This study explores the transformative potential and inherent challenges of Generative AI in the domain of knowledge creation and management, using the Socialization, Externalization, Combination, and Internalization (SECI) model as an analytical framework. Our qualitative research, based on content analysis from expert opinions, reveals that the integration of Generative AI in knowledge processes is inevitable and offers substantial productivity enhancements. These include providing diverse expression channels, simulating personalized interactions, and facilitating cross-disciplinary communication. However, significant risks accompany these benefits, such as threats to data security, personal privacy, and intellectual property, as well as issues of misinformation, data bias, and reduced human cognitive engagement. The findings extend the SECI model by highlighting specific challenges posed by AI technologies at each knowledge creation stage: socialization, externalization, combination, and internalization. The study underscores the necessity of a balanced approach, integrating technological, ethical, and socio-cultural perspectives to evaluate AI’s impact comprehensively. Our research contributes to the theoretical understanding of AI’s role in knowledge management and offers actionable strategies for its ethical and effective implementation, emphasizing the importance of interdisciplinary approaches and continuous regulatory adaptation.