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AI-generated SQL can fail even when the query is syntactically correct. One of the most common reasons is simple: > The AI used the wrong metric. A user may ask for “revenue,” “active customers,” “retention,” “margin,” or “customer lifetime value.” These terms sound straightforward, but in enterprise environments they often have multiple definitions, formulas, source tables, filters, and owners. If an AI system does not know which definition is approved, it may choose a field

Enterprise teams do not need to rebuild their entire data stack to make data usable by AI. They usually already have the core systems: - a warehouse or database; - BI tools; - dashboards and reports; - data models; - catalogs or documentation; - access policies; - transformation pipelines; - experienced data teams. The problem is that AI systems often cannot use this existing context reliably. An AI agent may connect to the warehouse and generate SQL, but it may not know whic

AI-generated SQL can make analytics faster. A business user asks a question. An AI agent interprets the request. A query is generated. A result appears in seconds. That speed is valuable, but it creates a new enterprise problem: > Can the team explain how the answer was produced? For internal experiments, a quick answer may be enough. For enterprise analytics, finance, operations, compliance, and executive reporting, the final answer is only part of the story. Teams also need