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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

AI agents can answer data questions quickly. But speed does not guarantee consistency. When an agent translates a business question into SQL, it must decide which metric to use, which formula is approved, which dimensions are allowed, which filters apply, and whether the user is allowed to access the result. If those rules are not available at query time, the agent may invent logic from table names, column names, or examples it has seen before. That can produce SQL that runs

## What Semantic Definitions Solve A semantic definition gives business meaning to data. For example, a company may define **customer lifetime value** as: > Total recognized revenue from a customer over a defined period, minus eligible refunds and credits, grouped by the approved customer region. This definition may include: - metric name; - business description; - approved formula; - source fields; - allowed dimensions; - default filters; - aggregation rules; - owner; - vers