The Speed-to-Skill Report, based on a survey of 1,500 U.S. employees and managers, uncovered a delay in the speed with which a company can spot a new needed skill and take the steps to put it to use.

Managers hit hardest by skills gap

The findings suggest most organizations are still working out the best way to adjust to this new reality. Only 16% of respondents say skill-building happens quickly when new needs arise. Yet 70% agree employees need faster ways to practice skills as job demands change. And managers, in particular, are feeling the impacts:

• 21% say their skills became outdated within the last year, compared to 10% of employees
• 12% say this happened within the last six months, versus 5% of employees
• 38% say it’s difficult to predict which skills their teams will need in the next 12 months
• 36% say they struggle to keep up with how quickly AI is changing their team’s needs

Twenty-four percent of respondents say the lack of a safe environment in which to practice skills before using them on the job is slowing progress. Meanwhile, training content that doesn’t match real job needs was cited by 28%, and 25% say training simply takes too long to develop and deploy, meaning by the time it arrives, the need has already moved.

“The challenge with predicting future skills is that the pace of change has outgrown the traditional planning cycle,” said David Kelly, an L&D executive, in the report. “Managers are being asked to prepare their teams for work that may shift dramatically before the next development plan is even finalized.”

Workers seem to be taking initiative independently, often with a workaround. Fifty-three percent of respondents say they learn new skills by figuring things out on their own. Forty-two percent turn to a peer who already has the skill, while 33% search their company’s learning platform. Formal training ranks close behind, at 32%.

Read more: Current hiring processes aren’t built to find AI-ready graduates, data finds

Advice for HR leaders

The report outlines six strategies for improving speed-to-skill:

• Make skill-building part of the work. Embed development into workflows rather than scheduling it alongside them.
• Build more responsive skill planning. Capture real-time input from managers and reassess priorities as business demands change.
• Shorten the path from learning to practice. Use simulations and scenario-based tools to close the gap between learning and application.
• Make training more dynamic. Modular content that can be updated quickly beats a fully developed course that arrives too late.
• Give clear ownership for speed-to-skill. Managers define needs, L&D provides structure and tools, employees build and apply.
• Measure skill application. Shift from tracking training activity to tracking how quickly learning turns into demonstrated capability.

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According to the report, Q1 2026 saw $2.8 billion flow across 97 HR tech deals, including key moves such as ADP acquiring WorkForce Software for $1.2 billion and Workday purchasing Sana for $1.1 billion. “As AI agents proliferate across HR workflows, the compliance surface grows with them,” the Norwest analysis states. The authors note that not knowing who authorized an action, whether a communication was appropriate or whether a given workflow creates regulatory exposure.

Legal and regulatory compliance

Employers deploying AI in hiring, performance management and workforce planning are currently operating under a patchwork of state-level requirements. As HR Executive has reported, these include Colorado’s mandate for annual algorithmic impact assessments on high-risk AI systems, Illinois’ restrictions on AI in video interviews and New York City’s bias audit requirement for automated employment decision tools. Meanwhile, federal guidance remains inconsistent, and attorneys advise that more state laws are coming.

Contracting with an AI vendor for recruiting or performance decisions does not transfer legal accountability to that vendor. Legal experts say that if a third-party tool produces a biased or opaque outcome, the employer remains on the hook under existing civil rights law and emerging state frameworks. And many HR teams that treated vendor contracts as compliance solutions are now facing harsh realities in court.

Britney Torres, co-chair of Littler’s AI & Technology Practice Group, told HR Executive that “courts will look to AI-specific and generally applicable discrimination authority to determine where liability lands for biased employment decisions arising out of AI tools.”

Read more: Where does compliance fit in the HR tech stack?

HR tech for compliance solutions

The compliance and HR service management category covers work that cannot be paused, according to the report authors. These include employee relations case management, compliance training and background screening. The Norwest analysis also questions whether the infrastructure in place now was built for an environment where AI agents are influencing HR decisions at volume.

Most organizations deployed AI tools in HR before putting governance frameworks in place, according to the report. Legal experts advise mapping where candidate and employee data flows into AI systems before deployment, building bias audits into vendor procurement, rather than retrofitting them after a complaint arrives.

The compliance and HR service management category rarely gets big attention, but the investment activity in the space, driven by recurring revenue and a growing accountability surface as AI agents take on more HR workflow, suggests this infrastructure is becoming harder to defer.

The post Compliance tech is becoming a strategic priority, as AI expands in HR appeared first on HR Executive.

Date & Time: Thursday, August 20, 2026 at 3:00 pm ET

Speakers:

Steve Boese, President and Co-founder, H3 HR Advisors

Trish Steed, CEO and Principal Analyst, H3 HR Advisors

Description:

What HR topics should be your focus in 2026?

In a July 2025 survey,* we asked 1,250 HR and finance professionals about the technology trends, strategies and goals occupying their focus. Their eye-opening responses give us powerful insight into HR’s ever-changing landscape.

In this webinar, we’ll discuss how to leverage the top HR trends and priorities of 2026 to:

• give your organization a strategic advantage
• prepare your staff for significant changes coming in the HR world
• improve productivity with upgraded HR tech

Join us as hosts Steve Boese and Trish Steed of H3 HR Advisors discuss the trends and priorities impacting HR’s expanding role. Register today and make sure you’re prepared to meet the HR challenges of 2026 and beyond!

*2025 survey conducted by PSB Insights and commissioned by Paycom.

—

This Program has been pre-approved for 1.0 (HR (General)) recertification credit toward aPHR®, aPHRi, PHR®, PHRca®, SPHR®, GPHR®, PHRi and SPHRi recertification through HR Certification Institute® (HRCI®).

Paycom is recognized by SHRM to offer Professional Development Credits (PDC) for SHRM-CP® or SHRM-SCP® recertification activities. This program is valid for 1.0 PDC. For more information about certification or recertification, please visit shrmcertification.org.

Replay here!

The post Top HR Trends and Priorities for 2026 appeared first on HR Executive.

Date & Time: Thursday, July 16, 2026 at 3:00 pm ET

Speakers:

Akshara Naik Lopez, Senior Analyst, Forrester

Description:

Everyone is talking about AI, from AI scheduling assistants to AI resume reviewers. But how will it work for your HR processes?

These are powerful tools already at work changing the environment we all live and work in. Before diving in headfirst, it’s important to understand what AI in HR can and can’t do, and how you can create a strategy to best leverage these new tools in your organization.

Join this webinar with Akshara Naik Lopez, a senior analyst from Forrester Consulting, to get your bearings in this exciting and ever-changing landscape. Lopez will discuss some of the latest trends happening in business today, from opportunities on the horizon to compliance concerns that will help you keep your operation secure.

Register now and make sure that you’re ready to capitalize on the seismic shift that AI-powered tech and automation are bringing.

Register here!

The post HR State of the Union: Understanding AI’s Impact on Your Business appeared first on HR Executive.

The rankings, derived from anonymous employee ratings collected between May 2025 and May 2026, named Paycom first among large companies when it comes to career growth, followed by Salesforce at No. 2. ADP landed at No. 5., while Workday, which sells HR and workforce management software to many of the same enterprises it competes with for talent, landed at No. 21 on the large company list.

Talent retention at HR tech firms

The presence of multiple HR software makers near the top of an employee-rated career growth list is notable at a moment when talent retention is a top concern across most industries. For HR leaders benchmarking their own development programs, the list offers a window into what employees at these organizations are saying internally.

Rankings were based on millions of anonymous ratings across tens of thousands of U.S. and Canadian companies, with additional weight given to organizations with higher participation rates relative to their size. Large companies on the list employ more than 500 people and were required to meet a minimum of 75 employee participants to qualify.

Key themes

Employee comments about the top finishers point to common themes: transparent leadership, visible internal mobility and active managerial investment in development. One Paycom employee wrote that the company “wants you to grow professionally and personally.” An ADP employee noted growing “more at ADP in 7 years than any other organization.”

AspireHR, a Dallas-based HR software company, earned a spot on the small and mid-size list. For HR leaders at smaller organizations, the SMB list also surfaces a consistent theme in employee comments suggesting that ownership and mentorship matter more than formal programs. Workers at top-rated smaller companies repeatedly credited individual managers and early responsibility, not structured development tracks, for their growth.

The post Paycom, Salesforce and ADP rank among top companies for career growth appeared first on HR Executive.

Despite the findings, two-thirds of employers use resume screening, either human or automated, as the first step in the hiring process, and many candidates expect this. The report notes that 92% of recruiting leaders say AI-generated resumes are now commonplace in their applicant pools, with half describing them as very common. This suggests that candidates using AI to write or optimize resumes may be responding to a system that rewards presentation over substance.

Read more: Current hiring processes aren’t built to find AI-ready graduates, data finds

What candidates think of resumes

Job seekers appear to understand the need to impress the tech, but research found that 68% of candidates would prefer a hiring process that deprioritizes the resume. Some potential employees, particularly those in roles where a traditional resume is not the norm, find the format a barrier before they have even started. Others are frustrated that AI-polished applications from less qualified candidates are crowding them out, meaning the candidates playing the game straight are losing to those who are focused on optimization.

A significant percentage of respondents say they wants to be evaluated on their potential and what they could do, not just on a list of past jobs that may say very little about what they are capable of. The report says this group is the one the current system handles worst, because the resume is a backward-looking document describing what someone has already done. For career changers, people re-entering the workforce or anyone whose most relevant capabilities did not develop in a conventional job sequence, the format doesn’t help them shine.

Read more: Could video hiring solve problems that resumes never could?

Do resumes surface the strongest candidates?

One recent academic study found that because generative AI makes it possible for everyone to submit polished, highly tailored applications, employers become worse at picking out the most capable workers. In a simulated market where written proposals no longer reveal who actually invested effort, workers in the top 20% of the ability distribution are hired 19% less often than in the pre-gen AI days, while workers in the bottom 20% are hired 14% more often. In other words, there are so many polished resumes to sift through that the strongest candidates are the ones most likely to be crowded out.

Organizations that rely on resumes as their primary hiring decision driver are 35% more likely to report a bad hire, according to the report. Meanwhile, around 64% of employers say they have already hired someone whose performance did not match what was on their resume, with 39% saying it has happened more than once.

The data hints that skills or work-based assessments are the most trusted alternative signal among employers, and structured interviews follow at 50%, with work samples or simulations rounding out the top three. Nearly all talent acquisition leaders say at least one of these approaches is a more reliable indicator of ability than the resume. Additionally, 68% of candidates say they would prefer to deprioritize the resume in favor of a chance to show what they can actually do.

The post AI is devaluing resumes, masking identity of top talent, research finds appeared first on HR Executive.

Here’s the problem: The majority of data informing today’s biggest workforce decisions is already out of date. Traditional sources of HR data like self-reported surveys, static HRIS records and manager intuition primarily capture perceptions of work, rather than real work patterns. A survey provides a dated snapshot, shaped by how the question is framed. An HRIS record shows an organizational skeleton: who’s in a role; their title. Manager intuition offers important context that shouldn’t be lost, but must be filtered for bias. Managers naturally notice the loudest or most visible employees, as well as those who are skilled at managing up.

With the explosive adoption of AI, work is changing faster than most organizations can measure it.

See also: How to take control of the future and make disruption your HR superpower

The scale of the work data blind spot is striking. The 2026 CHRO Survey Report found that 47% of CHROs haven’t established clear productivity measurements for AI, even as 91% name AI among their top priorities this year. HR leaders see parts and pieces as they make significant workforce decisions, but lack the integrated view to connect actions to outcomes. Which teams collaborate effectively? How does focus time translate to productivity? Which processes create friction? Too often, key patterns remain invisible to leaders who need to act on them.

That’s where behavioral data presents an opportunity. Behavioral work data is the digital trail that work leaves behind. It’s the patterns that show how employees spend time across applications, websites and tools; when and how teams collaborate; where focus breaks down or is maintained; what capacity is available or stretched too thin.

Behavioral work data reveals the gap between a job description and a job. For instance, data may show that a strategy leader spends 60% of their time on manual, repeatable tasks—not because they want to, but because the work demands it. That’s not visible in their job description or a performance review, but it shows up clearly in behavioral data. Data can flag precisely where AI could help free that person to fulfill their strategic role.

7 steps to close the visibility gap before your next workforce redesign

1. Be candid with yourself and your peers about what you know versus what you assume. Most organizations rely on assumptions more than they realize. Identify what’s anchored in repeatable, observable fact, and what isn’t.
2. Build a baseline understanding of how work flows before a redesign. Enable cross-functional visibility between HR, operations and people analytics teams, ensuring all are active participants in the design and decision-making process.
3. Make outcomes measurable, rather than aspirational. That means defining clear signals for whether new ways of working with AI are taking hold, such as adoption rates, time allocation shifts, employee capacity changes—and friction indicators. Organizations that close the AI measurement gap most effectively tend to have one thing in common: they asked sharper questions before the redesign began.
4. Resist the urge to treat AI adoption as a technology problem. It’s a human opportunity that technology enables. Gartner’s 2026 HR Trends report reinforces this point. When change becomes embedded in the natural flow of work, initiatives are three times more likely to end in healthy change adoption.
5. Design for employee experience, not just efficiency: The organizations navigating the human + AI work era well are thoughtful and intentional about the employee experience of AI, not just the efficiency case. They’re asking: What does it feel like to work in a new way? What happens to someone’s sense of purpose when their role changes? That’s central to whether people are thriving or just functioning.
6. Remember readiness will look different across the workforce — by role, function, tenure and individual. Leaders building real capability anchor AI rollouts in the why and meet people where they are.
7. Understand that any meaningful use of behavioral data requires employees to trust that data will be used to help them. Trust has to be earned through transparency: what’s being measured, and how decisions will and won’t be made from it. HR leaders who skip that step will face resistance that can undermine the entire effort. Bring employees into the conversation early and demonstrate through actions that the goal is to support a thriving workforce.

The two-word challenge for HR leaders

Periods of disruption can unsettle critical thinking. Leaders who consistently challenge assumptions and perceptions build resilience and support into their process. When advising HR leaders how to navigate change, I offer a simple, two-word challenge: Prove it.

If a leader comes to me and says, “Our employees are unhappy,” I gently ask follow-up questions. Where and how do they say that? How long has this been the case? Does their behavior reflect that? This approach forces leaders to bring evidence to the table. Many organizations will realize they don’t have it. But a lack of quantitative data is not a reason to ignore employee feedback. Rather, the feedback should be the signal to identify and track data toward a solution.

Employees should be empowered to use behavioral data to transform their work. With the increased outputs created by AI-powered work, an employee might feel burned out or disengaged, while a manager sees a lighter workload and fewer active hours of work. If managers use outdated metrics like hours worked or outputs created with their direct reports, the danger of burnout and disengagement may increase. Encouraging employees to track their metrics like focus time (the sustained, uninterrupted work that drives meaningful output) creates a foundation for a two-way conversation where manager and employee can diagnose problems and design solutions collaboratively.

Organizations that will thrive in the human + AI era aren’t necessarily the most tech-forward. They’re the most clear-eyed. They know what they know, measure what matters and build trust with the people doing the work. Clarity doesn’t happen by accident. It starts with HR leaders willing to ask better questions and demand better answers.

The post 7 ways your workforce can thrive through AI disruption appeared first on HR Executive.

The “cart before the horse” tension is now shaping the HR technology market. Buyers want a fraud agent or a voice agent, but many of them do not yet have the underlying data structure, career site, candidate relationship management (CRM) or workflow architecture to integrate the tools effectively.

The result is a kind of re-education moment, where vendors are having to explain not just what their products do, but why the customer’s environment matters just as much as the products themselves.

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

The infrastructure problem with AI in HR

AI in HR is not plug-and-play, yet organizations approach buying as if tools were à la carte, sitting neatly on top of systems of record without deeper preparation. That strategy ignores the layers of technology and process that determine whether the AI actually works.

A Society for Human Resource Management study found that 70% of HR leaders using AI reported challenges such as privacy concerns, employee resistance, limited resources and difficulty auditing algorithms. That goes to show that adoption is not the same as readiness. Even when the technology is installed, organizations still have to contend with trust, governance and change management.

Infrastructure is like the frame of a house. If the frame is weak, the shiny new features will not hold up for long. In HR terms, that means data quality, integration design and process consistency are the conditions that determine whether AI produces value or not. Without them, organizations risk building new technology on top of old weaknesses.

The concern is especially relevant as HR teams juggle multiple vendors and use cases. HR departments are still scarred by the integration challenges of the last decade, when multiple point solutions created fragmented systems rather than seamless experiences. That history is repeating itself, only now the packaging says “AI.”

The re-education moment

What makes the current market fascinating is that buyers are not always being pushed into bad decisions by vendors alone. Sometimes they are pushing themselves, driven by pressure to act quickly in a crowded field where every platform promises intelligence, automation and speed. People leaders are flooded with advice, but not always with enough experimental evidence to make confident choices.

That creates a familiar HR dilemma: Leaders know they need to modernize, but they are not always sure where to begin. Many are rediscovering basics they thought they had already solved, such as what a career site should do, what a talent CRM should enable and how systems of record actually behave in practice. The AI conversation is forcing HR and IT teams to revisit the plumbing before they install the faucet.

HR leaders who move too quickly risk buying novelty instead of capability. Those who move too slowly may miss the window to improve the candidate experience before competitors do. So where does the middle ground lie?

Here are three recommendations:

Map your process before you buy

Before evaluating any AI tool, HR leaders should document exactly how work moves through their organization today. Not how they wish it worked, but how it actually does. That means tracing the full lifecycle of a hiring decision, where requests originate, who touches them, where approvals stall, where candidates drop off and where recruiters are spending time they shouldn’t be.

The goal is to surface the real bottlenecks. The handoffs that rely on tribal knowledge, the steps that exist only because “that’s how we’ve always done it,” and the moments where volume overwhelms capacity. Those are the places where AI can do its best work, but only if the process is understood first.

There is a deeper challenge here, though. Many organizations assume that AI should map to their existing workflows, but it doesn’t have to. In fact, one of the most common mistakes in HR technology procurement is treating the current process as fixed and asking only whether a tool can fit inside it.

Just because a process exists today does not mean it should survive the transition/implementation. Some workflows were built around the limitations of older systems. Others were layered on over time, without anyone stepping back to ask whether the whole was still working. AI adoption is an opportunity, not just to automate what you already do, but to rethink whether you should be doing it that way at all.

The organizations that will get the most from AI are not the ones that digitize their broken processes fastest. They are the ones who use this moment to ask harder questions about where friction lives and why.

Start small, then scale

Business leaders should pilot AI with a narrow use case, rather than rolling it out everywhere at once. Think of it as testing a swatch of paint on a small section of wall before repainting an entire room. That approach is especially important in HR, where a misfire can affect candidates, employees and the employer brand at the same time.

Piloting lets teams see whether the tool actually solves the right problem, whether the data is clean enough to support it and whether employees trust the output. It also gives organizations a chance to spot integration issues before they become expensive mistakes.

Applied AI is better than generic experimentation. A broad “let’s do AI” mandate is unlikely to produce durable results, but a targeted use case tied to a specific business problem can reveal whether the technology is truly helping. For example, a company might test AI in one location, one workflow or one employee segment before expanding more broadly.

Seek trusted information

Attend HR industry conferences. They are better guides than the noise of the market, especially in a field where many new entrants are rushing in with little context and plenty of confidence. The more AI enters critical HR workflows, the more important it becomes to separate evidence from hype. HR buyers should not confuse availability with expertise.

The human layer still matters

Organizations can probably find 80% of the information they need, but the remaining 20% still requires judgment, context and human touch. That last stretch is where HR lives every day.

For HR professionals, that means the goal is not to automate everything. Instead, the focus should be to understand where automation makes the most sense. The best AI strategies will make judgments more informed.

The market is full of new agents, pop-ups and polished promises. But the real winners in HR won’t be the companies that buy the fastest or acquire the most tools, but the ones who build the strongest foundation first.

The post Caveat emptor: how the AI gold rush is repeating HR’s old mistakes appeared first on HR Executive.

At the summit, experts described a workforce that is exhausted, disengaged and increasingly disconnected from organizational goals. The Marsh data gives that observation a trajectory, with the share of employees who say they are thriving at work dropping from 66% in 2022 to 44% in 2026, erasing years of steady gains. One in four employees say they are unsatisfied at work, but don’t believe leaving is an option, while and another 12% plan to leave within six months.

Read more: AI is reshaping leadership roles faster than succession plans can keep up

AI is human transformation

Josh Greenwald, chief people officer at Sword Health, told summit attendees that organizations are approaching AI transformation incorrectly. “This is more about human transformation,” he said. “We put too much emphasis on technology.”

According to the Marsh report, mindset barriers to AI adoption rank sixth among people risks globally, but jump to third when the view narrows to C-suite respondents alone. The most commonly cited concern, flagged by 40% of respondents, is spending on AI before equipping employees to use it.

The report echoes what Brunswick Corporation CHRO Jill Wrobel described at the summit when she said that getting tech investments to pay off requires changing how work actually works. Marsh calls it work redesign, defined as deconstructing jobs to determine which tasks can be offloaded, augmented or automated, rather than layering AI onto existing roles and processes.

Read more: 10 stories that reveal what execs expect from CHRO leadership in 2026

The ‘how’ is elusive

Marsh’s analysis found that inadequate leadership skills trigger or worsen more downstream risks than any other single factor in the survey, including mental health deterioration, labor shortages, unsafe working conditions and poor investment decisions.

Only 14% of organizations Marsh surveyed report having “transformative” risk maturity, where risk management is deeply embedded in strategy and culture. Those that do have integrated risk management outperform peers by an average of 15 percentage points on talent-related risk mitigation.

The summit conversation reveals that that many leaders are isolated, under-supported and operating without clear direction. Harriet Harty, former CHRO at Allstate Insurance, said that leaders know what needs to happen, but don’t have the clarity around where they need to go. “The how has never been given to them,” she said.

The post Leadership gaps are driving multiple workforce risks, data shows appeared first on HR Executive.

See also: Why Oracle believes that EX must extend to the candidate experience 

Nevertheless, a small group of companies is reaching their savings targets by treating data access, governance and process redesign as CEO-level problems, rather than IT problems. Bain & Co. shared several recommendations to help businesses maximize AI savings:

• Pay down workflow debt before deploying AI. The single most costly mistake in AI deployment is automating a broken process. The question to ask before any AI program is approved is not “Where can we apply AI?” but “If we were designing this process from scratch today, what would it look like?” Only then should the technology conversation begin.
• Validate the investment case and name a governance owner before programs launch. Before approving the next wave of AI spending, CFOs should audit actual returns from prior automation programs, not projected returns. CEOs must answer one question their IT function cannot answer for them: “Who is personally accountable when an AI agent makes a consequential wrong decision in production?” Accountability must be established in advance.
• Use AI to solve the data problem. Imperfect data infrastructure is the most cited reason to defer AI investment, but also the least valid one. The more productive posture is to sequence AI investments to start where the data is already bound and accessible, and to use AI itself to improve how data flows through the organization. The fastest path to value is often automating one repeatable, high-value workflow where humans currently are pulling data manually, consolidating spreadsheets and producing reports, and replacing that entire sequence with AI.
• Redesign the operating model, not just the process. Deploying AI agents without changing how people work around them almost guarantees that an organization will underdeliver on the business case. The organizations capturing transformational savings have leaders who recognize that the human operating model is as important to redesign as the process itself.
• Measure outcomes at the enterprise level, not the program level. Programs will always optimize for what they were designed to measure, typically cost and hours saved. But what matters for the enterprise is whether AI investment is producing better decisions, faster responses and stronger customer outcomes. If those metrics aren’t on the CEO’s dashboard, programs will keep delivering the wrong things efficiently, and the value gap will persist regardless of how much the budget grows.

“The turning point for most companies is not finding the best AI technology,” the survey report concluded. “It’s the moment when leaders decide—before the next budget cycle, before the next vendor pitch, before the next program launch—that they have a personal responsibility to create the organizational conditions for AI success. The window to make that decision ahead of the competition is still open, but it’s narrowing faster than many executive teams realize.”

The post Only 4% of orgs are hitting AI savings targets: Here’s what they’re doing differently appeared first on HR Executive.