Your team builds an AI agent. It connects to your data warehouse. A product manager types "What was revenue last quarter?" and gets a number. The number is wrong. Nobody knows it's wrong until Finance runs the same query manually and gets a different result.
This happens constantly. And the problem isn't the AI model. It's the missing layer between the model and your data.
The Promise vs. the Reality
Natural language analytics is the most requested feature in every data platform survey. Business users want to ask questions in plain English and get accurate answers. No SQL. No tickets. No waiting.
The technology exists. Large language models can generate SQL from natural language with impressive accuracy. But accuracy on syntax isn't accuracy on meaning. An LLM can write grammatically correct SQL that returns the wrong answer because it doesn't understand your business definitions.
