Knowledge management was a central component of professional customer service long before it was integrated with artificial intelligence. It ensured efficient processes, guaranteed a uniform external image and prevented redundancies. It also ensured simple and smooth onboarding. Those who had their knowledge management under control had an advantage when it came to efficient processes, customer satisfaction and employee satisfaction. But since artificial intelligence has been integrated into traditional systems, knowledge management has taken on a new importance and become a game changer. To understand how far-reaching the new application possibilities of modern knowledge management systems are, we need to take a brief look at the topic.
Knowledge management is the collection, organization, distribution, application and preservation of knowledge in order to support the goals of a person or an organization. Knowledge management is therefore by no means exclusively concerned with the pure administration of knowledge, but rather with the active control and structuring of existing and yet-to-be-established knowledge. The overarching goal of using knowledge management systems is to optimally capture, organize, distribute, apply and preserve the knowledge of a person or organization in order to create a sustainable competitive advantage.
In order to approach the concept of knowledge management, the distinction between strategic and operational knowledge management is fundamental. Operational knowledge management enables employees to make decisions on the basis of existing data and is usually the focus of interest in knowledge management. Strategic knowledge management, on the other hand, is based on set goals and prepares the database with regard to these goals so that they can be realized (more) effectively.
Both forms of knowledge management comprise five sub-areas.
The process of knowledge capture involves collecting and documenting knowledge. A distinction is made between explicit and implicit knowledge: Explicit knowledge relates to objectively available information, such as manuals, documents, procedural instructions, rules and regulations, etc. Implicit knowledge is linked to personal experience and is therefore subjective in nature. In contrast to explicit knowledge, it must first be transformed into an objective state in order to be available and shareable.
The process of knowledge organization concerns the structuring and systematization of collected knowledge so that it is easily accessible and applicable. This includes categorizing, indexing and storing information in a way that enables easy retrieval.
Successful knowledge sharing requires a culture of knowledge sharing and transparency in which employees proactively share their knowledge and learn from the experiences of others. This can take place through formal mechanisms such as workshops, meetings, collaboration tools or informally through discussions and networking. Depending on the current corporate culture, a cultural change is necessary.
The process of knowledge application concerns the use of existing knowledge to improve business processes, decision-making and innovation. This involves the practical application of knowledge to increase operational efficiency, solve problems and identify new opportunities.
The process of knowledge retention ensures that relevant knowledge is retained within the organization, especially in the face of employee turnover.
This includes the storage of knowledge in databases, the documentation of best practices and experiences and the maintenance of a continuous knowledge base.
Modern knowledge management systems integrate technological solutions such as databases, intranet, collaboration platforms and artificial intelligence to increase the efficiency and effectiveness of the above-mentioned processes.
This enables the efficient use of AI-based language models. The risks of misinformation arising from generative AI are minimized by context control mechanisms. The focus is also on protecting customer data to ensure secure use.
The AI provides support through structured analysis as early as the integration of the system and the associated initial effort of processing knowledge. It creates summaries of longer texts and ensures that the latest version is always accessed.
Support for editors
In day-to-day use, AI provides support at various levels and reduces the workload for editors by identifying similar content to avoid redundancies and suggesting categories and tags to ensure intelligent findability.
With the help of so-called Large Language Models (LLM), editors can prepare knowledge optimally and with minimal effort, for example through automatic translation, quick instructions and their own prompts.
Chatbot and contact form
Bots based on LLMs can not only search through the knowledge database, but also present the available knowledge and the conclusions derived from it to the user in verbal form. The same applies to the contact form, which, provided a functional LLM is used, is actually transformed into a chatbot. This is because the customer no longer has to contact the service employee via the form but receives the answer directly without any time delay. This increases customer satisfaction and at the same time reduces the volume of contacts in the service center.
E-mail traffic
The benefits of LLMs when replying to emails are also obvious. These can be read by the AI in the shortest possible time in order to then formulate an answer as a suggestion for the agent based on the knowledge content.
The current state of AI-based knowledge management systems offers everything needed to establish both operational and strategic knowledge management in a targeted manner. This ensures satisfied employees, satisfied customers and efficient processes, „because knowledge is the only resource that increases with use“ (Probst, 1997).
Tabea Henrich – Senior Consultant
junokai