AI for the Experienced Professional: Why lived experience of disability is the key to success

Jane Hatton, CEO of Evenbreak, shares her thoughts.

The biggest myth about AI at work is that the winners will be the people who can code the best. In reality, the winners will be the people who can decide the best. AI fluency actually means asking better questions, not writing more code.

AI can write. It can summarise. It can generate ideas at speed. But it can’t take responsibility. That’s why the most future-proof skill for experienced professionals in 2026 isn’t learning to code. It’s about asking the right questions, and critically analysing the answers.

Major workforce research is already pointing in this direction: organisations are evolving toward models that blend AI capability with human decision-making, not human replacement. As WorkLab explore in the Work Trend Index Annual Report 2025.

And for disabled professionals, there’s a genuine opportunity here, because we often bring a form of expertise that doesn’t show up on a CV – lived experience and skills in navigating systems that weren’t designed for us.

AI fluency is a mindset: “How do we solve this?”

For most roles, AI fluency means:

• Framing problems clearly
• Providing context that matters
• Testing and verifying outputs
• Spotting risks (including bias and exclusion)
• Making decisions and owning outcomes

That’s why ‘human-led’ is increasingly showing up in how credible sources describe the next phase of work.

Why disabled professionals may be more ready than we realise

Many disabled people develop high-level professional capabilities through daily reality:

• Adapting to changing conditions
• Creating workarounds for barriers
• Planning for variability (energy, access, environment)
• Advocating and negotiating needs
• Seeing where processes break, and how to fix them

This is applied problem-solving. And it transfers directly to working well with AI, because AI needs human steering and human safeguards. (OECD, AI and the Future of Skills) 

There’s also the organisational proof point: disability inclusion is repeatedly associated with stronger performance and innovation—precisely because it expands how teams think and solve problems. (Source: Accenture, Getting To Equal: The Disability Inclusion Advantage)

The Judgement Checklist

Next time AI gives you an output, run this checklist:

1) Accuracy: What would I need to verify before this is used?
2) Assumptions: What did it assume that might not be true in my context?
3) Risk: What could go wrong, and who would be impacted?
4) Decision: What’s the right course of action given real-world constraints?
5) Inclusion: Is this accessible and fair, or does it quietly exclude people?

That last question matters. AI can scale accessibility improvements, but we know it can also scale barriers if we’re not careful, as acknowledged by the World Health Organisation in ‘Assistive Technology’.

In 2026, AI will enable speed. But it will rely on judgement more. And if you’re a disabled, experienced professional (someone who has learned to navigate barriers, adapt quickly, and solve problems under constraints) your judgement may be the missing link teams need.

(N.B. This article was written using a combination of AI and human judgement by a disabled professional).