AI adoption in German industry: Guide to success

20.11.2025

3 min read

TL;DR: For more on this topic: Additional articles on mybusinessfuture

The German technology conglomerate Siemens has joined forces with mechanical engineering firms – including Trumpf and Heller – to launch a KI (artificial intelligence) data alliance for industrial manufacturing. The goal is anonymized, cross-industry sharing of machine data to enable successful AI deployment across German industry.Connecting machines to IoT or MES platforms is becoming increasingly critical for industrial enterprises – enabling them to collect, analyse, and leverage the resulting data. Artificial intelligence is playing an ever-greater role in that process. Crucially, the higher the quality of the data, the more effective the training of deployed AI systems.

Guided by this principle, Siemens has co-founded a data alliance with leading mechanical engineering companies. The Munich-based conglomerate sees the initiative as a strategic opportunity for European industrial firms to gain a competitive edge over the US and China in AI development – unlike conventional AI approaches dominated by those regions. Among the European machine tool builders and tooling manufacturers that have joined the alliance are Grob, Trumpf, Chiron, Heller, the RWTH Aachen University Machine Tool Laboratory, and the Voith Group, according to Industry of Things.

Open Standard for Data Exchange as the Goal

Siemens CEO Roland Busch describes the data alliance formed with customers and partners as a “significant step toward scaling industrial AI.” He adds: “I see here a major opportunity for Europe’s economy – built on its strong industrial base. By making our companies’ unique data assets available to generative AI models, we can achieve entirely new levels of productivity.” The data alliance’s long-term objective is to develop and establish an open standard for exchanging machine data across the industry – all to enable successful AI adoption in German manufacturing.Unlike widely known AI models such as ChatGPT, Google Gemini or Claude, industrial AI demands the highest degree of reliability – because even the smallest error can quickly lead to costly and hazardous consequences.

Reliable Data Is the Foundation

That makes reliable data all the more critical for training AI. “Access to high-quality machine data from multiple manufacturers is the key,” stresses Busch. “Through this alliance, we can develop AI systems that understand the complexity inherent in engineering and manufacturing – making them powerful, trusted partners for skilled professionals,” adds the CEO.

Siemens cites one application example: generating machine programs that deliver results faster and with significantly lower error rates – while also relieving IT departments of routine tasks. Another widely cited use case in the context of the Industrial Internet of Things (IIoT) is predictive maintenance. Additional benefits include real-time adaptation to new conditions, improved manufacturing processes, and enhanced energy efficiency.

Image source: Adobe Stock/suldev

Read on

AI in Industry: The Next Wave of Productivity Is Building Momentum
AI in the German Economy: Widespread Adoption Underway
Connected Industry Increasingly Threatened by IoT Ransomware

More on this topic: More articles on mybusinessfuture

Frequently Asked Questions

What’s key to achieving the goal of open standards for data exchange?

Siemens CEO Roland Busch describes the data alliance formed with customers and partners as a “significant step toward scaling industrial AI.” He adds: “I see major economic opportunities here.”

Why are reliable data the absolute foundation?

The more critical it is to have reliable data for training AI. “Access to high-quality machine data from multiple manufacturers is the key,” stresses Busch. “Through this alliance, we can develop AI systems capable of understanding the complexity inherent in engineering and manufacturing.”