The Accessibility Problem with AI Interfaces

AI has enormous potential to make digital experiences more accessible. It can help generate alternative descriptions, simplify content, adapt interfaces, assist with navigation, and support people through natural language interaction.

But adding AI does not automatically make a product accessible. In some cases, it can create new problems. Generated content may be inconsistent, interfaces may rely too heavily on conversational interaction, and important states may be communicated visually without an equivalent accessible signal.

Predictability matters. Users who rely on assistive technologies often benefit from clear structure, consistent navigation, meaningful labels, and understandable feedback. An AI layer should complement those foundations rather than replace them.

Designers should also consider the accessibility of generated output. If an AI assistant creates a table, document, image description, or formatted response, the resulting content still needs to work with the tools and contexts users depend on.

Testing becomes especially important because AI behavior can vary. A flow that works perfectly in one scenario may behave differently when the user asks a different question or provides unexpected input. Accessibility evaluation therefore needs to include realistic variation rather than a single happy path.

The goal is not simply to make AI available to everyone. It is to make AI experiences understandable, controllable, and usable across different abilities, devices, environments, and ways of interacting with technology.