AI transformation depends on the daily choices employees make while learning, experimenting and adapting. A strong recognition system turns those choices into visible signals that guide action, build trust and sustain momentum.

AI deployments are moving faster today than the organizational conditions required to use them well. New tools may arrive in weeks, while confidence, clarity and working norms take far longer to develop. That difference helps explain why technically sound initiatives can struggle to produce meaningful adoption.

The 2026 State of Recognition Report from the Achievers Workforce Institute documents the scale of the readiness challenge. Globally, 19% of employees feel confident using AI tools, 18% have access to AI-enabled training and 18% feel supported in adapting to AI and technology changes. Only 18% feel informed when changes affect their jobs, while 23% say communication is clear during uncertainty.

These figures point to an operating problem for HR and IT leaders. AI transformation depends on learning, experimentation, collaboration, sound judgment and the willingness to redesign familiar ways of working. Employees need more than access to technology and a launch communication. And they need credible signals that show which behaviors matter, whether it is safe to try and how their efforts connect to the organization’s goals.

Recognition can provide those signals. When it is timely, specific and embedded in everyday work, recognition becomes part of the infrastructure that moves a workforce from strategic intent to sustained behavior.

The Missing Mechanism in Change Management

Most change programs devote substantial attention to capability and motivation. Training builds knowledge. Executive messages explain the vision. Process documentation establishes expectations. Each element is necessary, but employees still face a practical question in the moment of action: What happens when I try this new behavior?

Behavioral science describes change as an interaction among motivation, ability and a prompt at the right time. Recognition is a powerful prompt because it tells an employee that a particular choice was seen, valued and worth repeating. It also gives colleagues an observable example of what the organization wants to encourage.

That function becomes especially important during AI adoption. Employees are being asked to test unfamiliar tools, scrutinize outputs, share what they learn and exercise judgment where policies are still evolving. Acknowledging those behaviors quickly creates a feedback loop. People gain confidence that responsible experimentation is welcome. Managers gain a language for reinforcing progress. Leaders gain visibility into where adoption is taking hold and where support remains weak.

During a presentation at Achievers’ Recognition Next event in Toronto, Emma Harvie, Global Head of Recognition and Rewards Insights at Achievers, shared a telling example from her own work. After a session with a customer-facing team, a colleague wrote: “You didn’t just explain what AWI does, you connected the dots. You showed how recognition can drive real business success.”

The message worked because it named the contribution and linked it to impact. It reduced uncertainty about whether a new approach had landed and reinforced the behavior that should continue. That’s the same feedback employees need when a transformation asks them to operate in new ways.

Recognition Makes Priorities Usable

Strategy often reaches employees as broad language: innovate, collaborate, use AI responsibly, move faster. Those priorities become useful when people can see the actions behind them.

Specific recognition translates an abstract priority into a behavioral example. A manager can acknowledge an employee who checked an AI-generated analysis before it reached a customer. A project leader can recognize a team member who documented a failed experiment so colleagues could avoid repeating it. A peer can call attention to someone who used time saved through automation to solve a more valuable customer problem.

Each message carries information. It tells the recipient what to repeat, shows observers what good performance looks like and gives the organization a stream of data about the behaviors appearing across teams. Over time, those signals can reveal where new skills are developing, which managers are reinforcing change and whether stated priorities are showing up in daily work.

This is why recognition belongs in the middle of transformation activity. The learning phase, workflow redesign and early adoption period all create opportunities to reinforce progress before business results are fully visible. Waiting for a completed rollout or a major outcome removes recognition from the moments when employees are deciding whether a new behavior is safe, useful and sustainable.

The Recognition Gap Is an Enablement Problem

The willingness to recognize others is stronger than the systems supporting it. Achievers Workforce Institute research finds that 29% of employees give recognition weekly and 30% give it monthly. On the receiving side, only 17% are meaningfully recognized each week, 28% receive recognition monthly and 9% are never recognized. Ninety-two percent say they would put in additional effort if they were better recognized.

The barriers are practical. Twenty-three percent worry about saying the wrong thing or being misunderstood. Twenty-one percent do not know how to make recognition meaningful and 22% say recognition is not common in their workplace. These findings describe friction, ambiguity and limited reinforcement capability.

The gap widens for employees who are already struggling. Harvie’s presentation highlighted that employees performing below expectations are six times less likely to receive weekly recognition than colleagues who are meeting or exceeding expectations. However, 64% of those employees say recognition would make them more productive and 58% say it would make them more likely to complete tasks.

Managers may stay quiet because they fear that acknowledging progress will appear to endorse overall performance. This leaves a struggling employee with fewer signals about the behaviors that could improve results. Effective recognition can identify a useful action while preserving honest performance expectations. It keeps the feedback relationship active and gives improvement a foothold.

Organizations can address this gap through design. Managers need examples of effective recognition, prompts connected to real work and simple ways to respond while the contribution is still fresh. They also need permission to recognize learning, effort and sound decisions during a change process, when final outcomes may remain uncertain.

Human Credibility Sets the Boundary for AI

AI can help recognition operate at enterprise scale, though employees have drawn a clear boundary around authenticity. The State of Recognition Report by Achievers, a Brandon Hall Group™ Gold-level Eminence Partner, finds that 67% trust recognition more when it comes directly from a person. Only 14% feel comfortable receiving recognition written or suggested by AI.

That boundary offers a useful design principle for HR and IT. AI can surface opportunities, reduce administrative friction, support translation and help managers connect a contribution to relevant goals or values. The person giving recognition should retain authorship of the meaning. Their judgment, relationship and knowledge of the contribution create credibility.

The same principle should guide capabilities developed under the Achievers Intelligence umbrella. Technology can improve reach, timing and insight while keeping human intent visible. Organizations should be transparent about how AI supports the process and should avoid automated messages that feel detached from the work being acknowledged.

This balance matters because recognition data can become a valuable source of behavioral intelligence. Patterns in recognition can help leaders see where collaboration is increasing, where new skills are emerging and which teams are demonstrating the judgment required for responsible AI use. Human credibility makes those signals stronger.

Progression Brings Recognition into the Operating Model

Brandon Hall Group™ identifies four phases of progression in its Rewards and Recognition Progression Model for Empowering HR Excellence: Reactive/Ad Hoc, Standardized, Defined/Strategic and Optimized. Sixty percent of organizations have reached the strategic or optimized phases. At the highest level, rewards and recognition operate as a proactive transformation partner supported by integrated processes, real-time insight and advanced governance.

This progression is directly relevant to AI adoption. A reactive organization distributes occasional appreciation after visible accomplishments. A standardized organization creates consistent processes and broader access. A strategic organization connects recognition to business priorities and uses data to guide decisions. An optimized organization treats recognition as a continuous source of insight and a lever for business transformation.

Moving through these phases requires coordinated ownership. HR brings expertise in employee experience, behavior, leadership and fairness. IT brings expertise in workflow integration, data architecture, security and responsible AI. Together, they can build recognition into the systems where work happens and ensure that the resulting data is governed appropriately.

Three commitments for HR and IT leaders

Leaders can strengthen recognition as change infrastructure through three commitments:

- Define the behaviors that will make transformation succeed. Identify the learning, experimentation, collaboration, verification, knowledge-sharing and judgment expected during AI adoption. Recognize these actions early and connect them explicitly to business priorities.

Compensation Intelligence Becomes a Business Operating Lever

Brandon Hall Group™ believes the shift from compensation management to compensation intelligence represents an important change in how organizations connect workforce decisions with business performance. The real value is not simply faster benchmarking or the addition of AI. It is the ability to connect market data, job architecture, recruiting and compensation planning so leaders can understand the workforce and financial consequences of their decisions.

Payscale’s 2026 Compference was this week and had some compelling new topics. Payscale’s stated mission is to unlock an organization’s full potential through compensation intelligence, with a vision that compensation is an always-on strategic driver of performance, culture, and competitive advantage not just a once-a-year budgeting exercise.

One of the ideas that stayed with us after Payscale’s Analyst Day was the shift from compensation management to compensation “intelligence.” That language matters because compensation has traditionally been managed as a cycle: benchmark jobs, establish budgets, complete annual reviews, make adjustments and repeat the process the following year.

The business does not move that way anymore. Labor markets shift. Skills change. New positions emerge. Hiring demand increases unexpectedly. Pay compression develops. An organization loses several experienced employees in the same space. A competitor changes its wage strategy. A frontline operation suddenly needs more people on a particular shift or in a specific location. Those events do not wait for the annual compensation cycle.

From the perspective of a former CFO at Brandon Hall Group™, who also spent years working directly with payroll and alongside HR, this is where the compensation conversation becomes much more interesting. Compensation is not simply an HR expense to manage. It is an operating decision with implications for hiring, retention, scheduling, overtime, workforce availability, productivity and ultimately financial performance.

Payscale’s own 2026 research reflects that change. 68% of organizations say executive leaders now view compensation as strategic to business success, while 51% identify balancing employee pay expectations with financial constraints as their top compensation challenge. That tension is familiar to anyone who has sat in the CFO seat. The question is rarely just, What should we pay? It is also: What will happen if we do not adjust pay?

Will vacancies remain open longer? Will overtime increase? Will managers rely more heavily on agency or contingent labor? Will a critical employee leave? Will we struggle to recruit in one geographic market while another remains stable? Is the organization paying a premium for a skill that has actually become standard for the role? Those are business questions, and answering them requires more than a static salary survey.

From an Annual Process to Connected Compensation Intelligence

The market shift is larger than simply moving from old salary surveys to newer data. Compensation is becoming a continuous, enterprise-wide capability. Pay transparency, rapidly changing skill requirements, wage compression and increasing employee expectations mean organizations must make defensible pay decisions throughout the year not only during an annual compensation cycle.

Payscale’s strategy appears designed around that shift. The Payscale Intelligence Cloud connects market benchmarking, job architecture, talent acquisition and compensation planning so that HR, recruiters, managers and finance leaders can work from a more consistent foundation. The individual products play different but connected roles:

- Payscale Ascent is the intelligence and benchmarking foundation. It combines Payscale’s continuously refreshed Peer data with customer and third-party survey data, AI-assisted job matching, large-scale job pricing and natural-language reporting. Its purpose is to help organizations understand what roles should pay and explain the reasoning behind those recommendations more quickly and confidently.

- JobNav Recruiter brings that intelligence into talent acquisition. It uses governed job architecture including job families, levels, skills and market benchmarks to help recruiters create accurate, pay-transparent and compliance-ready job postings. This helps prevent the compensation strategy established internally from becoming disconnected from what candidates see in the market.

- Payscale Paycycle turns the strategy into an executable compensation process. It supports salary and compensation reviews through scenario planning, budgets, manager workflows, approval guardrails, dashboards and employee communications. Its continuous access also allows organizations to manage off-cycle adjustments and emerging pay issues instead of waiting for the next annual review period.

“From my perspective, the strongest element of this strategy is not any one product. It is the connection among them. Market intelligence informs the organization’s job and pay structures; those structures guide what recruiters communicate to candidates; and compensation planning helps managers execute decisions within established budgets and governance.” — Heidi Grecsek, Managing Director, HCM, Brandon Hall Group™

That connected approach matters because employees experience compensation as one continuous story from the job posting and initial offer to career progression, performance discussions and future pay decisions. When those moments are supported by different data, different job definitions or disconnected systems, trust can erode quickly. Payscale’s opportunity is to help organizations create a more consistent and explainable compensation experience across the entire employee lifecycle.

Payscale’s Ascent

Ascent is positioned as an AI-first benchmarking platform that combines market data, workflows and contextual intelligence so compensation teams can make decisions using information that changes with the market rather than relying exclusively on an annual survey cycle. Payscale also connects its peer data with skills, industry and hiring-demand signals to provide additional context around what is changing and why.

For finance and HR leaders, the value is not simply getting a newer number. The value is having enough context to make a defensible decision.

If a healthcare organization is struggling to hire nurses in one market, for example, leadership needs to understand whether the problem is compensation, labor availability, a specific skill requirement, schedule design or some combination of those factors. A pay increase without that context may increase labor cost without solving the underlying workforce problem. The same is true in hospitality, retail, manufacturing and other frontline-intensive industries.

Better compensation intelligence gives leaders a chance to move from reacting to labor problems after they appear to identifying signals earlier. That aligns with what Brandon Hall Group is seeing more broadly. In HR Outlook 2026, 64% of Excellence Award-winning organizations are developing predictive analytics, 57% are using AI or machine learning for HR decision-making and 55% are working toward unified data flows between HR and business systems. The progression is increasingly from collecting workforce data to using it to inform business decisions.

“Compensation intelligence becomes strategic when it helps leaders decide not only what a role should pay, but what the business can afford, where workforce risk is building and when the cost of inaction may exceed the cost of adjustment. That is where compensation moves from an HR process to a business-performance lever.” — Dr. Marline C. Duroseau, CPA, Managing Director, HR and Leadership Development, Brandon Hall Group™

Pay Decisions Cannot Stay Inside the Compensation Team

Another important theme from the briefing was the movement toward a more distributed compensation model. Historically, compensation professionals could operate as custodians of pay information. That becomes increasingly difficult as pay transparency expands and managers, recruiters, employees, finance leaders and executives all need access to consistent information. This makes governance just as important as access.

Payscale’s broader Intelligence Cloud strategy connects benchmarking, job management and compensation planning with systems used across HR, talent, payroll and the business. JobNav extends compensation intelligence closer to recruiting and job architecture, while Paycycle is designed to keep compensation planning available beyond a single annual window. Payscale publicly describes Paycycle as an “always-on” environment where organizations can respond to market shifts, compression, retention concerns and off-cycle decisions throughout the year.

That has important implications for frontline workforces. A manager does not necessarily discover a compensation problem during annual planning. It may surface when a high-performing employee receives another offer, when turnover suddenly accelerates on a particular shift or when recruiters repeatedly cannot fill a critical role at the approved range. Waiting six months for the next compensation cycle is increasingly disconnected from the speed of the problem.

The CFO and CHRO Need the Same Version of the Truth

Perhaps the larger opportunity is alignment. Compensation decisions often sit at the intersection of two legitimate priorities. HR is thinking about competitiveness, fairness, retention and employee experience. Finance is thinking about affordability, labor cost, productivity, return on investment and organizational sustainability. Those should not be competing conversations.

They should be the same conversation supported by the same data. When compensation intelligence is connected across benchmarking, job architecture and planning, the organization has a stronger foundation for CFO/CHRO decision making.

Organizational AI investment increased significantly in 2024 and 2025. Budgets grew, vendors expanded and pilot programs launched. However, many companies saw only modest results: adoption stalled, productivity gains were limited and leaders quietly reduced expectations.

The gap between AI’s potential and actual performance is well-documented. Brandon Hall Group™ research shows digital transformation frequently fails due to insufficient human capability, not technology. Without a capability infrastructure, employees do not change their work habits. Without manager-readiness programs, leadership behaviors remain unchanged. Organizations that neglect readiness infrastructure typically see poor returns on AI investments, showing that success with AI requires investing in both technology and human and managerial readiness.

Where AI Initiatives Actually Stall

Common explanations for AI adoption failure focus on change management: poor communication, one-time training programs and leaders who back initiatives but do not model desired behaviors. While these factors matter, they are symptoms of a more profound structural issue.

Brandon Hall Group™’s Learning and Development Progression Model measures L&D across four phases. When it comes to AI adoption, 39% of organizations lack foundational enablement infrastructure: no personalized pathways, no analytics to identify capability gaps and no way to scale tool access into skills.

The 44% of organizations who fall into Phase 3 (Defined/Strategic) face a different challenge. They have the structure to launch AI initiatives but often mistake deployment for adoption, viewing AI as a technology rollout instead of a capability-building effort that requires speed to sustained adoption. This approach leads to stagnant adoption rates. Only at Phase 4 (Optimized) do organizations achieve widespread AI adoption throughout L&D, supported by analytics expertise, data-driven decisions and forward-thinking learning infrastructure. Only 17% of organizations have reached this level.

NovoEd, a Diamond-level Brandon Hall Group™ Smartchoice Solution Provider, is designed to provide continuous, applied development infrastructure that helps organizations progress to Phase 4 instead of stalling at deployment.

From Program Deployment to System-Based Development

A key differentiator in award-winning AI adoption is the shift from thinking about AI rollouts as discrete programs to building it into continuous capability development systems. Here’s how these approaches differ:

What Award-Winning AI Adoption Actually Looks Like

Brandon Hall Group™ analysis of award-winning AI in Learning programs illustrates what effective AI adoption looks like and what distinguishes it from standard rollouts.

In these organizations, AI role-play and simulations appeared in 54% of case studies, AI content generation in 46%, as well as AI-driven personalized learning in 42%. The key differentiator was not tool sophistication, but the learning experiences built around them. Award-winning organizations combined AI capability with deliberate practice, using simulated environments, collaborative cohorts and structured feedback instead of demonstrations or passive modules.

An analysis of Excellence Award winners in AI for Business Impact demonstrates several findings: All winners implemented AI-driven personalized learning paths and real-time analytics as core infrastructure; Microlearning was integrated into workflows appeared in nearly all instances; Just-in-time performance support at the point of need was present fourth-fifths of the time. The unifying principle is application: learning is designed for direct transfer into practice, with AI accelerating and measuring that transfer. These experiences where peer collaboration, discussion and reflection are vital to learning. Teamwork spaces, threaded feedback and practice assignments in various media let learners interact with AI tools in realistic work conditions from the start, not for the first time after program completion.

Manager Readiness as the Choke Point

AI adoption programs do not succeed if managers lack understanding or trust in AI. Workforce resistance frequently stems from unprepared managers. This shows that manager readiness is the main point where most adoption efforts fail. Addressing this is essential for real change.

Managers need their own applied development experience, including opportunities to practice AI-enabled coaching before delivering it. NovoEd’s Mentor+ supports this need. Structured conversation frameworks and automatic action tracking provide managers with visibility into mentoring progress and outcomes. Automatic reminders keep development relationships active between sessions. NovoAI, NovoEd’s AI suite, offers Discussion Insights that synthesize coaching themes and highlight moments that need deeper guidance, providing real-time direction without requiring L&D to monitor every exchange manually.

Applied Learning as the Enablement Infrastructure

The ongoing challenge with AI adoption is not insufficient training content, but the lack of transfer to actual work behavior. Employees may complete training but fail to change their work habits. The key takeaway: effective enablement requires application and practice, not simply knowledge acquisition.

NovoEd’s Practice+ boosts the application layer beyond most platforms. Video practice rooms and real-time scenarios allow employees to rehearse AI-enabled workflows before high-stakes situations. NovoAI’s Practice Insights analyzes video and audio submissions for rate of speech, filler words and key terms, delivering targeted, actionable feedback instead of generic completion records. These insights are downloadable, giving L&D leaders a stronger evidence base for skill acquisition than course completion rates alone.

For organizations with global teams, NovoAI’s AI Translation eliminates a frequent barrier between scale and quality. Learners can translate lesson content and peer activity instantly within the same course, enabling cohort-based AI capability building for multilingual workforces without recreating programs for each language market.

Learn+, Practice+, Mentor+ and NovoAI demonstrate the principle found in award-winning research: skill acquisition must be combined with applied practice and feedback, with AI lowering administrative overhead through purposeful design and integration that promote meaningful behavior change.

Naming the Problem: The Readiness Trap

The Readiness Trap occurs when organizations invest in AI technology before developing the human capability to use it, then attribute adoption failure to technology, employees, or change management. The real issue is the lack of applied learning infrastructure. These organizations do not need different tools; they need a new approach to enablement that pairs skill acquisition with real work practice, not simply awareness or access.

The vast majority of organizations that fall below Phase 4 on Brandon Hall Group™’s L&D Progression Model need more than improved AI adoption; they require a human-centered learning infrastructure with systems designed for speed to sustained capability development. Closing the gap begins with building this base for effective capability transfer.

Employee engagement strategy is written in boardrooms and HR offices but lives among teams. A manager’s daily choices about who gets recognized, who gets a check-in and who gets stretched with a new project shape how engaged an employee feels far more than any company-wide initiative. Managers are the channel through which strategy becomes experience, and yet many organizations hand them tools built to inform HR, not to guide managers in real time.

Brandon Hall Group™ research points to a persistent gap. Organizations have invested heavily in engagement technology but the people closest to employees, their direct managers, often receive information without direction. They can see a dashboard. They cannot always see what to do next, for whom or how soon it will matter.

Brandon Hall Group™ analysis of HCM Excellence Award®-winning recognition programs shows how wide that gap is. Eighty-five percent of these organizations, already recognized for best-in-class practice, have deployed dedicated recognition platforms with dashboards and reporting. Far fewer have built the layer that turns that reporting into guidance. Even among organizations sophisticated enough to win industry recognition, the infrastructure for showing managers what happened is far more common than the infrastructure for telling them what to do next.

Closing that gap is less about adding another tool to a manager’s plate and more about changing what the existing tools tell them. Intelligence that points toward a specific action, aimed at a specific person, at a specific moment, is what turns an engagement platform from a reporting system into something a manager actually relies on during a normal week.

Why Managers Matter to Engagement

Managers control the moments that employees remember. A word of appreciation after a hard project, a conversation about workload before it becomes burnout, a nomination for a peer award: These small acts accumulate into an employee’s sense of whether their work matters and whether their organization notices. Brandon Hall Group™ analysis of award-winning recognition programs found that peer nomination systems now match manager-driven recognition in prevalence, each appearing in two-thirds of organizations studied. When recognition authority is shared more broadly, managers need to know where to focus their own attention so it lands where it will have the most effect.

Employees rarely experience “the organization.” They experience their manager, their team and their week. Engagement strategy succeeds or fails at that level, which means the quality of information managers receive determines the quality of the culture employees feel. A well-designed engagement program can still fall flat if the manager delivering it day to day has no clear signal about which employee needs a conversation, which contribution deserves a callout, or which team is quietly losing momentum.

The Limitations of Dashboards and Reports

Most organizations already give managers access to engagement dashboards. Participation counts, survey scores and recognition volumes sit neatly in a report, refreshed monthly or quarterly. The problem here is that it describes the past without pointing toward the next action.

A manager looking at a dashboard that shows declining engagement scores on their team still has to answer the harder question alone: Who is at risk, why and what should happen this week? Static reports tell a manager what happened. They rarely tell a manager what to do, for whom or when it will matter most. That gap between information and instruction is where good intentions quietly stall. Managers are busy, and a dashboard that requires interpretation before it becomes useful is a dashboard that gets checked less often than it should.

Identifying Recognition Opportunities

Recognition works best when it is specific and timely, but managers overseeing a dozen or more direct reports cannot track every contribution, milestone or quiet win happening across a team. This is where actionable intelligence changes the equation. Rather than relying on a manager’s memory or a quarterly reminder, platforms like Vantage Rewards can surface recognition opportunities as they arise, flagging project completions, work anniversaries or peer shoutouts that deserve a manager’s follow-through.

Brandon Hall Group™ research shows public recognition remains the most prevalent non-monetary reward mechanism among high-performing organizations, appearing in 77 percent of award-winning programs, while on-the-spot recognition tools reach 54 percent. Those numbers reflect a preference for immediacy. Recognition loses much of its power when it waits for an annual cycle. Vantage Circle’s AIRe Framework, built around appreciation, incentivization, reinforcement and emotional connect, gives managers a structure for treating recognition as a continuous practice rather than an occasional event, and the platform’s underlying intelligence helps managers act on that structure without adding to their workload.

Understanding Workforce Sentiment

Sentiment is harder to measure than participation, but it matters more. A team can hit every metric on a dashboard while quietly disengaging, and traditional annual surveys often surface that disengagement long after it has already shaped turnover decisions. Continuous listening changes the timeline. Vantage Pulse gathers eNPS data and open-ended feedback on an ongoing basis, and its AI sentiment analysis identifies recurring themes across that feedback so managers are not left parsing hundreds of free-text comments on their own.

What makes this useful to a manager rather than only to HR is segmentation. Sentiment broken out by team, role or location tells a manager something specific about their own group rather than an average that may not apply to anyone on their team. A manager who can see that sentiment around workload has shifted for their team specifically, rather than the company overall, has something to act on this week instead of something to note for next year’s planning cycle.

Detecting Engagement Risks Earlier

Every manager has been surprised by a resignation that, in hindsight, had warning signs. Formal exit interviews and annual engagement surveys tend to confirm disengagement after the decision to leave has already been made. Earlier signals, such as a drop in participation, a change in the tone of sentiment feedback or a quiet stretch without recognition, tend to appear well before an employee submits notice.

Vantage Pulse’s anonymous feedback channels and sentiment tracking are designed to surface those earlier signals, giving managers a chance to have a conversation while there is still a decision to influence rather than a departure to manage. Correlating recognition activity with engagement scores, a capability built into the platform, gives managers a second data point: teams where recognition has quietly dropped off often show the sentiment shift first. Catching that pattern early turns a retention conversation into a proactive check-in instead of a counteroffer.

AI-Enabled Manager Support

None of this requires managers to become data analysts. The value of AI in this context is translation: turning raw engagement data into specific, manageable actions a manager can take without a background in analytics. Brandon Hall Group™ research into AI adoption among award-winning organizations found that real-time analytics and performance tracking has reached universal implementation among top performers, with behavior change and performance analytics appearing in 83 percent of leading programs. Managers increasingly work in an environment where this kind of intelligence is expected rather than optional.

Vantage Circle, a Brandon Hall Group™ Platinum Eminence Partner, brings that expectation to engagement specifically. AI-assisted award recommendations reduce the guesswork in recognition, suggesting who has earned acknowledgment and why, rather than leaving a manager to remember on their own. Sentiment analysis across Vantage Pulse turns open-ended survey responses into identifiable themes without requiring a manager to read every comment. Together, these capabilities shift the manager’s role from someone who reviews a report to someone who receives a signal and a suggested response, a meaningfully different relationship with the same underlying data.

This kind of support also scales in a way that manual review never could. A manager with five direct reports might reasonably track recognition moments and sentiment shifts through memory and observation alone. A manager with 15 or 20 cannot, and organizations with distributed or hybrid teams have made that span of control more common, not less. AI-enabled intelligence closes that gap by doing the tracking so the manager can focus on the conversation.

Moving from Reactive to Proactive Management

Reactive management responds to problems once they are visible: a resignation, a complaint, a missed deadline. Proactive management responds to patterns before they become problems. The difference is not effort. Many managers already want to catch issues early. The difference is access to intelligence that surfaces those patterns in time to act on them.

Organizations that build this kind of support into their engagement strategy are effectively extending their HR function into every team, without adding headcount. A manager equipped with recognition prompts, sentiment visibility and early risk indicators operates with the same awareness an engaged HR business partner would bring to a struggling team, available continuously rather than during a scheduled check-in.

Closing Thoughts

Engagement strategy has never lived in a policy document. It lives in the many small interactions between managers and their teams, and those interactions are only as good as the intelligence behind them.

There are conferences we attend where you learn about an organization’s philosophy and then there are conferences where you experience it. That distinction stayed with me throughout the duration of O.C. Tanner’s Influence Greatness 2026 at Black Desert Resort in Southern Utah.

I attended as an invited analyst guest representing Brandon Hall Group™, with O.C. Tanner hosting my participation. Going into the event, I expected to learn more about the company’s research, recognition platform, technology roadmap, partnerships and perspective on the future of workplace culture. Of course, I did. However, what I did not fully anticipate was how intentionally the philosophy behind the technology would show up in the experience itself.

For an organization with such a long history in employee recognition and workplace culture, O.C. Tanner had an opportunity to tell us what meaningful recognition should look like. Instead, they showed us.

When the Experience Matches the Message

From the moment I arrived in UT, there was an unmistakable level of care behind the experience. The setting at Black Desert Resort was exceptional, but beautiful surroundings alone do not create connection. It was the consistency of the human interactions that stood out.

Members of the O.C. Tanner team stopped to talk. They checked in, remembered conversations, made introductions and made certain people knew where they needed to be and had what they needed. I especially appreciated Mindi Cox and Raphael Magre, who went out of their way throughout the event to make sure I felt welcomed and connected. That same attentiveness extended far beyond my own experience. I saw it in how O.C. Tanner interacted with analysts, clients, speakers, members of its Client Advisory Council, its own employees and the broader attendee community.

That really matters because recognition loses its power when it feels like a transaction or an isolated HR program. It becomes meaningful when people consistently experience signals that say: We see you. Your contribution matters. You belong here. Influence Greatness demonstrated that principle in practice.

Recognition Is Moving Closer to the Work

Our analyst discussions also provided a deeper look at how O.C. Tanner is evolving its technology and product strategy. One theme surfaced repeatedly: Recognition needs to become easier to access and more naturally integrated into the way people already work.

For many organizations, another standalone platform is not necessarily the answer. Employees are already moving among HR systems, collaboration tools, communication platforms and multiple applications throughout their workday. If recognition sits somewhere outside that flow, participation becomes harder and the experience can feel disconnected.

O.C. Tanner is increasingly addressing this through integrations, partnerships, APIs and expanded ways for recognition to reach employees across different work environments. The company’s partnership with Kudoboard, for example, expands employee-led celebrations around milestones and meaningful life and work moments. Integrations with systems such as Workday and Microsoft Teams similarly reflect a broader shift toward bringing recognition into the rhythm of work rather than requiring employees to leave it.

That is particularly important as organizations think about frontline employees, distributed teams, global workforces and employees who do not spend their days sitting behind a laptop. This is notable because technology access should not determine recognition access.

Another important conversation involved measurement. Organizations increasingly want to understand not only whether employees are using recognition programs, but what those programs are influencing. Retention, engagement, trust, collaboration, performance, employee experience and ultimately business outcomes all belong in that conversation.

From my own perspective, as a former Chief Financial Officer, this is an important evolution. Participation is useful information but, impact is the more important question.

The 2027 Global Culture Report: Growth Cannot Be Extraction

One of the most substantive parts of Influence Greatness was the release and discussion of findings from the O.C. Tanner Institute’s 2027 Global Culture Report. The research, based on nearly 26,000 employee perspectives across 23 countries, examines four areas organizations should consider as they navigate continued disruption: organizational compassion, growth cultures, active trust and innovation.

The growth conversation especially caught my attention. Organizations talk frequently about upskilling and reskilling. Those investments are necessary, but employees do not simply want to be developed so organizations can extract more productivity from them. Growth needs to be reciprocal.

Employees should be able to see how development expands their own capabilities, opportunities, confidence, mobility and future, while the organization benefits from greater adaptability, innovation and performance. That difference is significant and the research reinforces the connection.

When recognition is integrated into growth cultures, employees are substantially more likely to innovate, trust the organization and respect its values. That gives leaders a different way to think about development. Instead of asking only, “What skills do we need employees to acquire?” we should also be asking, “What are we creating that allows employees to grow because they are here?” When those two interests meet, growth becomes a partnership and stops being yet another organizational demand.

Recognition as a Cultural Amplifier

Another idea I kept returning to throughout the event was the concept of amplification. Recognition does not create a healthy culture from out of the blue. It reinforces what an organization chooses to notice.

If leaders recognize collaboration, employees learn that collaboration matters. If experimentation is acknowledged even when every idea does not succeed, employees gain permission to innovate. If compassion, service, problem-solving, mentoring and quiet contributions are consistently noticed, those behaviors become increasingly visible within the organization. That makes recognition much more than saying thank you. It becomes a cultural signal and those signals matter even more during periods of change.

Several sessions explored organizations operating under pressure, whether from AI, economic uncertainty, workforce shortages, burnout, rapidly changing skills requirements, or shifting employee expectations. The common thread was that sustainable performance cannot be separated from the human conditions in which people are being asked to perform.

Leadership Still Determines What People Experience

That theme carried through many of the speakers. Former NASA astronaut José M. Hernández opened the conference with a story grounded in extraordinary perseverance. His path to becoming an astronaut included repeated rejection, deliberate preparation, continued education and a willingness to keep investing in himself. But one of the ideas that stayed with me was his emphasis on validating people and helping them rise to the occasion. Leadership is not simply about setting the destination. It is also about creating the conditions that allow people to believe they can reach it.

Well-being expert Jennifer Moss brought another important dimension to the conversation through her discussion of burnout, trust, hope, purpose and the changing experience of work. Her reminder that hope can be developed as a skill was particularly meaningful in a workplace environment where uncertainty has become almost constant.

Another well-being expert, Ashish Kothari, challenged organizations to look beyond individual well-being interventions and examine the systems creating the conditions employees are working within. One question from that conversation deserves more attention: organizations review operational, sales, staffing and financial data constantly, so why is meaningful people data so often reviewed only periodically? That question connects directly to the work HR leaders are being asked to do today. If people truly are among an organization’s most important assets, our measurement and management practices should reflect it.

AI Can Amplify Culture, but It Cannot Substitute for It

Of course, AI was part of the conversation as well. AI expert Allie K. Miller challenged attendees to move beyond using AI as a basic productivity tool and think more expansively about how it can become part of work systems, workflows, roles and organizational capability. One of the most useful ideas from that discussion was also one of the simplest: AI scales culture, it does not create it.

Technology can increase the speed and reach of what already exists. If an organization has trust, curiosity, strong leadership, knowledge sharing and psychological safety, technology can help amplify those strengths. If those conditions are absent, technology cannot simply manufacture them.

Even O.C. Tanner’s own use of AI within recognition reflects this tension. Technology may help someone create a stronger recognition message or expose an opportunity to appreciate someone, but the human intention behind the recognition still matters. Efficiency should support human connection, not replace it.

From Pickleball to Grease: Designing Moments That People Remember

Some of the strongest examples of recognition during Influence Greatness did not happen in a breakout room. During the analyst experience, O.C. Tanner arranged time with professional pickleball player Tyler Loong. I had never played pickleball before, which made the experience both humbling and fun. We learned, played, laughed and connected in an environment completely different from the analyst discussions earlier in the day. That mattered too because relationships are often built in the spaces around the formal agenda.

I have spent a lot of time over the past several years studying skills.

At Brandon Hall Group, we have researched skills-based organizations, built frameworks around skills architecture, talked with technology providers and, most importantly, advised organizations trying to turn the idea of skills into something that actually changes how they hire, develop, deploy and plan their workforce.

Our recent research, Skills, Work and Talent: Why Skills Architecture Is Becoming the New Operating Logic for the Enterprise, looks at that journey through our five-stage Skills Architecture Progression Model: Skills Visibility, Skills Insight, Skills Evidence, Skills Integration and, ultimately, Skills Orchestration. The progression reflects something we see repeatedly in our work. Defining skills is only the beginning. The real value comes when organizations can use trusted skills intelligence to make better workforce decisions across learning, talent, workforce planning and the business.

Through all of those conversations, I have noticed something.

When we get deep enough into an enterprise skills discussion, TechWolf inevitably comes up.

Sometimes the organization is already working with them. Sometimes they are evaluating them. Sometimes another technology provider brings them into the conversation. But their name has become increasingly difficult to avoid.

That is why SAP’s announcement that it intends to acquire TechWolf is significant, but not particularly surprising.

What is more interesting is what the acquisition tells us about where the skills market is going.

Skills are becoming part of a much bigger intelligence layer

It would be easy to look at this as SAP adding another skills technology to SAP SuccessFactors. I think that misses the bigger story.

TechWolf itself has been evolving beyond that definition.

The company increasingly talks about Skills Intelligence, Work Intelligence and Market Intelligence together. SAP’s announcement reinforces that direction, describing a model that connects the work itself, down to individual tasks, with the skills people have and apply and what is happening in the external labor market.

Together, these capabilities provide a much more current understanding of the workforce, the work being performed and what is happening outside the organization.

For years, organizations have talked about building a skills taxonomy or becoming a skills-based organization. Those are important steps, but the business does not ultimately need a better list of skills. It needs better answers to workforce questions.

What capabilities do we have today? What will we need tomorrow? How is the work changing? Which tasks can AI augment or automate? How should roles change as a result? Where can we redeploy people rather than hire externally? Where should we invest in development?

Those questions require more than skills data.

They require context about people, skills, jobs, tasks, work and the market around them.

That is much closer to what SAP is acquiring.

AI makes the context layer even more important

As AI moves from copilots into agents and increasingly autonomous workflows, enterprise AI needs context.

An agent can retrieve information or complete a task, but to make meaningful workforce decisions it needs to understand how an organization actually works. It needs to understand roles, skills, tasks, relationships and business context. And that information needs to be current, trusted and governed.

SAP specifically points to TechWolf’s context graph as a grounding layer for agent queries. SAP says that foundation can make token usage more efficient while making Joule more intelligent in areas such as skills-based hiring, workforce planning and role redesign.

That may ultimately prove more consequential than the skills use cases that initially made TechWolf well known.

The next stage of this market is not simply about having better skills intelligence. It is about connecting skills intelligence to work intelligence and making that intelligence available to the systems and AI agents that increasingly influence workforce decisions.

It is also why we are seeing strategic workforce planning, job architecture, role redesign and AI impact assessment move much closer together.

These used to be relatively distinct conversations. AI is collapsing the distance between them.

There is an ecosystem question worth watching

There is also a less obvious part of this announcement that I find particularly interesting.

TechWolf has built much of its value around being an intelligence layer that can work across the enterprise technology ecosystem. Its ability to coexist with the systems organizations already have is part of the value proposition. SAP’s own announcement specifically notes that TechWolf works with SAP SuccessFactors as well as non-SAP applications.

That dynamic changes when SAP owns the company.

SAP has been clear that its current plan, subject to the transaction closing, is for TechWolf to remain an independent entity and for its platform to remain available to SAP and non-SAP customers.

But it also raises an interesting question: How will other enterprise technology providers respond to relying on a workforce intelligence layer owned by one of their largest competitors?

There is no reason to assume those relationships disappear. TechWolf’s interoperability is part of what has made the platform valuable in the first place.

Still, ownership matters.

How SAP preserves that neutrality, how deeply TechWolf becomes embedded within SuccessFactors, and how competing platforms respond will be important to watch as the transaction moves forward.

Brandon Hall Group will be in the middle of the conversation

The timing of the announcement is particularly interesting for us at Brandon Hall Group.

On October 21, our Chief Strategy Officer, Michael Rochelle, will be at UNLEASH World in Paris participating in SAP’s Skills at Scale: Build and Develop a Workforce that Drives Your Business Forward Explorer Tour.

The conversation was already set to explore how large organizations establish a skills foundation, connect skills across the talent lifecycle, introduce intelligence and agentic experiences, improve internal mobility and reskill people as AI changes roles.

TechWolf was already part of that conversation as an SAP ecosystem partner before this announcement.

Now the context has changed considerably.

For years, the industry has asked how organizations become skills-based. I think the more important question now is what happens after you have the skills intelligence.

Can you connect those skills to the work actually being performed? Can you understand how AI changes that work? Can you redesign roles around that understanding? Can you give leaders and AI agents enough trusted context to make better workforce decisions?

That is where this market is going.

SAP’s planned acquisition of TechWolf reinforces something we have been seeing through our research and advisory work for some time: skills are not the destination. They are becoming part of the intelligence infrastructure organizations will use to understand and redesign work.

And that is a much bigger conversation.

Talent management is changing so fast that it is easy to mistake disruption for decline. The technology is evolving. Many of the practices we have relied on for years are shifting. The lines between learning, performance, career development, succession, workforce planning and talent acquisition are becoming less distinct.

But none of that means talent management is disappearing.

I think we need to be careful not to confuse the traditional talent management technology category, or the way organizations have historically practiced talent management, with the discipline itself. They are very different.

Talent management exists because organizations must make decisions about the people and capabilities they need to execute their business strategy. They must understand the talent they have, what people can do, where capability gaps exist, how people should be developed and where talent should be deployed. They need to think about succession, performance, careers, leadership and the capabilities they will need in the future.

AI doesn’t make any of those things less important. In many respects, it makes them more important and certainly more complex.

What is changing is how we do the work.

For years, talent management was built around a set of relatively independent processes. We had a performance-management process, a succession process, a learning process, a workforce-planning process and so on. Technology largely mirrored that structure. We bought systems and modules to automate each activity.

The problem is that the business doesn’t experience talent that way.

A capability gap doesn’t necessarily mean we need to train someone. We may need to hire someone. We may already have someone elsewhere in the organization who could do the work. We may need to redesign the job. We may need to develop someone for the capability over time. And now we have another possibility: some portion of that work may be better performed or augmented by AI.

That changes the conversation considerably.

This is where I believe talent management is headed. It is becoming less about administering a series of talent processes and much more about understanding and managing the capabilities the business needs.

AI is accelerating that shift because we can now see and understand far more about the workforce than we could before. We can bring together information about skills, learning, performance, experience, career interests and work itself. We can identify relationships in that information that would be difficult, if not impossible, to detect manually.

But having more intelligence about the workforce doesn’t eliminate the need for talent management. What would be the point of having all that intelligence if we aren’t going to do something with it?

If we identify a skills gap, we still have to decide how to close it. If we identify someone with potential, we still have to decide how to develop them. If we know someone has adjacent skills that could make them a candidate for another role, someone still has to create the opportunity. If AI tells us that a job is changing, we still have to determine what that means for the person doing the job today.

Those decisions are talent management.

There is another aspect of this that I think deserves more attention. We are entering a period when organizations will need to manage human and AI capabilities simultaneously.

That is a significant change.

Workforce planning can no longer be only about how many people we need and what skills they should have. Organizations increasingly need to understand the work itself: What work needs to get done? What should people do? What can AI do? Where should AI augment people rather than replace an activity? And what new capabilities do people need now that AI is part of how the work gets done?

That doesn’t make talent management obsolete. It expands its mandate.

It also means some traditional boundaries within talent management probably won’t survive. Learning can’t operate independently of skills. Skills can’t be disconnected from workforce planning. Workforce planning can’t be disconnected from the changing nature of work. Performance can’t be separated from development. Career mobility can’t work well if we don’t understand people’s capabilities.

We have spent years connecting these concepts while often managing them separately in practice. AI gives us an opportunity, and I would argue a business requirement, to finally bring them together.

That is why I see what is happening as a metamorphosis of talent management rather than its demise.

Some of the technologies associated with traditional talent management will undoubtedly change or disappear. Some longstanding practices should be retired. I don’t think anyone will miss cumbersome annual processes that produced more administration than insight.

But the discipline isn’t going anywhere because the business need remains.

In fact, the opposite may hold.

As work changes faster, skills have shorter shelf lives, career paths become less predictable and AI takes on a larger role in getting work done, organizations need a much better understanding of their people and capabilities than they have today.

That is a talent management challenge.

Talent management is alive and well. It is simply becoming something different from what we have known before.

And I think that is very good.

Workforce Intelligence is becoming one of the latest terms in HCM technology. More technology providers are using it to describe their capabilities, but they aren’t necessarily talking about the same thing.

Some approach it through people analytics. Others through skills intelligence, talent acquisition, workforce planning, internal mobility or learning. Each brings something valuable, but no single technology category provides the complete picture.

For corporate HR leaders, this creates a challenge. How do you know what Workforce Intelligence actually means, which capabilities matter, and whether the technology you already have can deliver some of what these providers are promising?

The interesting part is that HR leaders have been responsible for many of these decisions for decades. Understanding workforce capabilities, anticipating talent needs, managing turnover, planning for change and connecting people decisions to business outcomes are hardly new responsibilities.

What’s changing is the technology’s ability to connect information that has historically lived in separate systems and turn it into intelligence that can inform decisions.

At Brandon Hall Group, we’ve been studying this convergence and began tracking Workforce Intelligence as a distinct HCM technology category in 2025.

My goal with this article is to help define the emerging category from the corporate practitioner’s perspective. What does Workforce Intelligence actually encompass? Where does the technology come from? And how can HR leaders evaluate the capabilities they already have, identify what’s missing and bring the right pieces together?

It’s an evolving category, and I expect the definition to mature as the technology does. But we need a starting point.

What Is Workforce Intelligence?

Brandon Hall Group™ defines Workforce Intelligence as:

The continuous understanding of the workforce, the work being performed, the capabilities and skills available, and the external talent market, using connected data and intelligence to improve workforce and business decisions.

An important part of that understanding is workforce movement. Who is joining the organization, who is leaving, who is moving internally, and what patterns are emerging across different employee populations? Changes in turnover, attendance, scheduling, compensation and employee sentiment can provide early signals of workforce challenges or opportunities. When viewed together and in context, these signals help organizations understand what is happening across their workforce and where action may be needed.

HR has employee information. Finance understands workforce costs. Talent Acquisition monitors external labor markets. Learning and Development assesses capabilities. Workforce Planning forecasts future talent requirements. Business leaders understand the work that needs to get done.

Advances in AI, data integration and workforce modeling are creating new opportunities to bring these perspectives together and understand how people, capabilities, work and business requirements influence one another with greater speed and precision.

That’s what’s driving the emergence of Workforce Intelligence as a technology category.

Where Did Workforce Intelligence Come From?

One of the most interesting aspects of this emerging market is that technology providers are approaching it from different directions.

Some began in People Analytics, helping organizations understand workforce trends, turnover, performance and organizational effectiveness.

Others emerged from Skills Intelligence, using AI to identify workforce capabilities, infer skills and uncover gaps that traditional job architectures and employee profiles often miss.

Talent Intelligence providers have historically focused on recruiting and external talent markets, but many are expanding into internal mobility and broader workforce planning.

Strategic Workforce Planning platforms connect business requirements with workforce supply, demand, costs and future scenarios.

Labor Market Intelligence providers bring an external perspective, helping organizations understand where skills exist, how much they cost and how competitive the market has become.

And emerging Work Intelligence capabilities examine the jobs, tasks and workflows themselves, particularly as AI changes how work gets accomplished.

These technology categories aren’t disappearing. Their capabilities are increasingly overlapping.

Workforce Intelligence is the convergence of these different approaches into a more complete understanding of the workforce and the work it needs to perform.

How Is Workforce Intelligence Different from Talent Intelligence?

Talent intelligence is one of the foundations of Workforce Intelligence. It helps organizations understand available talent, skills, capabilities and opportunities, both internally and in the external labor market.

Workforce Intelligence brings additional dimensions into the picture, including the work being performed, workforce movement, operational conditions, organizational capacity and business requirements.

For example, knowing which employees have a particular skill is valuable. Understanding whether the organization needs more of that skill, whether AI is changing the work, whether employees with that capability are leaving, and what action the business should take brings us closer to Workforce Intelligence.

This distinction matters when evaluating technology. A provider may deliver excellent talent intelligence capabilities without addressing every dimension of Workforce Intelligence. That doesn’t make the technology less valuable. It helps organizations understand where it fits in their broader technology ecosystem.

Brandon Hall Group Began Recognizing Workforce Intelligence in 2025

In 2025, Brandon Hall Group introduced Best Workforce Intelligence & People Analytics as a distinct category within our Technology Awards.

Four organizations earned recognition:

- Capgemini (Gold): Orgvue-Enabled TransformX Operating Model

- ActivTrak (Silver): Workforce Intelligence for Maximum Business Impact

- Tata Consultancy Services (Bronze): TCS USA Workforce Intelligence and People Analytics

- UST (Bronze): Data Quality Dashboard: A Foundation for Scalable Workforce Intelligence

What makes these winners interesting is the range of problems represented by their submissions.

The entries address different dimensions of workforce intelligence, including data quality, analytics, organizational transformation and business impact.

This illustrates why corporate buyers need to understand the category before evaluating technology providers. Two vendors may use the same terminology while approaching very different workforce challenges.

The Five Domains of Workforce Intelligence

At Brandon Hall Group, we see five connected domains that help explain what a more complete Workforce Intelligence capability should address.

- People: Who is in the organization? Where are they? What are their experiences, performance, aspirations and workforce costs?

- Skills and Capabilities: What can employees actually do? What capabilities exist internally? Where are the gaps, and which adjacent skills could be developed?

- Work: What work needs to be accomplished? How are jobs and tasks changing? Where can AI augment, automate or fundamentally redesign work?

- External Market: What talent and capabilities exist outside the organization? Where are they located? How available are they, and at what cost?

- Business Context: What is the organization trying to accomplish? What workforce capabilities, capacity and investments will be required to execute its strategy?

A technology platform doesn’t necessarily need to own all five domains. But corporate leaders should understand which domains a solution addresses, how it connects to the others and which decisions those connections improve.

The real opportunity comes from connecting these different perspectives.

What Does Workforce Intelligence Actually Solve?

Consider a common challenge: A business leader says the organization needs to hire 200 people with a particular set of technical capabilities.

A traditional approach might begin with headcount approval, recruiting plans and external labor market analysis.

Workforce Intelligence allows the organization to ask a broader set of questions.

Do we already have employees with these capabilities? Are there people with adjacent skills who could be developed? What work will these employees actually perform? Could portions of that work be automated or redesigned? What does external talent availability look like? And which option makes the most sense financially?

Those questions change the nature of the workforce decision.

Our 2026 HCM Excellence Awards research provides a useful example.

FPT Software reported that approximately 40% of its existing skills information was outdated, incomplete or contradictory. Managers lacked reliable visibility into available capabilities, sometimes considering external hiring when qualified engineers were already employed within the organization.

As the company improved its approach to skills intelligence and matching, it began identifying internal talent beyond managers’ existing networks.

The lesson extends well beyond skills technology.

An organization can have extensive employee data and still lack a reliable understanding of the workforce it already has.

Workforce Intelligence creates the opportunity to address that problem before organizations make expensive talent and workforce decisions.

What Should Corporate HR Leaders Look for in Workforce Intelligence Technology?

This is where I would encourage buyers to spend their time.

The category is still developing, and technology providers will naturally emphasize the capabilities where they have the strongest history.

Rather than starting with a vendor’s definition, I would evaluate Workforce Intelligence through six questions.

- What workforce decisions does the technology actually improve?

Earlier this year, I wrote about UKG’s evolution toward becoming a Workforce Operating Platform and why I believed it was an important advancement for the HCM technology market. What stood out to me was the company’s recognition that workforce technology needed to do more than manage transactions, maintain records and support administrative processes. It needed to help organizations make better decisions and improve how work is accomplished.

I explored that direction in my earlier article, UKG Analyst Day 2026: Why the Future of Workforce Technology Is Becoming an Operating Platform.

UKG’s announcement today takes that strategy in an exciting and important direction.

UKG, a Brandon Hall Group™ Smartchoice® Preferred Provider, has announced a headless AI vision that will enable employees, managers and leaders to initiate workforce activities through AI experiences, including Anthropic, Google Gemini, OpenAI and consumer voice assistants. UKG’s Workforce Operating Platform provides the intelligence, business rules and operational controls that govern execution.

This is one of the more significant strategic developments in workforce technology because it addresses a fundamental challenge organizations face as they scale their AI investments: integrating intelligent technology with the operational realities of managing people and work.

Jennifer Morgan and Suresh Vittal Are Connecting the Right Priorities

What makes UKG’s direction particularly compelling is the alignment between CEO Jennifer Morgan’s emphasis on employee empowerment and Chief Product Officer Suresh Vittal’s focus on making enterprise AI actionable.

Jennifer has consistently emphasized the importance of improving frontline employees’ lives. In today’s announcement, she links AI to something that matters enormously to employees: greater agency over their schedules, pay, benefits and working lives.

I think that is an important perspective.

Much of the enterprise AI conversation has focused on productivity, efficiency and automation. Those are legitimate priorities, but the employee experience deserves equal attention. Frontline employees often have limited flexibility in accessing workforce information and services, even though scheduling, compensation and work arrangements directly affect their personal lives.

Jennifer’s perspective recognizes that making these interactions easier can improve the employee experience and support workforce stability and business performance.

Suresh brings an equally important dimension to the strategy.

In his accompanying article, Why the Future of Enterprise AI Is About Action, he argues that enterprise AI needs to move beyond providing answers and become more directly connected to the systems and processes that get work done.

I agree with that assessment.

Organizations have made substantial investments in AI assistants, conversational interfaces and increasingly sophisticated agents. But the real business value emerges when those capabilities support decisions and trigger appropriate actions within established operational processes.

Jennifer is articulating what AI should achieve for employees and organizations. Suresh is advancing the technology strategy that can make those outcomes possible.

I see alignment as an important strength of UKG’s approach.

From a Workforce Operating Platform to a System of Action

One of the most interesting aspects of UKG’s announcement is its positioning of the Workforce Operating Platform as a system for action.

Traditional HCM systems have been highly effective at maintaining records, processing transactions and supporting established workforce processes. Those capabilities remain essential, but organizations increasingly need technology that helps them respond to evolving workforce conditions.

UKG’s headless approach separates the experience through which someone initiates work from the platform that executes it.

Through Model Context Protocol tools, pre-built connectors and related technologies, approved AI experiences can access selected UKG capabilities and initiate workforce workflows. The UKG platform continues to enforce the appropriate permissions, policies, approvals and business logic.

This is a smart architectural decision.

Employees and managers can interact with workforce technology through convenient, familiar experiences, while organizations maintain the operational structure necessary to execute workforce activities responsibly.

It also allows UKG to build on its customers’ existing technology investments rather than requiring them to adopt another disconnected application.

This approach represents a natural progression of the Workforce Operating Platform strategy and an important example of how enterprise software can evolve beyond traditional application boundaries.

Addressing the Growing Challenge of AI Fragmentation

One concern I have about the rapid expansion of enterprise AI is that organizations may unintentionally recreate the technology fragmentation they have spent years trying to eliminate.

Companies are introducing AI assistants, agents and specialized applications across functions. While these technologies offer tremendous opportunities, they can also create additional complexity when they operate independently of the systems that run the business.

UKG’s approach recognizes that organizations will increasingly use multiple AI experiences rather than relying on a single interface for all activities.

Instead of requiring employees to interact exclusively through UKG applications, the company is extending selected workforce capabilities to approved AI environments while maintaining a consistent operating foundation.

I think this is a particularly strong strategic decision.

It gives organizations flexibility in how they adopt AI while preserving the workforce’s intelligence, governance and operational processes they already depend on.

It also shifts the conversation about enterprise AI investments. The question is no longer simply which AI assistant offers the most impressive capabilities. It is whether those capabilities can integrate with the underlying systems that support business execution.

UKG is directly addressing that question.

The Frontline Workforce Is Where This Strategy Becomes Especially Powerful

The potential impact on frontline employees and managers is one reason I am particularly enthusiastic about UKG’s direction.

Frontline environments are dynamic. Staffing requirements change, employees call out, customer demand fluctuates and operational priorities shift throughout the day.

A retail manager facing unexpected absences needs to understand which employees are available, whether they have the appropriate skills and what the scheduling implications might be.

A healthcare supervisor responding to increased patient demand must balance qualifications, staffing requirements, employee availability and compliance obligations.

These situations require more than access to information. They require the ability to make informed decisions and to act quickly.

UKG’s strategy enables managers to initiate these activities through conversational AI experiences, while the Workforce Operating Platform provides the underlying intelligence and operational controls.

The opportunity is equally important for employees.

Accessing scheduling information, understanding pay-related questions, or initiating workforce requests should be straightforward. Making these interactions easier can reduce frustration and give employees greater control over their work lives.

This connects directly to Jennifer Morgan’s emphasis on employee agency.

It also creates business value. When managers spend less time on administrative processes, they can devote more attention to coaching employees, serving customers and improving operations.

UKG is making an important connection among AI innovation, employee experience and organizational performance.

Workforce Intelligence and Governance Make the Difference

Another strength of UKG’s strategy is how it brings together workforce experience, intelligence and AI.

Workforce Experience, powered by the next generation of UKG Bryte™, provides conversational ways for employees and managers to engage with workforce capabilities.

Workforce Intelligence, powered by the Workforce Intelligence Hub, connects workforce signals, benchmarks, labor market insights and business context.

Workforce AI uses agentic workflows and intelligent automation to translate that intelligence into action across HR, payroll and workforce management.

Together, these capabilities connect understanding workforce conditions with responding to them.

Suresh Vittal’s emphasis on context, authority, action and accountability is particularly relevant here.

For AI to support workforce decisions responsibly, it must understand the context of a request, identify who is authorized to act, operate within established workflows and maintain accountability for the outcome.

These requirements are especially important in workforce management, where decisions can affect compensation, compliance, employee well-being and operational performance.

UKG’s approach shows that governance and innovation are not competing priorities.

Strong operational controls can make it easier for organizations to expand AI adoption because they provide confidence that activities are being executed appropriately.

That combination of intelligence, flexibility and governance is an important differentiator.

What This Means for the HCM Technology Market

UKG’s announcement also has broader implications for enterprise technology providers and buyers alike.

As AI becomes more deeply embedded in business operations, the value of workforce technology will increasingly depend on how effectively platforms integrate intelligence, operational context and execution.

I returned from the WMHH Summit in Chicago grateful for the people I spent time with and reflective about the workforce challenges healthcare leaders are preparing to address.

A personal highlight was the quality time and meaningful discussions I shared with friends like Andrew “Andy” S. Lawrence, Vice President of HR, Talent Development at the University of Maryland Medical System and Brittany Taggart, Senior Learning Architect, of Intermountain Health. I also appreciated sharing the experience with my colleague Amoi Black. Getting to know one another and exchanging perspectives made our time in Chicago especially rewarding. These connections matter. They give us opportunities to understand the people behind the priorities and build relationships that continue long after an event ends. I will look forward to seeing Andy at the BHG AI event next week and Brittany at our HCM Excellence Conference® in January in Florida.

For Brandon Hall Group™, attending had a clear purpose. Healthcare leaders need meaningful benchmarking, practical best practices and research that helps them make informed decisions amid significant workforce challenges. We came to listen, better understand their needs and share evidence they can use.

My breakfast session, “Megatrends Shaping the Future of Healthcare HR: The Soft Skills Are Now the Hard Skills,” focused on a question that will become increasingly important: How do we help people perform, collaborate and lead effectively as healthcare work changes? I prepared a review of 27 Brandon Hall Group HCM Excellence Awards® Excellence in Practice blueprints across 11 healthcare organizations that deliver patient care. The examples connected human skills with leadership reinforcement and the conditions under which employees work. Together, they offer practical lessons for the future.

Human Skills Need to Be Defined In Everyday Actions

In healthcare, communication, empathy and judgment have consequences for patients and employees. Developing these capabilities requires clarity about what people should actually do. Listening without interrupting, explaining what happens next, checking understanding and responding constructively to a concern are behaviors employees can practice and managers can observe.

- Ascension’s Elevated Service Actions initiative translated service commitments into six observable behaviors. Leaders reinforced one behavior each month through huddles, staff meetings and coaching. Its case reported a 13 point improvement in care team responsiveness and nearly five points in clinician interaction scores.

- Acibadem Healthcare Group used actual patient complaints to create realistic simulation scenarios for 2,268 Patient Services employees across 22 hospitals. Employees practiced empathy and communication in situations reflecting the pressure of their work.

These examples suggest a useful direction for healthcare HR: Make human capabilities specific, build realistic practice into development and evaluate whether those behaviors carry into the workplace.

Leadership Development Must Extend Into the Next Shift

An employee’s ability to use a skill depends partly on how their leader pays attention and responds. If an organization encourages employees to raise safety concerns, managers need to know how to listen and follow through. If employees learn new ways to communicate, leaders/managers need to coach and reinforce those behaviors. IHH Healthcare’s Speak Up for Safety program recognized this relationship through a separate manager pathway. Managers learned to receive concerns professionally, provide feedback, follow-up and protect employees from retaliation.

The Acibadem case also offered an important implementation lesson. Investing in managers as coaches mattered alongside the learning experience itself. As healthcare organizations prepare future leaders, they should give managers practical support for these daily responsibilities. Development has greater value when it changes what happens during a difficult conversation, a team huddle, or a demanding shift.

AI Will Bring New Questions About the Value and Design of Work

The priorities represented in the attendee briefing included AI education, reduced administrative burden and improved staff efficiency. Those priorities create important work and contemplation for HR.

As technology changes tasks, organizations will need to reconsider responsibilities, skills and performance expectations. Employees will need opportunities to learn, ask questions and understand how their roles are evolving. One question I believe leaders should ask early is: What will we enable people to do with the time technology gives back? There may be opportunities for more patient interaction, stronger coordination and continued development. Achieving those benefits requires intentional decisions about workflows and staffing.

Workforce Readiness Depends on Retaining Experience and Developing New Talent

Retirement, succession and early tenure retention were also reflected in the attendee priorities. These issues need to be considered together. Organizations need to preserve experienced employees’ knowledge while helping newer colleagues build capability and confidence. Mentoring, structured onboarding and opportunities to apply learning can support that transition.

- Advocate Health’s Emerging Leader Program illustrated one approach to preparing people for greater responsibility. It combines development in areas such as feedback, emotional intelligence and collaboration with mentoring and applied projects. Its case reported an 87% conversion or internal mobility among graduates. For healthcare HR, the broader opportunity is to connect development with visible pathways forward. Employees need to understand how they can grow, while organizations need greater visibility into the people preparing for future roles.

Supporting Retention Requires Understanding People’s Circumstances

Workforce support also requires a closer look at the specific barriers employees face. This particularly resonates with me, as over the course of my career you never know what someone may be facing. Advocate Health’s Teammate Success Coaching found that departures attributed to personal reasons could reflect difficulties with transportation, childcare, housing, or finances. Coaches identified barriers during the first 90 days and coordinated practical assistance. The case reported early tenure attrition falling from more than 70% to as low as 16% in key participating sites. Those results are specific to the settings described, but the underlying lesson has wider relevance: organizations need to understand the problem early enough to help. An empathetic conversation becomes more useful when managers and support teams know exactly what the issue is and have a way to act on what they learn.

Addressing Workforce Strain Means Examining Working Conditions

- Oak Street Health’s PROPEL Coaching Program identified potential burnout and paired providers with experienced peer coaches. Studies show that people respond to their peers and direct managers, I loved reading about this program. Managers and coaches worked together to address workflow barriers and support needs. The case reported a 15% reduction in first year provider turnover and 92% of leaders reported improved performance. Those findings describe retention and performance outcomes, rather than a measured reduction in burnout. They demonstrate how coaching can connect with practical changes to the employee’s work experience.

Brandon Hall Group Perspective

Healthcare’s workforce challenges require more than filling vacancies or introducing new technology. Leaders need to strengthen the conditions that help people succeed: clear expectations, practical development, supportive managers and opportunities to grow. The award-winning blueprints offer concrete examples of how organizations are putting these priorities into practice. For healthcare HR leaders, the next step is to identify which approaches address their own workforce challenges, adapt them to their environment and measure the results.

This is why Brandon Hall Group’s engagement with the healthcare community matters. Through research, benchmarking, HCM Excellence Awards® Excellence in Practice blueprints and advisory support, we help leaders connect evidence with action. Conversations with healthcare executives also help us focus our research on the decisions and challenges that matter most to them.

I left Chicago with a deeper appreciation for this community and the work ahead. To Andy, Brittany, Amoi and everyone who shared their time and perspectives, thank you for making the experience so meaningful. I look forward to continuing these conversations, building on new friendships and helping healthcare leaders support the people shaping the future of care.