Navigating the AI revolution: challenges and opportunities for integrating emerging technologies into knowledge management systems. Systematic literature review

Abstract

Introduction: Artificial intelligence (AI) is transforming organizational knowledge management (KM) by leveraging techniques such as machine learning, neural networks, and fuzzy logic to enhance knowledge discovery, capture, storage, and sharing. While this shift promises improved efficiency and personalization, it also poses challenges related to data quality, employee resistance, and alignment with existing workflows. Methods: This study presents a systematic literature review (SLR) of 40 peer-reviewed publications focused on the integration of AI in KM. The review follows PRISMA guidelines and includes thematic coding to identify patterns, critical success factors, and knowledge gaps. Results: Findings indicate that successful AI-enabled KM depends on strong leadership commitment, adaptable governance structures, and context-sensitive technology selection. AI’s role is evolving from supporting routine tasks to enabling dynamic, real-time knowledge flows. The review also highlights a critical need to balance automation with human oversight. Discussion: Key gaps were identified in understanding cost–benefit trade-offs, ethical implications, and governance mechanisms. These insights suggest directions for future research focused on practical, accountable, and empirically validated KM strategies. As part of an ongoing research project, the synthesized findings will inform the design of future empirical studies. The evidence suggests that, when strategically implemented, AI can serve as a competitive enabler in knowledge-driven organizations.