"Humans will lag behind!": Why skills must be put at the heart of the UK's AI strategy
"People learn best when they see how AI works in the real world, experiment, get feedback and solve problems that matter to them."
As AI transforms the UK economy, the risk is not that machines will suddenly replace us. The most immediate danger is that humans without the skills to adapt and harness these new technologies will lag behind.
Which is why preparing Britain's workforce for AI will require more than investment in infrastructure.
The Government has committed £2 billion to position the UK as a leader in AI. Spending alone won't do the job because even the most advanced technology delivers little value if people aren't trained to use it properly.
Which is not what's happening. Even though 90% of global IT leaders are investing in AI, businesses are still having to make difficult decisions because of tight budgets and rising compute costs. Nearly 20% of UK companies are actively reducing their employee training budgets, which threatens long-term competitiveness.
The lesson for leaders is clear. To deliver lasting transformation, skills development must be built into the strategy from day one.
Building sustainable foundations for AI
AI transformation roadmaps often start in the same place: infrastructure, including platforms, computing, and models. All too frequently, however, skill development is viewed as optional or put aside until "later." Often, later never arrives.
Businesses end up automating processes without giving employees the freedom to evolve as a result. Although this type of automation lowers costs in the short term, it hinders innovation, lowers morale and creates aversion to change.
Instead, we need to help people work with AI. That doesn’t mean turning everyone into a machine learning engineer. It means giving teams the confidence and capability to use AI to solve problems, make better decisions and focus on higher-value work. That shift is what delivers lasting ROI, not just from the technology but from the people behind it.
Unlocking the potential of the existing workforce
The positive is that the majority of businesses already have in-house experts in their fields. Existing teams can use AI tools to automate the repetitive and elevate the creative, allowing them to further develop their positions with the correct support. However, this cannot occur in isolation.
From the start, skill development must be incorporated into AI strategy, change initiatives and everyday operations. It demands more than a single centre of excellence or training module. It requires a culture that demystifies AI and encourages lifelong learning.
This also means ensuring training isn’t limited to abstract or academic environments. People learn best when they see how AI works in the real world, where they can experiment, get feedback and solve the problems that matter to them.
Driving inclusive growth through AI training
The movement to democratise access to AI education is gaining traction on a global scale. High-quality, practical training, particularly when it is provided for free, helps create a more varied pipeline of AI-literate talent and level the playing field for skilled people everywhere.
Access to enterprise-grade tools and learning materials can help anybody, whether they are a student, an early career professional, or someone changing careers, to acquire useful skills in data engineering, analytics and AI development. By removing barriers to entry, more people get practical experience in applied problem-solving, which reflects the difficulties that businesses deal with on a daily basis.
Enterprises, too, should invest in domain-specific upskilling. Most of tomorrow’s AI-specific jobs won’t be brand new; they’ll be existing roles transformed by access to real-time intelligence and automation. This means training must be tailored, contextual and aligned to what teams actually do day-to-day.
Scaling AI with transparency and trust
Ultimately, when workers see AI in action in their own domain, their confidence in it builds. Scaling and safely integrating technologies into actual workflows is crucial. This means breaking down data silos, putting in place transparent governance and deploying AI agents into the platforms that people currently use. The most inclusive innovations meet users where they are, rather than requiring teams to start from scratch.
This strategy lowers friction, encourages trust and motivates cross-functional teams to work together towards common objectives. AI moves from being a technical initiative to a business-wide asset when it is integrated into decision-making across operations, customer service, and products.
Placing people at the heart of AI leadership
Making skills a key component of innovation is essential for leadership in the AI economy. The most successful businesses and people will be those that can use data and AI to address practical issues at all levels of the workforce.
Investing in upskilling should not only be a business need but also a national goal. We must act immediately to ensure that this generation of AI is characterised by opportunity, inclusivity, and shared progress.
Skills must now be the cornerstone of the UK’s AI policy; they can no longer be considered an afterthought. Encouraging people at all levels to interact with and use AI will be the key to success.
Michael Green is UK&I Managing Director at Databricks
