Building AI That People Actually Use.

I started in enterprise technology in the early 2000s, close to the machines — voice engineering and telecom, where a dropped packet is something a customer feels. Over the years the work moved up the stack: enterprise infrastructure, then cloud, then the harder human problems of digital transformation, program leadership and enterprise partnerships. The common thread was never the technology itself. It was helping large organizations actually adopt it.

When AI arrived, the obvious move was to leave and chase it full-time. I didn't. I kept my seat inside the enterprise and started building products in the hours around it — shipping real things to real users, learning what held up in practice and what only looked good in a demo. That constraint turned out to be a gift. It forced everything I built to be useful before it was impressive.

The belief underneath all of it is simple. AI should augment people, not replace them. The best software doesn't ask you to work its way — it quietly does more of the work you'd rather not, and hands you back your attention.

Away from the screen, I'm a musician — handpan, didgeridoo, and a growing shelf of instruments — as well as a photographer, podcaster, and teacher of AI. These aren't a separate life from the products. They're the same instinct pointed at a different surface.

Music teaches restraint: what you leave out matters as much as what you play. The same is true of an interface. Photography teaches attention — what to keep in frame and what to let fall away. Teaching forces clarity, because you can't explain what you don't truly understand. Every good product decision I've made borrows from one of these.

I enjoy conversations about building practical AI, the reality of enterprise adoption, and what two decades in the field actually teaches you. If it's useful to your audience, I'm glad to join.

Open to podcasts, universities and student programs, enterprise events, and AI conferences — as a guest, speaker, or collaborator.