When AI moves faster than your strategy, how do you catch back up?

A recent McKinsey report delivers a clear warning: Frontline employees are racing ahead with gen AI, but managers are stuck in the governance weeds. While 90% of surveyed workers use AI tools informally, only 13% say their organization is a formal early adopter.

That’s a huge gap. And it’s growing.

The wrong metaphor

Most organizations still treat innovation like carpentry: they draw up plans, select their tools, and build in sequence. But AI’s pace doesn’t allow for that kind of linear rollout. By the time your strategy is approved, your competitors have already tested five tools, dropped three, and operationalized two.

The better metaphor is gardening. Look for the shoots. Where are teams already solving problems with AI? What’s working informally? Instead of prescribing from the top, nurture what’s already growing.

This gardener mindset aligns with what Harvard professors Michael Luca and Max Bazerman describe in The Power of Experiments: forward-looking organizations foster cultures of experimentation. They don’t wait for perfect plans. They test, learn, and adjust.

Design to learn, not just scale

Leaders often jump to, “How do we scale this?” But first ask: “What are we learning?”

Luca and Bazerman emphasize that smart experimentation is deliberate. It means crafting hypotheses, documenting both wins and failures, and embracing small-scale pilots. Experiments aren’t just good for providing success metrics. They identify blind spots, test assumptions, and expose what truly matters.

Tech firms like Amazon and Microsoft lead the way not by betting big all at once, but by iterating fast. A change as small as ad size on Bing yielded $50 million in revenue. That kind of impact comes from testing, not committees.

Elevate what matters

When organizations run lots of experiments, not everything can (or should) scale. That’s why leaders need to be selective about what they spotlight.

Celebrating every effort equally dilutes attention and muddies focus. Instead, purposeful leaders highlight the experiments that yield real insight or signal broader potential. They ask teams: What did you learn that surprised you? What actually shifted behavior, saved time, or revealed risk?

Framing results this way makes learning visible and repeatable. It also reinforces a culture where progress isn’t defined by perfection, but by clarity.

What this means for learning leaders

L&D teams are uniquely positioned to spot and support early experiments and to turn them into repeatable practices. But only if they shift from gatekeeping to greenhouse tending.

Want to accelerate AI adoption? Stop treating it like an IT deployment. Start treating it like a culture shift.

The future isn’t waiting for your strategy. It’s already sprouting in the margins.

Take a deeper dive…

Read the getAbstract summary of The Power of Experiments

Read the McKinsey Report: “The Learning Organization: How to Accelerate AI Adoption”