My (evolving) AI UX Approach
My process and principles for designing trustworthy AI experiences.

My AI-augmented process
AI compresses the pass. The judgment, the taste, and the read on what the user actually needs — that part's still mine.
Synthesizing research notes into themes.
Drafting blockflows I refine rather than starting from a blank canvas.
Generating variants I react to and refine — often surfacing options I wouldn't have considered.
The handoff gap between design and engineering shrinks, and I stay closer to how the product actually behaves in users' hands.
Designing for Trustworthy AI
Lately, I have been focused on how best to design for AI. Thinking about it, I believe the friction people feel with AI isn't primarily a capability problem, it's a shared-understanding and alignment problem (and ultimately, a trust issue).
As humans and AI work together, an effective working relationship depends on knowing what's being handed over, why decisions are made, and how to correct course when something goes wrong. Most AI products haven't been designed around those mechanisms yet.
Below are some emerging principles for trustworthy AI that I am noodling on:
Good delegation isn't built in a single handoff. You establish how you work, what's important, where the boundaries are, and what success looks like. Trust builds through demonstrated understanding.
Most AI products skip this step. They execute immediately and hope the output matches what you intended.
People ask AI to triage email, book travel, or draft reports. Then they hover over every action and double-check every output.
That's not user resistance. It's appropriate caution for a relationship that hasn't earned trust.
AI products are still optimized for speed to output rather than speed to understanding. Demo culture rewards instant answers, while small cues like "thought for 5 seconds" reinforce the idea that faster means smarter.
But speed doesn't create the shared understanding required for delegation.
