How do I keep inaccessible features out of each release?

Accessibility is more dependable when it is considered while digital experiences are being designed and built, rather than being added as a final-stage correction. AI code assistants can help developers build accessible features, including customised dropdown menus that work for mouse, keyboard and voice users. They can also help teams apply accessibility standards from the outset, making inclusive design part of the digital foundation.

This approach does not remove the need for ownership across design, content and development. It gives those teams a practical way to address repeatable requirements early, while preserving time for the decisions that need judgement. The aim is not simply to complete a check before launch, but to create releases that account for different ways people navigate, perceive and use digital content.

AI can reduce effort on accessibility tasks that are repetitive or time-consuming. It can support accessible code and generate image descriptions using both the image and its surrounding page content, which is more useful than an empty filename or generic placeholder. These capabilities can help teams spend more time on the parts of accessibility that require deeper consideration.

However, AI is probabilistic. It can misinterpret an image, miss cultural context or reproduce bias from its training data. A description may sound plausible while failing to communicate the meaning a screen-reader user needs. Human designers, developers and accessibility advocates should therefore review AI output, especially where an image carries an important message, the surrounding content changes its meaning, or the interaction affects a key user task.

A release should be assessed through the ways people actually use it, not only through a single automated score. Review key journeys with keyboard navigation, screen readers, mobile devices and slower connections. Check whether content remains understandable, controls can be reached and operated, forms communicate clearly, and media has suitable text or caption alternatives.

Automated checks are useful for identifying repeatable issues, but they cannot establish whether the experience makes sense in context or supports a person through a complete task. Testing should therefore combine automated checks with informed human review. Where possible, include people who use assistive technologies, because they can identify barriers that are difficult to infer from code or visual inspection alone.