AI-Assisted UX Governance & Accessibility Review System
Challenge
As accessibility adoption grew, designers needed more support than one person could realistically provide. Reviews, questions, and coaching requests increased while project timelines continued to move quickly.
The challenge became: How can accessibility expertise scale without requiring a subject matter expert to participate in every design conversation?
Approach
I explored how AI could serve as a first-pass coaching and review resource for designers.
Rather than replacing human expertise, the goal was to provide designers with immediate access to accessibility guidance, review criteria, design expectations, and examples that could help them evaluate their own work before formal review.
The concept focused on using AI to reinforce learning, improve consistency, and support self-service design reviews.
What Was Created
- AI-supported accessibility review assistant
- Structured review guidance and evaluation criteria
- Accessibility knowledge base
- Design defect reference library
- Remediation guidance framework
- Confidence and escalation model
- Human review process for complex issues
Outcomes
- Created a scalable model for sharing accessibility expertise
- Reduced dependency on one-on-one support
- Encouraged stronger designer self-assessment
- Improved access to accessibility knowledge and examples
- Explored practical applications of AI for performance support and coaching
Ask Me About
- Using AI to scale organizational knowledge
- Balancing automation with human expertise
- Accessibility governance in AI-assisted workflows
- Designing AI as a coaching tool rather than a replacement
- Future opportunities for AI-enabled quality reviews