GenAI-Assisted Knowledge Generation: A Case Study on Human-Machine Collaboration Through the SECI Model

Abstract

This paper analyses the impact of Generative Artificial Intelligence (GenAI) on the traditional phases of knowledge creation theorized Nonaka's SECI model. To the purpose, an exploratory single-case study was conducted using semistructured interviews, direct observation and document analysis within a company operating in the cybersecurity sector and software development. The case company was selected based on its strong innovation orientation, technological culture, and moderate organizational complexity, which are three factors influencing technology adoption in business environments. Interviews were conducted with employees and managers from the R&D and Operations departments, and data were triangulated with secondary sources. Qualitative data were analysed through content analysis methodology, generating an inductive coding tree. The study reveals that GenAI significantly impacts knowledge creation across existing SECI phases. Specifically, while it supports externalization, combination and internalization by facilitating knowledge transformation processes, its impact on socialization presents both opportunities and risks, particularly in the replacement of human interactions. Moreover, results reveal differentiated effects of GenAI across the SECI phases. GenAI enhances externalization, combination, and internalization by supporting the generation of formal templates, code synthesis, report creation and personalized feedback, while its effect on socialization is more ambiguous, raising concerns about critical thinking and the erosion of informal peer learning. These findings suggest that GenAI holds transformative force within knowledge dynamics, offering a unique opportunity to reconsider how human and machine-generated knowledge co-evolve. The paper's novelty and significance reside not only in the analysis of GenAI impact on well-established KM model but also in its capacity to offer organisations interesting insights on effectively integrating it into their workflows.