In response to the Skills England report published in October 2025, which highlights a significant tech and AI skills gap hindering the uptake of artificial intelligence across the construction sector, this piece reflects on what that gap really means for the industry and how we can bridge it.
The AI Skills for the UK Workforce report by Skills England shows that while AI has the potential to transform productivity and safety across key growth sectors, including construction, many organisations are struggling to adopt AI due to poor digital literacy, unclear understanding of relevant skills, and a lack of structured training pathways. Without addressing these barriers, the construction sector risks lagging behind others in realising the benefits of AI tools that could enhance safety, compliance and operational performance.
AI isn’t about replacing people on site; it’s about giving them better information, stronger safeguards, and more time to do their jobs safely and productively.
However, the Skills England report is right: construction is facing a significant AI skills gap. But the gap isn’t simply a lack of technical capability; it’s a gap in confidence, clarity, and practical use cases that genuinely add value on live sites and, most importantly, the digital foundation to extract maximum benefit from this emerging technology.
From my perspective in digital transformation, AI is often talked about in abstract terms, especially given the buzz surrounding it at the moment. Its value becomes very real when we connect it directly to the industry’s biggest operational challenges: getting people on site safely, ensuring compliance, improving productivity, and giving project leaders and site teams visibility they’ve never had before.
These challenges appear across every level of the construction companies we work with—from commercial teams trying to evidence compliance, to site teams dealing with labour bottlenecks, through to leadership struggling to consolidate data from multiple systems.
AI is becoming most valuable where construction leaders need faster decisions, validated data, and clear risk indicators. But it only works when workforce data is accurate, real-time, and verified. The industry, and organisations within it, must first standardise how they capture, check, and share digital information, because AI cannot compensate for incomplete or unreliable site data—especially if it is still reliant on paper forms or basic digital forms.
Without this, there is a risk that AI’s value is limited to task-based, individual productivity gains, such as capturing meeting minutes and actions, rather than real transformation and properly leveraging AI’s capability in analysing and presenting back vast amounts of data.
And we have to be honest about the pitfalls. AI is not a silver bullet. It can recommend, flag, and predict—but it should never replace human judgment, particularly where worker wellbeing or legal compliance is concerned. Those skilled and experienced individuals on sites need to help determine the guardrails for the technology, ensuring it is not over-relied on and remains a tool to support decisions and raise risks, not the decision-maker in its own right.
When implemented correctly, AI can have a measurable impact, especially in creating safer site environments. By analysing patterns around access control, incident logs, shift overlap, and high-risk activities, it can highlight where overcrowding is likely or where fatigue may be creeping in. These insights help organisations evidence their responsibilities under the BSA, CDM, and wider HSE frameworks—all of which rely on knowing exactly who is on site, whether they are competent, and how work is being supervised.
Some of the most exciting progress is happening in ESG and social value reporting. Demonstrating ethical labour practices, diversity, local employment, and carbon reduction is now fundamental to tender success. AI can help consolidate data from multiple projects and supply chain tiers, producing clear, auditable evidence where organisations previously relied on manual spreadsheets or incomplete records.
But there’s also a cultural challenge. Adoption won’t happen if AI is perceived as something being “done to” the workforce rather than “done for” them. Many site teams we speak to are already juggling too many systems, unclear processes, or tools that duplicate effort. That’s why implementation and associated training must be role-specific, practical, and centred on real benefits and real problems: faster access to site, less paperwork, and clearer visibility of who is safe, competent, and ready to work.
The future of the industry isn’t a choice between human insight and machine intelligence—it’s the combination of both. Working together, they can create safer, more sustainable, and more productive projects. With the right foundations and a workforce-first approach, the AI skills gap is absolutely bridgeable, and the companies investing in its capability today are well positioned to lead the sector in the years ahead.