter, and more importantly, the employees themselves matter more. What this results in is more speed and confidence, less confusion and, ultimately, bigger impact.

This is a leadership shift, not just a technology one

The organizations that will get the most value from AI will be the ones that do the hard work of aligning culture, management practices and ways of working so that employees can use tools with confidence and sound judgment. This requires leaders who adopt a “learn it all” versus a “know it all” perspective, modeling growth mindset and vulnerability while also providing clarity of vision. Corporate cultures that normalize experimentation and failure are also those where mistakes pave pathways to solutions, compounding progress over time.

Because in the end, AI adoption isn’t defined by what a company deploys. The companies that come out on top will be those that align mindsets, skillsets and toolsets to make change stick—and that make their people feel seen and heard in the process.

Hear more from Ana White during Unlocking Performance: AI + the Culture to Power It, a breakout session at HR Tech. The conference will be held Oct. 20-22 in Las Vegas. Click here to register.

The post AI adoption is a people challenge before it’s a technology one appeared first on HR Executive.

According to LinkedIn’s own commentary, VP of Product Laura Lorenzetti described the target as generic material that lacks authenticity and originality, judged by how well a post separates real perspective from content that simply repeats existing ideas. “When content appears to be generated by AI and lacks clear perspective, it is less likely to be widely distributed beyond a person’s immediate network, which helps keep space for more thoughtful contributions,” wrote Lorenzetti.

According to LinkedIn, AI-assisted writing is still allowed, as long as the post carries an original idea or invites real conversation. Lorzetti wrote that, in addition to identifying sloppy posts, the tool will recognize:

• “comments members create and post at scale using automation tools, with little or no human involvement” and
• “responses that simply restate the original post without sharing anything new.”

What makes a post ‘slop’?

According to the Wall Street Journal, LinkedIn Editor in Chief Dan Roth laid out the patterns that actually trip the flag. He describes these as pushing bland, interchangeable takeaways, unusual formatting, recycling-symbol emoji paired with prompts to comment for more and dramatic one-line paragraphs known as “broetry.”

Roth also flagged the phrasing pattern chatbots default to, calling it a “not this, but that” construction. According to the Journal, he said the single most common red flag is a comment that simply summarizes the post it’s replying to and described the overall effect on readers as landing in an “uncanny valley.”

A leader who uses AI to organize a thought and then edits it into something specific with useful context or a real opinion doesn’t appear to be the target of the slop button. If the org’s comms team is running executive accounts through the same prompt, this will likely be a red flag, regardless of who or what typed the words.

For leaders working on social media communication, there are a couple of things worth doing now. First, look at how executive content actually gets produced across the leadership team. If several leaders’ posts share the same rhythm, takeaway bullets or format, or the cues that Roth described, that pattern will be visible. Second, treat the button less as a risk and more as a test. If the poster manages to say something interesting or meaningful, this can build an executive’s credibility over time.

“Valuable conversations happen when professionals share their unique perspectives,” according to Lorenzetti. “These updates are designed to protect the experience on LinkedIn so when you’re engaging, you’re interacting with real people offering their real point-of-view.”

The post What LinkedIn’s editor in chief says gets a post flagged as AI slop appeared first on HR Executive.

However, new research suggests that using a “one-size-fits-all” strategy can hide a fundamental truth: Employees don’t respond to AI the same way.

In fact, the research study from Shrihari Sridhar, a senior associate dean at Texas A&M University’s Mays Business School, and Huachao Gao, an associate professor of marketing at Mays, concludes that AI adoption has often been treated as if employees share a single attitude toward the emerging technology. Their findings suggest quite the opposite: People experience AI in markedly different ways depending on their individual perspectives and the context in which they’re using it—a concept they call “AI-heterogeneity.”

“What we’re seeing is that leaders are almost mandating that employees need to be AI-ready,” Sridhar says, and that’s one reason they’re encouraging employees to become familiar with AI. Another could be market pressure. A third possibility is they don’t yet know exactly how to use AI to transform their business, but they believe getting employees to use more of it will help drive that change, Sridhar says.

Employers that are mandating AI usage may be creating a “compliance rather than adoption” situation, Sridhar says, adding that this perspective is driven by the Texas A&M research that drew on a nationally representative survey of 2,144 U.S. adults.

Rather than falling neatly into pro- or anti-AI camps, he explains, many survey respondents expressed mixed feelings about the technology, seeing it as both beneficial and threatening.

“Those attitudes often shifted depending on the task at hand. Someone might enthusiastically use AI for one task while distrusting it for another, suggesting adoption isn’t a simple yes-or-no decision,” he says.

The survey also identified what the researchers call the “skepticism-usage” paradox: Some of the people most concerned about AI are among its most frequent users.

“The most anxious people are often the ones using more AI,” Sridhar said. “They’re using it, and they’re anxious about it at the same time.”

He believes this can be attributed to several factors, and he offers ways employers can resolve this AI outcome. The findings, in fact, led the researchers to reframe AI adoption as a segmentation challenge, rather than simply a technology challenge.

Sridhar suggests that rather than asking how to get more people to use AI, employers should ask how different groups experience AI and tailor their approaches accordingly in the following ways:

Audit your adoption dashboard

According to Sridhar, most organizations measure AI adoption by usage: logins, prompts, active users. Their research suggests usage alone can mislead, because the employees using AI most heavily are often the most anxious about it. “For an HR leader, the practical move is to measure confidence alongside usage,” he says. “High use with low confidence is not a success story; it is a retention and burnout risk wearing a productivity costume.

Segment your workforce the way marketers segment customers

Employees hold “a portfolio of attitudes” that shift with the task and with what the technology means for their livelihood. For example, a senior attorney whose value rests on judgment and a junior analyst whose daily tasks are exactly what AI does well are living through different events. “One all-hands email treats them as the same person, and it will miss them both,” he says.

Start with the ‘Monday morning’ problem, not the strategic vision

Sridhar says the most effective adoption message is not “AI is the future.” It is “Here is the most tedious part of your week, and here is how to hand it off.” To Sridhar, sustained behavior change comes from usefulness people can feel in their own role, not from enthusiasm for a technology in general.

Say the honest thing out loud

Employee anxiety about AI is a reasonable response to a real situation, and employees know it, Sridhar says, adding that messages that acknowledge the legitimate concern before making the ask are more credible than pure cheerleading. “HR is often the only function positioned to insist on that honesty in official communication,” he says.

Train before you measure

Sridhar says that if an employer raises the expectation of AI fluency faster than it builds the skill, employees default to shallow, anxious use that looks productive on a dashboard while confidence quietly erodes. The better path, he says, is to “close the preparation gap first” via role-specific training. The adoption numbers that follow will mean something, he says.

“Start from the workflow. Start from the job to be done,” Sridhar concludes. “Seeing how the job was done earlier, how you can do it better and where you can bring in AI is a much more productive, calming conversation.”

The post AI adoption isn’t one-size-fits-all. 5 strategies to make it work appeared first on HR Executive.