Tacit Knowledge Management with Generative AI: Proposal of the GenAI SECI Model

TL;DR

This paper introduces the GenAI SECI model, an innovative framework that leverages generative AI to manage both tacit and explicit knowledge through digital fragmented knowledge, enhancing knowledge creation processes.

Contribution

It proposes the GenAI SECI model, integrating tacit and explicit knowledge management with generative AI, and introduces the concept of digital fragmented knowledge within cyberspace.

Findings

Introduction of the GenAI SECI model for knowledge management

Concept of digital fragmented knowledge combining tacit and explicit knowledge

Comparison with existing models demonstrating improved integration

Abstract

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.