When AI meets accessibility

When accessibility isn’t part of the picture from the start, AI-powered experiences can end up working really well for some users while quietly excluding others. And because AI operates at scale, that exclusion happens at scale too.

Take chatbots, for example. They're replacing traditional navigation and search on a lot of websites, and for many users that's genuinely more convenient. But for someone who relies on a screen reader, a conversational interface without proper semantic structure, keyboard support, or clear feedback can be a real barrier. What feels like a smoother experience for one person becomes a dead end for another.

The same goes for AI-generated content. It's appearing everywhere, and often it's perfectly fine in terms of information. But without proper headings, alt text, or a logical reading order, that content becomes difficult to navigate for people who depend on assistive technology. And adaptive interfaces that change based on user behaviour sound clever in theory, but they can break the consistency that some users genuinely rely on.

In the EU alone, an estimated 87 million people live with some form of disability. And with the European Accessibility Act now in effect, digital accessibility isn't just a nice-to-have anymore. It's a legal requirement. So this isn't a niche concern. It's something every organisation building digital products should be thinking about.

Here's what makes this topic so interesting: the same technology that can create barriers can also remove them, often in ways that weren't possible before.

AI can generate meaningful alt text for images, not just generic descriptions but text that actually conveys what's happening in a picture. It can provide real-time captioning and translation, making audio and video content accessible to people who are deaf or hard of hearing, or who speak a different language. It can power interfaces that adapt to individual needs, offering larger text, simplified layouts, or voice-first interaction for people who struggle with traditional input methods.

For users with motor impairments, voice interfaces can remove the need for precise clicking or typing. For users with cognitive disabilities, AI can simplify complex content on the fly, adjusting reading level without losing the core message. These aren't futuristic ideas. The building blocks exist today. What's often missing is the intent to actually use them.

A recent development that neatly illustrates this connection is WebMCP, a new W3C proposal backed by Microsoft and Google. It gives web developers a standard way to expose their application’s functionality as structured, machine-readable tools that AI agents can interact with directly. No more screen-scraping or guessing at buttons. The interesting side effect: when websites describe their actions and content in a structured way for AI, they’re simultaneously making that information more accessible to screen readers and other assistive technologies. It’s a concrete example of how building for AI and building for accessibility aren’t competing priorities. They’re fundamentally the same work.

Accessibility is sometimes treated as a compliance exercise. Meet the WCAG criteria, pass the audit, check the box. And while compliance matters, that framing misses something important.

Accessibility is fundamentally a design principle. And one of the most interesting things about designing for accessibility is that it tends to make things better for everyone. It's what's sometimes called the curb-cut effect: captions are helpful in noisy environments, voice control is useful when your hands are full, and clear content structure makes it easier for anyone to scan and find what they need.

AI features should follow the same logic. When you build them with accessibility as a core requirement rather than an afterthought, you end up with more robust, more thoughtful experiences across the board.