This master thesis explored on a conceptual basis whether a Large Language Model (LLM)-supported knowledge capture tool allows senior business consultants to document and externalize their tacit knowledge in a way that makes it accessible and useful for junior business consultants. The central research question explores how an LLM-supported knowledge capture tool can be designed to encourage senior business consultants to document their tacit knowledge in order to facilitate its transformation into explicit knowledge that is accessible and applicable to junior business consultants.

Using a design science research methodology, the thesis follows an iterative process involving literature analysis, expert interviews, and progressive refinement of the artefact.

While the tool itself was not implemented technically, its structure, usability, and perceived value are evaluated through feedback from senior and junior business consultants. Hence, this study contributes to the growing field of AI-supported knowledge management by offering a user-centered concept for facilitating human-AI collaboration in knowledge-intensive environments. It also lays the groundwork for future studies aiming to implement and evaluate such systems in real-world consulting practices.