Databases

Database. That is where many enterprise AI demos quietly go to die. The user asks one clean natural-language question: “How many customers are in California?” The AI assistant smiles politely, searches something, finds a table that looks relevant, and returns a confident answer. The problem is not that the model cannot understand English. The problem is that five internal databases may all contain customers, states, locations, stores, loans, accounts, or sales regions. Some can answer the question. Some can almost answer it. Some merely smell like they can answer it. ...

A data analyst asks a database a question. An AI agent interrogates it. That distinction sounds theatrical until the query logs arrive. The human analyst usually knows roughly where to look, asks a small number of targeted questions, waits for answers, adjusts, and eventually presents a result. The agent is less graceful. It checks schemas, samples columns, guesses joins, inspects distinct values, tries partial SQL, abandons it, starts again, validates, retries, and occasionally recruits more agents to repeat the exercise in parallel. It is not being stupid. It is compensating for a missing sense of the underlying data. ...