Your AI strategy might be slowing you down

I’m working with companies navigating how to bring AI into their business, and the pattern I keep seeing is this: leadership knows AI matters, so they do what they’ve always done with big initiatives.

They plan. They align. They document.

Meanwhile, nothing actually gets built—and, more importantly, nothing gets learned.

Judgment comes from doing

In my experience, people build real AI skill by using it: testing prompts, judging outputs, and figuring out where a tool fits and where it doesn’t. That judgment develops through active experimentation. A committee or strategy session can’t supply it on its own.

The technology is also moving quickly. A static plan can fall behind while people are still discussing it. Staying in the practice of using these tools matters.

Start with small bets

What I recommend is a series of small bets across departments:

• A product team building a working demo instead of another wireframe.
• A marketing team testing AI-assisted SEO content production.
• An operations team automating a weekly dashboard they’ve been building manually for years.
• A support team testing an AI-assisted response workflow.

Each experiment builds intuition about what works and what doesn’t. It gives the next decision something concrete to rest on.

Small starts compound. Distributed experiments build capability across the business.

If you’re waiting until the strategy is “done” to start, it may never feel done enough. Start with a useful problem, build something small, and let what you learn shape the next step.