European manufacturers need to adopt Industrial AI at greater scale if they are to compete with manufacturers in North America and Asia, speakers at an Automation News webinar have argued.
The webinar, ‘Can Industrial AI Make Europe More Competitive?‘ brought together Cécile Vercellino of Schneider Electric, John Mackey of WAGO UK & Ireland, Dan Smalley of Siemens and Sanjit Shewale of ABB Process Industries to examine how AI is being deployed in factories, and what is holding wider adoption back.
The panellists agreed that AI is already delivering value in areas including predictive maintenance, energy management, quality inspection and process optimisation. But they also pointed to data, skills, cybersecurity, workforce adoption and the difficulty of modernising older factories as barriers to wider deployment.
For Schneider Electric’s Vercellino, the challenge is not simply finding an industrial problem that AI can address, but building the foundations to take a successful pilot into production.
“Customers implement some proof of concept, they test the model, they test potentially the technology, but they don’t think about the scaling of this technology overall,” she said.
That means identifying the desired business outcome, establishing the necessary data infrastructure and defining how success will be measured before selecting the technology, she said.
Siemens’ Smalley similarly argued that industrial AI needs to be treated differently from consumer applications because mistakes can have physical consequences.
“We have real-world impact when there’s mistakes and things like this,” he said. “It’s really important from our perspective that we’re perhaps maybe moving a little bit slower than some other industries because we have to take into account safety, trust and reliability, cybersecurity, and all these sort of things.”
Among the applications already being deployed are machine-vision inspection, predictive maintenance and closed-loop process control, Smalley said.
ABB’s Shewale pointed to applications in process industries where AI is being used to improve uptime, production yield and energy consumption. He cited ABB deployments in the steel industry where AI optimisation had reduced heat consumption by more than 2%, while greater automation had reduced operator intervention.
“I think one of the biggest challenges is not really the solution; it’s internal adoption,” Shewale said. “It’s with the workforce, and it’s also being able to demonstrate the right ROI for the specific use cases.”
That focus on measurable returns is particularly important in Europe, where manufacturers face high energy costs and significant investment requirements.
The region also has a distinctive challenge: much of its industrial infrastructure is decades old.
Vercellino said European manufacturers could not simply abandon existing factories and build new facilities from scratch. Instead, AI will have to be introduced into legacy production lines, equipment and automation systems.
“We cannot ask our customers to rebuild from scratch,” she said. “They need to build the future and the digitisation of their operations from their legacy systems.”
WAGO’s Mackey agreed that brownfield infrastructure presents a significant challenge, particularly when manufacturers are trying to connect data from different machines and systems.
“Access to data is a challenge, but also the underlying cybersecurity and that defence in depth approach of both the products and the people and the processes around manufacturing has to be probably the precursor before we can start to look at AI,” he said.
The speakers nevertheless argued that Europe’s ageing industrial base could also become an advantage if its accumulated engineering knowledge is combined with newer technologies.
“There is really deep domain know-how within Europe, within all our manufacturing industries, and we’ve been doing it for a long time,” Smalley said.
Watch the full webinar ‘Can Industrial AI Make Europe More Competitive?’ below.
