Why Your AI Experiments Don’t Need to Be Perfect to Be Worthwhile

In a world of polished demos and viral product launches, it’s easy to feel like your experiments with AI need to be flawless from day one. But the truth? The most valuable experiments are often the messy ones.

Here’s the thing I tell clients all the time: your first AI use case doesn’t need to scale. It doesn’t need to be revolutionary. It just needs to help you - or your team - understand how this technology can fit into what you already do.

The most useful insights come from:

• Trying a tool on one real workflow, and noticing where it trips up

• Spotting tasks that feel almost automatable, but need a human eye

• Learning which types of prompts get you halfway there vs. all the way through

• Discovering unexpected side benefits - like how an AI summary of your meeting notes might help new team members get up to speed faster than ever.

These small, imperfect experiments become the building blocks of real capability.

Sometimes, an experiment fizzles out - and that’s fine. Knowing what doesn’t work is part of knowing what’s worth fixing. And often, those failures help you ask better questions next time.

So if you're holding back because your idea feels “too scrappy,” “too manual,” or “not ready” - take this as permission. Run the test. See what happens. Use the result as a stepping stone, not a final product.

Innovation doesn’t start with polish. It starts with play.