Validate·Expert Review·Automation·Established·VAL-034

Accessibility Audit

Value hypothesis

Catches accessibility issues before development by automating WCAG compliance checks across designs and live interfaces, reducing remediation costs and compliance exposure.

Quality · Efficiency

AI scans design files or live products against WCAG criteria, checking colour contrast, focus order, alt text, heading structure, touch target size, and component labelling. Identified violations are returned in a generated report, which the designer or accessibility specialist reviews for errors or false positives, then prioritises true errors remediation. Advanced implementations automate scanning within CI/CD pipelines to flag regressions at the point of code commit.

Risks in application

Shallow Solutions

Automated scans cover a subset of WCAG criteria reliably but cannot assess cognitive load, plain language, or contextual usability for screen reader users; a clean scan result may create false confidence that a design is accessible when significant barriers remain undetected.

Pseudoproductivity

Resolving AI-flagged violations may be mistaken for achieving accessibility; teams that address automated findings without user testing with disabled participants may ship designs that pass tools but fail people.

Expertise that differentiates

Ethical Assessment

Understanding which AI-flagged issues represent genuine barriers for users with disabilities vs. technical violations with minimal real-world impact; prioritising remediation based on actual user harm rather than violation count.

Interaction Design

Evaluating whether AI-proposed fixes preserve intended interaction patterns and do not introduce new usability problems.

AI Fluency that assures

Platform Awareness

Understanding tool and platform limits, recognising what automated scans cannot test, and when clean-reports may miss other issues or more diffuse problems.

Accessibility Audit

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Enables

IMP-065Enrich the project knowledge base with compliance evidence.