Organizations are facing an urgent change management challenge. Leaders are convinced that artificial intelligence will transform their business, yet the people needed to carry that transformation forward have stopped trying, or so it appears. According to McKinsey’s Superagency in the Workplace report, employees are already using generative AI three times more than their leaders realize. Yet only 1% of companies say AI is fully integrated into how work gets done. Workers are moving. Organizations aren’t. Much of that activity, as we’ll see, is happening outside approved systems entirely—less a sign of resistance than a signal of unmet need.
We’ve seen this pattern across industries from both sides—Tomer as chief customer officer at WalkMe, on the frontlines of digital adoption, and Jenny as an executive coach and organizational change consultant. What looks like resistance is usually a rational response to a system that changed at the top without bringing people along. Leaders who close the gap don’t begin by tightening control. They begin by resetting the system. Here are three strategies to do it.
First, understand why employees resist
When employees disengage from AI, we call it resistance. WalkMe’s State of Digital Adoption Survey tells a more nuanced story. A 52-point trust chasm separates executives and workers: 61% of executives trust AI for complex decisions; only 9% of workers do. According to McKinsey’s State of AI Survey, while 88% of organizations use AI in at least one business function, nearly two‑thirds are still running pilots rather than scaling. Leaders believe the tools are working. Employees are living a different reality. These are not two sides of the same conversation. They are two different belief systems.
Beneath that chasm are five recognizable patterns:
- “I don’t know what I’m supposed to do with it.”—Gallup research links resistance directly to loss of control and unclear expectations.
- “I’ve tried it, and it wasted my time.”—Over 80% of AI projects fail, with skill gaps, data readiness, and poor workflow integration as core causes.
- “I’m afraid of what it means for my job.”—FOBO (Fear of Becoming Obsolete) is real. Workers see layoff headlines and connect the dots.
- “Nobody showed me how.”—Most organizations provide one-time or outdated training without structured learning paths people need day‑to‑day.
- “I’m good at my job. I don’t need this.”—This is craft identity, and it’s more asset than obstacle. As Jenny has explored in her research on healthy friction, the tension between expertise and new tools, when channeled well, becomes a driver of growth, not a barrier.
These are not obstacles to push through. They are signals to read.
1. Give people a clear destination, not just a directive
Across industries, we see the same pattern repeat. An enterprise AI platform launches with fanfare—executives send a memo, IT provisions licenses, a training webinar gets posted to the intranet. And then, not much changes. Research consistently finds that the majority of AI initiatives fail to meet expected outcomes. The employees aren’t rebelling. They simply don’t know what “use AI” means for their role. The directive is clear. The destination is not.
One WalkMe customer faced exactly this pattern. Employees had access to multiple AI tools but were writing vague prompts, getting inconsistent results, and giving up. To solve this challenge, reduce cognitive load, and reinforce desired behaviors, the customer’s digital adoption team created a custom prompt library organized by role and use case—over a thousand templates—that gave each person a concrete starting point. An engineer knew exactly which prompt to use for code review. A marketer had ready-made templates for campaign briefs. Within a month, abandonment dropped, and thousands of interactions were logged. Same tools. Same people. Different destination. That outcome was the result of a defined business target. The goal wasn’t “increase AI adoption”—it was “cut first-draft time in half for every role that touches client work.” Measurable. Owned. Tied to outcomes that already mattered to the business.
Rather than “use AI more,” try: “By next quarter, your first draft of any client deliverable should take half as long, and here’s exactly how.” That’s a destination.
Questions to direct your team:
- Have you defined what AI-enabled success looks like for each role?
- Does each employee have a concrete use case to start with?
- Is your destination specific enough that someone could confirm they’ve reached it?
- What does “using AI well” look like in your team’s daily workflow?
2. Connect AI adoption to what people already care about
People are not moved by logic or mandates. They move toward what feels rewarding, identity affirming, and safe. This is precisely where most AI rollouts fail—treating adoption as a compliance issue rather than a human one.
What people actually want from their work doesn’t change because AI enters it: to feel competent, not exposed; to do work that’s seen, not invisible; to do work that matters, not work that could be done by anything. AI adoption succeeds when it’s framed against those needs instead of against a mandate—a dynamic McKinsey has tied to self-determination theory, which holds that employees become autonomously motivated when their needs for competence, autonomy, and relatedness are met. The reframe is simple but consequential: Stop asking employees to “adopt AI” and start asking them what kind of professional they want to become. A skilled analyst who sees AI as a threat to their expertise will resist. That same analyst, invited to become the person who produces better insights faster, leans in. Same tool. Different frame.
One organization Tomer works with evolved its digital adoption team from SaaS enablement to a team focused on helping to build AI fluency enterprise-wide: human-AI experience design, AI-enabled workflows, and role-based prompt curation. The team’s framing shifted from “we have to use AI” to “understanding AI and driving AI fluency is a big opportunity to make a meaningful impact.”
The expanded scope gave the team a different kind of work: less repetitive, less friction-driven stress, and more room to focus on higher-value work. That’s an identity shift, and it spreads. What made it durable was that IT, Learning, and business leaders were operating from a shared definition of success. Each function owned a piece—infrastructure, competency, outcomes—and together they could see the whole picture.
Questions to motivate your team:
- What does your team already care about, and how does AI help them do more of it?
- Have you created visible career markers for AI fluency, or is adoption invisible and unrewarded?
- Have you invited employees to publicly commit to one specific AI use case? Small commitments made visible tend to stick.
- Are you framing AI as a threat to their skills or as an amplifier?
- Is there psychological safety to experiment, fail, and try again, or only pressure to perform?
3. Make the right behavior easier than the wrong one
Nearly half of workers admit to using AI tools without employer approval, many sharing sensitive data in the process. The instinct is to clamp down. But that misreads what’s happening. Workers aren’t rebelling against governance—they’re following the path of least resistance. Approved tools are harder to access, less integrated, or simply unknown.
A global professional services firm Tomer worked with had a persistent bottleneck: identifying the right cost center for a client engagement required manual searches across dozens of options. They embedded AI directly into that step—what had required multiple searches became a single click, in the same place employees already worked. Adoption was immediate; not because behavior changed, but because it didn’t have to. Don’t ask people to adopt AI. Make AI part of how they already work.
Questions to shape the path:
- Where could AI be embedded directly into existing workflows?
- What makes bypassing approved AI easier right now than using it?
- What small changes—a template, a shortcut, a default prompt—could make the right behavior feel automatic?
- How can you treat shadow AI as a diagnostic rather than a disciplinary issue?
Getting unstuck—together
Closing the AI adoption gap doesn’t require better tools or stronger mandates. It requires directing people toward a clear destination, connecting change to what they already care about, and building an environment where the right behavior is also the easiest one.
Your people aren’t waiting to be pushed. They’re waiting to be led. Mandates move behavior. Meaning moves people.
