Nearly two-thirds of German industrial companies are already using AI in their ongoing production processes. This is shown by a recent survey conducted by networking equipment provider Cisco on the occasion of HANNOVER MESSE 2026, which polled more than 1,000 executives from 19 countries, including around 100 from Germany. This puts German industry at the forefront in Europe. The shift has been rapid: as recently as 2023, AI was not a priority for half of all German companies, according to Bitkom. A study by ZEW Mannheim and KfW Research presented at a press conference in Frankfurt on April 23, 2026, confirms the correlation—the greater the level of digitalization, the more productive the company.

Physical AI Instead of Chatbots

It turns out that for the factory floor, various language models such as ChatGPT, Gemini, or Claude are less relevant than hoped. More decisive is physical AI—meaning robots and self-controlling production systems. This industrial AI is tailored to the requirements of manufacturing and relies on companies’ production data, which is often part of their trade secrets. Language models, on the other hand, are trained using publicly available data.

Germany’s trump card: the excellent historical quality of production data

According to Antonio Krüger, head of the German Research Center for Artificial Intelligence (DFKI), this is precisely where Germany’s trump card lies: the excellent historical quality of production data allows for the training of small, specialized models that perform exceptionally well in industrial applications. This results in a certain competitive advantage over the U.S. Augsburg-based AI researcher Markus Sause also sees Germany as “right at the forefront”—now it comes down to translating the factory data collected daily into new business models.

Use Cases with Measurable Impact

These applications have long been in practice. In predictive maintenance, AI calculates in advance when machines might fail, allowing for timely maintenance—which improves processes and saves energy. In quality assurance, sensors and camera systems detect defects during production and enable immediate corrections, resulting in significant cost savings. Digital twins—data-driven copies of real-world systems—are already widely used; in the future, production facilities are expected to monitor themselves to a large extent.

Generative AI can increase profit margins in mechanical and plant engineering

There are many concrete examples: BMW is testing humanoid robots in production, while Siemens is deploying an engineering agent that independently plans and executes complex technical tasks. According to DFKI researcher Paul Lukowicz, AI could “completely transform processes and entire markets.” The extent of this impact is demonstrated by a study presented at HANNOVER MESSE by VDMA and Strategy&, PwC’s global strategy consulting firm: generative AI can increase profit margins in mechanical and plant engineering by up to 10.7 percentage points.

Routine tasks are eliminated, new professions emerge

This transformation is accompanied by concerns about jobs. According to estimates, around 1.6 million jobs could be affected by AI-driven structural change over the next 15 years—either eliminated or newly created. However, the researchers surveyed do not anticipate mass unemployment, but rather a shift: routine tasks are eliminated, new professions emerge. The key is to shape the transformation in a socially responsible way, for example through retraining, and not to recklessly trade the vast wealth of experience held by employees for AI.

Pick up the pace now – the race is wide open

The starting point is strong—thanks to robust university research, high levels of public investment, and specialized software firms that offer AI solutions locally and with data sovereignty, ensuring sensitive data does not end up on U.S. servers. Yet some companies lack the courage—due to financial constraints or fear of revealing trade secrets. According to Krüger, the biggest shortcoming is the pace of development in infrastructure and ecosystems. If Germany hesitates too long, the US and China could pull ahead. For decision-makers, this means: The data foundation is there, the technology is available, the lead is real—but not permanent. Those who invest now in data infrastructure, expertise, and concrete use cases will secure tomorrow’s competitiveness.