Data Governance Practices for Generative AI Powered Organizational Knowledge Management Systems Using Retrieval Augmented Generation

This study examines how data governance supports the success of generative AI-based Knowledge Management Systems (KMS) using Retrieval-Augmented Generation (RAG) in large enterprises. Drawing on a multi-case study methodology, the research identifies 17 distinct data governance practices and synthesises them into a conceptual framework that theorises their contribution to KMS success. The adoption of these practices is shaped by the dynamically evolving technological affordances of generative AI and RAG, as well as the contextual challenges posed by the predominantly ingested semi-structured and unstructured textual data. While the identified practices enable value-add, they also introduce strategic trade-offs, particularly in balancing data protection and expected benefits. This study contributes to the evolving discourse on data governance by extending its scope beyond structured data and highlighting its dynamic, context-sensitive role in AI-enabled KMS.