Britain's technical and industrial sectors face a widening shortage of skilled workers, yet the real problem may not be recruitment or training alone. According to Cathie Hall, Chief Customer and Product Officer at IFS, the issue runs deeper: when experienced engineers leave, they take with them thirty years of unwritten judgment and decision-making logic that was never documented. The gap widens further because the UK lacks mid-career technicians with five to fifteen years of experience—the very people who would normally pass accumulated wisdom to newcomers.
Hall sees Industrial AI as the missing bridge. By connecting operational data with the reasoning behind decisions, the technology can preserve institutional memory as a working asset rather than letting it walk out the door. When this context is woven into daily tasks, newer workers gain access to insights that traditionally required years to develop, dramatically shortening their learning curve.
Beyond Generic Digital Literacy
Conversations about AI skills often focus on broad digital competence, but Hall argues this misses the real opportunity in asset-intensive industries. "Focusing only on general digital literacy risks missing the real opportunity for industrial organisations. Too many businesses approach AI as another piece of software, rather than a capability that can fundamentally change how people work. It's not about using AI to remove people or reduce entry-level opportunities; it's about helping workers make better decisions and access the knowledge they need at the point of work," she explains.
Effective AI training in industrial settings must move beyond treating the technology as just another tool. Instead, it should help workers access relevant operational history, previous decisions and context directly within their workflows. By enabling staff to interact with complex processes using natural language, organisations can develop capability across both experienced and newer workers while keeping human expertise central to operations.
Capturing Expertise for Faster Learning
The mechanism is straightforward in principle: AI maps the relationship between informal human reasoning—notes, emails, conversations—and formal operational records in enterprise systems. Once this connection is established, the context becomes embedded in everyday workflows.
When a junior technician steps onto the shop floor, the system surfaces relevant history and prior reasoning alongside the task at hand. Instead of fumbling through disconnected folders or relying on guesswork, they gain access to knowledge that would normally take years to accumulate. This approach raises the baseline performance level across the team. When junior workers can safely navigate complex processes using plain language, backed by the organisation's collective memory, their confidence grows significantly while risk and safety concerns are managed more effectively.
A Workforce Equaliser Rather Than a Threat
In the UK, six in ten young people classified as NEET (not in education, employment or training) have never held a paid job, creating a significant barrier to entry. Industrial AI can help lower that barrier by providing newcomers with the knowledge and support they need to contribute effectively.
When an AI system surfaces the right context and allows newer workers to interact with multi-layered processes using plain language, it removes the traditional requirement to master years of specialised jargon or institutional knowledge. Lack of experience no longer becomes an insurmountable obstacle. Instead, AI helps newer workers build confidence and capability while preserving human expertise and judgment as the foundation of decision-making.
Real-World Impact on Capacity
Organisations deploying Industrial AI are discovering that the real benefit lies not in replacing workers but in enabling existing teams to work more effectively. By providing faster access to information, insights and expertise, companies reduce time spent reacting to problems and free up staff for higher-value work.
William Grant & Sons offers a concrete example. The spirits company is using AI-driven predictive maintenance to combine live sensor data with historical asset information, allowing engineers to spot potential issues earlier and minimise disruption. At its Girvan site, this approach is projected to generate £8.4 million in annual savings through improved maintenance, reduced downtime and enhanced operational efficiency. The outcome demonstrates how AI can support experienced workers by removing operational friction and allowing them to concentrate on tasks requiring experience, judgment and creative problem-solving.
A Message for Industrial Leaders
For executives still hesitant about Industrial AI, Hall offers direct counsel: "Stop viewing Industrial AI as a future technology trial and recognise it as an immediate operational priority. The organisations that benefit most will be those that use AI not only to automate tasks, but to make expertise more accessible across the workforce. By combining the knowledge of experienced employees with the capabilities of the next generation, businesses can accelerate learning, strengthen capability and build a more resilient workforce for the future."
Solving the skills gap demands a shift in how organisations approach workforce development. Rather than relying solely on external hiring or standard training, embedding the expertise of seasoned employees into systems that support those building their careers can accelerate learning, deepen capability and create a workforce better equipped for the future.
Source: TechRound
