Design leadership that stays close to the craft.

I voice-dump half-formed thinking on a walk and let Claude shape it into a structured brief or testable direction. Feed it a mountain of research and ask it to surface the five decisions worth a real human conversation. Before any project kicks off, an interview loop surfaces the hard questions upfront so designers aren't discovering gaps mid-sprint.

I use AI to build faster fluency in my partner's worlds. I'll have it synthesize the engineering tradeoffs buried in a technical spec, stress-test a PM's prioritization logic against customer evidence, or interrogate whether a DS team's success metric actually captures what users care about. That depth is what lets me push back meaningfully - not just advocate for design, but shape the product direction. AI turns me into a more informed, harder-to-dismiss voice in the room.

I've built a connected layer across our docs, tokens, and decision history that surfaces inconsistencies early - components diverging, patterns reinvented, tone going off. It catches the small things so the team can focus on the hard ones. And it frees me to show up as a design leader, not a design librarian.

Before a crit, AI handles the baseline - accessibility flags, pattern inconsistencies, copy gaps - so the room stays focused on the harder questions of narrative and emotional resonance. In 1:1s, I use it to stress-test edge cases live. For dogfooding, it synthesizes the messy feedback - Slack threads, recordings, bug reports - into clear craft debt patterns, so we're fixing the right things, not just the loudest ones.

By offloading context synthesis and routine validation to AI, I convert 1:1s into coaching sessions instead of status meetings. That discipline protects my time and lets me re-engage as a hands-on designer when the stakes are high.