The human side of AI adoption: Why executive mindset will determine its success

It’s the gap everyone sees but few fix.

Three facts keep showing up in boardrooms I work with.

• First, AI is on every agenda.
• Second, the stakes feel high.
• Third, progress is slower (if at all) than anyone wants to admit. The data backs that tension.

Boston Consulting Group’s AI Radar 2025 reports that “75% of executives rank AI/GenAI as a top three strategic priority.” Yet most leaders still haven’t moved from intention to action. McKinsey’s State of AI 2025 found that “23% of respondents report their organizations are scaling an agentic AI system somewhere in their enterprises (that is, expanding the deployment and adoption of the technology within a least one business function) and an additional 39% say they have begun experimenting with AI agents.” Everyone’s talking about AI — far fewer are doing something about it.

There’s a riddle that goes, “Three birds are sitting on a wire. One decides to fly away. How many are left?”

The answer is three — because the bird only decided to fly; it didn’t actually fly away.

That riddle perfectly captures what’s happening in executive circles. Leaders are deciding AI is important, but few are actually experimenting with real solutions.

As an executive coach, I see the human story inside those numbers. Knowing you need to act and being ready to act are not the same. AI adoption is a leadership development challenge as much as a technology one. The roadblocks are emotional and behavioral — fear of getting it wrong, resistance to change and whether or not to embrace an early-adopter mindset without having all the answers. That’s where leadership either accelerates or stalls adoption.

The real barrier isn’t AI

When executives say they’re “waiting for a clearer roadmap,” what I often hear underneath is a more honest truth: uncertainty feels too risky and we’ll stick with the (perceived) safety of the status quo. Many senior leaders have succeeded by having strong opinions, controlling key decisions and moving fast with conviction. Many have also succeeded by taking risks. That begs the question: Why does AI feel more dangerous than past risks?

AI requires a different posture — curiosity before certainty, experiments before scale and collaboration before control and command.

And it triggers something deeper: fear.

Fear of being replaced. Fear of losing relevance. Fear of failing publicly.

A client of mine said his boss acknowledged using AI to review his work. During their one-on-one, my client went from engaged and motivated to nervous. In a split second, his brain was no longer focused on the matter at hand, but on how AI might impact his job. He was distracted and hijacked.

His story captures the unease many people feel about AI. People don’t need their executives to be AI experts. They need leaders to set direction, resource the work and remove barriers.

Growth mindset at the executive level

For years, we’ve encouraged lower- and mid-level managers to adopt a growth mindset — to see challenges as opportunities to learn, not verdicts on their ability. Interestingly, the higher you go, the less often executives hear that feedback. Somewhere along the line, making mistakes or saying I don’t know has become kryptonite.

Psychologist Carol Dweck, author of “Mindset: The New Psychology of Success,” wrote, “In a fixed mindset, people believe their basic qualities, like their intelligence or talent, are simply fixed traits. They spend their time documenting their intelligence or talent instead of developing them.”

In other words, they believe they’ll never be smarter or more talented than they are right now — and why even try?

Quite frankly, I do NOT subscribe to a fixed mindset.

In practice, executives with a growth mindset do three things differently:

1. They name the uncertainty out loud. Saying “We don’t have all the answers yet” invites teams to bring their best ideas forward instead of waiting for an official playbook. It’s the open invitation teams need to know it’s safe to brainstorm.
2. They set learning goals in concert with outcome goals. Early efforts are measured by validating and invalidating hypotheses, the workflow modifications we tried and what was learned on both the micro and macro levels. This is not the time for artificial harmony and unchecked agreement.
3. They seek feedback, honestly. They ask the people closest to the work where AI helps, where it hurts and what’s missing — then respond to that feedback with action. As the adage goes, “those closest to the job know best.”

None of this is soft. It’s disciplined. It requires executives to manage their own reactions and stay focused on evolving the systems and processes they’re reengineering. This is the time to think expansively, with a growth and curious mindset, and avoid the trap of fear and the “we’ve always done it this way” thinking of the fixed mindset.

Let curiosity be your guide

The instinct to control every variable is often strong with high-performing leaders — but when innovating, progress depends on curiosity.

The leaders I see moving fastest do three things consistently:

1. They frame AI as an experiment with purpose. They use pilot programs as a way to effectively inform where to go next. What problem are you most curious about solving? And what variables are you curious to experiment with? That’s exactly where you start!
2. They make it safe to surface the downside. Organizational psychologist Amy Edmondson defined psychological safety as “a shared belief that the team is safe for interpersonal risk taking.” If people fear consequences for honesty, you’ll never hear about bias, errors or breakdowns until it’s too late.
3. They insist on evidence. Opinions matter. Gut instincts count. Data decides. Leaders who curiously ask, “What are we learning?” move faster than those waiting for absolute certainty.

Define measures that matter. Track them. Celebrate what works. Learn fast from what doesn’t.

Adoption at scale is a people system

AI isn’t just a standalone project. It’s an entirely new way of working. Adoption is less about tools themselves and more about the people who either will or won’t adopt, advocate and champion. In my work with executives, I focus on four levers:

• A clear strategy people can act on. “Reduce time-to-resolution by 20%” beats “do more with AI.” When people know the why, they’ll figure out the how. By the way, “do more with AI” is a common reframe nowadays — avoid falling into that trap.
• Roles and trust. Executives who delegate with trust see more experiments, faster learning and better decisions up and down the organization. Your message is simple: I expect progress, I expect trial and error and I expect you to report what you learn.
• Learning infrastructure. Upskilling isn’t a one-time event. People need safe spaces to practice — sandboxes, red-team drills and cross-functional partnerships. Leaders who build learning into the work see greater adoption.
• Measurement that matters. Focus on the metrics that matter most. Keep a short list, review it often and make sure teams see their progress — especially when it’s not linear.

What executives can do this quarter

Try this in the next 90 days: Start with two meaningful use cases.

One customer-facing, one internal productivity. Give each a small, cross-functional team and a single exec sponsor who can unblock decisions fast.

Set three measures per use case: One learning metric, one operational metric, one financial metric. Publish them. Track them. Review every two weeks.

Model curiosity in the open: Tell your organization what you’re testing, what you’re learning and what you’re changing. When leaders talk about learning on the fly, teams start experimenting too.

Invest in the human skills that make AI useful: Executives don’t need to be prompt engineers. They need to be question engineers. Ask better questions. Give clearer feedback. Coach people through uncertainty. That’s leadership range — and it’s teachable.

The challenge

Most executives already believe AI is critical to their strategy. BCG’s research makes that clear. McKinsey’s data confirms most still aren’t acting on it.

The next edge won’t belong to the leaders who talk about AI best. It’ll belong to the ones who build habits that help their organizations learn faster than everyone else.

If you want to be in the top percentile of leaders actually realizing AI’s promise, what’s holding you back? Now’s the time to embrace the uncertainty, test the ideas that keep nagging at you and model the curiosity you expect from your teams.

Clarity won’t show up before you act. It’ll come because you did.

AI transformation isn’t just about mastering the technology. It’s about engaging leaders who are willing to learn, be early adopters and champion change through uncertainty.

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