Bug or feature? Investigating the impact of generative AI on knowledge creation in software engineering

Purpose This paper aims to investigate how the adoption and use of generative artificial intelligence (GenAI) impact organizational knowledge creation. Specifically, it provides empirical evidence on the effect of GenAI tools on knowledge socialization, externalization, internalization and combination processes. Design/methodology/approach This study uses an exploratory research approach, utilizing semistructured interviews with 22 software engineers who regularly use GenAI tools to create new knowledge on a daily basis. Using the SECI model as a theoretical lens, a thematic analysis of the transcribed interviews is performed. Findings This study contributes to the growing yet limited body of empirical research on the impact of GenAI on KM, focusing especially on the knowledge-creation process. The application of the SECI model as a theoretical framework provides valuable and novel insights into the complex interplay between GenAI technologies and the dynamics of organizational knowledge creation. Building on these insights, this paper introduces a GenAI-impacted SECI model that illustrates how GenAI reshapes each mode of knowledge conversion, highlighting both its enabling and constraining effects. Research limitations/implications This study is subject to the common limitations of generalizability inherent to all qualitative research. Practical implications The study’s findings provide valuable insights and recommendations for managers seeking to integrate GenAI effectively within their organization’s knowledge creation processes. Originality/value To the best of the authors’ knowledge, this research is among the first empirical investigations addressing the interrelationships between GenAI and knowledge creation through the lens of the SECI model. This approach enables the identification and in-depth discussion of issues that have been largely unexplored in the existing literature. By proposing the GenAI-impacted SECI model, this study advances the theoretical understanding of how AI-mediated processes transform the social, cognitive and procedural dimensions of organizational knowledge creation. Furthermore, it provides insight for future research on the intersection of GenAI systems and knowledge workers.