AI was meant to make technology more human: to understand us, adapt to us and open up new possibilities for everyone. For people with disabilities, its potential is even greater. AI can bridge communication gaps, describe the world through sound and vision, and make everyday tasks more independent and dignified.
Despite all the hype, that potential still feels out of reach.
5 Steps to Improve AI Accessibility
- Design with accessibility in mind from day one.
- Invest in inclusive data sets.
- Measure accessibility as a success metric.
- Involve people with disabilities in every phase.
- Build clear standards and accountability.
We have amazing prototypes and inspiring demos, but not nearly enough real-world systems that are accessible to everyone. The technology is powerful, yet the benefits haven’t reached the people who need them most. The question is: why hasn’t AI lived up to its promise for accessibility?
As a senior software engineer who’s spent close to a decade building cloud systems and integrating AI into real products, I’ve seen firsthand both the breakthroughs and the blind spots. The problem isn’t that we can’t build accessible AI, it’s that we often don’t design it with inclusion in mind from the start.
How AI Is Already Transforming Accessibility
The potential for AI in the accessibility space is huge. In many ways, the technology capability is already there. Just look at these examples:
- Speech recognition can make conversations accessible for people who are deaf or hard of hearing.
- Computer vision can describe environments for people who are blind or low-vision.
- Language models can simplify dense text or adjust tone for neurodiverse users.
- Predictive tools can anticipate accessibility needs before users even have to ask.
There are already great examples.
- Voiceitt and Whisper are helping people with speech impairments communicate clearly by recognising non-standard speech patterns.
- Microsoft’s Seeing AI and Google’s Lookout narrate the world through smartphones, reading text and describing objects in real time.
- Generative AI tools like ChatGPT are being used to simplify complex documents or translate visuals into text for people with dyslexia or low vision.
When these tools are designed thoughtfully, they give people a sense of independence that technology should always deliver. But these success stories are still the exception, not the rule.
Why AI Hasn’t Fulfilled Its Accessibility Promise
Despite all of that promise, those tools have failed to have a bigger impact. There are a number of reasons for that, including:
1. Accessibility Still Comes Too Late
Too often, accessibility isn’t part of the first conversation; it’s an afterthought. Teams design, train and ship products for the end user, then try to bolt on accessibility features later.
By that point, it’s too late. Accessibility becomes a patch instead of a principle. And while it might tick a compliance box, it rarely delivers a great experience.
Accessibility should be treated like performance or security, something you think about from day one, not when you’re already deploying.
2. Biased Data Equals Biased AI
AI systems learn from the data we give them. If that data doesn’t include people with disabilities, the model won’t serve them well.
Most speech models are trained on clear, standardized English, not on slurred speech, regional accents, or stutters. Image models sometimes misread people with mobility aids or facial differences. Even large language models can unintentionally mirror biases against people with disabilities present in online data.
If the data isn’t inclusive, the AI won’t be either. Until our data sets reflect real-world diversity, the gap will remain.
3. The Economics Don’t Encourage Accessibility
Let’s be honest. Accessibility doesn’t always make it to the top of the priority list.
Building inclusive systems takes time and resources. You need to collect diverse data, test with a range of users, and sometimes use specialised tools. In companies under pressure to release fast and show growth, accessibility can seem like a “nice-to-have.”
But that thinking is short-sighted. History shows that accessibility drives innovation. Voice typing, captions and dark mode all started as accessibility features, but now they’re mainstream. The same will be true for today’s AI accessibility breakthroughs, if we invest in them early enough.
4. Building For People With Disabilities, Not With Them
One of the biggest missed opportunities is not involving people with disabilities early in the design process.
Many AI systems are built for people with disabilities, not with them. That’s a huge difference. When you include the people who will actually use your system, you uncover problems and solutions you wouldn’t find otherwise.
5. No Clear Rules or Standards
While areas like data privacy have clear global standards (think GDPR), AI accessibility is still the Wild West.
We have the Web Content Accessibility Guidelines (WCAG) for websites, but nothing equivalent for AI models or interfaces. That lack of structure means companies aren’t held accountable, and accessibility depends on culture rather than compliance.
It’s time for industry, policymakers, and advocacy groups to create shared standards frameworks that make accessibility measurable and mandatory in AI development.
4 Signs of AI Accessibility Progress
Thankfully, there are reasons to be hopeful. Across the tech world, new efforts are starting to close the gap between intention and impact:
- Open data sets like Mozilla Common Voice and Project Euphonia are collecting diverse speech samples to improve recognition for people with disabilities.
- Generative AI is being fine-tuned for accessibility tasks simplifying text, describing images, or translating tone for neurodiverse users.
- Wearable AI tools are emerging that use computer vision and natural language to help people navigate spaces, recognize faces, and read printed materials.
- Governments and companies are beginning to fold accessibility metrics into ESG and AI ethics reporting, signaling a real cultural shift.
These are small steps, but they’re moving us in the right direction toward proper accessibility as a default, not a differentiator.
5 Steps to Move AI Accessibility Forward
To make AI truly inclusive, we need to do a few things differently:
- Design with accessibility in mind from day one. Don’t bolt it on later.
- Invest in inclusive data sets. If your model doesn’t represent everyone, it won’t serve everyone.
- Measure accessibility as a success metric. Treat it like uptime or accuracy.
- Involve people with disabilities in every phase. Co-design, test, and refine together.
- Build clear standards and accountability. Accessibility should be built into policy, not just principle.
AI’s future isn’t just about smarter systems; it’s about fairer ones. The real goal isn’t to make machines think like humans, but to make technology work for humans.
The question isn’t whether AI can transform accessibility. It already can. The question is whether we’ll choose to make inclusion a core part of how we build.
Empathetic AI isn’t just good ethics. It’s good engineering. And when we design with empathy, we don’t just make technology better, we make the world better too.
