The goal is ambitious: to create a joint generative AI model for industry that will support development and manufacturing as naturally as ChatGPT is already used in offices. At the core of this cooperation lies the exchange of anonymised machine data — a significant development for a traditionally closed sector.

As Siemens CEO Roland Busch put it: 'Together with customers and partners, we are taking a significant step today to scale industrial AI. I see a great opportunity here for Europe’s economy, which has a strong industrial base”.

The idea sounds simple, but the execution is not. Anyone familiar with mechanical engineering knows how sensitive data can be. Nevertheless, the opportunity is real. The more companies contribute their data to a shared pool, the more powerful the model becomes. Busch stressed that access to high-quality machine data is the “decisive lever” – and the prerequisite for making AI in industry more than just a glossy vision.

The crucial task now is to bridge the gap to practical application. At the Mechanical Engineering Summit in Berlin, Cedrik Neike, a board member of Siemens' Digital Industries, put it plainly: 'After the peak of expectations around AI, we now need to build the bridge to practice — it's about concrete applications.'

One example is automated machine programming. Instead of manually writing NC codes over the course of days, AI could generate programs in minutes in the future – more quickly, with fewer errors and far more productively. If such use cases can be widely adopted in manufacturing, then 'Deutschland-Tempo' will become a reality.

If Siemens and its partners maintain this pace, the alliance's initial results are likely to be presented at Hannover Messe 2026, that will again focus on AI in industry. This would be a win not only for the companies involved, but also a signal that Europe can position itself as a frontrunner in the race for industrial AI.