Tacit Knowledge Management with Generative AI: Proposal of the GenAI SECI Model
This work addresses the problem of fragmented knowledge management for organizations using AI, but it is incremental as it updates an existing model.
The paper tackles the insufficient integration of tacit and explicit knowledge management with generative AI by proposing the 'GenAI SECI' model, which introduces 'Digital Fragmented Knowledge' and includes a concrete system architecture and comparison with prior models.
The emergence of generative AI is bringing about a significant transformation in knowledge management. Generative AI has the potential to address the limitations of conventional knowledge management systems, and it is increasingly being deployed in real-world settings with promising results. Related research is also expanding rapidly. However, much of this work focuses on research and practice related to the management of explicit knowledge. While fragmentary efforts have been made regarding the management of tacit knowledge using generative AI, the modeling and systematization that handle both tacit and explicit knowledge in an integrated manner remain insufficient. In this paper, we propose the "GenAI SECI" model as an updated version of the knowledge creation process (SECI) model, redesigned to leverage the capabilities of generative AI. A defining feature of the "GenAI SECI" model is the introduction of "Digital Fragmented Knowledge", a new concept that integrates explicit and tacit knowledge within cyberspace. Furthermore, a concrete system architecture for the proposed model is presented, along with a comparison with prior research models that share a similar problem awareness and objectives.