Case study summary
FROM IDEA TO EVIDENCE—AT AI SPEED
Reengineering ideation, prototyping and research into one AI-native workflow built for faster, evidence-based decisions
- Role: UX leader — operating model and workflow design
- Company: Gap, Inc.
- Focus: AI-native ideation, prototyping and research
- Disciplines: Product Design, UX Research, Content Design, Design Operations
Ideation, prototyping and customer validation were separate stages handled by separate teams. Early product discovery moved slowly as a result, and decisions frequently ran ahead of the evidence that should have informed them.
I led the UX team in rebuilding those stages into a single accelerated workflow. We partnered directly with Figma to connect our design system to rapid prototyping through Figma Make and Claude Code, so a cross-functional team could move from a focused half-day ideation workshop into working prototypes that same afternoon.
In parallel, UX Research prepared participant screeners and study protocols so validation could launch immediately. AI-native moderation and analysis tools let the team apply qualitative methods at quantitative scale, with researchers keeping human oversight to curate insights, protect quality and interpret nuance.
Running design and research concurrently compressed the distance between an idea and real customer evidence dramatically. Teams could prioritize with better information, make tradeoffs sooner and ground their decisions in what customers actually did rather than what the room believed.
What the full case study covers
- The operating model and how the stages were recombined
- The Figma Make and Claude Code toolchain and design-system connection
- How research kept human oversight over AI-assisted analysis
- What changed in cycle time and decision quality
The full study is access controlled — it contains confidential work.