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For many manufacturers, AI still carries a reputation for being costly, complex and out of reach. But according to Esther Cornell, Managing Director of Cambridge Rapid, that perception is holding businesses back from opportunities that are already within their grasp.

Speaking ahead of the upcoming Manufacturing Digitalisation Summit, Cornell said rather than viewing AI as a single, high-cost investment, we should look at it as a spectrum, one that includes everything from simple productivity tools to advanced automation.

“What SMEs are missing is often the smaller things that could make a big difference.” 

At Cambridge Rapid, that means using accessible tools like Claude to support everyday tasks, from answering technical queries to building small internal applications that calculate machine rates. These are not headline-grabbing use cases, but they are delivering tangible value with minimal investment.

The message is clear: manufacturers don’t need to wait for a large-scale transformation programme to begin their AI journey. Incremental improvements can unlock immediate efficiencies while building confidence and capability.

Closing the digital skills gap

However, technology adoption is only part of the challenge. As manufacturing becomes more digitised, Cornell highlights a growing disconnect between ambition and understanding when it comes to skills.

“You would have an engineer say, ‘I’d like to learn to code’… and people often assume they want to get involved with gaming. So, there’s a lack of knowledge, within manufacturing organisations, on the skills that are actually needed.”

This lack of clarity, she argues, is exacerbating the skills gap. Businesses risk misdirecting training efforts without a clear view of how digital tools—from basic coding to AI-driven visual recognition—can be applied in practice.

Cornell calls for a more structured, industry-wide approach to defining and communicating these skills, enabling organisations to upskill their workforce in a more targeted and effective way.

Turning data into decisions

If AI is the opportunity and skills are the enabler, data is the foundation. Yet many manufacturers are still struggling to translate growing volumes of data into meaningful insights.

Cornell believes the problem lies in collecting too much of the wrong data.

““Data analytics is about getting the data you need, not the data the system will fling at you.” ”

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At Cambridge Rapid, this principle is shaping a more deliberate approach to digital transformation. Following a recent shift to a new ERP system, the focus is not on maximising data collection, but on identifying what information actually supports better decision-making.

This includes embedding standard operating procedures and exploring how shop floor data can be integrated over time, while resisting the temptation to invest in tools or technologies that don’t align with clear business needs.

A pragmatic path forward

Across AI, skills and data, Cornell’s message is consistent: progress doesn’t have to be complex. By focusing on practical use cases, building understanding and prioritising relevant data, manufacturers can take meaningful steps forward, without the need for major upfront investment.

Esther Cornell

Managing Director, Cambridge Rapid