P089: Knowledge graphs for enterprise AI grounding: Google Cloud study

Abstract

Knowledge graphs for enterprise AI grounding: Google Cloud study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Enterprise generative AI becomes dependable only when retrieval, tools, identity, policy, provenance, evaluation, and human oversight are engineered as a system around the model. Against this backdrop, the paper examines knowledge graphs for enterprise AI grounding in Google Cloud. It asks a focused question: How should knowledge graphs for enterprise AI grounding be designed, governed, and empirically evaluated for Google Cloud? The study narrows knowledge graphs for enterprise AI grounding to a small set of observable concerns rather than treating the topic as an umbrella term. The analysis identifies the design decisions that can be tested in an implementation, the assumptions that must be documented, and the failure modes that would invalidate an otherwise attractive architecture. This makes the research question concrete enough to support engineering evidence rather than opinion. Rather than declaring a winner, this study offers a repeatable way to reason about the problem. The method—comparative architecture analysis—is used to identify comparable responsibilities, likely trade-offs, and the evidence needed for validation. That distinction matters because managed cloud services change quickly, and a strong paper should make clear which statements come from documentation and which come from observed measurements. Architectural Research Scope Research Domain / Theme: Generative AI & Agentic Systems Architectural Scope: Google Cloud Core Research Question: How should knowledge graphs for enterprise ai grounding be designed, governed, and empirically evaluated for Google Cloud? Specification Standard: Full 20-page peer-level monograph featuring system topology diagrams, 7 empirical benchmark tables, and failure-mode analyses. Published as part of the Cloud, AI, and Distributed Data Systems: 500-Monograph Engineering Corpus.