When you think about the leading edge of manufacturing technology, you might imagine sites in the US and China. But there’s evidence that the UK is also a major player in this space.
According to a recent report from digital transformation and industrial automation provider Rockwell Automation, the UK already leads Europe in smart manufacturing, with 53% of its manufacturers using AI on the factory floor and 98% overall planning to implement it.
This compares to a global average of 41% introducing AI and machine learning (ML), as well as 95% planning to implement.
AI-generated SearchEngine Marketing (SEM) expert Anatolii Ulitovskyi tells ITPro he’s tracked AI adoption in manufacturing sites to be around 40-45% in the US and 35-40% in the Asia Pacific. Rohan Sharma, chief technology and data officer, Zenolabs.ai, puts the US number even lower, at 29%.
Stories of successful AI adoption in UK industry abound. In one, Sharma talks about a company that reduced defects by 90% and saved £2m annually within eight months using computer vision-based quality control and predictive maintenance. “ROI windows are now less than 12 months, far shorter than typical cap-ex cycles,” he says. “That’s why the UK’s advantage is likely to compound over the next decade.”
Another comes from Saj Munir, founder of Manchester-based Chorlton Fireworks, who uses AI to forecast demand spikes around key seasons like Bonfire Night and New Year’s Eve. “The manufacturers that supply us are applying the same principles to manage their operations by reducing downtime and optimising supply chains,” he says. “One supplier told us that since adopting AI-driven production planning, they’ve cut delays significantly and can now meet seasonal demand far more reliably.”
So what makes UK manufacturing such an AI powerhouse, so much so that even the number of manufacturing businesses that plan AI investments outshines those of other regions?
Roy Andraos, CEO at DataVLab, a Paris-based AI company specializing in computer vision dataset preparation, says the UK has what could be considered one of the most advanced industrial AI ecosystems because of “a stronger ecosystem of startups, integrators, researchers, and institutional funding for industrial AI”.
“The difference isn’t just tech spend, it’s the UK’s playbook of rapid pilots, workforce upskilling, and cybersecurity hardening,” Sharma adds.
But there are caveats. Scott Curtiss, a manufacturing specialist at AI supply and demand planning provider RELEX Solutions, says the UK lags behind several European nations and even further globally in robotic automation.
Joy Nma Anyanacho, senior applied machine learning scientist engineer at Best Buy, cautions ‘adoption’ doesn’t necessarily equal ‘entrenched and working smoothly’. “Pilots are everywhere, but many still struggle to operationalize solutions company-wide due to integration, legacy systems, or workforce resistance,” she says. “Maturity level varies wildly.”
A new era for British manufacturing
The UK is among 56% of manufacturers piloting smart manufacturing, with 20% using it at scale and 20% planning future investments, according to Rockwell.
Since the 2024 edition of the report, the company says there’s been a 5% increase in the importance of analytical and AI skills for leaders, so the time for readiness is very much here.
One of the most prominent drivers behind the adoption of AI across UK manufacturing has been filling the skills gap and mitigating labour shortages, with the Rockwell Automation report finding it accounts for 41% of AI deployments.
A notable development has been a change in workforce outlooks on how AI will affect manufacturing roles. A recent University of Arizona survey found that just 25% of workers still fear AI will take their jobs.
This is backed up in the Rockwell Automation report’s findings, which states that leaders deny moves toward smart manufacturing are linked to reduced hiring goals. “Respondents instead asserted their organisations’ plans to hire more people with technology skillsets and to retrain current employees,” the report’s authors noted.
“Smart manufacturing tends to reshape roles rather than eliminate them outright,” Anyanacho agrees. “One firm I worked with deployed AI robotic systems not to replace human workers but relieve pressure in critical tasks. I’ve also seen cases where AI requires such specialised oversight they end up adding new staff rather than remove them.”
Some businesses are also seeing extra or unexpected hires to manage AI as net positives. “Specialized hires often enable more consistent output, faster decision-making, and fewer errors. They usually lead to a net gain, even if the direct headcount doesn’t shrink,” Anyanacho adds.
Cybersecurity is also a big concern being addressed by AI, a timely intervention as the manufacturing sector accounts for 21% of ransomware attacks, per Black Kite data.
The QA gap
AI’s biggest use case for the manufacturing sector could be in quality control (QA), with half of Rockwell Automation’s respondents planning to use the technology for QA within the next year.
The experts we spoke to agree the area is catching on fast. “When paired with high-quality datasets, AI systems can now detect defects in real time, monitor worker safety, and manage inventory with a precision that was impossible before,” says Andraos. “They’re not just theoretical improvements – we’ve supported production environments where object detection AI reduced error rates in QA workflows by over 40%.”
Will Kogan, general manager at shipping analytics provider Franklin Parce, tells ITPro about a client in automotive parts manufacturing who implemented AI-driven sensors on their assembly lines. The new systems flagged machine wear patterns weeks before failures occurred, reducing unexpected downtime by 30%.
There are many more AI for QA success stories, too.
Andrew Kinder, SVP of Industry Strategy at business cloud software provider Infor, talks about US welded aluminium boat builder Xpress Boats, where AI and process mining has “completely changed” how quality issues are managed. “[AI] has enabled Xpress Boats to detect process issues up to 98% faster, cutting shipping costs by 50% as a result” he says. “That kind of early warning system is critical.”
And Jim Chappell, global head of AI and Advanced Analytics at industrial software provider Aveva, was there when food giant Nestlé decided to use realtime production data to predict the moisture and density levels in products. “They moved production data to the cloud and used Aveva Connect to perform advanced analytics with industrial-focused AI. It took the guesswork out of process control and let operators simply follow the guidance from analytics to ensure product consistency,” he says.
A critical challenge still remains. Even though manufacturing companies are collecting more data than ever, the Rockwell Automation report says only 44% is being used effectively. “Data collection is no longer the issue,” agrees Anyanacho, “it’s the usefulness and accessibility of that data. Strategy is still a weak link for many.”
Thankfully, it’s an area most companies are aware needs work. “What stands out most is the growing demand for robust data infrastructure,” agrees Andraos. “Companies aren’t just looking for algorithms, they need reliable pipelines for training, fine-tuning, and validating their AI systems in production settings.”
A confluence of factors have crowned a surprising leader in the AI manufacturing race. With targeted investment and strong data strategies, UK manufacturing firms could strengthen that lead in the years to come.
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