11. «Industrieforum 2025»

I started my pitch by holding up a notebook and saying: "Imagine all my knowledge and experience were captured here. But unfortunately something like that does not really exist." So much implicit knowledge is lost in organisations when employees leave. That was exactly the problem I wanted to address.

My pitch was part of the 11th "Industrieforum 2025", held under the motto "Technology, People, Culture in Harmony". The forum provided a platform for inspiring exchange between industry leaders and over 25 exhibitors presenting the latest technologies and innovations. The goal of the event was to discuss digital transformation in harmony with human values and explore new ways of working together.

After my one-minute pitch, the audience voted on which talks should be given in the afternoon. I narrowly missed enough votes, so my presentation was not delivered live and is only available as a video. Even so, the pitch was a lot of fun and vividly showed how artificial intelligence can help preserve implicit knowledge and make it actively usable. This idea is central to the future of knowledge management.

Securing Company Knowledge with Artificial Intelligence - A Practical Example

In many companies, the most valuable knowledge is not in manuals or databases but in the minds of long-serving employees. This "implicit expertise" is quickly lost, however, once key people leave the company or move to other departments. The result: other employees suddenly lack the required knowledge, have to rethink the same problems from scratch and must work through time-consuming processes to find solutions. In the long run this means not only a loss of innovative capacity and efficiency, but can also cause considerable costs.

At the 11th Industrieforum 2025 on 4 June 2024 I gave a presentation together with bbv Software Services AG that focused precisely on this problem. Under the title "Securing Company Knowledge with Artificial Intelligence" we showed how the application of modern AI systems, in particular Large Language Models (LLMs), can help to capture, structure and make implicit knowledge accessible to all employees. The central approach is based on the idea of "AI agents", virtual assistants that interact in principle like colleagues: they answer questions, conduct research, consult experts where necessary and summarise newly gained information in a central location.

The Underlying Problem: Implicit Knowledge Gets Lost

Implicit knowledge is hard to grasp because it is not written down but exists "between the lines". An employee who has looked after a particular product or a specific process for years often knows exactly where the pitfalls lie and what measures must be taken to ensure quality. This knowledge is rarely documented. You simply know it from daily practice. When that key person leaves the company, however, this know-how is difficult to reconstruct. Moreover, remaining colleagues are often not familiar with the details of the processes and must reacquire the knowledge through laborious effort. The result: rising costs, longer production and development times and recurring errors.

The AI Approach: Knowledge Management with AI Agents

An important part of the presentation was the demonstration of a use case from food production. The example revolved around the question of how to maintain the freshness of products throughout the entire delivery process. An AI agent can act here as a kind of "virtual employee". It receives the query: "How do we ensure that our products stay fresh from production to delivery to the customer?" If it turns out that the answer is not in its existing data, it forwards the question to a recognised expert. Once their answer is available, the AI agent stores this information in the knowledge database and also creates a glossary entry, in this example for the technical term "Frost Flow". This approach makes it possible not only to pass the knowledge acquired to those immediately involved, but to make it permanently available to all employees.

LLMs and Prompting: How Artificial Intelligence Gains Language Understanding

The technical foundation for AI agents is provided by Large Language Models, trained on vast amounts of data. At their core, these models learn to predict the next word in a sentence. In doing so they develop a remarkable understanding of grammar, sentence structure and even complex relationships. In practice, however, they are only as good as the instructions they are given, the so-called "prompting". Here, for example, one must define from which perspective the model should respond, what information is available to it and what kind of answer is desired (particularly detailed, or short and concise).

In addition to this basic structure, "Retrieval Augmented Generation (RAG)" can be used to integrate company-specific databases. This means the agent draws not only on its internal training but actively accesses current and specific knowledge from within the organisation. This interplay makes it possible for answers to be not only linguistically correct but also to incorporate the concrete context of the company and deliver relevant details.

Multi-Modal Interaction and Tailored Answers

Furthermore, modern AI systems are capable of handling not only text data but also speech, images or other files. For example, the agent could evaluate an uploaded photo or analyse a voice message to derive context-based answers. Also interesting is the possibility of presenting the same information adapted to different roles or departments. While an operations manager might need a precise technical description, an environmental officer may only require a brief summary focused on environmental aspects.

Costs, Effort and Opportunities

Of course, introducing such AI systems involves considerable effort. Companies must first decide whether to train their own model or use an existing model and fine-tune it for their needs. Both options require investment, whether in hardware, AI expertise or high-quality data intended to optimise the model. Nevertheless, early experience shows that these efforts pay off in the long run, particularly when knowledge is thereby secured permanently and duplicate work is reduced.

Especially in times when workforces change rapidly, securing company knowledge is crucial for maintaining competitiveness and a drive for innovation. AI agents that collect, structure and deliver knowledge on demand can make a decisive contribution to this.

Conclusion

With my talk at the 11th Industrieforum 2025, together with bbv Software Services AG, I was able to show that AI agents are not merely a theoretical concept but deliver tangible results in practice. From capturing implicit knowledge through creating glossary entries to delivering context-sensitive answers: modern AI systems offer companies a powerful tool for preserving know-how and making it quickly accessible to new employees. This prevents valuable experiential knowledge from being lost the moment an employee walks out the door.

Artificial intelligence thus offers not only potential for process automation and data analysis, but also opens up an entirely new perspective on knowledge management, a perspective that places people at the centre and ensures that their knowledge and experience are not lost but instead become a central resource from which the entire organisation benefits.