The biggest barrier to AI adoption isn’t accuracy — it’s belief.

Every AI project eventually hits the same wall: the moment when people stop asking what it can do and start asking can I trust it?

I saw this recently while working with a startup building an AI tool that measures patterns of human performance.

It’s a fascinating space — but what has really stuck with me isn't the tech. It was something we kept hearing from users:
“I just need to know it’s right.”

They didn’t want to take the data at face value. They wanted proof — to see the moment the system captured, to say “yes, that’s correct,” or “no, not quite.” In other words, to teach the system for a bit before trusting it.

And that made me realize: early AI adoption is less about accuracy and more about relationship-building. Trust doesn’t just come from flawless algorithms — it comes from giving people a hand in shaping them.

Bringing AI into an organization isn’t just a technical challenge — it’s a human one. We’re naturally skeptical. We want to see for ourselves, to understand, to believe. Designing for that moment — when curiosity becomes confidence — is the real UX of AI.