Skilled labor shortages, loss of knowledge, and an information overload: The event demonstrated how companies can take a pragmatic approach to getting started with knowledge management—and use AI to efficiently preserve and leverage knowledge.
Many companies in the mechanical and plant engineering sector are currently facing similar challenges: a shortage of skilled workers, an aging workforce, and a steadily growing flood of information. At the same time, valuable experiential knowledge often remains in the minds of employees and is inadequately documented. Added to this are heterogeneous system landscapes and isolated data silos, which make it difficult to access relevant knowledge. Without structured knowledge management, these issues can hardly be resolved in a sustainable manner—yet for many companies, getting started seems complex and resource-intensive. This is precisely where the ProduktionNRW event on June 16, 2026, came in, offering a practical demonstration of how to successfully get started.
The Knowledge Database
Thomas Riegler, a speaker at VDMA Software & Digitalization, highlighted the fundamentals of pragmatic knowledge management. It became clear that increasing product complexity, individual customer requirements, and growing competitive pressure are placing new demands on how knowledge is managed. At the same time, the “employee” resource is becoming scarcer, making the systematic preservation and utilization of knowledge even more important.
A key challenge lies in the multitude of distributed data sources—ranging from ERP and CRM systems to technical documentation, unstructured information, and tacit knowledge stored in employees’ minds. As a result, employees spend a great deal of time searching for information, while existing knowledge is not utilized efficiently.
Against this backdrop, the importance of systematic knowledge management was emphasized, particularly in light of demographic change. Methods such as structured interviews, expert discussions, and storytelling help to systematically capture critical experiential knowledge. At the same time, a concrete approach for building a knowledge database was outlined—from raising awareness among management, through analyzing the current state and defining goals, to pilot projects and rollout. Key factors for success include, above all, the involvement of relevant user groups, clear rules for structure and storage, and continuous maintenance of the content. Looking ahead, knowledge management will also be increasingly supported by artificial intelligence, for example in troubleshooting or through assistance systems in customer service.
Knowledge Management Reimagined: How AI Simplifies Knowledge Modeling in the Enterprise
Dr. Achim Steinacker, Principal Consultant at Empolis Information Management GmbH, then demonstrated how knowledge management can be reimagined through the use of artificial intelligence. The starting point is the observation that many companies are already attempting to introduce self-service offerings or AI applications—but these efforts often fail due to insufficient data quality and poorly organized knowledge.
The central approach, therefore, lies not only in improving access to knowledge but, above all, in its structured generation and processing. The focus is on the intelligent extraction of knowledge from various sources such as support tickets, emails, or technical documentation.
AI-based solutions make it possible to automatically identify and process this previously unstructured knowledge and convert it into a consistent knowledge base. For example, knowledge articles can be created automatically, service processes can be supported by structured checklists, or relationships can be mapped in so-called knowledge graphs. This makes knowledge documentation significantly more efficient and allows it to be seamlessly integrated into existing work processes.
Exchange of Experiences and Discussion
In the concluding discussion, it became clear that knowledge management is not a one-time project but an ongoing process. While traditional approaches focus primarily on structures, processes, and organization, the targeted use of artificial intelligence opens up new possibilities for automatically capturing knowledge, keeping it up to date, and making it available as needed.
However, the quality of the underlying data and its consistent integration into daily workflows remain crucial. The event made it clear that companies can both take a pragmatic first step and benefit in the long term from systematic and increasingly AI-supported knowledge management.
Organizer
The event is organized by ProduktionNRW and the Northern Regional Association. ProduktionNRW is the cluster for mechanical engineering and production technology in North Rhine-Westphalia and is operated by VDMA NRW. ProduktionNRW sees itself as a platform for connecting, informing, and promoting companies, institutions, and networks with one another and along the value chain. Significant portions of the services provided by ProduktionNRW are funded by the Ministry of Economic Affairs, Industry, Climate Protection, and Energy of the State of North Rhine-Westphalia.