AI-aware design practice
AI does the legwork. Judgement stays human.
I use AI to accelerate research operations, synthesis, critique and documentation - so I spend more time on the parts that actually need product judgement, user evidence and stakeholder context. It's a method, not an identity.
Where it helps - and where it doesn't
AI does the legwork. The judgement stays mine.
AI-accelerated
Where I lean on AI
+Analysing thousands of app-store comments to validate the real problems
+Note-taking across research sessions, then summarising for stakeholders
+Testing tools like Gemini, ChatGPT and Otter side by side to find what works
+Keeping stakeholders in the loop without burning hours on check-ins
Human-led
What never gets handed over
Building trust and navigating stakeholder chaos
The human stories behind the data, from users and the support team
Deciding what is actually worth prioritising
Adding the semantic meaning that makes a design system usable
A typical research-to-decision loop
01
Human-led
Gather
Sessions, support conversations, store visits and analytics, collected first-hand.
02
AI-accelerated
Analyse
AI clusters thousands of comments and transcripts into themes, fast.
03
Human-led
Verify
A human note-taker sits alongside the AI; I check every theme against the room.
04
Human-led
Decide
Prioritisation and the design response sit with the team and the evidence.
↻Every decision feeds the next cycle - evidence compounds, judgement stays human.
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