The UK skills system is not lacking in ambition. The government’s commitment to provide free AI training to 10 million workers, around a third of the workforce, by 2030 signals a clear intention to position the UK as a leader in AI adoption. At a time when AI is already reshaping the way organizations operate, building baseline competencies across the workforce is an important and necessary step.
However, knowing how to use AI is only part of the picture. Knowing where to apply it is what makes the real difference. As someone who works closely with employers on AI implementation and workforce development, the disconnect between training and real-world application is becoming increasingly apparent.
Much of the focus now is on access, how many people can be trained, how quickly, and at what scale. Although important, access alone does not lead to improved performance. Free AI training alone won’t make you more productive. It must be based on applied intelligence.
Across the sector, there is growing recognition that skills delivery is not just about curriculum delivery, but needs to be more closely aligned with economic outcomes. The question is moving from how many people can you train to what impact that training will actually have on your organization.
This is the challenge. This is not just due to the availability of AI training, but also to a lack of employer demand and, in many cases, an unclear understanding of how AI provides tangible business value.
Most organizations don’t require their own training. They want results: ways to work more efficiently, increase output, and reduce inefficiency. Training only makes sense if it ties directly to those priorities.
Applying AI skills
The government’s proposed training is rightly focused on teaching people how to use essential AI tools. But it’s equally important to help people and organizations understand where to apply AI. In doing so, AI delivers real productivity gains and is used safely and ethically.
An easy way to think about it is learning to drive. Mastering control is one thing. Real skill comes from understanding the road, the conditions, and when it’s appropriate to drive.
Apprenticeships offer an ideal way to integrate these elements. Our AI apprenticeships incorporate hands-on training through productivity use cases and proof-of-concept projects to help your employees use AI in ways that directly benefit your organization.
This complements our out-of-work training on AI tools by showing both employers and employees how AI actually delivers value, while supporting safe, secure and responsible use.
Gap between training and application
Current training models are not always able to close this gap. Programs like the AI Skills Hub are designed to help you gain confidence in using tools in your daily work, and this can be a useful starting point. However, knowing how to use a tool is not the same as understanding where the tool should be applied to provide value.
Despite increased investment, many organizations are still in the early stages of adoption, with recent government-commissioned research finding that only 16% of UK businesses are currently using AI technology. The challenge is to identify where tools and courses are rarely accessible but where AI can truly improve the way we do work. Without that clarity, you risk becoming disconnected from the realities of your day-to-day work.
Why AI needs context
Much of the AI training available today focuses on tools, features, and prompting techniques. They do not provide value on their own.
Lack of context can lead to misperceptions of functionality and, in some cases, inefficient or inappropriate usage. AI for AI’s sake doesn’t work.
What is needed instead is a shift to applied intelligence, where AI is based on real-world use cases, tailored to specific roles, and focused on improving the way work is done. Just using a tool doesn’t make it effective, but knowing when and how to apply it does.
start with results
Organizations reaping the most benefits from AI are not starting with the technology.
The starting point is results, focusing on where effort is being wasted, how processes break down, and what performance actually improves. From there, work backwards to first define the outcome and then build the capabilities needed to achieve it.
This approach changes the role of training. It is not a starting point, but a means to delivering a clearly defined result.
feature already exists
There is also a tendency to believe that AI capabilities must be brought in externally.
In many cases this is not necessary. The people who understand the business best have a clear understanding of where processes are going wrong and where improvements can be made.
Many organizations continue to look for solutions externally, even though the most pressing opportunities already exist within their own employees.
When employees are supported to apply AI to their daily roles, the benefits are often immediate. Work becomes more efficient. Improved decision making and increased production. Importantly, these improvements are more likely to last because they are built into existing ways of working, rather than being introduced as an external layer.
What success should look like
If we want to succeed in this national ambition, we need to be clear about what success actually looks like.
It is not defined by course completion or certificates, but by whether the organization’s performance improves as a result. This means being more competitive, more adaptable, and better positioned to grow, with people who can perform their roles more effectively.
what happens next
The UK has taken an important first step by expanding access to AI skills. The next stage will determine whether that ambition translates into real impact. This requires a shift in focus from just access to applications, productivity, and outcomes.
AI itself won’t transform your business. When applied in context by people who understand the organization and the job, it can be a powerful driver of growth.
The ambition is there. Now we need to actually deliver it.
Mo Issup OBE, IN4 Group CEO
