Why AI Can Increase Accessibility Debt (And What Development Teams Should Do About It)

AI is transforming software development. Teams are shipping features faster, writing more code, and automating repetitive tasks like never before. But there's a hidden cost that many organizations are only beginning to recognize: accessibility debt.

Recent industry research suggests that while AI accelerates development, it can also accelerate the accumulation of accessibility issues if organizations don't adapt their engineering practices. The challenge isn't that AI produces "bad code." It's that it enables teams to create and ship much more code than traditional accessibility validation processes can realistically keep up with.

In this article, we'll explore why AI can increase accessibility debt, what that means for engineering teams, and how organizations can build accessibility into AI-assisted development.

What Is Accessibility Debt?

Accessibility debt is similar to technical debt.

It's the collection of accessibility issues that accumulate over time because they weren't identified or addressed during design and development.

Examples include:

• Missing alternative text
• Poor keyboard navigation
• Incorrect ARIA usage
• Low color contrast
• Unlabeled form controls
• Broken focus management
• Inaccessible custom components

Each individual issue might seem small.

Collectively, they make applications harder, or even impossible ,for many people to use.

Like technical debt, accessibility debt compounds. The longer issues remain unresolved, the more expensive and disruptive they become to fix. Recent industry reporting notes that fixing accessibility issues after release can cost many times more than addressing them during design and development.

AI Is Making Teams More Productive

There's no question that AI coding assistants have changed software development.

Developers now use AI to:

• Generate components
• Build APIs
• Write tests
• Refactor legacy code
• Create documentation
• Prototype interfaces

Development velocity has increased dramatically.

That's good news.

But higher velocity also means organizations are producing significantly more code than before.

Every additional component, page, form, or feature is another opportunity for accessibility issues to appear.

The Hidden Problem: AI Scales Output Faster Than Validation

Many teams assume that if they ask an AI to "generate accessible code," the problem is solved.

Unfortunately, accessibility is rarely that simple.

Accessibility depends on context.

For example:

• A button may have the correct ARIA attributes.
• But is its focus order logical?
• Does it work correctly with a keyboard?
• Does it announce meaningful information to screen readers?
• Does it remain accessible after JavaScript updates the interface?

Those questions require validation.

AI can generate a good starting point.

It cannot reliably guarantee that an entire user experience meets accessibility requirements.

Deque's recent survey of engineering leaders highlights this growing gap: organizations are increasing their use of AI-generated code while many acknowledge that accessibility risk is also increasing because validation processes have not scaled at the same pace. 

AI Doesn't Understand Your Entire Product

Accessibility isn't determined by individual components alone.

It's about how users complete real tasks.

Consider an online checkout.

AI may correctly generate:

• Accessible buttons
• Proper labels
• Semantic HTML

Yet the overall experience can still fail if:

• Focus gets trapped inside a modal
• Error messages aren't announced
• Dynamic updates aren't communicated to assistive technologies
• Keyboard users can't reach important controls

Accessibility emerges from the complete interaction, not just from isolated snippets of code.

AI Can Also Reinforce Existing Problems

AI learns from existing code and patterns.

Unfortunately, not every example on the internet follows accessibility best practices.

If developers don't review generated output carefully, AI can reproduce:

• Poor semantic structure
• Incorrect ARIA usage
• Non-descriptive button labels
• Missing accessibility attributes
• Inconsistent interaction patterns

The issue isn't that AI is uniquely error-prone. It's that it can replicate mistakes at scale if those mistakes go unnoticed.

Research into AI-generated code has also found that AI-assisted commits can introduce quality issues that persist in production repositories, underscoring the need for strong review and testing practices.

Continuous Accessibility Is the New Standard

The solution isn't using less AI.

It's changing how teams build software.

Modern development workflows should include:

Automated accessibility testing

Run accessibility checks regularly

Developer guidance

Provide actionable remediation guidance while developers are writing code.

Manual testing

Automated tools cannot detect every accessibility issue.

Keyboard testing, screen reader testing, and usability evaluation remain essential.

Continuous monitoring

Websites change constantly.

Accessibility should be monitored continuously, not just before launch.