Artificial intelligence in knowledge management: Identifying and addressing the key implementation challenges

In today's digital landscape, Knowledge Management (KM) is crucial for organisational competitiveness. Artificial Intelligence (AI) offers transformative potential for KM practices, yet its integration presents multifaceted challenges. This study addresses significant gaps in the literature by identifying and prioritising critical challenges associated with AI integration in KM. Employing a tripartite methodological approach, this research combines a literature review on KM and AI’s challenges, a Delphi study with domain experts, and confirmatory factor analysis (CFA) across four KM processes. Data from retail sector professionals validate the challenges identified by experts. Findings reveal a comprehensive landscape of challenges, categorised into technological, organisational, and ethical domains, with variations across different KM processes. The study contributes to the field by comprehensively exploring AI-related challenges in KM, offering a quantitative ranking, and enhancing understanding of the AI-KM interplay. This research provides valuable insights for business leaders, facilitating the development of strategies to foster robust knowledge ecosystems. By addressing these challenges proactively, organisations can enhance their KM practices, leveraging AI to maintain competitiveness in an increasingly digital business environment. The study contributes to theoretical discourse and offers practical implications for organisations navigating AI integration in their KM practices. • AI integration in KM entails three key challenges: technological, organisational, and ethical. • Ethical considerations are paramount in AI's integration into KM frameworks. • Job security and privacy are foremost among the challenges in all KM models. • Concerns about knowledge storage overshadow other challenges in KM. • Security and privacy are prominent technological challenges in all KM models.