Europe’s manufacturers are increasingly turning to artificial intelligence to address workforce shortages, rising costs, and operational complexity, industry leaders said at the Hannover Messe Press Preview today.
The annual trade fair, taking place from 20–24 April, will showcase how AI is transforming factories—from SMEs to large corporations—through robotics, data platforms, and integrated production solutions.
Norbert Jung, CEO of Bosch Connected Industry, opened the discussion by highlighting the sector’s pressures. “Despite the hype around AI, 95% of projects today fail to deliver economic value,” he said. Jung argued that the solution lies in combining AI, machines, and humans in a co-intelligent approach. “We help customers with taking data out of their silos, out of their functions, into a shared semantic data layer across the broad life cycle, and we industrialise the generative AI. That is what we do,” he said, emphasising the need to move beyond pilot projects to industrial-scale implementations.
AI is emerging as a central focus for Hannover Messe because it directly addresses the manufacturing sector’s biggest challenges. From labour shortages and the retirement of experienced workers to rising production costs and the proliferation of industrial data, AI solutions—from robotics to data platforms—promise to optimise efficiency, maintain continuity, and enable SMEs to compete with larger companies. The trade fair will showcase practical applications and live demonstrations of AI, highlighting how the technology is transforming factories across Europe.
The panel contrasted adoption patterns between large manufacturers and SMEs. While Germany’s major companies are increasingly implementing AI, SMEs remain more cautious. “Smart factories with a little bit too big what? Let’s put it this way,” Jung said, adding that smaller firms tend to adopt AI selectively, focusing on proven high-ROI use cases.
Lilija Kucinskaja, Product Owner for AI and Analytics Solutions at German Edge Cloud, discussed her company’s mission to make AI accessible to SMEs. “SMEs often struggle to access AI use cases due to complexity and cost,” she said. “Our middleware solutions are designed to be easy to deploy, providing immediate business value, and complement initiatives such as the Industrial AI Cloud established by Telecom. We truly believe this is a game changer for the European economy.”
Kucinskaja emphasised that successful AI adoption begins with digitisation. “Before we talk about using data for AI, we first need to talk about data at all, about digitalisation itself,” she said. Factories often contain disparate data sources that were never designed to interoperate. “When data gets cleaned and digitised, this is the first industrial magic moment, when the inefficiencies in production become visible through digitalisation. And this is the first business value you see of your factory in one place.”
The panel stressed that leadership, data maturity, and structured implementation are essential. “It all starts with leadership,” Kucinskaja said. “Leaders need to be in the game. They need to be in the project. They really need to look into it deeply. If this is not there, we think that you’re not going to reap the benefits.”
Sven Parusel, Head of Research Partnerships at Agile Robotics, highlighted the role of physical AI in the factory environment. “This is the right time to talk about how we actually make this reality, how we can benefit from that,” he said. Agile Robotics, spun off from the German Aerospace Centre in 2018, develops robotic solutions ranging from dual-arm assembly systems to humanoid robots. “We really look into how we can bring smart solutions onto the factory floor… bringing AI into the factory itself.”
Jung noted that measurable outcomes are critical for industrial AI. “The thing that delivers value is starting from the KPI that you want to improve. We want to improve that process, and we know exactly what we want to improve. Then with domain know-how and a proper data foundation, the agents can produce the results,” he said, citing examples where AI agents replicate the expertise of senior staff to maintain production lines overnight or optimise processes across multiple sites.
The panel discussed the so-called “data growth paradox”: industrial data volumes are increasing rapidly, but value extraction has not. Kucinskaja explained: “We have more and more data, but the value from data has not doubled at the same time. Data is sitting in source systems, data lakes… and in order to make it translatable to other functions, you need to have a semantic context. Machines don’t understand that on their own; we need to teach them.”
Concrete examples illustrated practical benefits. Diamond Trucks has implemented a dual-arm AI-powered system for gearbox assembly, combining computer vision with high-level system integration. Another initiative used a digital industrial engineer platform to leverage both machine and expert knowledge to optimise SME production processes. “It was very inspiring to work on this use case with an SME, looking at what the factory is doing and asking: what knowledge-based use case would help you bring immediate value in your working processes?” Kucinskaja said.
Panellists emphasised integrating AI across multiple layers of the factory, from low-level machine control to higher-level robotics and production management. “AI needs to be integrated very deeply. It needs to be involved in the hardware, in cells and systems,” Parusel said. “This high level of integration is very important for it to bring value.”
Looking ahead to 2030, the panel agreed that broad adoption of industrial AI across European SMEs would mark a successful outcome. Jung said: “If SMEs participate as fully as large companies, we can achieve significant efficiency gains, cost savings, and innovation. Failing to do so risks losing business opportunities and talent to regions that embrace AI more boldly.”
Kucinskaja noted that initiatives such as the Industrial AI Cloud could help smaller manufacturers access advanced technology even if it is not their core expertise. “We hope that one day a panellist in 2030 will be an SME telling us exactly what the cost savings are, how efficiency has improved,” she said.
Parusel highlighted the synergy of physical and digital AI. “With the foundation models nowadays, we can train models that already bring a lot of intelligence and allow AI-based manufacturing on the shop floor,” he said. “This ensures that AI is integrated where it is needed and works together with the whole infrastructure.”
