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AI can generate SQL that looks correct. The syntax may be valid. The query may run. The result may even look reasonable in a dashboard. But in enterprise analytics, a query can be technically correct and still be wrong. One of the most common reasons is the join path. AI-generated SQL often fails not because the model cannot write SQL, but because it does not know how the data should be connected. --- ## The Problem Is Not Just “Can These Tables Join?” When an AI system sees

AI-generated SQL often fails for a simple reason: the system does not know which data relationships it can trust. A model may find two columns with similar names and generate a join. The query may run. The result may look reasonable. But if the relationship is weak, outdated, incomplete, or valid only for a different business use case, the answer can be wrong in a way that is hard to detect. This is why enterprise AI workflows need more than a list of tables and columns. They

# Why Foreign Keys Are Not Enough for Enterprise Join Discovery Foreign keys are useful. They tell systems how tables are supposed to connect. But in many enterprise environments, they are only a small part of the relationship story. Modern data stacks contain operational databases, warehouses, data marts, BI models, dbt projects, ETL pipelines, spreadsheets, third-party systems, and years of analyst-written SQL. The relationships that matter to business reporting and AI-gene

# Metadata-Only AI Data Readiness: What It Means and What It Does Not Mean Enterprise teams want AI to answer business questions, generate SQL, assist analysts, and help data teams move faster. But before AI systems can safely query enterprise data, they need context. They need to understand business definitions, table relationships, join paths, access rules, and lineage. Without that context, an AI system may generate SQL that looks correct but uses the wrong metric, the wro