Could AI Create More Value in Compliance Than on the Factory Floor?

Most discussions about artificial intelligence in manufacturing begin with productivity. AI will predict equipment failures, optimise production, improve inspection or reduce the time engineers spend on repetitive tasks. Those applications are important, but Stephen Phipson, CEO of Make UK, points to an area where the prize may be even larger: compliance.

For businesses functioning in regulated sectors such as aerospace and medical devices, the cost of producing a component is only part of the commercial equation. Organisations also have to prove that the component, process, and supporting evidence meet the required standard. Documentation, traceability, review, configuration control and audit activity can consume significant technical resources.

Phipson argues that AI applied to this compliance burden could create value beyond incremental shop-floor improvements. The opportunity is easy to understand. A system capable of interrogating large bodies of technical information, identifying missing evidence, checking consistency, and helping teams navigate requirements could reduce the time highly skilled people spend searching, comparing, and assembling information.

The risk is equally obvious. Compliance is not an area in which an organisation can accept a convincing answer without understanding its source. Any AI-supported process would need strong data governance, traceability, validation and clear human accountability. A system that produces a faster answer but weakens the chain of evidence would create more risk than it removes.

That suggests a useful distinction between AI making the compliance decision and AI supporting the people responsible for it. The second is likely to offer more immediate value. Engineers and quality specialists could use AI to locate relevant requirements, compare documents, flag inconsistencies, prepare evidence packs or determine areas that require expert review while retaining responsibility for the final judgement.

The same thinking could apply to change control. When a design, material, supplier or process changes, the impact can spread across drawings, work instructions, test evidence and certification documentation. AI may help identify those relationships more quickly, particularly when information is held across multiple systems.

There is also a wider productivity point. Technical specialists are scarce. Every hour spent searching for low-value documents is an hour that cannot be spent solving an engineering problem. If AI can reduce administrative effort without reducing assurance, the benefit is not simply faster compliance. It is a better use of specialist capability. It could also shorten the time between completing technical work and demonstrating that the work meets the required process.

This may prove especially important for smaller suppliers. Large regulated organisations can build substantial compliance teams, whereas SMEs often carry the same assurance expectations with far fewer people. Tools that make regulatory evidence easier to manage could improve their ability to enter and compete in high-value supply chains.

AI on the shop floor will continue to attract attention because its results are highly visible. The quieter opportunity may sit in the layers of evidence and assurance behind the product. In regulated engineering, reducing that burden safely could unlock capacity, shorten lead times and make growth easier in ways that a small cycle-time improvement never will.

Advanced Engineering takes place at the NEC Birmingham on 4-5 November 2026.