The UK faces an expanding skills shortage across technical and industrial roles, and Cathie Hall, Chief Customer and Product Officer at IFS, believes the core mistake is treating it strictly as a recruitment or training issue.
External hiring and standard training modules can’t replicate thirty years of tacit, unwritten judgment that simply leaves the business whenever a senior engineer retires, given that the rationale behind those decisions was never recorded in a manual. Compounding the problem, the UK has a severe shortage of technicians with five to fifteen years of experience, leaving no intermediate tier to pass that wisdom down the line.
Hall argues this is precisely where Industrial AI steps in, bridging the gap between operational data and the context behind it. By embedding that reasoning directly into daily workflows, decades of institutional memory become an active operational asset, compressing the learning curve and giving newer workers access to insights that previously took years to build.
Cathie caught up with us to break down how to deploy AI in asset-intensive industries, why the technology is empowering junior staff, and her direct message to leaders sitting on the fence.
AI skills discussions often focus on general digital literacy. What does effective AI adoption and training look like in technical and industrial environments?
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.
Industrial environments don’t just need tools that answer generic queries. Industrial AI can help asset-intensive industries surface operational context, access relevant information and improve decision-making in real-world environments. Effective AI training therefore needs to focus on capability, not workforce reduction.
Applied AI training should help workers use AI to access relevant history, previous decisions and operational context within their workflows. By enabling people to interact with complex industrial processes using natural language, organisations can help both experienced employees and newer workers build capability, while keeping human expertise and oversight at the centre.
How exactly does AI capture institutional expertise so younger workers perform at a senior level faster?
It comes down to mapping how unstructured human reasoning influences structured operational data. AI maps the link between informal notes or email exchanges and formal ERP records, then embeds that context into the daily workflow.
When a younger engineer enters the shop floor or field environment, the relevant history, prior decisions and underlying reasoning can be surfaced alongside the task, giving them access to knowledge that would traditionally take years to build.
AI elevates the baseline performance ceiling. Instead of leaving a junior technician to guess or blindly navigate disconnected folders, the system actively surfaces relevant historical choices and prior reasoning pertinent to the job at hand.
When junior workers can direct these complex processes safely through plain language, supported by the organisation’s collective memory, their confidence can grow significantly. Risk is mitigated for the employer, safety is enhanced and the work is performed with the benefit of accumulated organisational knowledge.
Why is AI proving to be a workforce equaliser, rather than a threat, for younger and less experienced workers?
Six in ten young people in the UK’s NEET cohort have never held a paid job, highlighting the challenge many face when entering the workforce. AI can help lower that barrier by giving newer workers access to the knowledge, context and support they need to succeed.
When an Industrial AI system surfaces the right context and allows a newcomer to interact with complex, multi-layered processes using plain language, it reduces reliance on years of specialised jargon or institutional knowledge built over time. A lack of traditional experience doesn’t have to become a barrier to contributing effectively. AI can help newer workers build confidence and capability, while keeping human expertise and judgement at the centre.
What impact are organisations seeing from using AI to expand workforce capacity?
Organisations are increasingly using AI to expand workforce capacity by helping employees spend less time reacting to problems and more time focused on higher-value work. The biggest opportunity isn’t replacing people, but enabling existing teams to work more effectively by giving them faster access to the information, insights and expertise they need.
We’re seeing this play out across industrial environments. For example, William Grant & Sons is using AI-driven predictive maintenance capabilities to combine live sensor data with historic asset information, helping engineers identify potential issues earlier and reduce disruption. At its Girvan site, this approach is expected to deliver £8.4 million in annual savings through improved maintenance, reduced downtime and increased operational efficiency.
This demonstrates how AI can support skilled workers by reducing operational friction, allowing engineers to focus on tasks that require experience, judgement and problem-solving.
To finish, what is the single piece of advice you would give to industrial leaders who are currently sitting on the fence about deploying Industrial AI?
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.
Addressing the skills gap requires a different approach to workforce development. By putting the expertise of experienced employees into the hands of those building their careers, Industrial AI can help accelerate learning, strengthen capability and create a more resilient workforce for the future.
