The rapid advancement of artificial intelligence (AI) is catalyzing a profound transformation in how individuals, organizations, and societies create, disseminate, and apply knowledge. We argue that the accelerated evolution of AI applications necessitates a critical reexamination of core concepts, definitions, and methodologies traditionally considered foundational to the field of knowledge management (KM). Established models such as the data, information, knowledge, wisdom (DIKW) hierarchy, the socialization, externalization, combination, internalization (SECI) model, and the people, process, and technology (PPT) framework have long guided KM research and practice. However, the integration of AI technologies challenges these foundational constructs, calling for their reassessment and adaptation. This chapter aims to bridge the divide between AI’s rapidly advancing capabilities and KM’s enduring conceptual principles. By examining the intersections of AI and KM, it investigates how generative AI tools, particularly large language models (LLMs), are reshaping fundamental KM elements, including key processes like knowledge acquisition, documentation, sharing, and application.