Enterprise AI analytics is hitting a hard limit: connecting LLMs directly to raw databases via text-to-SQL is both structurally unreliable for complex queries and increasingly expensive as schemas and usage scale. This whitepaper presents benchmark evidence that Strategy Mosaic’s semantic layer solves both sides of that problem—accuracy and cost—by giving AI the business context and validated query paths it lacks today. The study benchmarks Strategy Mosaic against direct text‑to‑SQL execution, using a real ACORD‑aligned insurance claims dataset and 17 representative analytics questions. Mosaic is evaluated as a semantic layer sitting between AI and the database; PostgreSQL represents the baseline of querying raw schema via LLM‑generated SQL. 

The results show two things: first, that Mosaic materially improves AI answer quality on complex, multi‑table questions where text‑to‑SQL against raw schemas tends to produce plausible but wrong numbers. Second, that Mosaic and Strategy AI Agents significantly reduce the tokens and cost required to run AI analytics at scale. The paper examines why these differences emerge, focusing on how Mosaic’s encoded business logic and canonical join paths change query behavior, and how its semantic APIs compress the context an LLM must process. Together, the findings demonstrate that comparing AI on raw SQL versus AI on a semantic layer is not a minor implementation choice; it is an architectural fork with measurable implications for accuracy and cost. For organizations evaluating AI analytics infrastructure, the conclusion is direct: a semantic layer is now a prerequisite for trustworthy, scalable AI on enterprise data.