AI doesn’t replace the designer. It reveals what only a designer can do.

I believe our best can always be better. It’s a principle I hold for myself and the teams I lead — that curiosity isn’t a trait you either have or don’t, it’s a practice you choose every day. The designers I most want to work with are the ones who get genuinely excited about a new tool, who want to tinker with it and understand it before they’re asked to. That instinct is what separates people who grow from people who stagnate.

AI is the clearest test of that principle I’ve seen in my career. And I apply it to myself the same way I’d apply it to anyone on my team: I have to understand it, not just have an opinion about it.

Early in my career I was doing everything — building prototypes, writing test scripts, moderating sessions, synthesizing observations, and crafting presentations to bring findings back to the business. A moment that still informs how I think: running moderated usability tests for AutoZone.com’s mobile-first redesign, a major migration to React covering both mobile and desktop experiences. I could run about ten moderated sessions a week. The business wanted more data, so we scaled to fifty unmoderated tests.

More participants, more data — but the synthesis didn’t scale with it. I still had to watch all fifty sessions and take notes by hand. It took twice as long to reach conclusions as the moderated approach had. The data was richer. The insight wasn’t.

I share that story not because research is my current work, but because it shows exactly where AI changes the game — and why I want the teams I lead to understand that deeply. Pattern recognition across fifty sessions is precisely what these tools do well. A designer today could receive a summary of what surfaced, then choose which session deserved a closer look, rather than sifting through everything hoping nothing was missed. The judgment about what matters remains human. The sifting doesn’t have to be.

That’s the leadership opportunity I see right now. Not just adopting AI tools, but helping teams understand what they’re actually good for — and what they’re not. The hours freed from production work and data aggregation can go toward the harder, more human questions: What problem are we actually solving? Whose voice is missing? What are we optimizing for, and is that the right thing?

Don Norman also warns that AI systems must be transparent — that trust erodes when decisions are made without explanation. That is a design problem. And it is one of our professions that is uniquely equipped to solve. How AI communicates its reasoning, earns trust, and fails gracefully — these are UX problems at their core. They need designers who think strategically, not just designers who know the tools.