Three stories about AI and accessibility that every CTO will recognise

AI is changing delivery. Accessibility risks are changing too.

AI is now part of mainstream product development. It is helping teams write code, create content, analyse data and prototype ideas at a pace that would have seemed unrealistic only a few years ago.
For technology leaders, that creates a practical challenge. The benefits are easy to see. The risks are often less visible until they begin affecting real users.

Much of the current discussion around AI governance focuses on transparency, accountability, bias and risk management. These are important concerns, but they can often feel abstract when organisations are trying to make decisions about products and services already in production. Accessibility offers something more tangible.

When disabled users struggle to understand what a system is doing, cannot control its behaviour, cannot recover from errors or cannot complete key tasks, those are not simply accessibility issues. They are signals that trust, usability and governance may also be breaking down.

This is one reason accessibility is becoming increasingly important in conversations about AI assurance. It provides practical evidence about how systems behave in the real world and whether users can interact with them safely, confidently and effectively.

The examples below are drawn from real client situations. Details have been changed, but the underlying challenges are genuine and increasingly common.

Story one: the team moved fast. The accessibility debt moved faster

A large organisation had introduced AI-assisted development tools across multiple teams. Delivery was faster and productivity metrics looked encouraging. The concern emerged later.

Many of the generated interfaces relied heavily on an existing component library. Some of those components contained long-standing accessibility weaknesses. Individually, the issues were manageable. Once AI tools began drawing on those patterns repeatedly, the scale changed dramatically.

In this case, recurring problems included form fields without reliable labels, custom controls that could not be operated consistently from the keyboard, and modal dialogues with broken focus management. None of these issues were unusual. What changed was the speed and scale at which they appeared.

A developer using a coding assistant might unknowingly generate the same inaccessible implementation dozens of times across different services. A problematic date picker becomes the default date picker. An inaccessible custom dropdown appears throughout an estate of applications. The problem is no longer confined to a single release. It becomes embedded in the organisation's delivery process.

This is a familiar pattern in accessibility engineering. Defects that once appeared occasionally can become systemic when they are baked into reusable components, design patterns or code templates. AI increases the speed at which those patterns spread.

The solution was not a larger backlog of accessibility fixes. Instead, attention shifted upstream. Core components, design patterns and implementation guidance were reviewed and strengthened. The focus moved from identifying defects after deployment to reducing the likelihood of those defects appearing in the first place.

For the Chief Technology Officer (CTO), the value was straightforward. Less rework, greater consistency and a clearer understanding of what AI-enabled development was producing. For accessibility specialists, it created a stronger foundation on which future work could build.

Story two: the AI assistant that passed testing but failed users

Another organisation was introducing AI-driven conversational interfaces within a service where users often needed support during stressful or time-critical situations.

Technical testing suggested the feature was performing well. Questions were answered accurately. Response times were acceptable. System reliability was strong.

When disabled users became involved in testing, however, a different picture emerged. Screen reader users struggled to understand when new information had appeared. Keyboard users found parts of the interaction difficult to control. Some users were uncertain whether the system had correctly understood their request or what would happen next.

One user described feeling as though the interface was "having a conversation with itself". Content was changing dynamically, focus was moving unexpectedly, and important status messages were not always announced through assistive technology.

Another participant successfully completed a task but had little confidence that the system had actually done what they intended. The transaction had worked, but the experience lacked clarity and reassurance.
None of these issues would have been obvious from a performance dashboard.

This reflects a broader challenge with AI interfaces. Many are dynamic, adaptive and conversational. Traditional accessibility checks remain important, but they do not always capture whether an experience feels understandable, predictable and trustworthy when someone is trying to complete a real task.

The organisations making the most progress in this area are investing in truly diverse user research alongside technical evaluation. They recognise that accessibility is not simply a question of compliance. It is also a question of confidence. People need to understand what a system is doing, what choices are available and how to recover if something goes wrong.

There is also a wider trust issue. As AI systems become more capable and more autonomous, users need confidence that the service remains understandable and under their control. Accessibility and trust are becoming increasingly difficult to separate.

Story three: the organisation with data everywhere and answers nowhere

A third organisation had invested heavily in accessibility monitoring, reporting and governance. Dashboards existed. Metrics were being collected. Accessibility featured in discussions about AI strategy. But despite all of this activity, a recurring question remained unanswered; Was accessibility actually improving?

The organisation could count issues, track audit findings and monitor compliance activities. What it struggled to understand was how AI-driven changes were affecting users over time and who ultimately owned decisions when risks emerged.

One dashboard showed that more than 95% of tested pages passed automated accessibility checks. At the same time, disabled users were encountering difficulties completing key customer journeys.
The data looked reassuring. The user experience told a different story. This is not unusual. Automated testing tools remain valuable, but they only identify a subset of accessibility issues. A dashboard can report strong compliance metrics while users are still struggling with confusing interactions, poor task completion rates or inconsistent behaviour across channels.

Many accessibility governance approaches were developed for relatively stable digital products. AI systems introduce a different level of variability. Interfaces may adapt, content may be generated dynamically and user journeys may evolve in ways that are difficult to predict in advance.

Regulatory developments are adding further pressure. The direction of travel is clear; organisations are increasingly expected to demonstrate transparency, accountability and effective oversight of AI-enabled systems.
For this organisation, the breakthrough came from establishing clearer ownership, stronger baselines and a more direct connection between technical findings and user impact. Accessibility reporting became less focused on counting issues and more focused on understanding risk and prioritising action.

That shift proved valuable well beyond accessibility. It gave senior leaders a clearer view of where AI was introducing uncertainty and where additional assurance was needed.

What CTOs and accessibility leaders should take from this

Across all three stories, the underlying challenge is managing rapid change while maintaining confidence in the user experience.
AI is making digital products more dynamic. It is shortening development cycles and influencing decisions throughout the delivery process. Those shifts create opportunities, but they also increase the chances that accessibility problems will spread unnoticed.

The stories also highlight something broader. Accessibility is becoming one of the most useful lenses through which organisations can assess the trustworthiness of AI-enabled systems. If users cannot understand what an AI system is doing, cannot predict how it will behave, cannot maintain control of an interaction or cannot recover when something goes wrong, there is usually more at stake than accessibility alone.
Those same weaknesses often point to wider concerns around governance, assurance, transparency and user trust.
This is why specialist accessibility expertise remains important, even as AI capabilities improve.

Independent review, disabled user insight, governance support and practical testing provide a perspective that automated tools cannot replace. They help organisations understand not only whether something works, but whether it works well for the people who rely on it.

For CTOs, that means greater visibility and stronger control over technology risk. For accessibility professionals, it provides evidence that can influence decisions earlier and more effectively. For organisations as a whole, it helps ensure that innovation does not come at the expense of usability, trust or inclusion.

If AI is becoming part of the delivery model, accessibility needs to be reflected in design systems, procurement decisions, testing approaches, governance frameworks and success measures. Leaving it until the end of the process is becoming an increasingly expensive option.

Where organisations go from here

Many organisations are still working out where accessibility sits within their AI strategy. Some are reviewing procurement processes. Others are looking at governance, testing approaches or the maturity of their design systems. Most are trying to balance innovation with assurance.

What matters is creating enough visibility to understand where risk is emerging and enough evidence to make informed decisions before issues become embedded in products and services. This is where independent accessibility expertise can be valuable. AbilityNet works with organisations to assess AI-enabled products and services, review design and development practices, carry out diverse, disabled user research and strengthen accessibility governance. The aim is not to slow delivery. It is to help teams move forward with greater confidence that accessibility, usability and trust are being considered throughout the process.

As AI becomes more deeply woven into digital services, organisations that understand its impact on disabled users will be in a stronger position to deliver experiences that work well for everyone.

Accessibility has always been about removing barriers. In an AI-enabled world, it is becoming something more than that. It is one of the clearest ways of understanding whether technology is behaving in ways that people can understand, trust and use with confidence.

For CTOs, that makes accessibility more than a delivery consideration. It becomes an important part of AI assurance itself.

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