Building foundations for AI at scale
How I unified design across Google's experimental AI and ML teams — creating foundational patterns that scaled from prototype to production as these products matured.
The challenge
Google's emerging AI products division was brilliant at engineering but struggling with design coherence. Each team had developed its own patterns, components, and visual language in isolation — users were confused moving between products, and the brand experience was fragmented.
We needed to prove that great design could enhance technical excellence, not slow it down — while navigating the ambiguity of AI products still finding their market fit.
Research & discovery
Understanding the fragmentation before building unity.
Component inventory
Catalogued UI elements across 5 ML products, identifying patterns unique to AI interfaces — confidence UI, model states, training views, pipeline visualization.
Workflow analysis
Shadowed teams for two weeks to understand how ML engineers actually interact with their tools, and established baseline metrics for design velocity and consistency.
The journey
Leading through ambiguity to create order from chaos — 14 months.
Discovery & alignment
Interviewed 25+ ML engineers and designers. Discovered deep mistrust between design and engineering. First design review was brutal — “this will slow us down by months.”
Failed prototype
Created a rigid design system. Teams rejected it immediately. I was solving the wrong problem — they needed flexibility for experimental AI features, not rules. Pivoted to a modular approach.
Building trust
Embedded with ML engineering teams. Co-created components WITH them, not FOR them. First successful implementation in the AutoML team.
Scaling success
Other teams saw AutoML's velocity increase and adopted voluntarily. The system evolved from real usage — engineering became its biggest advocates.
Full implementation
Rolled out across all 5 teams with a design review process and self-service tools. Teams shipped 45% faster with better quality.
The audit — five teams, five languages
The component inventory made the fragmentation impossible to argue with: 23 button styles across five products, then one shared foundation.
The transformation
Before: 5 design languages
- 5 inconsistent approaches
- Limited component sharing
- Siloed team decisions
- Inconsistent user experience
- 2-week design cycles
After: 1 unified system
- Shared design foundation
- 67 foundation components
- Weekly design syncs
- 85% core pattern adoption
- 1-week design sprints
“This design foundation let us experiment rapidly with AI interfaces while maintaining quality.”
— Design Lead, Google Cloud AIKey learnings
Start with empathy, not enforcement.
Understanding why teams made different choices was crucial. Authority comes from value, not title.
Make it easier, not mandatory.
The system succeeded because it made teams' lives easier. Adoption through attraction beats compliance.
Embrace engineering culture.
By speaking their language and understanding their constraints, design became a valued partner in ML innovation.
Design for the unknown.
Building flexibility into the system let it accommodate AI features we couldn't yet imagine. Ambiguity is a design constraint.