AI tools fit into accessibility auditing as efficiency aids that support human auditors, not as replacements for manual evaluation. An audit is a human process. A trained auditor conducts screen reader testing, keyboard testing, visual inspection, and code inspection against WCAG success criteria.
AI assists around the edges of that work, speeding up documentation, explanation, and remediation guidance.
Where AI performs well right now: translating WCAG requirements into plain English, drafting code for accessibility fixes, answering developer questions instantly, and generating VPAT and ACR documentation from audit data. Inside a conformance platform, AI can pull from audit findings to give contextual remediation guidance and project-level insights. That reduces reliance on expensive technical support hours and gets teams moving faster on fixes.
Where AI does not belong: conducting the audit itself. AI accessibility scans flag more potential issues than traditional scans, but with significant uncertainty. Many flags require manual verification, which cancels out the efficiency gain.
Reliable AI coverage of 75% of WCAG criteria remains a future prospect, not current reality. Traditional automated scans still cover about 25% of issues with high accuracy, and the remaining 75% needs a trained auditor.
The practical setup today pairs traditional scans with a fully manual WCAG audit, then uses AI inside the platform to accelerate remediation, reporting, and documentation. Human judgment leads. AI supports.