AI Knowledge Architecture for Enterprise Knowledge Management

Retrieval-augmented generation improves access to enterprise information but typically leaves query-time semantic interpretations dependent on each model invocation. This conceptual study applies AI Knowledge Architecture (AIKA) to enterprise knowledge management through two persistence responsibilities. First, semantic distinctions that have been explicitly established and accepted are externalized as Attributes. Second, accepted relationships among semantic units are externalized as persistent Relations rather than repeatedly inferred. Claims identify semantic units, while Qualifiers, Grounding, Viewpoints, and derived Domains make the resulting knowledge state bounded, inspectable, and reusable. An enterprise case demonstrates how this architecture converts transient interpretation into governed organizational knowledge across multiple functions. The paper defines a semantic unit as a meaningful assertion available for inference.