Generative AI & Web Accessibility: How Audits Will Evolve in 2025

Reading time: 10 minutes

The intersection of artificial intelligence and web accessibility is rapidly transforming how we approach digital inclusion. As we enter 2025, generative AI models are no longer just experimental tools — they’re becoming integral to how we detect, understand, and fix accessibility barriers.

What is AI-Powered Accessibility Auditing?

AI in accessibility audits refers to the use of machine learning models, particularly generative AI, to enhance traditional rule-based testing. Unlike conventional tools that check against predefined criteria, AI-powered systems can:

• Understand context beyond rigid rules
• Generate solutions rather than just identifying problems
• Learn from patterns across millions of web pages
• Predict user experience issues before they occur

Current Capabilities of AI in Accessibility

1. Automated Alt Text Generation

Modern AI can analyze images and generate descriptive alt text that goes beyond simple object recognition. It can understand context, identify important elements, and create meaningful descriptions.

2. Color Contrast Analysis

AI tools can analyze entire page designs and suggest color modifications that maintain brand identity while meeting WCAG contrast requirements.

3. Code Remediation Suggestions

When accessibility issues are found, AI can suggest specific code changes to fix them, taking into account the existing codebase and coding patterns.

4. Natural Language Understanding

AI can evaluate whether content is written in plain language and suggest simplifications for better comprehension.

Limitations and Risks

Despite the promise, AI in accessibility has significant limitations:

• False confidence: AI may miss nuanced issues that require human judgment
• Bias in training data: If models are trained on inaccessible content, they may perpetuate problems
• Over-reliance: Organizations may skip manual testing, assuming AI catches everything
• Context blindness: AI may not understand the specific needs of your user base

Best Practices for AI-Assisted Auditing

1. Use AI as a Starting Point

Let AI handle initial scans and obvious issues, but always follow up with manual testing.

2. Combine Multiple Tools

No single AI tool catches everything. Use a combination of automated scanners, AI assistants, and manual review.

3. Include Users with Disabilities

AI cannot replace the insights of actual users with disabilities. Include them in your testing process.

4. Stay Updated

AI capabilities are evolving rapidly. Regularly evaluate new tools and update your processes.

The Future of AI in Accessibility

Looking ahead, we can expect:

• Real-time accessibility suggestions during development
• Personalized accessibility adaptations for individual users
• AI-powered assistive technologies that adapt to user needs
• Automated compliance monitoring and reporting

Conclusion

AI is transforming accessibility auditing from a periodic compliance check to a continuous improvement process. While it’s not a replacement for human expertise and user testing, it’s an invaluable tool for identifying and fixing barriers at scale.

The key is balance: leverage AI’s strengths while acknowledging its limitations, and always keep the end user at the center of your accessibility efforts.