Designed an internal AI-enhanced design system platform that uses machine learning to suggest components, check accessibility, and maintain design consistency across 8 product teams.
Context
Capita PLC, a FTSE 250 company with 45,000+ employees, was struggling with design inconsistency across its digital products. With 8 different product teams working in silos, each team had developed their own component libraries, color palettes, and interaction patterns.
The result? A fragmented user experience, wasted design effort (teams rebuilding the same components), accessibility compliance gaps, and significant technical debt as developers maintained multiple codebases for similar UI elements.
Key Problem Statement: How might we create a unified design system that not only ensures consistency but actively helps designers make better decisions faster using AI-powered assistance?
Discovery
I conducted interviews and shadowing sessions with 15 designers, 8 developers, and 5 product managers across different Capita teams to understand their workflows, pain points, and aspirations.
5 years at Capita
Works on customer portal
3 years at Capita
Works on B2B platform
6 months at Capita
Recently onboarded
I know we have a component for this somewhere, but I've spent 45 minutes looking for it. At this point, it's faster to just build it from scratch.
Define
❌ Current State Problems
✓ Desired Future State
Ideation
Working with the data science team, I identified four key AI capabilities that could transform how designers work with the design system:
Natural language search that understands intent. Type "button for submitting forms" and get the right component with all variants, not just keyword matches.
Real-time WCAG compliance checking with automatic suggestions for fixes. Scans color contrast, touch targets, focus states, and ARIA labels.
Analyzes designs against the system and flags deviations. "This button uses #0066CC but your primary blue is #0052CC" with one-click fixes.
Based on what you're designing, suggests relevant components and patterns. "Designing a form? Here are the most-used form patterns by your team."
Design
I designed and documented 240+ components across 12 categories, each with variants, states, and accessibility specifications.
Design
The AI assistant guides designers through component selection and customization, reducing decision fatigue and ensuring best practices.
"I need a confirmation dialog for deleting user accounts with a warning about permanent data loss"
Identifies: destructive action, needs warning, requires confirmation, high-stakes decision
Returns: Destructive Modal variant with red accent, warning icon, two-button layout, recommended copy patterns
Component includes: focus trap, ARIA labels, escape key handling, color contrast compliant
Deliver
Results
The AI assistant has completely changed how I work. Instead of spending time searching and second-guessing, I describe what I need and get the right component instantly. It's like having a senior designer looking over my shoulder.
For the first time, what I see in Figma matches exactly what I can build in code. The design-to-dev handoff that used to take days now takes hours.
Recognition
🏆 This project contributed to winning the Center of Excellence Award 2023 at Capita, recognizing exceptional contribution to design operations and innovation.
Reflection