That tension is becoming more visible as HR’s role expands across hiring, employee experience, accommodations, investigations and workforce planning. In many organizations, new tools arrive before governance practices are fully mature. The result is a familiar pattern in which leaders focus on efficiency and capability first, then address trust concerns only after confusion or resistance surfaces. The challenge is no longer just about policy design. It is about confidence in HR’s judgment.

See also: Why fiduciary governance matters more than ever

The challenge is especially clear in hiring. AI-enabled tools can help recruiters manage volume, speed communication and support more consistent workflows. But they can also reinforce narrow assumptions about what a qualified candidate looks like, especially when screening systems favor continuous, traditional career paths and deprioritize applicants with gaps or non-linear experience. That matters because many qualified candidates do not fit a conventional pattern, even when they are fully capable of succeeding in the role.

Research from Harvard Business School and Accenture has highlighted a large population of “hidden workers” who are often overlooked because of rigid hiring filters and assumptions about career progression. The Society for Human Resources Management (SHRM) has similarly emphasized the need to unlock untapped talent pools and rethink how employers define readiness and fit. When automated screening tools reinforce those patterns, organizations risk narrowing their talent pipeline rather than expanding it. That creates both a fairness concern and a talent strategy problem.

Regulators beginning to respond regarding AI hiring tools

Regulators are also beginning to respond. New York City’s Local Law 144 requires certain employers and employment agencies using automated employment decision tools to complete a bias audit, make information about that audit publicly available, and provide required notices to candidates or employees. That does not mean every organization must build its hiring strategy around one local law. It does mean the direction of travel is clear, with accountability, transparency and governance becoming part of the HR technology conversation.

A second pressure point is workplace monitoring. Productivity dashboards, badge data, collaboration metrics and activity-tracking tools are often implemented as operational or technology decisions, but employees rarely experience them that way. What leaders may view as efficiency or risk management can easily be interpreted as surveillance, especially when purpose and limits are poorly explained. HR has a critical role here because trust is shaped not only by what data is collected, but also by whether employees believe the organization is using that data fairly and appropriately.

A third pressure point involves sensitive employee information, including accommodation-related and medical-related data. This is where trust often breaks down fastest, not because of malicious intent, but because confidentiality boundaries are poorly understood or inconsistently applied. Managers may be told more than they need to know, ask questions they should not ask or share details too casually in the name of coordination. For HR leaders, this is a governance issue as much as a compliance issue because inconsistent handling of sensitive information quickly undermines employee confidence and increases the risk of disclosure beyond those with a legitimate business need to know.

HR leaders do not need to become privacy officers or AI specialists to govern employee data well. They do need a disciplined way to think about purpose, transparency, proportionality, access and accountability. This is not just a vendor-management issue or a technology-policy issue. It is a leadership issue because HR owns how these decisions are experienced by candidates, employees and managers. In AI governance, that approach aligns with the broader risk-management model reflected in the National Institute of Standards and Technology (NIST) AI Risk Management Framework.

How does employee data trust work in practice?

In practice, that means asking a disciplined set of questions before expanding data use. Why are we collecting or using this information, and is the purpose specific enough to defend? Are we collecting more than we need, or retaining more detail than the business objective requires? Could we explain this clearly to candidates, employees and managers in language that builds trust rather than suspicion? Who truly needs access to this information, and who is accountable for ensuring managers and HR staff handle it appropriately, including review and escalation when problems arise?

These questions matter because data governance in HR is no longer only about compliance checklists. It is about whether people believe HR is exercising sound judgment in moments that directly affect opportunity, dignity and confidentiality. Organizations that perform best in this area are not necessarily the ones with the most sophisticated tools. They are the ones with the clearest boundaries, the strongest communication and the discipline to match innovation with accountability.

For CHROs and senior HR leaders, the strategic question is no longer whether employee data will play a larger role in the function. It will. The more important question is whether HR can govern that data in ways that are fair, explainable and worthy of trust. In the years ahead, some of HR’s most important leadership decisions will involve not just what the organization can do with employee data, but what it must do to remain worthy of employee trust.

The post Beyond compliance: A strategic HR framework for employee data trust appeared first on HR Executive.

That’s according to a new report from the University of Phoenix, based on surveys of 5,000 employed Americans and 1,000 employers. The research found that—amid low turnover and near-daily headlines about AI-driven layoffs—employees are recognizing the key capability they need to survive in today’s job market: AI skills. With that, a majority are feeling more confident in their ability to leverage the tech—which is driving up concern among employers that, without the right opportunities, employees with newfound AI skills will take their skills elsewhere.

John Woods, provost and chief academic officer at University of Phoenix, says that, as workers embrace AI’s career growth potential, employers are faced with an “important moment” in which they need to “lead with AI clarity—because organizations that make AI part of a broader growth strategy for their people may be better positioned to support engagement, satisfaction and prevent a mass exodus.”

See also: The great AI skills paradox: When employee adoption outpaces organizational support

AI skills: where things stand today

The potential for such an exodus is building, researchers write. Beneath the “seeming stability” of low turnover, “a new power dynamic is emerging: Workers are quietly preparing for their next move.”

AI is primed to be a key catalyst. University of Phoenix found that among employees who use AI, three-quarters say their confidence at work has increased, and more than 80% say the tech is helping guide their career progression. Among all survey respondents, about two-thirds feel positively about their job prospects—a figure that rises to three-quarters when employees are knowledgeable about AI.

Employers share that optimism: More than 70% of employers surveyed said AI-fluent employees are more likely to advance in their company than others. Yet, the research found, organizations aren’t necessarily owning the AI upskilling journey.

About half of workers are teaching themselves how to use the tech, and 60% want their employer to do more on that front. Majorities of workers also report that they “don’t know where to begin” when it comes to incorporating AI into their work and crave better products and processes to support their AI journey.

Importantly, job satisfaction rises when employers provide a clear plan for how AI can enable career growth. Employers seem to acknowledge this reality, and worry about gaps, as nearly half say they fear they won’t be able to retain AI-fluent talent.

“When workers can see how AI skills translate into advancement, they are far more likely to stay,” researchers say.

4 must-haves for AI skills-building as a retention lever

University of Phoenix researchers outlined four strategies HR can help leaders look to as they aim to build an AI-fluent workforce that stays:

1. Define AI career pathways: More than half of employees surveyed say AI is not mentioned in their job description, which reflects how few organizations have created formalized structures around how AI will impact career potential.

2. Assess skills: Workers are craving new skills, and want their employer to take the lead, which organizations concerned about retention should do, researchers say—especially given that more than three-quarters of employees say they would be more likely to stay at their organization if they could more easily apply new skills to their work. Any skills development and internal mobility strategies, they caution, should be informed by a comprehensive skills assessment.

3. Expand training and support: Even though employees are often willing to undertake their own AI education, they want support from their organizations. Yet, there is a perception gap, the report finds: Employees “report significantly less access to AI resources than employers believe they provide,” a divide that HR can help expose in order to make more meaningful progress with AI skills-building.

4. Empower managers: The research found a strong link between manager knowledge about AI and their direct reports’ job satisfaction: Nearly 80% of those with AI-fluent managers feel positively about their own careers—compared to 61% of employees whose managers aren’t as skilled around AI.

The post The AI training mistake that could turn job hugging to job hopping appeared first on HR Executive.

Recent reporting in HR Executive highlights a critical shift: Focus time has dropped to a three-year low. As AI-driven workflows accelerate and digital interruptions increase, employees are losing the uninterrupted cognitive space required for deep thinking, reflection and effective decision-making.

At the same time, expectations are rising. Employees are being asked to process more information, make more decisions and respond at greater speed than ever before. This combination is creating a new and under-recognized pressure in AI-enabled workplaces—what might be called the Cognitive Crunch.

The hidden ROI problem in AI

Organizations have invested billions in artificial intelligence with the expectation of productivity gains. Yet, emerging evidence suggests a growing disconnect between investment and return.

Despite massive global investment in AI, research from Boston Consulting Group indicates that most organizations are still seeing little to no measurable impact on profits. However, while employees report that AI tools can improve individual productivity, organizations are not consistently seeing gains at the system level.

This reveals a critical gap: AI may be accelerating work, but it is not yet improving organizational performance. The implication is significant. If productivity gains are not materializing, then something else is absorbing the value AI is expected to create.

One increasingly visible explanation is cognitive load or overload.

The rise of cognitive overload in AI-enabled work

Research is beginning to describe what many employees are already experiencing. A recent Harvard Business Review study of nearly 1,500 full-time workers using AI tools identified a phenomenon referred to as “AI brain fry,” where employees reported higher levels of cognitive load, information overload and mental fatigue when interacting extensively with AI systems. Related insights from Harvard Business Review further highlight how AI-driven work places employees in a continuous cycle of evaluation, correction and decision-making, intensifying cognitive strain.

Importantly, this strain is not driven by task volume alone, but the need to continuously evaluate, verify and refine AI-generated outputs—placing leaders and employees in a constant state of cognitive engagement.

At the same time, emerging reporting suggests that AI may be creating a form of false productivity—where time saved is offset by time spent correcting, refining and verifying AI-generated outputs. Employees are completing tasks faster, but not necessarily with less effort, as the need for critical thinking becomes central to the work itself.

The nature of work has not just changed—it has intensified.

When faster work becomes harder work

One of the central paradoxes of AI is that it speeds up individual tasks while increasing the cognitive demands surrounding them. AI systems can generate reports, recommendations and analyses in seconds. Yet these outputs rarely remove responsibility from employees. Instead, they introduce a new form of work: cognitive supervision.

Employees are no longer only performing tasks—they are continuously monitoring, validating and interpreting machine-generated outputs. As AI increases the volume and speed of outputs, employees must engage in more frequent judgment, more rapid critical thinking and decision-making, and more constant attention-switching.

Meanwhile, the reduction in focus time means that this cognitive work is occurring under increasingly fragmented conditions. The result is not simply more work—it is more cognitively demanding work performed under less favorable conditions.

From burnout to accelerated burnout

Traditionally, burnout has been associated with long hours, emotional labor or excessive workload. Importantly, burnout occurs when there is a mismatch between the worker and the context. The context of work is changing with expectations to embrace and use AI. However, the next phase of workplace fatigue may be driven by sustained cognitive pressure.

This may be understood as accelerated burnout.

Rather than developing gradually, burnout may now emerge more quickly as employees face continuous streams of information, compressed decision timelines and limited opportunities for mental recovery. Emerging evidence suggests that employees experiencing AI-related cognitive fatigue are more likely to make errors and consider leaving their roles.

This directly links cognitive load to both performance risk and retention risk.

The emerging risk: Always-on judgment

In many organizations, a consistent theme is emerging: a sense of perpetual mental fatigue. Employees describe the experience as being constantly “on the clock” for decision-making. As AI usage increases, work increasingly involves frequent cycles of verification—reviewing generated emails, reports and data outputs—creating a sustained cognitive demand that can lead to exhaustion well before the end of a working day.

One of the least visible consequences of AI-enabled work is the rise of always-on judgment. Because AI continuously produces outputs, employees are required to continuously interpret, critically evaluate and respond. The boundaries between tasks begin to blur, and opportunities for cognitive recovery diminish.

The mental load does not reset—it accumulates. While organizations routinely measure engagement and productivity, very few currently measure cognitive load.

This may be the missing metric in understanding why AI investments are not translating into expected returns.

Leaders at the center of the strain

Recent global data from Gallup’s State of the Global Workplace adds a critical dimension to this shift. Leaders report higher levels of engagement and overall wellbeing than employees. However, they also report significantly higher levels of daily stress, anger, sadness and loneliness.

This reveals an important shift: Those who are most engaged in their work are not protected from burnout—they may be the most exposed to it.

In the context of AI-enabled work, this is significant. Leaders are not only responsible for their own performance, but for interpreting AI outputs, making high-stakes decisions and guiding teams through ongoing change. As the Cognitive Crunch intensifies, the burden of sustained judgement and decision-making is increasingly concentrated at the top of organizations.

If this pressure is not managed, it risks cascading through teams and amplifying both cognitive load and burnout across the organization.

Strategic imperatives for HR leaders

For HR executives, the challenge is no longer simply overseeing the management of workload. It is managing cognitive capacity. If AI changes the nature of work, then job design, performance expectations and wellbeing strategies must evolve accordingly.

Organizations will need to redesign roles to reflect cognitive demands rather than task outputs, while also protecting focus time as a strategic resource and wellbeing initiative rather than a personal preference. They will also need to begin monitoring cognitive load alongside engagement and productivity and clarify accountability in human-AI decision-making environments where responsibility can become blurred.

Without these adjustments, organizations risk achieving short-term efficiency gains at the expense of long-term workforce sustainability.

The real constraint in the AI era

AI will undoubtedly reshape the future of work. But technological capability alone does not determine organizational performance. The Cognitive Crunch highlights a growing risk: As work becomes faster and more cognitively demanding, employees may reach burnout more quickly—particularly those in high-responsibility and decision-intensive roles.

If this continues, organizations risk not only declining performance and rising errors, but the loss of experienced, high-performing employees. As AI continues to reshape the workplace, organizations will need to rethink how work is designed—including how much cognitive demand is placed on employees; how time for focus, reflection and recovery is protected; and how sustainable employee performance is supported over time.

If burnout is accelerating under the Cognitive Crunch, how must organizations redesign work to sustain both their employees and the leaders responsible for guiding them?

The post The cognitive crunch: Why AI is accelerating burnout appeared first on HR Executive.

The report found that many leaders today still use legacy office-era metrics such as visible activity to judge performance, rather than measuring “outcomes, alignment and impact.” The report, based on conversations with more than 100 CHROs, urges leaders to adapt to the times.

Stephanie Larson, principal, strategic research at Seramount, explains that the research points to an “AI productivity paradox” at work: AI is able to make work faster, but not necessarily better. “And that,” she says, “is what makes it a productivity problem.”

At the same time, she adds, AI can lower the cost of production, but not the cost of judgment. So, if employers focus only on using AI to speed up processes, they may get more output, but also more need for reviews, more rework, more ambiguity—and ultimately, longer cycle times.

“AI actually can weaken engagement, because people lose clarity about what good performance looks like and where accountability sits,” she explains. Because of that tension, organizations need to be asking whether they are building the “human judgment” needed to make AI’s acceleration valuable.

HR’s role in confronting the AI productivity problem

To Larson, AI should act as a “thought partner, not just a tool,” adding that AI can be most useful when it helps people think better, not when it does the thinking for them.

“I believe we miss how their strengths can help us interrogate and improve our work. For HR leaders, that means building a workforce that knows how to use AI—not just to produce more, but to question more,” she says.

Employees also need to ask: Is this accurate? What might I be missing? What context or nuance got flattened? What risk am I taking on if I rely on automated output?

“Fluency with the AI tool is not the same as judgment,” she says.

Larson explains that many organizations are racing to scale AI adoption so quickly that it may favor deployment speed over building employee judgment and decision-making capabilities. And that can drive four significant risks, she adds, including:

• Reputational risk: Polished but lower-quality work can be circulated before anyone catches potential problems.
• Revenue risk: Managers can end up spending time correcting output that only looked efficient upfront.
• Leadership risk: Many of the tasks AI is absorbing were never just tasks; they were training grounds where people learned judgment.
• Inclusion risk: AI tends to amplify the systems already in place, so early differences in access to training, manager support and room to experiment can quickly widen into larger gaps in capability and opportunity.

“When it comes to talent development, HR should be prioritizing ways to ensure employees can effectively review, challenge and refine AI-generated output,” Larson says.

Larson would focus on critical thinking, writing, revision, communication and problem-solving—normally framed as “soft skills.” However, she adds, there is “nothing soft” about the ability to communicate clearly, weigh competing perspectives, anticipate counterarguments or make sound decisions during complex moments.

“I spent nearly 15 years in higher education, most recently as an English professor, and that background still shapes how I think about AI,” she says. “We need humanists and social scientists now more than ever, because critical thinkers know how to question, critique, contextualize and challenge something, not just accept it at face value.”

The best use for AI

Looking ahead, Larson says, organizations that lead the pack will be those that understand the objective is not merely faster workflows, as “smarter goals” offer better judgment, wider trust and more equitable access to growth.

“In a world where AI can help nearly everyone be productive faster, the real differentiator becomes whether an organization is still advancing its people: whether employees are learning to think critically,” she says.

To Larson, that means the strongest, most successful organizations will use AI to strengthen human capability.

“They will protect the developmental experiences, mentorship and accountability structures that build future leaders,” she says. “That will show up in performance because the work is better, in culture because people trust the system more and in retention because people will stay where they can continue to grow.”

The post Why the ‘AI productivity paradox’ calls for HR’s intervention appeared first on HR Executive.

Who made the list?

This year’s top-five companies include:

• JPMorganChase
• Alphabet
• Microsoft
• Amazon
• Wells Fargo

LinkedIn researchers note that the standouts are embracing new opportunities for building the workforce’s skills and offering meaningful career growth opportunities amid AI transformation. For instance, they cite how JPMorganChase is “going all in” on AI, with a $2 billion annual investment across the enterprise that extends to employee upskilling and training programs focused on AI integration.

At Alphabet’s Google, the tech giant is meeting a new demand for skilled labor—as it scales up the creation of data centers—with paid training for skilled workers in local, underserved communities. Meanwhile, Microsoft is empowering its workforce to take the lead on agentic AI integration with a training program that teaches employees—no coding experience needed—to create their own specialized AI agents.

“AI is reshaping roles at an accelerating pace,” writes LinkedIn Senior Editor Juliette (Faraut) Bell about the outsized influence of AI investment among companies that are setting their workforces up for sustainable career growth. “Finding a company that helps future-proof your next step is more vital than ever.”

Career growth opportunities across industries

Interestingly, many of the organizations that landed on the Top Companies list are in the tech industry, which is undergoing massive AI disruption that many credit with the waves of layoffs. Tech is, by far, the industry that has seen the most job cuts this year: Facebook parent Meta, for instance, is expected to slash its workforce by 20% this year, with the first half of the cuts coming this spring.

Notwithstanding those layoffs, a recent report found there were more than 80,000 tech layoffs already this year, as organizations reorganize and reallocate in the face of AI transformation.

Yet, LinkedIn data finds, there are still plenty of opportunities for long-term career growth in tech. Three of the top 10 Top Companies are in tech. Microsoft, in particular, recently made headlines for its strategy to cut headcount by about 7% with an early retirement program.

At the same time, financial services and banking—which have been at the forefront of the push to bring workers back into the office full-time—are also well-represented, with four such orgs in the top 10. Top-ranked JPMorganChase, in particular, took a hardline approach to RTO, with CEO Jamie Dimon reportedly blasting an employee-led petition protesting the company’s RTO mandate and telling workers unhappy with the rule to quit.

Despite the obstacles this landscape presents for talent attraction and retention, LinkedIn’s Top Companies list highlights that leading companies can still secure candidate and employee buy-in with a demonstrable investment in future-proofing employees’ skills.

After all, Bell writes, employees’ career choices are largely driven by wanting to build skills, get promoted and set themselves up for long-term success. And they want to find out which employer can meaningfully do that.

“The question hasn’t changed,” she says. “The world of work has.”

The post JPMorganChase, Alphabet, Microsoft: Why LinkedIn tells candidates to bet on these orgs appeared first on HR Executive.

Date & Time: Wednesday, May 27, 2026 at 2:00 pm ET

Speaker:

Dennis Hill, Ph.D., HRIP, SHRM-SCP, SPHR, Chair & CEO, Board Director, International Association for Human Resource Information Management

Description:

AI is no longer a promise; it’s operational. Across leading organizations, AI is fundamentally changing how HR delivers value: accelerating onboarding, resolving employee inquiries instantly, guiding smarter benefits decisions, and orchestrating workforce transitions with precision. This session cuts through the noise to show exactly where AI is working today across the employee lifecycle and where it delivers measurable business impact.

Grounded in real practitioner benchmarks and field-tested insights, this session connects the dots between your team’s daily pain points and the highest-impact AI use cases already producing results. More importantly, it provides a practical framework to help you prioritize, sequence, and activate AI in your own environment—because the right approach makes fast deployment possible, not aspirational.

If your organization is still treating AI as an experiment, this session shows you how to move quickly, pragmatically, and confidently from isolated pilots to integrated intelligence — so your HR leaders can shift from firefighting to the strategic work that actually moves the business.

Three Takeaways You Can Apply Immediately

1. Where AI Actually Delivers ROI
Identify the highest-impact use cases across onboarding, service delivery, benefits navigation, and workforce transitions—mapped directly to cost, speed, and experience improvements.

2. What “Good” Looks Like
Understand real-world benchmarks for service cost reduction, time-to-productivity, case deflection, and employee satisfaction—so you can measure progress against meaningful standards.

3. Your 90-Day AI Action Plan
Leave with a clear, executable framework to:

• Pinpoint your top 3 high-friction HR processes
• Prioritize use cases based on impact and readiness
• Launch a focused AI pilot within 90 days

Register here!

The post AI in Action: Real HR Use Cases, Real Outcomes appeared first on HR Executive.

So, where will new grads go? New LinkedIn data culled from millions of member profiles has some clues about the jobs and industries holding the most opportunity for entry-level professionals—and they’re not all tech-focused.

Where are the opportunities for new grads?

LinkedIn’s Grad Guide 2026 offers a forecast for young job seekers, detailing the current market they’re facing. But it also has some important insights for HR.

In particular, the research highlights the fastest-growing roles for new grads. The top titles are:

1. AI engineer
2. Marketing coordinator
3. Recruitment assistant
4. Legal specialist
5. HR operations specialist

“LinkedIn data shows that many of the fastest-growing job titles for career starters are tied directly to how companies operate today—from revenue-generating positions like business development representative to technical roles supporting the rise of AI,” researchers write.

Interestingly, two of the top five roles are in the HR space, highlighting that the people function will continue to be a critical driver of business, even in the age of AI and automation. It’s a reality that Box CEO Aaron Levie recently predicted: “You can automate software creation; you can’t automate people creation,” he said.

Also of note, only two of the top 10 roles—AI engineer and machine learning engineer, which ranked eighth—are AI-specific jobs.

On a broader scale, the tech, information and media industry is considered the best bet for new grads to break into. Other industries most friendly to early-career talent include:

• real estate
• financial services
• utilities
• construction

HR’s new mandate

What can HR glean from the data? Apart from pointing new jobs to potential paths for growth, LinkedIn’s guide also describes a generation willing to think outside the box when it comes to building their careers—a message HR can embed into its current talent strategies.

For instance, LinkedIn research finds that more than half of Gen Z job seekers globally are considering non-traditional work arrangements, like freelance and contract work. This demographic is the most likely to rethink the full-time, 9-to-5 standard, researchers say. They are also more likely than others to be interested in starting their own business.

“Together, these trends point to a broader rethinking of what an early career can look like,” the report says. That vision is “one that’s less defined by a single full-time job and more shaped by flexibility, ownership and multiple paths to building experience.”

That backdrop suggests HR may need to lean into creativity to attract new grads, while offering the empowerment and flexibility they’re looking for.

Gen Z is “recalibrating the formula for career success,” SurveyMonkey’s Wendy Smith recently wrote for HR Executive. They are “prioritizing wellbeing, flexibility and alignment with personal values over traditional markers like company loyalty or the single-track corporate ladder.”

This shift is creating a new organizational imperative that falls squarely on the desk of HR leaders. They “cannot afford to ignore this emerging mindset if they want to attract, retain and effectively engage the future workforce,” Smith writes.

Yet, the entry level is increasingly on the chopping block, as AI and automation take over some of the routine tasks generally assigned to junior talent. Recent research from Korn Ferry found that nearly 40% of organizations plan to replace entry-level roles with AI.

IBM CHRO Nickle LaMoreaux says her firm sees that approach as a mistake that could have significant effects on long-term talent strategy. Apart from creating a future dearth of middle managers, she recently said at a Wall Street Journal Chief People Officer Council Summit that axing the entry level in favor of AI restricts the real growth potential of the the tech.

Instead, IBM has committed to tripling entry-level hiring over the next three years. As AI takes away the rote aspects of some early-career jobs, young talent is instead being given new opportunities to stretch across functions, build skills and chase opportunities that weren’t available due to time and labor constraints duringthe pre-AI landscape. It’s a signal, she says, that the company views AI as a driver of growth, not just productivity.

Gen Z is uniquely situated to meet that moment like no generation before, Teuila Hanson, LinkedIn chief people officer, told HR Executive this past fall. Digital natives are used to figuring out how to innovate with technology and make decisions quickly, often without all the information—after all, she says, this class of new grads started coming of age in the middle of a pandemic.

Their unique experiences likely provided them high-valued adaptability.

“Why would companies not want to bring that talent in?” Hanson asks.

The post AI is reshaping entry-level hiring. Where will new grads go? appeared first on HR Executive.

A recent AirOps study found that your owned content accounts for only 15% of brand mentions in early AI search discovery, with 85% coming from external sources. And according to Similarweb, 35% of U.S. consumers now use AI at the product discovery stage, compared to 13.6% who use search. And according to Indeed’s own data, 70% of candidates are using generative AI to research companies and prepare for interviews. This is no longer a future state prediction. This is your current candidate pipeline.

See also: Why are 93% of workers looking for a new job?

Job candidates use AI to find out employer branding

Increasingly, AI platforms are exactly where candidates are starting their research.

LinkedIn recently reported losing 60% of its B2B traffic to AI-driven environments. The detail that matters: Its Google rankings barely moved. Pages still appeared in roughly the same positions. Nothing looked broken from a traditional SEO standpoint. But the traffic fell because users were getting answers directly inside tools like ChatGPT and Google’s AI Overviews. The click never happened.

That pattern is now playing out in talent acquisition. Candidates ask conversational questions: “Does [Retailer] pay weekly or biweekly?” “How competitive is [Tech Company] for senior engineers?” “Is [Company] actually remote-friendly, or is that just marketing?” If your content isn’t structured for how AI systems retrieve and cite information, the answer comes from somewhere you don’t control.

The problem is further compounded by job-specific searches. I ran this test with KeyBank. Google results for “What jobs are open at KeyBank in Washington state?” show KeyBank’s careers site as the second and third organic search results (Indeed is number one). The same query in ChatGPT? KeyBank’s careers site is an afterthought, as the vast majority of links are to job searches from Indeed, LinkedIn, ZipRecruiter and more.

This reinforces a structural problem that most career sites have today: Company job listings are often completely invisible to AI search engines.

Indeed has been publicly cited as one of the largest OpenAI API users, reportedly processing over a trillion tokens. Most corporate careers sites aren’t meaningfully participating in that environment at all. So, when a candidate asks an AI about your open roles, the platform with the knowledge of how to structure their content gets cited and dominates search results. An organization’s careers page gets skipped.

The financial consequences compound in ways most dashboards don’t capture. When a candidate sees a recruitment ad and then asks an AI what it’s like to work there, they’re looking for validation. If the answer comes from a two-year-old Glassdoor thread instead of your own content, you’ve paid for the impression and lost the candidate. The budget didn’t change. The outcome did.

Or even simpler, if a candidate is looking for KeyBank jobs near them in ChatGPT and is directed to Indeed to then apply—your brand equity dissolves and Indeed now controls the jobs your potential candidate sees. And you’d never see it in your traffic reports because your Google rankings look fine.

Try this yourself. Open ChatGPT or Claude. Ask five or six questions that a candidate would ask about your company: compensation, remote work policy, open roles in specific locations and what the culture is actually like. Map where your brand shows up, where it’s invisible and where third parties are answering for you. That audit takes 30 minutes and will tell you more about your real employer brand reach than your quarterly traffic report.

3 ways to close the branding gap with AI

Closing this gap starts with three things.

• Emphasize governance. Mandate content structure that prioritizes AI citations through explicit content structuring. This means features like clear heading hierarchies, FAQ pages and schema markup (JobPosting, Organization) to ensure that AI systems cite your truth first. Because pages built for Google don’t automatically translate to AI citation.
• Enforce consistency. Make your story consistent across every surface. When your careers page says one thing, your Glassdoor profile says another and your job descriptions use internal language, AI systems either pick the wrong source or skip you entirely. Consistency is what makes AI choose your narrative over someone else’s.
• Implement AI-first metrics. The old scorecard of rank, get clicked, drive a visit, convert no longer reflects how candidates find you. LinkedIn shifted its own success framework to “be seen, be mentioned, be considered, be chosen.” If you’re only tracking Google rankings and site visits, you’re reporting on legacy SEO scorecards. Success in AI environments is now measured by whether your company is seen, mentioned or cited in candidate research, not just whether your content ranks in traditional search.

We’re still early in this shift. Employers can close the gap quickly if they’re paying attention. But the competitive window for early movers is shorter than most HR leaders realize. The employers that establish visibility in AI environments now are the ones that won’t have to buy it back later.

The post Who controls your employer brand in the age of AI search? appeared first on HR Executive.

Findings from enterprise interview scheduling platform GoodTime’s 2026 Hiring Insights Report, which surveyed 504 senior talent acquisition leaders in the U.S., showed that teams reported spending 38% of their time scheduling interviews, making it the single largest operational burden in the hiring process.

In this vendor-commissioned research, the talent pros shared that the most common bottlenecks were scheduling delays, limited interviewer availability, cancellations and hiring manager conflicts. As HR leaders know, each rescheduled interview can trigger multi-day delays and each delayed response increases the odds that a qualified candidate accepts another offer.

AI agents are a success story in scheduling

The research found that the organizations that reduced time-to-hire treated scheduling as a system, not a task. They were more likely to use AI agents for interview scheduling and to prioritize improving scheduling efficiency.

Teams using automated or AI-driven scheduling were 1.6 times more likely to achieve near-perfect hiring goal attainment, with 13% hitting 90%-100% of goals compared to 8% of non-users. The faster cohort was also 40% less likely to rely on sourcing bots and 56% less likely to use chatbots for early candidate engagement.

Read more: Inside the scheme placing North Korean IT workers in U.S. firms

However, AI is also inhibiting quality hiring

In 2025, skills gaps and a shortage of qualified candidates were the top hiring challenges. Now, an emerging threat is expected to take the lead in 2026, and that is fake or AI-assisted candidates.

“Resumes, answers to application questions, even online portfolios … have all lost value in the era of gen AI,” wrote Hung Lee, founder of Recruiting Brainfood, in the report. “It’s one of the most urgent tasks of TA teams today to find methods of assessment which are suitable for this era.”

Already identified by nearly one-fourth of talent acquisition leaders as a current issue, AI-generated applicants are putting pressure on hiring pipelines and making candidate evaluation more complex.

As Becky McCullough, vice president of talent acquisition and mobility at HubSpot, wrote in the GoodTime report, AI has raised the bar on verification needs. “A single background check at the end of the process isn’t enough anymore,” she said. “TA teams now have to confirm identity and authenticity at multiple points.”

Read more: AI hiring is creating a sea of sameness

What top-performing TA teams measure

There is a surprising twist revealed by the research. Top-performing TA teams were less likely to grow headcount than their underperforming peers. While 60% of underperforming teams expanded their recruiting staff, fewer than half of top performers did the same.

Instead, high performers reorganized roles, reassigning coordinators away from logistics and toward candidate experience and strategic work. They scaled by building better systems, not bigger teams.

Top-performing teams also anchor their measurement in outcomes. They were 42% more likely to choose quality of hire as their top metric and 21% less likely to focus on cost-per-hire in their measurement priorities.

The report points to a few areas that all HR leaders can check in with their own hiring processes:

• Automate interview scheduling end-to-end, not for convenience, but as a structural investment.
• Centralize candidate communication. Top performers were 58% more likely to use a centralized texting platform, reducing dropout and improving accountability.
• Build fraud detection into multiple stages of the hiring process, not just the end.
• Reorient measurement around quality of hire and funnel health rather than speed and cost alone.

“Recruiters aren’t slow, scheduling is,” wrote Shelby Wolpa, founder of Shelby Wolpa Consulting, in the report. “Until companies adopt systems that absorb complexity instead of pushing it onto humans, scheduling will remain the single biggest bottleneck in hiring.”

The post Nine out of 10 companies missed hiring goals in 2025. Here’s what went wrong appeared first on HR Executive.

If an AI agent ran the initial screen, scored the resume and ranked that candidate in the last 30 days, that fact almost certainly does not appear in the record. No agent name. No model version. No timestamp. No note that a non-human actor touched this person’s file before a recruiter ever saw it.

The same is true when an agent approves a time-off request, routes an HR service case or flags a performance concern. Work happens. The HRIS records nothing.

That’s the gap this article addresses. This piece goes deep into the systems and what CHROs can actually do about it.

What the HRIS debate has missed so far

Josh Bersin put the stakes plainly in his February 2026 piece in this publication: the market is moving from task-level automation to full process automation, and HR’s operating model is being rewritten. Peter Cappelli challenged the “treat agents like employees” framing in his piece the same month, arguing the real issue is that agents have to be supervised.

Both are right. The agents are already here, and they’re doing real work.

But the conversation has stayed at the conceptual level. The harder question is architectural: Most enterprise HRIS platforms were not built to store, manage or audit workflows that involve non-human workers. And almost no one is asking what that means in practice.

The assumption your HRIS was built on

Every HRIS built in the last four decades was designed around one central concept: the employment relationship.

That assumption runs deeper than a software design choice. The employment relationship is a specific, regulated construct. It has a legal start date, a compensation structure, a reporting hierarchy, protected-class obligations and an audit trail HR is legally required to maintain. Your HRIS was built to manage that relationship. Payroll, performance and succession all sit on top of it.

AI agents carry no employment relationship with your organization. They’re not employees or contractors. They carry no W-2, no 1099, no position in your job architecture, no manager of record.

And yet they’re doing work that used to be done by people who had all of those things.

This is what I’d call HR’s conceptual debt. We’ve spent decades building workforce management systems on one shared assumption: “Worker” means “person with a legal relationship to this organization.” That assumption stopped being complete. The system was never wrong. The world outran it.

3 gaps that open the moment agents go live

Once AI agents start doing real work in HR processes, three specific gaps open up inside the HRIS. I’ve seen each of them firsthand.

The visibility gap. No single place in most HRIS platforms shows which agents exist, who owns them and what HR processes they’re touching. Agents are provisioned by IT as service accounts. HR finds out they’re operating the same way it finds out about most things—after the fact.

The audit gap. When an agent screens a candidate, approves a time-off request or routes an HR service case, the system of record typically captures the outcome but not the actor. A candidate is moved to the next stage. A request is approved. A case is closed. The record shows a result with no indication that a non-human process produced it. This is the gap that creates legal exposure.

The authority gap. Traditional workforce management runs on delegation chains your HRIS captures. A CHRO delegates to a VP, who passes authority down to directors and managers. When something goes wrong, HR can trace back to who delegated what and when. AI agents break that chain entirely. When an agent screens out a candidate based on a skills matching model, it isn’t acting under a delegation HR approved. Legal accountability flows upward to HR. The actual decision traveled down a path HR wasn’t part of. I call this the reverse delegation problem, and it has no precedent in HR’s history.

A 3-step framework for CHROs

This is not a developer problem. It’s an organizational governance problem, and it belongs in the CHRO’s portfolio. Here’s where to start.

Step 1: Name the workforce. Before HR can govern AI agents, it has to know they exist. That means building or requiring an inventory that treats agents as workforce entities, not just IT service accounts. Each agent doing work in an HR process needs a record: what it does, who owns it, which HR processes it touches and what data it can access. This inventory doesn’t have to live in the HRIS on day one. It can start in a spreadsheet. The point is that HR owns it, not IT.

Step 2: Build the audit trail before the problem arrives. Every AI action in an HR process should leave a record in the system of record: which agent acted, on which record, using what inputs, with what outcome and whether a human reviewed or changed the result. This is the architectural requirement CHROs need to put in writing with their HR technology teams and their vendors. If an agent screens a candidate and that decision is later challenged, HR needs to produce that trail from the HRIS, not from a vendor dashboard or a server log.

Step 3: Draw the authority line. Every agent operating in an HR process needs a named human owner and a documented scope of authority: what decisions the agent can make independently, what decisions require human review and what triggers an escalation. This governing structure doesn’t exist in most organizations today. Building it is the CHRO’s job, not the CTO’s. It’s workforce governance, not systems engineering.

What to demand in 2026 contract renewals

Most enterprise HR technology contracts renew this year. CHROs and HRIS leaders should use that moment to put specific requirements on the table. Here’s what matters.

Agent registration capability. Can the platform register and manage AI agents as distinct workforce entities alongside human employees? Workday’s Agent System of Record, generally available in early 2026, moves in this direction. Ask every vendor where their roadmap stands.

AI action logging. When an AI agent takes an action inside an HR process, does the platform capture it in the system of record? Not in a separate analytics tool—in the record itself, where HR and legal can access it on demand.

Transparency controls. Can HR define what data an agent is permitted to access, at what level of sensitivity and in which geographies? Can that scope be changed or revoked without an IT ticket?

Audit export. Can HR pull a complete log of every AI-involved decision in a given time period, in a format legal counsel can use, without relying on the vendor’s support team to produce it?

Vendors who can answer these questions with working features belong on your renewal list. Vendors who answer with a slide deck belong on your watch list.

The org chart is a legal document

Regulators, courts and plaintiffs’ attorneys examine the org chart as a document that defines the authority structures behind challenged employment decisions. As this publication explored in its reporting on whether CHROs still hold real strategic authority, HR’s influence is only as credible as the structures it actually controls.

The ghost org chart accumulates legal exposure outside any structure HR controls right now. The problem isn’t the technology or the platform maturity. The problem is that agents are doing real employment work, and the system of record shows none of it.

The organizations that address this early will close all three gaps: visibility, audit trail and authority. They’ll hold vendors accountable to deliver the infrastructure in 2026 renewals. They’ll treat agent governance as workforce governance, owned by HR, not IT.

The post Your HRIS has a ghost org chart. And it’s already running the show appeared first on HR Executive.