Manufacturers that have adopted AI on the shop floor are growing their headcount – the chief executive of the UK’s national manufacturing body told Parliament on Tuesday.
Giving evidence to the Business and Trade Committee’s inquiry into Artificial Intelligence, business and the future of the workforce, Stephen Phipson CBE, Chief Executive Officer of Make UK, told Liam Byrne MP, Chair of the Committee:
“We actually see employment increase as a result, not decrease, because they’re more productive […] When they see an average 20% improvement in productivity, you’re dealing with much bigger volumes. Their overall business is much more productive, much more profitable, but in doing so, they’re actually increasing employment as a result.”
The news comes as official Government statistics confirmed unemployment fell to 4.9% (from 5%) between February and April this year. Manufacturing accounts for 130,000 businesses and 2.5 million jobs in the UK, the overwhelming majority in small and medium-sized enterprises (SMEs), which suggests wider deployment of such tools could provide significant gains for more modest-sized businesses in terms of employment as well as productivity.
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Other figures providing oral evidence included Karen Dewar, Chief Data and Analytics Officer at NatWest Group, Daniel Smalley, Industrial AI Lead and Business Manager for Factory Automation Digitalisation at Siemens, and Kayur Raughani, Managing Director for Data and AI (UK and Ireland) at Accenture.
Throughout the session, the committee heard evidence that AI’s biggest gains have arrived where organisations didn’t plan for them. That position has been echoed in previous research. Morgan Stanley research published in January found UK businesses reported an average 11.5% increase in productivity aided by AI. At NatWest, the impact landed most sharply in a team of 220 wealth relationship managers.
NatWest’s Karen Dewar told MPs the bank deployed AI to automatically summarise client meetings, saving those managers 72.5% of their note-taking time. Freed from capturing notes, she claims they became more present in client conversations – and NatWest’s net promoter score improved as a result, with a further 70,000 hours saved across the bank’s retail call-handling teams.
Siemens’ work on the shop floor has produced results in similar territory. The company’s AI Lead, Daniel Smalley, described how AI deployed on the Kellanova production line – the snacks company spun out of Kellogg’s – achieved a 10% capacity increase, a 13% reduction in waste and a 7% improvement in energy efficiency. In a separate project developed with Yorkshire Water and Sheffield University, Smalley told MPs, the team built a system that “allows them to predict a blockage three to four times more efficiently than they were previously able to do so.”
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The distance between those results and where most businesses currently sit is significant. Make UK’s data shows just 2% of UK manufacturers have AI widely embedded in their operations, with 83% using it only in back-office functions such as HR and finance. Accenture’s Generating Impact report, based on surveys of more than 500 executives and 2,000 employees across 17 industries, puts the broader picture starkly: Kayur Raughani told MPs that only 10% of organisations could be described as scalers – those generating real, measurable returns from the technology.
“The level of activity is huge,” he said, “but actually, the level of progress, right at the front end, where people are scaling this and getting real benefit from it, is still relatively small.”
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Kayur offered the committee a case that didn’t go to plan. A regulated customer service team had AI transcription deployed at the end of each client call.
“The person was then spending twice as long comparing the auto notes with their own personal notes to create a superset of notes,” he said, “and the whole exercise was taking much longer.”
The problem, when investigated, wasn’t the technology. “What really had been missed was adequate training for that team to say, we don’t need to do both of these things. There’s a slightly different way of capturing notes, so you really use the technology as a copilot rather than trying to sort of combine both to yourself.”
The harder barriers, Kayur said, are rarely the technology itself. Often, he told MPs, organisations treat procurement as the hard part – but getting business data into a usable state, bringing people through the change, and aligning decision-makers who cut across organisational silos is where implementations stall. Giving an example, he mentioned a case of an Accenture client spending nine months on that preparation ahead of a three-month implementation.
Asked by the Chair what was holding back wider adoption in the sector, Stephen added two barriers specific to manufacturing: ageing equipment and legacy systems on the shop floor that are difficult to retrofit, and the absence of leadership capability to recognise when and how to act. With fewer than 2% of UK manufacturers fully through that journey, Stephen told MPs the pace needs to accelerate.
The stakes for UK small businesses are already visible in recent figures. PwC’s 2026 AI Jobs Barometer found this week that companies at the leading edge of AI adoption grew headcount by 52% between 2018 and 2025, against 36% for those using it least.
And Employment Hero research has also found that 44% of UK employees already use AI tools at work – and those doing so at least weekly are 23% more likely to say they’re making an impact. Among business leaders, AI tools are already the number-one priority for improving productivity over the next 12 months, though just over half of businesses have integrated AI to at least a moderate degree. From the perspective of Make UK, the employment gains already visible in larger manufacturers will translate directly to small businesses – but the pace of getting there is where the real challenge lies.
