A Conceptual Framework for the Role of Large Language Models in Knowledge Management

Large language models are reshaping knowledge management by mediating how organizations create, store, share, and use knowledge. This paper proposes the LLM-KM Interaction Matrix (LKIM), a human-centred framework that aligns the KM cycle with specific LLM capabilities and with transparent human-machine roles. The model blends three layers: a foundation of governance, data quality, ethics, and skills; a capability layer featuring retrieval-augmented generation, summarization, adaptation, and conversational access; and a lifecycle layer that links these capabilities to creation, storage, sharing, and utilization. To preserve trust and accountability, the framework joins SECI with the GRAI extension, which distinguishes human and machine participation without surrendering human judgment. It also integrates knowledge graphs to strengthen provenance, and promotes RAG to ground outputs in verifiable sources. We synthesize recent research and practice evidence, identify adoption risks and benefits, and derive design rules for enterprise deployment. The paper closes with implications for theory, practice, and policy, and with a research agenda on evaluation, governance, and collaboration that can promising pilots into reliable KM at scale. Results aim to guide leaders toward effective and measurable adoption.

Keywords:

Knowledge Management , Large Language Models , Retrieval Augmented Generation , Knowledge Graphs , Human in the Loop Governance