Make room for useful experiments.

Start with a real problem, a time boundary, and a learning goal. Make it safe to surface what went wrong. Celebrate useful discoveries and successful outcomes, then decide what deserves another round.

In practice

I start small on purpose, and I start where it hurts. When the first thing AI does for someone is take away the task they resent, they get curious about the next one. Starting small also lowers the stakes. I might test meeting prep for an account I already know, where I can check the output against familiar material before using it in a customer conversation. That gives me a way to learn where the model is strong and where it’s jagged. A weekly lunch-and-learn or a channel where people share one thing that worked keeps the momentum going.

The tradeoff

Pain-first can pick a low-value target, and a well-run experiment can prove something nobody needed proven. So every experiment gets a hypothesis, a baseline, a review date, and a stated decision it’s meant to inform. And documenting the painful process before automating it usually reveals a step that shouldn’t exist at all. Delete before you automate.

Go deeper

Make the experiment safe to learn from in the field guide.