This paper aims to revise Nonaka and Takeuchi’s SECI (Socialization, Externalization, Combination, Internalization) Model in light of recent advancements in Generative Artificial Intelligence (GenAI), proposing an updated framework for enhancing knowledge management (KM) processes. The study conducts a systematic literature review, following the guidelines outlined by Fink (1998), using "Web of Science", "EBSCOhost", and "IEEEXplore". The review identifies 57 relevant articles published since 1995, meeting the inclusion criteria of discussing KM and the SECI Model with specific AI applications in organizational contexts. The search terms used were: SECI Model AND Knowledge Management; SECI Model Challenges; SECI Model Evaluation; SECI Model Limitations; Hypertext Organization AND Knowledge Management; Knowledge Management AND AI; SECI Model AND Knowledge Management AND AI. Articles were categorized into five thematic clusters: Integration of Artificial Intelligence into the Knowledge Management Process, Criticism and Limitations of the SECI Model, Re-evaluation and Extension of the SECI Model by GenAI, Enhancements and Perspectives of the SECI Model, and Theoretical and Practical Implications of the SECI Model in the Context of Hypertext Organization.

While existing research highlights the transformation of individual knowledge into collective organizational knowledge as a crucial factor for innovation and competitiveness of a company, a comprehensive investigation of the influence of GenAI on the SECI Model, especially regarding capturing and transforming tacit knowledge, is lacking. This paper synthesizes the identified literature to discuss the potential impact that the integration of GenAI may have on the SECI Model and the associated implications for KM in organizations. The aim is to show how GenAI could act as a bridge to make tacit knowledge more accessible, thus demonstrating and exploring the potential to facilitate the transformation of tacit knowledge into explicit knowledge with GenAI to support dynamic knowledge sharing in organizations.

Therefore, a use-case scenario is described to illustrate how the phases within the SECI Model could be implemented in a software tool. Furthermore, it examines the transformation within a hypertext organization and shows how GenAI can distribute knowledge dynamically, rather than in a fixed sequence, from the individual to the organizational level. The study discusses how, similar to the previous expansion incorporating the influence of Web 2.0 by Jia et al. (2011), the SECI Model is updated to include GenAI as a vital component, leading to an additional dimension of the SECI Model. This updated model considers the interaction between human knowledge and AI systems, enabling a more efficient and dynamic distribution and utilization of knowledge.

The integration of GenAI into the SECI Model has the potential to improve KM, thereby contributing to the competitiveness of organizations. This study demonstrates that the SECI Model remains a robust framework for understanding knowledge creation in organizations, ensuring its relevance and effectiveness in contemporary KM practices. By using the updated SECI Model, organizations can enhance their knowledge management capabilities, drive innovation, and maintain a competitive advantage in an increasingly digital world.

Keywords: SECI Model, Knowledge Management, Generative Artificial Intelligence, Tacit Knowledge, Explicit Knowledge, Hypertext Organization, Organizational Knowledge, Knowledge Transformation.