The Tool Desk
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What an accessibility scanner can establish
A scanner can identify candidate issues in the pages and states it examines. Its results are evidence for investigation, not proof that a product is accessible or conforms to WCAG. The W3C Web Accessibility Initiative puts the limit plainly: “Web accessibility evaluation tools can not determine accessibility, they can only assist in doing so.” The W3C also notes that some checks require manual intervention. W3C guidance on selecting evaluation tools
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Automated and semi-automated tools can miss problems, and results may be false or misleading. Accessibility often depends on context, interaction, and whether people can complete a task—not just whether a page matches a detectable rule. A clean scan therefore means only that the tool did not report certain issues in the material it tested.
Why more scanners do not mean complete coverage
WCAG 2.2 is the current W3C Recommendation, and W3C advises using it to maximize future applicability. WCAG 2.0 and 2.1 remain Recommendations. WCAG 2.2 includes testable success criteria for web content across device types, but does not address every need of every person with a disability. A scanner’s supported rules are not the same as a complete WCAG evaluation. WCAG 2.2
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Tools vary in the standards and criteria they support, the content they can reach, and the evidence they provide. One may check a single page, while another may crawl a broader site or support a particular development workflow. Password-restricted pages, mobile apps, documents, interaction states, and complete user journeys may be outside a given tool’s scope. The relevant question is not how many scanners exist, but what each one actually examines. W3C overview of evaluation tools
What AI changes—and what it does not
AI can help detect or interpret some accessibility issues, but it does not make every WCAG criterion machine-testable. A 2025 FSE paper, “Enhancing Web Accessibility: Automated Detection of Issues with Generative AI,” distinguishes criteria that can be assessed through static analysis, those requiring dynamic analysis, and those it classifies as not testable. That distinction matters: a more capable model may help with some checks, while other questions still require human judgement or interaction with the product. The paper’s taxonomy is not evidence that all commercial AI scanners perform alike. ACM Digital Library
A September 2026 arXiv preprint evaluated agentic scanners on 250 page–criterion records from 24 pages across 11 scholarly platforms. Its worker agents recovered 0.86 of positive reference labels, compared with 0.36 for axe-core and 0.67 for an uncued vision-language model. Their corresponding precision values were 0.56, 0.90, and 0.61. In that evaluation, the agents found more positive labels but also produced more findings absent from the reports. These are study-specific results, not a general accuracy ranking: the authors cite inferred negative labels, configuration differences, and the saved-page corpus as limits. “Agentic Web Accessibility Auditing”
There is no general, independently established percentage for how many accessibility problems AI scanners detect. Any coverage figure needs its denominator, criteria, tool version, and test conditions to be meaningful.
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How to decide whether another scanner is worth using
Compare tools by the job they do and the evidence they produce, rather than by an “AI-powered” or “WCAG compliant” label. W3C notes that tools differ and that combining them can serve different roles and project stages. W3C overview of evaluation tools
- Purpose: Does it run automatic checks, support manual testing, simulate experiences, or combine these functions?
- Product and scope: Can it examine your website, app, document, restricted content, or only a single page? Does it reach relevant states and journeys?
- Standards and criteria: Which WCAG version and specific success criteria does it support?
- Workflow: Does it fit how your team works—for example, through a browser extension, command-line tool, desktop or mobile app, online service, or development integration?
- Evidence and reporting: Can reviewers see the context for a finding, reproduce it, export results, and distinguish uncertain results?
- People and usability: Can the team use the scanner itself, and does it support the languages needed?
- Human evaluation: Does it help reviewers perform manual checks, evaluate complete processes, and conduct assistive-technology or disability-led testing?
A second tool is most useful when it adds a capability the first lacks—not simply another list of overlapping alerts. Even then, greater detection can mean more false positives to triage, so inspect how the tool explains findings and supports confirmation.
Build an evaluation around real user journeys
W3C’s WCAG-EM 2.0 sets out a process for evaluating a representative sample of a digital product against WCAG 2. It is a Group Note, not a new normative addition to WCAG. Its method begins by defining scope and the conformance target, then explores functionality and sample types, selects representative samples, evaluates criteria and complete processes, and reports findings. Evaluating a subset alone generally does not support a claim that an entire site conforms. WCAG-EM 2.0
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- Explore the product. Note common views, essential functionality, technologies, and types of content or interaction that need to be represented.
- Run scanners and record their limits. Use one or more tools to find candidate issues, documenting which pages, states, criteria, and tool versions were actually tested.
- Review the results. Have knowledgeable evaluators confirm findings, record why a result is dismissed as a false positive, and investigate criteria that require judgement or interaction.
- Test complete processes. Check that important tasks can be completed, including with assistive technologies. Involve people with disabilities where feasible; WCAG-EM assumes relevant expertise and strongly recommends their participation.
- Report the method and boundaries. State the scope, sampling, tools and versions, findings, unresolved criteria, and limitations. Do not turn a partial sample into a whole-site conformance claim.
So, do we need more AI accessibility scanners?
We need better, more transparent coverage more than we need a larger number of scanners. A new tool earns a place when it tests relevant criteria or product states that existing tools miss, gives reviewers inspectable evidence, or helps teams act on findings in their workflow. It does not replace a standards-aware evaluation, assistive-technology review, or the experience of disabled people using the product.
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