<![CDATA[Enterprise deployments of large language model (LLM)-powered natural language-to-SQL interfaces continue to exhibit a critical failure pattern that conventional error handling cannot adequately detect: systems generate syntactically valid SQL against operational databases while producing answers that misrepresent user intent. This study examines the semantic layer as an architectural mechanism for addressing the gap between organizational business meaning and the structural representation of enterprise data. The analysis identifies four principal failure modes in schema-only natural language-to-SQL architectures: schema hallucination, terminology ambiguity, omission of implicit business logic, and metric definition drift. These failure modes are examined in relation to four corresponding components of the semantic layer: business glossaries, metric registries, entity resolution pipelines, and contextual disambiguation frameworks. Based on this mapping, a four-layer integration architecture is proposed in which the semantic layer serves as a governance-critical interface among natural-language input, enterprise data structures, and LLM inference. The proposed architecture establishes explicit representations of business terminology, metric definitions, entity relationships, and contextual interpretation before query generation, thereby reducing dependence on direct model interpretation of raw database schemas. The analysis further shows that the effectiveness of the semantic layer depends not only on technical integration but also on organizational governance mechanisms. Defined ownership, data stewardship, standardized semantic definitions, change-control procedures, and continuous drift detection are required to maintain semantic consistency as enterprise data and business rules evolve. The study concludes that reliable enterprise natural language-to-SQL systems require semantic governance to complement LLM capabilities, because model fine-tuning and additional tooling cannot independently resolve inconsistencies between organizational meaning and database structure]]>
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