The easy part is connecting an AI tool to a database. The hard part is explaining, six weeks later, which tables it can see, what credentials it uses, where the queries are logged, and who owns the access model. That is where most AI database demos quietly become production risks. The mistake Teams ask: Can we connect AI to the database? That question is too broad. A better question is: Which team needs which answers from which tables, and what should the AI never be able to do? A customer success usage-drop workflow, a finance revenue workflow, and an engineering incident workflow should not all have the same database scope.…