AI disruption: 7 ways your workforce can thrive

The decisions HR leaders make right now about roles, team structures and AI investments will shape the workforce for the next decade.

Here’s the problem: The majority of data informing today’s biggest workforce decisions is already out of date.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.