Knowledge management in the age of generative artificial intelligence – from SECI to GRAI

Abstract

Purpose – Generative Artificial Intelligence (GenAI) models are now able not only to recognize complex patterns from large amounts of input data but also to display them in context. This fact invites a critical analysis of the SECI model and its further applicability as an analytical framework for knowledge generation and transfer in organizations. This conceptual paper aims to take the SECI model with the individual SECI phases and analyze how GenAI changes the assumptions and descriptions of the original SECI framework. More specifically, the aim is to propose a revised SECI framework. Design/methodology/approach – This paper aims to contribute to theory development of theories present in the literature. More specifically, it seeks to make a conceptual contribution that draws on one of the four types of conceptual contributions proposed by Deborah J. MacInnis, namely, envisioning, and is based on previous literature and the authors’ thoughts and experiences to propose a revised SECI framework called GRAI, which stands for Generative Receptive Artificial Intelligence. Findings – A better understanding of the further applicability of the SECI framework that arises with the introduction and application of GenAI models is not only relevant to the existing knowledge management (KM) theory but also to organizations. The proposed revised perspective of the SECI model, summarized in the GRAI framework, reflects the use of GenAI technologies in the corporate environment and thus allows the necessary stimulation of a discussion on how KM in general, and knowledge generation, in particular, will be affected and augmented by AI. Originality/value – To the authors’ knowledge, this paper is the first to systematically and comprehensively examine the established SECI framework and its wider applicability in terms of the potential impact of GenAI models on KM practices in organizations. The proposed GRAI framework is seen as a relevant contribution to the further development of KM theory.