Pattern recognition
AI is great at scanning large volumes of notes, feedback, and data to spot themes, repeats, and outliers I might miss.
My approach to using AI
I use AI to move faster on the work that benefits from speed, and I keep judgment, craft, and systems thinking human-led.
AI’s strengths → Where AI helps most
Summarization & synthesis
AI helps turn messy, unstructured inputs into clearer summaries I can sanity-check, refine, and share with the team.
Speed and iteration
AI helps me generate options quickly, explore variations, and unblock early thinking without over-investing in one direction.
Scalability and consistency
AI is reliable for repeatable tasks: applying rules, formatting, and keeping outputs consistent across docs, specs, and systems work.
AI’s weaknesses → Where I keep it human-led
Deep problem framing
AI can restate what it’s given, but it often misses the “why” behind behavior: motivations, constraints, and what’s actually at stake.
Emotional understanding
AI doesn’t carry lived context. It can miss tone, trust, and the emotional weight behind user decisions.
Complex system thinking
AI struggles with multi-step dependencies. It can miss how a change in one place affects edge cases, states, and downstream surfaces.
Taste & craft
AI can produce options, but it can’t own the final call. Great design is choosing the right direction and sweating the details.
How this all fits together
AI accelerates the work that benefits from speed and scale. I handle the parts that require judgment, systems thinking, and craft. The result is faster execution without sacrificing quality.
Let’s build something great.
Always open to connecting about roles, systems work, and solving messy problems.