bbv KI Webinar - Knowledge Management

On 3 January 2025 bbv held the second episode of its webinar series on artificial intelligence. The event was moderated by Stefan Herberling and Alan Ettlin, who together with me offered in-depth insights into modern knowledge management using AI agents. The goal of the webinar was to look beyond the current hype about artificial intelligence and to illuminate the topic from both a business and a technical perspective.

Knowledge Management in Focus

At the start of the webinar I introduced the DIKW pyramid, a fundamental structure running from data through information and knowledge to wisdom. This pyramid serves as a theoretical framework for understanding the different levels of knowledge management and designing them effectively.

"Knowledge management is not just the administration of databases; it encompasses processes, methods and practices for the shared use and management of information."

This statement underscored the importance of efficient knowledge management as a decisive success factor for achieving organisational goals. I explained that knowledge management goes far beyond the simple administration of data. It encompasses the shared use and management of information that is necessary to achieve organisational objectives. This is not only about technical systems but also about the processes and practices that ensure knowledge is effectively shared and used.

Definition and Context of Knowledge Management

Knowledge management is a complex field that encompasses both strategic and operational aspects. In my presentation I emphasised that it is not merely about storing and managing data but rather about fostering a culture of knowledge sharing and continuous improvement. "In modern organisations we have increasingly become knowledge-based enterprises. Effective knowledge management is therefore indispensable for remaining competitive and driving innovation."

I explained further that a central goal of knowledge management is not only to collect knowledge but also to make it accessible and usable. This requires a combination of technological solutions and organisational measures that ensure knowledge is effectively shared and used. Both explicit knowledge, which can easily be documented and shared, and implicit knowledge, which often resides in employees' heads, play an important role.

Live Demonstration: AI Agents in Action

The highlight of the webinar was the live demo in which I showed how AI agents can proactively support knowledge management. Using a fictitious company called "Eisbeit" operating in food production, I demonstrated how new employees can quickly access company-specific knowledge through interaction with AI agents.

"Examining information from different perspectives and thereby generating wisdom," I explained during the demo, which significantly increased the efficiency and accuracy of knowledge transfer.

In the demonstration it became clear how AI agents not only make explicit knowledge accessible but can also actively promote and integrate implicit knowledge. For example, a new employee could obtain information about internal processes and specific company terms through a simple query to the AI agent, without needing to complete extensive training. The AI agents were able to bring together data from different departments, contextualise it and put it into usable form. This means knowledge is not only centralised but also provided dynamically and interactively.

Application Examples from Practice

During the demo I showed concrete examples of how AI agents can be used in a company like "Eisbeit". One example was the management of company-specific terms such as "Frostflow". Through the integration of a glossary, the AI agent could automatically recognise and explain these terms, which is of great benefit especially for new employees. This significantly reduced onboarding time and promoted efficiency in the workplace.

A further example was the monitoring and analysis of production data. The AI agent could monitor temperature data in real time and immediately raise an alarm on deviations in order to ensure product quality. This impressively showed how AI agents can not only respond passively to requests but actively contribute to improving business processes.

Future Perspectives and Roadmap

After the live demonstration I presented a comprehensive roadmap for implementing AI agents in knowledge management. The first step is to start with simple language models like Fast GPT and Smart GPT. These models form the foundation and enable rapid introduction without immediate adaptations.

Building on this, the integration of proprietary knowledge databases should follow to provide internal company knowledge. In the third step, proactive agents could be introduced that actively ask follow-up questions and continuously expand the knowledge database. This means AI agents not only respond to requests but can also proactively gather information and expand knowledge within the organisation.

Finally, additional information channels could be monitored and the agents individually tailored to employee preferences. This includes adapting communication styles and integrating personal working habits to make interaction with the AI agents as effective as possible.

"There are many ways we can further improve these agents," I summarised, emphasising the flexibility and scalability of the proposed solutions.

Long-Term Visions

The roadmap closes with long-term visions in which AI agents function as an integral part of the organisational structure. This includes the continuous further development of agents through machine learning and the integration of new technologies in order to always remain at the cutting edge of development. The goal is to create an intelligent and adaptive knowledge management infrastructure that flexibly adapts to the changing needs of the organisation.

Interactive Q&A

In the subsequent Q&A section numerous interesting questions from the audience were addressed. Topics at the centre included automated creation of glossaries, the four-eyes principle for expert knowledge, security and data protection when integrating ERP systems, and the lifecycle management of content.

On the question of how to avoid incorrect knowledge I explained:

"Algorithms can be used to detect contradictions in the data and take appropriate measures."

I emphasised the importance of automated processes combined with human verification to ensure the quality and reliability of knowledge management. Further questions addressed the integration of data from ERP systems and the possibility of developing AI agents to automatically anonymise sensitive data. Particularly noteworthy was the discussion on data security and data protection, where I explained how important it is to choose trustworthy providers and establish clear guidelines for handling sensitive information.

Challenges and Approaches

A central topic was the integration of AI agents into existing IT infrastructures. It was emphasised that careful planning and step-by-step implementation are necessary to ensure compatibility with existing systems. I explained various approaches to data integration and underscored the importance of interfaces that enable seamless communication between AI agents and existing IT systems.

Another important topic was the scalability of AI solutions. I explained that scalability plays a central role in meeting the rising requirements of a growing organisation. Through the use of modular architectures and flexible cloud solutions, organisations can ensure that their AI agents grow with the organisation and adapt to changing requirements.