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AI training company, QA, explains the AI workforce readiness gap and what small businesses can do to overcome the main barriers
- Many organisations remain in the early stages of their workforce transformation which in turn is creating a growing divide between sectors ready to benefit from AI and those at risk of falling behind.
- The findings echo wider research from the British Chambers of Commerce, which found 97% of UK organisations report at least one significant AI skills gap.
- Measure capability, just as you would with any other rollout.
AI implementation is accelerating across UK industries, yet many organisations remain in the early stages of their workforce transformation which in turn is creating a growing divide between sectors ready to benefit from AI and those at risk of falling behind.
New analysis by QA, the UK’s leading AI technology and digital skilling partner, shows the gap is not just about who has adopted AI, but who has actually built the confidence and capability to use it well from within.
In healthcare, for example, the AI market continues to grow fast, yet separate research into frontline clinicians found 73% have never used AI in their own work, with fear of clinical error the leading barrier. In manufacturing, AI distribution is being driven predominantly by skills shortages and labour gaps rather than efficiency gains alone, with over 40% of deployments aimed at plugging capability gaps rather than replacing them.
The findings echo wider research from the British Chambers of Commerce, which found 97% of UK organisations report at least one significant AI skills gap.
Where UK sectors stand today with AI adoption
| Sector | AI readiness signal | What’s driving it |
| Technology and IT | Leading | Highest measured adoption of any UK sector |
| Financial Services | Leading, fastest-growing | Adoption has grown fastest of any sector since 2022 |
| Professional Services | Advancing | Rapid generative AI uptake for drafting, research, knowledge work |
| Manufacturing | Advancing, skills-driven | 41% of AI deployments driven by labour/skills shortages, not efficiency alone |
| Healthcare | At risk (trust gap, not tech gap) | Growing AI market, but most frontline clinicians have never used AI at work; fear of error is the leading barrier |
To help organisations close this gap, Jo Bishenden, chief learning officer at QA, is sharing practical guidance on building AI capability across a workforce.
1. Start with where your sector actually stands
It’s vital to start with a realistic view of AI maturity in your sector. Every sector is moving at a different pace, so organisations need to understand their own starting point before they can make informed decisions about where AI can deliver the greatest value.
2. Don’t mistake adoption for capability
Introducing new AI tools is only one part of the story. Sustainable success comes from building the skills, confidence, and behaviours that enable people to use those tools effectively in their day-to-day roles.
3. In safety-critical sectors, address trust before technique
In highly regulated and safety-critical environments, trust is fundamental. Employees, customers, and stakeholders need confidence that AI is being used responsibly, transparently, and with the right safeguards in place.
4. Use skills-shortage sectors as an upskilling opportunity
Used effectively, AI can help people to build capability faster, reduce administrative burden, and focus on areas where human expertise adds the most value. For sectors with ongoing skills shortages, AI present an opportunity to rethink workforce development.
5. Keep measuring capability, not just rollout
The real measure of progress isn’t how many people have access to AI, but how effectively they’re using it. Organisations should be looking at capability, confidence, and outcomes alongside adoption metrics to understand whether they’re creating lasting value.
Jo Bishenden continued: “Adopting AI tools is the easy part. Building the confidence and skills to use them well is where the real magic happens. But it doesn’t happen automatically.
“Organisations need to treat capability as something to track deliberately, the same way they’d track any other rollout, rather than assuming it improves on its own once the technology is in place. The gap between adoption and capability doesn’t close by itself – it closes because someone is paying attention to it.”
For more information on QA’s AI training and workforce upskilling programmes, please visit www.qa.com.
