A report by Skills England – AI skills for the UK workforce – analysed the current state of play in 10 growth sectors in the UK to identify some of the pinch points that might hamper progress. Helpfully, it also includes a series of tools to support employers and training providers. But it’s the shortfall in skills that should be ringing alarm bells.

“Some sectors are more advanced but still face gaps in ethics, governance, and interpretation,” said the report. “Other sectors need stronger infrastructure, training, and workforce support.”

It goes on to say that poor digital literacy, patchy training and general confusion and lack of specialist knowledge have left many firms unable to make use of the technology.

Report warns of talent gap

In construction, for example, it warns that the lack of technical skills is holding back the introduction of advanced drone technology for surveying. While in manufacturing, it’s being blamed for a lack of progress towards greater automation in areas such as predictive maintenance and robotics.

It goes on to say that a common barrier to adoption is poor employer understanding, particularly among small and medium-sized enterprises (SMEs), of what is meant by AI skills and what their staff need to learn.

For me, this report confirms what most of us working in this space already know. At Passion Labs, we see that gap every day. While it’s important that people know how to use common tools like ChatGPT or Copilot, we must not lose sight of the need to develop the deeper analytical, technical, and creative skills that make these very tools truly effective in the first place.

After all, real progress in AI depends on the people who can shape, train and apply it with purpose, not simply prompt it. That means encouraging expertise in data science, machine learning, and human-centred design.

And as the report rightly points out, it also means building multidisciplinary teams that understand not only how AI works but also how it touches on issues such as ethics, governance, and how best to solve real-world problems.

AI future needs curious minds

At Passion Labs, we’re fortunate to have recruited – and work with – some of the brightest minds in the field. We have researchers, data scientists, and engineers who’ve spent years studying how intelligence actually works. They remind us daily that AI isn’t just about automation or efficiency, but understanding problems in new ways.

For instance, we recently worked with a data analytics company that blends human insight with data science. But they wanted to push beyond traditional analytics and use AI to make their work smarter and more predictive. 

The result has allowed the business to “reimagine what’s possible with AI” and “transform how [they] generate insights, deliver client value, and stay ahead of the curve”.

Similarly, we helped a leading fashion brand reinvent its design process by using AI to eliminate waste and speed up production. Our team helped them develop a model that turns simple text prompts into precise, production-ready sewing patterns, all but eliminating the need for physical samples.

As a result, the firm has delivered sizeable savings in terms of fabric, labour, and overheads.

But just like the examples quoted in the government report, these only scratch the surface of what’s possible. Every day, we speak to companies that want to use AI to make their operations smarter and more efficient.

Human skills hold key

Whether that’s using reinforcement learning to predict when critical components need servicing or how time-series forecasting with pattern recognition can identify complex patterns in stock market data, the use cases are endless. But to do that, you need people with the vision to understand the problems they face and ask the right questions. And you also need people with the technical skills to deliver the right solution.

That’s why we believe the conversation around skills needs to go deeper. It’s not enough to teach people to use off-the-shelf tools. The real opportunity lies in developing a new AI-savvy workforce grounded in problem-solving. That means having the ability to think like a scientist, reason like an engineer, and build systems that adapt and learn. Because if we don’t, then any aspiration we have as a country to be part of the AI revolution will struggle.

We only need to look east to see what happens when skills are treated as strategy. China has spent more than a decade building its AI workforce from the ground up. It’s invested heavily in research, university programmes and technical infrastructure. And as a result, it produces tens of thousands of AI specialists each year. Plus, it’s rapidly scaling domestic chip production to support them.

If the UK wants to lead in AI, it must take the same long-term view and invest in the people – in human intelligence – upon which all artificial intelligence is built.