A recent report by the business magazine ARD PlusMinus shows that while the use of artificial intelligence in German industry is on the rise, its widespread adoption still lags behind the available potential. Key challenges cited include access to suitable data, the need for qualified specialists, and structural barriers within companies. This observation points to a pivotal phase in technological development: the transition from the availability of powerful AI methods to their systematic integration into industrial value-added processes.
From the perspective of AI research, this shifts the focus of the debate
The fundamental methods—such as those in the fields of machine learning, data-driven process optimization, or automated quality control—are well-developed and have been tested in many areas of application. The scientific expertise and necessary technical knowledge are also generally available, according to Prof. Dr. Katharina Morik, founding director of the Lamarr Institute for Machine Learning and Artificial Intelligence. What matters now, she says, is how consistently companies integrate AI into their processes. This applies in particular to the integration of AI into existing system landscapes, the availability and quality of data throughout the entire process chain, and the adaptation of organizational structures. AI applications generally do not deliver their benefits in isolation, but rather through the interaction of multiple process steps, such as when data from production, logistics, and quality assurance are linked and evaluated together.
Accordingly, the use of AI requires not only technological solutions but also investments in data infrastructures, process adjustments, and interdisciplinary collaboration. The competitiveness of Germany as an industrial location depends significantly on how successfully existing AI technologies can be transferred to a broad range of industrial applications and effectively and sustainably integrated into existing value-added systems.