Comprised of business, tech, academic, public policy and workforce transformation leaders—including from Microsoft and Harvard University—the council is an effort to uncover where there are gaps that could lead to women being left behind during tech advancement and formulate practical strategies to help HR close such chasms.

“The story we tend to hear about women and technology is one wherein women are usually cast as the losers. They fall behind, are left out of jobs and opportunities, and are victims of bias,” says Dr. Ginna Santy, executive director of Women in Revenue and co-chair of the new AI Leadership Council. “We’re starting to see the same thing happen to women with AI. The most common narrative is that women will be disproportionately displaced by AI in the workforce.” 

To change the narrative, HR needs to first know how AI is affecting women’s jobs, what training and access to tools looks like and who is being selected to lead AI initiatives, Santy says.

“Without that information,” she says, “organizations cannot know whether AI is expanding opportunity or reinforcing existing inequities.”

See also: AI hiring tools can invent their own bias, research says

Apart from gathering data about the state of AI and women in the workforce, the council aims to identify practical frameworks: inclusive leadership models that involve pairing tech leaders with women leaders; targeted efforts to give women more decision-making over AI strategy and implementation; sponsorship programs that give women more say in and opportunity around AI projects.

“Why does it have to follow the same old story? Instead, why can’t we push ourselves to see AI as a great opportunity for women?” Santy says.

While AI is advancing quickly, seemingly by the day, “we are early enough in the AI journey that ‘could be’ is a productive, inventive phrase, rife with possibility,” she says. “But it won’t do anything for us if the questions aren’t being asked. That’s the gap we are seeking to fill.”

Santy recently spoke with HR Executive about the council’s vision, and what HR leaders should be doing today to make AI a “great equalizer” instead of a divider.

HRE: How do you intend to measure what success looks like in the Council’s future?

Santy: Success looks like things that exist in the world now, not things we said. It includes frameworks people can pick up and use, new definitions of what leadership looks like when the routine work is automated, developmental models that don’t assume a career ladder built for 1985, sponsorship playbooks that put real weight behind the women coming up. In the first year, we want those in the hands of the HR leaders and policymakers making decisions right now. Beyond that, we will look at whether organizations are actually building AI strategy differently because of what we found. We’ll know it worked when we start hearing our questions asked back to us by people who never heard of this Council.

HRE: You’ve called this council possibly the last real opportunity to intervene before yesterday’s biases become tomorrow’s workplace infrastructure. Why “last”? What’s the closing window you’re seeing?

Santy: The “last” refers to the critical moment we’re in. AI is being built and deployed at a scale and speed that will define workplaces for decades. Right now, those systems are still being designed, which means the assumptions inside them are still up for debate. Those assumptions stop looking like choices and start looking like infrastructure, getting embedded in procurement contracts, trained into the next generation of models and inherited by every company that buys off the shelf rather than builds. At that point, the question is no longer what AI could be for women, but how much damage we can retrofit. There may be later chances to fix things around the edges, but much tougher to undo the harm to the foundation.  

HRE: What is HR leaders’ role in advocating for equity for women in the workplace when it comes to setting AI strategy?

Santy: HR leaders will play a pivotal role in this moment. They are uniquely positioned to advocate for equity because they understand both organizational culture and workforce dynamics, and they directly control many of the systems in which workplace AI is being deployed. CHROs should audit their own AI-enabled hiring screens, performance reviews, promotion processes, pay decisions and scheduling tools to determine whether they are creating unequal outcomes for women, particularly women from historically underrepresented groups.

They must also demand transparency and accountability from AI vendors and developers, insisting on inclusive design processes that reflect the diverse experiences and needs of the women these systems will evaluate. Finally, they should also push for data and metrics that reveal where AI is creating disparities, and use their influence to ensure AI strategy is aligned with broader equity and talent goals, not just efficiency or cost.

HRE: What’s one thing you want every CHRO reading this to go do on Monday morning to ensure their AI strategy isn’t embedding bias?

Santy: On Monday morning, identify the woman in your workforce most exposed to harm from an AI-driven decision—perhaps an hourly worker of color who has a disability, speaks English as a second language, has caregiving responsibilities and little power to challenge a scheduling or performance decision. Then, design and test your safeguards from her perspective: Can she understand the decision, question it and reach a human empowered to correct it? When you design for the woman facing the greatest barriers and the least recourse, you build a fairer, more accountable system that works better for everyone—including employees with far more privilege.

Learn more about how organizations are leveraging AI to level the playing field for women at the Women in HR Tech as part of the HR Tech Conference in Las Vegas. Click here to register.

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Neural data might be a new term for many HR leaders, but in the AI age, this concept has crept into the workplace. If enacted, AB 1883 would target specific uses of AI-enabled surveillance while allowing employers to use workplace-monitoring tools for safety and other purposes when those tools do not engage in the prohibited conduct. 

Read more: How the EU AI Act impacts global standards for AI in hiring 

‘HR leaders should pay close attention’ to AI emotion recognition

The proposal comes as the European Union’s AI Act prohibits AI systems intended to detect the emotional state of people in workplace and education settings, except when the systems are placed on the market strictly for medical or safety reasons. The regulation defines an “emotion-recognition system” as an “AI system for the purpose of identifying or inferring emotions or intentions of natural persons on the basis of their biometric data.” 

Danielle Ochs, a San Francisco shareholder at Ogletree Deakins, said the bill comes amid growing concern about workplace surveillance. Specifically, there is an eagle eye on “AI-driven tools that possess potentially intrusive monitoring capabilities,” Ochs said in an exclusive email to HR Executive. 

The bill comes amid broader scrutiny of employee-monitoring technologies, including tools used for productivity management and other operational purposes. “It seems like almost weekly, employers are introduced to new AI-driven workplace tools with ever-expanding capabilities,” she said. “HR leaders should pay close attention to the rapidly changing legal landscape regulating these tools.” 

Read more: Privacy risk? Employee-monitoring apps sharing worker data with Big Tech 

What the bill would prohibit 

AB 1883 would amend California’s Labor Code, according to Ochs. If signed by Newsom, the bill would prohibit employers from using AI-driven workplace-surveillance tools that: 

• recognize an individual’s emotional state 
• make inferences or predictions about an individual’s emotional state 
• collect an employee’s neural data 

The bill defines a “workplace surveillance tool” as “any system, application, instrument or device that collects or facilitates the collection of employee data, activities, communications, actions, biometrics or behaviors by means other than direct observation by a person.” 

The definition includes, among other technologies: 

• video or audio surveillance 
• continuous incremental time-tracking tools 
• geolocation 
• electromagnetic tracking 
• photoelectronic tracking. 
• photo-optical systems or other similar tools 

“‘Neural data’ means information that is generated by measuring the activity of an employee’s central or peripheral nervous system, and that is not inferred from nonneural information,” Ochs said. 

The California Labor Commissioner and public prosecutors could enforce the measure, and employers that violate it could face civil penalties of up to $500 per violation. The bill does not expressly allow individual employees to sue employers, although other legal avenues could raise separate questions. Ochs said the bill’s language “may leave the door open for representative claims under California Private Attorneys General Act (PAGA), which allows [eligible employees] to pursue Labor Code-based penalty claims on behalf of the state.”  

What the bill does, and doesn’t, do 

Ochs said the use of employee-monitoring tools “has been on the rise for some time,” citing the Society for Human Resource Management and Ogletree Deakins’ observations. She identified productivity tools, safety systems and scheduling software as common examples. 

“Presumably based on objections to prior versions of the bill, this bill seeks to narrowly tailor the kinds of monitoring it seeks to proscribe,” Ochs said. 

Ochs said opponents had expressed concerns that the legislation would have banned facial-recognition technology or inadvertently swept in standard security systems. She also cited concerns about AI surveillance tools used for safety analytics, “such as detecting distracted or fatigued driving in commercial vehicles.” 

Employers may want to examine how their systems work, particularly when a tool makes inferences about an employee rather than simply recording observable conduct. 

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Close to 12% of Irish job postings now reference AI skills. In the U.S., that figure is 4%. In the U.K., it is 6%. Microsoft’s AI Diffusion Report puts Ireland second in Europe for overall AI adoption, behind only Norway, and large-enterprise AI usage here has climbed from 31.2% to 57.3% since 2021. A market of 5 million people is running ahead of the big ones.

We built TalentHunter inside that environment, and inside a regulatory one that got there before America did. Recruitment was classified as high-risk under the EU AI Act before we had our first paying client, and a candidate’s right to a plain explanation of a hiring decision was close to law before most vendors had thought about it. We joined the European AI Pact, the voluntary framework the European AI Office set up ahead of the Act’s deadlines, because it put us in the room where the standards were being worked out.

That environment shaped the product. Every résumé score a candidate receives can be sent to a human reviewer on request. Our terms of service state plainly that no candidate is ever automatically rejected by an algorithm. Our policies open in ordinary language before they open in clauses. Our client base spans hospitality, security, facilities, construction and healthcare staffing across Ireland, businesses that mostly do not have a dedicated compliance team, which meant the standard had to work at that scale or it did not work at all.

See also: The countries winning the global talent race

U.S.-based companies still have to abide by the EU AI Act if their AI system is used there

Here is the part that matters if you are sitting in Chicago or Dallas and hiring into Dublin or Berlin: the EU AI Act does not care where your company is headquartered. It applies to any organization whose AI system’s output is used inside the EU. Screen applicants for a role in your Cork office with a tool running on a server in Virginia, and you are a deployer under the Act, with the obligations that follow.

That means using the system according to the provider’s instructions, keeping a human genuinely in the oversight loop, telling candidates and workers that the system is in use and keeping the logs to prove all of it. Penalties scale to global revenue, the same design as the GDPR fines American firms already know.

The timeline has just shifted, and it is worth being precise, because the shift is being misread. The high-risk obligations for hiring tools were due to bite on Aug. 2 of this year. Under the EU’s digital omnibus package, that deadline has moved to December 2027. Some vendors are treating the delay as a reprieve. It is a short one. The Act’s transparency rules still took effect this August, the GDPR’s Article 22, which restricts fully automated decisions about people and gives candidates a right to human review, has been in force the whole time, and the recruitment tools employers buy this year will still be in service when the high-risk rules land.

What compliance actually required of us was not a document produced at the end. It was an audit trail that exists from the first decision, and that is not something anyone can retrofit.

America is heading toward the same place through a different route, city by city and state by state. New York City requires an independent bias audit and candidate notice before an automated tool is used on anyone residing there. Illinois amended its Human Rights Act from Jan. 1 of this year, so employers must disclose when AI is used in an employment decision, and using AI that discriminates is a civil rights violation. California’s civil rights regulations, in force since October 2025, treat automated decision systems like any other employment practice, and whether anyone ran bias testing counts as evidence in a discrimination claim. Colorado’s sweeping 2024 law was blocked by a federal court in April and replaced in May with a narrower notice-based statute taking effect in January 2027. Underneath the patchwork, the federal picture is instructive.

The EEOC withdrew its guidance on AI hiring in early 2025, but Title VII, the ADA and the ADEA did not move. They apply to an algorithm exactly as they apply to a person, and “the algorithm did it” is not a defense.

If you are evaluating AI hiring tools against that patchwork, four questions will tell you most of what you need to know. Can a person point to the exact moment they, not the algorithm, made the call? Can a rejected candidate get a real explanation in language they understand? Can the vendor hand you the documentation behind that answer, the bias testing, the logs, the audit trail that New York already demands and other states are drafting toward? And is fairness something the product does or something the marketing page claims? We have been answering those questions under a regulator’s eye since before we had revenue. American employers will be asking them of every vendor soon enough.

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More than 40 years after the Cold War-era thriller WarGames, I can still see the blinking screen and hear the computer voice invitation to teenage hacker Matthew Broderick, and the creeping realization that the machine missed one fairly important point: The game in question was one humans could not afford to lose.

For decades, Hollywood churned out stories about machines with too much access and the absence of perspective, usually leading to a climax in which tech overtook human society.
Today, that plot feels less cinematic and more like an agenda item.

Recent safety disclosures from leading AI labs make that old storyline feel less far-fetched. OpenAI reported that models found a path to the open internet during testing and exploited vulnerabilities in the process.

Anthropic disclosed separate cybersecurity evaluation incidents involving models reaching the internet and gaining unauthorized access to real systems. Controlled research has also shown advanced models taking misaligned actions in testing, including behavior tied to shutdown scenarios and preserving assigned objectives.

These lab incidents attract attention to the broader commercial issue, which lands on the underwriter’s desk. If an AI system used by a business causes harm, where does the risk belong?

See also: When layoffs become a safety risk, HR is the first line of defense

What changed from predictive analytics?

I spent nearly two decades helping insurers turn models into operational tools, so I have seen a consistent pattern: The human and operational pieces were often the hardest part of model implementation.

As insurers consider how to assess their insureds’ AI risk, it is useful to look at how the deployment of advanced analytics evolved inside insurance organizations. There are parallels across industries, especially in how companies move from experimentation to production, from manual workarounds to system controls, and from policy-based oversight to embedded governance.

Predictive analytics created plenty of practical challenges. In the earlier days, some companies deployed models through spreadsheets because it was the fastest way to get a score into the hands of the business. In one survey I conducted more than a decade ago, roughly 50% of models were deployed that way. The problem was obvious to anyone who has ever managed a production model: Spreadsheets were sent as email attachments opened on each user’s desktop. There was no way to gauge if a simple typo was made in an input field, and managers had little ability to govern whether the model was being used accurately.

Then, the industry improved. More companies moved models directly into workflows so inputs and scores could be generated inside the system where the work happened. That was a meaningful step forward because it reduced manual errors and helped connect analytics to the business process.

But a lot of governance still lived around the model instead of inside the workflow.

Reporting on model usage, adoption, model results and whether users followed recommendations often required data pulls, spreadsheet analysis and help from IT. Evaluating performance, drift, model age and the need for refresh or rebuild was a separate effort rather than a living management process. Underwriting, claims and/or pricing considerations were often implemented as post-model adjustments, which introduced complexity and made it harder to audit whether the model was performing as intended.

Those workarounds were imperfect, but they were workable because most predictive models scored, flagged, ranked or recommended. A human still took the next step (with the notable exception of personal auto).

AI agents change the level of responsibility placed on the system. They can use tools, access systems, communicate externally, write code, trigger workflows or act across a chain of tasks. The human and operational work remains critical, but model governance now has to rise to the same level. The way the model is built, integrated, monitored and constrained matters as much as the change management plan around it.

For insurers assessing AI risk in their insureds’ businesses, this history is instructive. The evaluation should include how the AI moved from experiment to production, what workarounds still exist and whether governance is built into the workflow or sitting outside it.

In a second installment, Kristin Marr explores how to evaluate human review of AI, the new bar in model governance and what backup information underwriters need to cover AI exposures. See that at PropertyCasualty360.

This article was originally published on PropertyCasualty360, a sister site of HR Executive. For more content like this delivered to your inbox, sign up for PropertyCasualty360 newsletters here.

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The news, as I see it, is this: The belief among the leaders of many of the big consulting firms is that AI tools are—or perhaps will be soon—able to take over the more straightforward tasks of consulting. That might mean clients can do them without the help of consultants, although it turns out that the issue has been less about the client’s own skills and more about not having staff to do them. More likely, the clients will not want to pay as much for the work. We have already seen big clients arguing that their law firms should be cutting their rates because AI is taking over more of the work.

This issue is a double-whammy for any business billing by the hour. If AI can take over more work, then the firms get a whole lot more done in an hour and finish projects faster. That means less time the hourly advisor can bill. If the clients are also demanding cuts in the per-hour rates, that will certainly leave a big mark on their finances.

What the consulting firms are saying is that more of their future success will be based on what cannot be so easily automated or turned over to AI. Of course, that sounds right.  Exactly what can’t be done by AI keeps changing, but their bet is that it will be based on interpersonal skills.

See also: AI isn’t replacing human decision-making at work just yet

Here’s where remote work comes up short

That is where the connection to remote working comes in. Their belief, which sounds right to me, is that new hires, in particular, learn interpersonal skills in face-to-face relationships. Learning doesn’t work well in virtual meetings, in part, because we simply cannot watch how others react. That assumes we are actually paying attention. In most meetings now, it seems that participants (and I use that term loosely) have their cameras off, or worse, just send their agent to take notes.

The idea is also that employees lose interpersonal skills when they don’t see each other, and new hires never develop them. Why might this be especially so for consulting firms? Because they have a business model based on learning-by-doing. New hires are given a task that their mentor/supervisor, who might only be a year or so ahead of them, knows how to do, and the new hire initially can watch to see how it is done. Then they do it. In other words, the company is not paying for training. What is expensive about training is mainly when employers pay for someone to sit in a classroom. These models are cheap because the learner is actually producing something useful as they learn, as with apprentice systems in the trades.

So, the solution is to bring employees together more often by reining in time spent working at home. That is unpopular with employees who have to come back but may well be necessary for the next generation: The employees who already have the skills and feel that they could do their work remotely are also the ones who need to be there so others can watch them.

The caveat is that one could make a reasonable case that interpersonal skills could be taught better and faster through training. One of the problems of learning by watching is to figure out what the right lessons actually are from what we see. Many people who have been managing employees for years or even decades are still really bad at it. If you are a new hire watching a supervisor for the first time, how can you tell what is good versus bad practice and what to copy?  The worst behaviors that violate social norms are pretty obvious, but good supervisors sometimes make us do important things we don’t like and might therefore seem bad to us. So, how do we calibrate that?

One of the hidden benefits of remote work is that it may have made the value of interpersonal skills more obvious, and it appears that some of the big consulting firms are now providing training and coaching on it. Maybe we had to lose them to see why they were important.

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Nirit Peled-Muntz, chief people officer at HiBob, has been at the center of the growth, building the HR function largely from the ground up, as well as creating the org design and processes to enable HiBob to tap high-potential talent from around the world.

The leadership philosophy that has come to define Peled-Muntz’s 30-year HR career—which includes HR leadership roles at Caeserstone, Lumos Global and IQVIA—helped her create an HR function primed to be a strategic driver of growth.

“I always work to bring together people I appreciate, who I think are smarter than me, who add value. Remove the barriers for them, get them the budget and resources and then let them work,” she says, noting she sees herself as a “facilitator” for others’ innovation. This philosophy helped earn Peled-Muntz the title of HR Executive’s 2026 HR Leader of Distinction.

Alongside HR Executive of the Year Monique Herena, Peled-Muntz will be honored at the Icons Awards dinner Oct. 19 during HR Tech in Las Vegas, followed by a celebration in partnership with the Institute for Leadership and Work in Boston.

Listening and leadership

Peled-Muntz joined HiBob in 2020, just a month after the COVID pandemic started. As she worked to grow both her function and the organization, the disruption caused by the pandemic prompted her to lean heavily into continuous listening to ensure employees’ needs were being met. This strategy persists to this day, as leaders hold frequent roundtables, one-on-ones, and conduct employee surveys multiple times a year—which have a participation rate of about 85%.

“When I run a survey, the most important part is the action plan afterwards and the communication it takes to make people understand their feedback is valued,” she says.

The trust leadership built with employees over the years is evident in the metrics: Engagement sits at 4.2 out of 5, voluntary attrition remains below 10% and eNPS is between 35 and 40. The organization has also received accolades, including being named as one of the UK’s Best Workplaces, as well as receiving nods as a top employer for wellbeing and employee development. HiBob’s Leadership CODE Framework—built around the leadership pillars of connect, own, disrupt and evolve—has also earned awards.

An ongoing investment in manager and leader development is essential to such broad talent successes, Peled-Muntz says.

Leadership development is all created in-house—“we never take anything off the shelf”—and designed to specifically speak to the issues managers and leaders identify. In fact, leadership development programs spearheaded by Peled-Muntz have garnered participation from 91% of leaders, while such initiatives have reported improvements in leadership dimensions of up to 20%.

‘Reimagining’ what’s possible with AI

Leadership development is particularly critical as organizations navigate the influence of AI. Just a few years ago, the C-suite was setting strategies that looked ahead a handful of years; now, strategy may change every two months, given how quickly AI is evolving.

“Leaders have one of the most difficult roles,” she says, “because there’s no perfect playbook on how to implement AI, and they need to be there day-to-day with employees and give them confidence.”

At HiBob, Peled-Muntz says leaders are resisting “just putting AI powder on everything.” Throwing agents on top of all of the tasks associated with talent acquisition, for instance, won’t meaningfully move the needle; rather, the work needs to be redesigned from scratch, with consideration given to where AI could augment and where a human touch is critical.

Since Peled-Muntz rolled out the organization’s AI Readiness and Workforce Transformation strategy—leveraging learning, AI champions, leadership modeling, experimentation and workflow integration—more than 95% of HiBob employees have become active AI users. Employees have conducted more than 2,500 AI experiments and undergone 1,200 learning hours on AI, leading to approximately 200 AI proofs-of-concept and more than 150 AI agents embedded into workflows.

“We knew we had to have employees play and think and reimagine how things could look, and that has to start from the top,” says Peled-Muntz, noting HiBob CEO Ronni Zehavi advocated several years ago for a philosophy to drive how employees should view AI integration and stated that the tech can help employees “do more with more.”

“In the beginning, there was so much anxiety. We said, ‘We have to change this and create something that gives people confidence.’ We knew they would reject change if it feels threatening.”

Culture at the core

HiBob’s AI transformation embodies the philosophy that threads through Peled-Muntz’s work: The company and the people have to grow together.

“Whenever we talk about how we grow as a company, that’s connected to how we grow our people,” she says. “All of our people initiatives have to be connected to our business strategy; we’re always thinking about how we’re going to support the company’s success.”

The function is equally intentional about the role of culture in people and organizational success.

When HiBob pivoted to become a remote-first, global company, it “honed in” on building relationships among employees around the world. For example, when designing leadership programs, attention is given to grouping leaders from different geographic regions in an effort to help them build connections across barriers.

As the company has expanded into new regions, her team has worked to lead with a global perspective alongside a strong local approach. For instance, wellness initiatives in Germany may involve sporting programs like biking, sailing and running, while in the UK, employees are more inclined to initiatives that involve food and drink, games and entertainment.

“We create a global framework, but give people the freedom to decide what works with their teams locally,” she says.

In a nomination letter for the Leader of Distinction award, CEO Ronni Zehavi cited Peled-Muntz’s capacity to link culture and business performance.

“She didn’t simply build a people function; she helped build the culture that became the foundation for HiBob’s growth,” he wrote. “As we’ve scaled globally, expanded into new markets, navigated acquisitions, embraced hybrid work and adapted to the AI era, she has ensured we never lost what makes HiBob special.”

The breadth of what Peled-Muntz has touched at HiBob has made her not just an exemplary people leader but “one of the architects of the company we have become,” Zehavi says.

“She has created an environment where people feel they belong, leaders are empowered to succeed, and high standards go hand in hand with trust and care,” he adds. “The result is a culture that attracts talent, develops leaders, drives engagement and enables sustainable growth.”

Daniel Marsili, president of the National Academy of Human Resources and a judge for this year’s HR Executive of the Year and HR Leader of Distinction competitions, calls Peled-Muntz an “outstanding choice” for the honor.

“It is clear that she is regarded as one of the company’s key architects,” Marsili says. “She possesses a unique ability to connect culture to business performance and has helped foster an environment defined by belonging, high performance and sustained growth.”

That Peled-Muntz was able to help grow the organization so much, so quickly while still delivering exceptional experience for employees is a testament to the caliber of her leadership, wrote Kelly Durkan Bean, principal and CEO of Heron Strategy Partners, in a recommendation letter.

“While many organizations can scale their workforce,” she writes, “few can do so while preserving the culture, engagement, belonging and employee experience that fueled their success in the first place. Nirit has been instrumental in helping HiBob scale without losing its soul.”

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“I really wanted to completely sync up the human resources strategy with the business strategy, which has always been a hallmark of everything I do,” says Herena, noting she also wanted the employee experience to mirror the mobile-first, user-friendly experience customers encounter with American Express. “I wanted our colleagues to have an experience where they have easy ways to get what they need, when they need it. When you start to improve the most important and most frequent journeys for colleagues, people really feel the investment.”

It’s a philosophy that has overlaid everything from learning to benefits to internal mobility, with sprawling talent wins that helped earn Herena the title of HR Executive’s 2026 HR Executive of the Year. Along with HR Leader of Distinction Nirit Peled-Muntz of HiBob, Herena will be honored at the Icons Award dinner at HR Tech next month, followed by a celebration with the Institute for Leadership & Work in Boston.

Streamlining learning for impact

In a recommendation letter for the HR Executive of the Year award, CEO Steve Squeri said Herena was adept at aligning the vision for HR with the Framework for Winning, the company’s strategic plan.

“She took what we do so well for our customers and applied it to our colleagues, putting them at the center of all we do with a vision of providing the best colleague experience to fuel growth,” Squeri wrote. “The Colleague Experience Group’s (CEG) vision, mission and strategy she designed are focused on driving deeper connections with all colleagues across the business so we can gain a deeper understanding of their needs and improve their experiences so they can be and deliver their best.”

Herena’s work streamlining learning and development is a prime example of her efforts to strategically enhance the colleague experience for American Express’ 77,000-person workforce. Earlier this year, the organization launched Amex U, an enterprise learning ecosystem unifying previously disparate learning into one integrated experience.

“We had a lot of learning happening across all the different business units, but we’re at a size and scale where it just wasn’t efficient or the best way for colleagues to experience and access learning,” Herena says.

Colleagues now have access to more than 20,000 on-demand courses, including workshops and hands-on experimentation, with an emphasis on bite-sized offerings and self-paced learning. In just two months, the program reached more than 45,000 unique users—about 1,400 per day—and more than 231,000 course completions through sessions that cover leadership, business and technical skills.

The offering was designed with an end-to-end perspective in mind: Making learning more available, digestible and meaningful in order to enable employees to enhance the skills they need to advance their careers with American Express, and in a way that helps the organization drive toward business goals.

That investment in learning is paying off. In the last three years, 40% of Amex colleagues have moved into new roles or been promoted. At the same time, more than 80% of workers surveyed through Amex’s annual Colleague Experience Survey now say they feel equipped to manage their careers and are satisfied with the development conversations with their managers.

Doubling down on benefits investment

Herena’s work to enhance employee experience has paid dividends: Last year’s Colleague Experience Survey found that 91% would recommend the company as a great place to work. Amex has also received a number of external awards for EX: It ranked No. 4 this year on Fortune’s “100 Best Companies to Work For” after seven years running of being in the top 10, and 26 years on the list. 

This year, the Business Group on Health recognized Amex for the 15th time, with its Excellence in Global Health & Wellbeing, Mental Health & Health Equity Award.

Benefits is one space where Herena has innovated and invested in pursuit of a stronger colleague experience.

Among the enhancements added in recent years are new mental health training, increased bereavement support, free financial wellness tools, and family support—including free consultations with doulas and expanded IVF and cryopreservation resources. She also led the enhancement of Amex’s Healthy Minds program, doubling the number of free sessions offered and ensuring access to licensed counselors. As of June, more than 23,000 colleagues and family members registered, taking part in more than 122,000 counseling sessions.

In its annual survey, the colleague experience team found that about 90% of employees report that the company’s benefits offerings now support their wellbeing.

Notably, the decision to invest in employee benefits comes at a time when some organizations are slashing their offerings in an effort to keep up with rising costs.

But Amex views employee benefits not as a cost, but rather a long-term investment. “It really pays dividends if our colleagues are healthy and able to be at their best for our customers and each other,” Herena says.

“Investing in our colleagues means that we’re investing in our business growth. Backing our colleagues means showing up for them in the moments that matter, at every stage of their life,” she adds. “And that can be in times of growth or in times that are a little tougher.”

Change leadership: the key to AI transformation

AI integration is also increasingly becoming a key driver of colleague experience. While HR leaders across industries are facing pressure to move fast on AI transformation, to become true “architects” of work in the age of AI, they need to move intentionally, focus on job and org design, and emphasize effective change leadership, Herena says.

“It’s about using human judgment, taking all the information you have and asking, ‘Is this the right area in which to apply technology or automation to move faster and create value or not?’ ” she says.

Such decisions need to be undertaken with a focus on colleague experience and core values. At Amex, that includes doing what’s right, respecting one another and working cooperatively.

Building trust is also central to these values. In an environment where AI transformation is happening quickly and employees are worried about job displacement, trust between employees and leaders is going to require frequent communication and an emphasis on transparency, Herena says.

“That’s the way trust is built. We don’t pretend that things don’t change on a daily basis, and many times within the same day,” she says. “As the external environment changes, we need to change with it to innovate, provide great service and experiences, and compete effectively.”

While flexibility is critical, HR can also help anchor the workforce around those things that aren’t changing: leadership behaviors, values, and the foundations of the business strategy. Learning to balance adaptability and stability requires today’s HR leaders to remain focused on the importance of education.

“We’re all learning as we go through this,” Herena says. “The most important thing you can do—and what you can control—is to be a learner. Be open to learning, doing things differently, and adding value, whatever your role is.”

Squeri says Herena embodies what it means to be a “courageous and transformational change leader.”

“We are a 176-year-old brand, and while it is important to respect the past, you also need to build on it, innovate and challenge the status quo,” he wrote in his recommendation letter. “That is what Monique does every day at American Express.”

Since joining Amex, Herena “laid out a new vision, mission and HR strategy to support the strategy of the company,” says HR Executive of the Year judge Fred Foulkes, professor emeritus at Questrom School of Business and interim co-director and founder of the Human Resources Policy Institute, now known as the Institute for Leadership & Work. “One has to be impressed with all that she has accomplished,” he says.

Her influence extends far beyond the walls of American Express, Foulkes adds.

“In addition to her visible leadership at AmEx, she has given back much to the profession—from serving on several prominent HR advisory boards to teaching at leading colleges and universities,” he says. “She is an outstanding role model for students and future HR leaders who want to contribute and make a difference.”

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In global payroll, the market has moved beyond the traditional processing services toward a platform approach for automated processing, instant payments, funding, FX, digital wallets, pay modality and money movement. With payroll commonly the largest line item on most P&Ls, it’s quickly becoming a liquidity strategy for global enterprises.

Buyers are no longer asking providers to calculate payroll in a country. They’re increasingly asking whether that vendor can orchestrate the full pay obligation across worker data, compliance, approvals, funding, FX, payments, statutory remittance, employee visibility and reconciliation. All of which requires a very different infrastructure, and why global payroll providers are beginning to lean on and look more like fintech than legacy global payroll solutions.

Payroll is essentially becoming unbundled, split into layers, enabled by tech and repackaged by solution providers, increasing the value of capturing this opportunity.

The calculation layer, compliance layer, funding layer, treasury layer, payment rails, payout destination, employee experience, income data layer, wallet, rewards and bank relationship are no longer automatically bundled into one linear journey. They are becoming separate market opportunities with different niche providers competing to own each layer in the stack, and the employer is increasingly positioned as the gateway to the most important financial event in an employee’s life: getting paid for their work.

Global payroll and EOR (Employer of Record) providers like Papaya Global hope to own it all, betting big on consolidating more of the payroll delivery chain into a single platform experience for end-to-end compliant support for a hire-to-payment operating stack. Papaya Global also bet massively as an early mover on global payments, investing in the infrastructure and positioning around a one-stop shop for all things global employment and payments. It supports workforce payments across 180-plus countries, through its proprietary Papaya Global Workforce OS, Contingent OS, Payments OS and OnePay solutions as part of its broader orchestration model.

Global EOR Multiplier announced its strategic direction, North Star and path to enabling a “Global Exchange for Work.” Essentially, the infrastructure where the key elements of global employment converge: countries, companies and talent to transact, similarly to a stock exchange. Underpinning Multiplier’s strategy is global employment, compliance and payments infrastructure with 150 owned legal entities globally, localized gross-to-net calc engines, a compliance intelligence engine and dynamic API’s.

It is also bringing global payments in-house, owning the orchestration and underlying rails and pursuing a money movers license. Aside from its compliance intelligence engine and expanded embedded payroll and EOR capabilities, one of Multiplier’s other big bets is workforce treasury with a roadmap that aims to bring to market a workforce wallet, stablecoins tied to USD, payroll forecasting, an FX optimization engine and treasury intelligence.

Payoneer, Revolut also entering the SMB payroll/paycheck space

Global payments providers like Payoneer and Revolut are coming after SMB payroll and the paycheck all the same but from a different direction.

Already thriving on the payments side, Payoneer scooped up two global EOR providers: Singapore-based SKAUD and Ireland-based Boundless, pairing its payments capabilities and rails with two global payroll and employer of record specialists. Revolut turned on payroll and EOR for the U.K., targeting its more than 800k customers, again pairing a well-adopted consumer and SMB offering with proven payments infrastructure, worker-targeted payments and compliance services where its customers already move money.

In case you haven’t noticed, Walmart has also quietly entered the fintech + HR tech game.  Back in 2022, Walmart and Ribbit Capital teamed up for a start-up called Hazel to build financial services for Walmart associates and customers. Shortly after, Hazel merged with EWA provider Even, and quickly paired that with ONE, a digital banking platform it also acquired around the same time. The goal was and remains to build out an all-in-one app to support both its millions of consumers that shop at its stores weekly and its 2.1 million associates that run them. The solution now operates under the OnePay banner with OnePay@Work now a certified Workday Wellness partner and targeting large enterprise employers with the solution it launched to support its own workforce wellness challenges.

A deeper look at the consumer side where work, money and life intersect

Stablecoins are now moving from crypto-native adoption into mainstream payments infrastructure. Visa just launched USDC stablecoin settlement in the U.S. and reported more than $3.5 billion in annualized stablecoin settlement volume at launch.

Deel announced that companies holding its stablecoin, USDC, can fund payroll and payments through Coinbase, with contractors able to withdraw funds in more than 150 currencies, including crypto payouts. That same week, Deel announced the Deel Stablecoin Wallet—a dollar-backed balance embedded in Deel where contractors can hold wages in stable currency, earn rewards and spend through the Deel Card. It’s launching in Argentina first, expanding across Latam, with APAC, the Middle East and Africa up next.

This is further signaling that global workforce platforms increasingly see payroll funding, contractor payouts, currency flexibility and digital money movement as part of the broader employment operating model.

Stablecoins are huge for payroll because they can help companies move money (and payroll) faster and more efficiently, especially across borders. Today, MNCs operating globally often require companies to pre-fund accounts in different countries, currencies, banks and payroll partners days before payday. That can leave cash sitting idle, create FX timing issues and make last-minute payroll changes hard to manage.

A stablecoin or digital dollar can move 24×7, 365 days a year, instead of waiting on bank cutoffs, intermediaries or local banking timing.  A company or payroll provider can now move funds quickly to where they are needed, convert them into local currency or pay workers through approved payout options. This is especially useful for contractor payments, emergency off-cycle payroll, compliance requirements, countries with unstable currencies or that are difficult to fund, or globally distributed workers, offering organizations a faster liquidity and settlement rail that provides the infrastructure for real-time payroll and workforce payments.

Part four continues with a look at the employee-consumer experience, where this convergence is heading and my advice to HR leaders.

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The study by research and advisory firm idealis finds that 72% of employees who use AI are concerned about the safety of their jobs, compared to just 46% of non-users.

“U.S. workers are fearful of the impact of AI on their livelihoods,” researchers write. “Greater exposure does not mitigate concern; it may exacerbate it.”

When employees become AI adopters, the study finds, they “are the first to witness the immense change it’s bringing to the world and the workplace. This knowledge can directly contribute to concern about their own place in the workforce of the future.”

Addressing the ‘high level of concern’

Gen AI use is widespread, and growing quickly. The firm found that 62% of workers surveyed use AI for professional purposes, an increase of 16 percentage points from 2025, which researchers say reflects “a pace of adoption that is hard to keep up with at scale.”

The technology is evolving so quickly that as soon as an employee starts using a tool, there’s something new to learn—preventing adopters from getting comfortable with their AI knowledge and driving up concern.

“As organizations continue to accelerate their AI use expectations, it is essential to remember and acknowledge the high level of concern employees are feeling,” researchers write. “As the technology continues to evolve, workers may perceive technology advancement as a barrier to personal success, reducing adoption out of fear.”

Concern about AI varies across demographics. Those with a graduate degree or more, for instance, report more worry about AI’s impact than do those with no college degree. At the same time, white and Hispanic employees are more concerned than Black and Asian survey respondents.

A new mandate for leaders

There are also differences across job levels. Leaders are also more likely than individual contributors to use AI—76% versus 61%—and are also more concerned about AI. Eighty-eight percent of leaders report being concerned about the proliferation of AI tools, compared to 76% of non-leaders.

“As workers are searching for guidance from everyday and enterprise leaders,” researchers write, “they may be met with deep concern about the impacts of AI in society and in the workforce.”

Despite this, leaders are more likely than employees to see productivity gains from using AI and to agree that AI improves their quality of work.

Leaders who lean into “human-centered” leadership will be best positioned to drive AI confidence in their teams, the report finds.

“Leaders who pair the speed of AI adoption,” researchers write, “with visible, consistent care for their people will be the ones who carry their organizations through this transition with trust intact.”

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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.

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