The "Dashboard" is a failure of imagination. We build them because we don't know what questions people will ask, so we give them every possible answer and hope they can find it. The result? "Dashboard Fatigue." Executives ignore the beautiful Tableau charts and just email the Data Analyst: "Hey, can you pull the sales numbers for France?"
Text-to-SQL: The Holy Grail
Generative BI breaks this cycle. It allows a user to ask a question in plain English, and the AI translates it into SQL (Structure Query Language) to query the database directly.
User: "Show me the churn rate breakdown by region for Q3, compared to Q2, excluding enterprise accounts."
AI (SQL): `SELECT region, churn_rate FROM customers WHERE ...`
Result: A generated bar chart showing exactly that data.
The "Semantic Layer"
The challenge is making the AI understand the business logic. What is "Churn"? Is it voluntary or involuntary? Does "Revenue" include tax?
This requires a "Semantic Layer"—a dictionary that defines these metrics. Once this is set up, the AI becomes a trustworthy analyst. It democratizes data. Marketing managers don't need to know SQL. HR directors don't need Excel pivot tables. They just need to know how to ask a question.
From "What" to "Why"
Standard BI tells you "Sales are down 5%." Generative BI can tell you why. "Sales are down 5% because the 'Enterprise' segment in Germany had 3 large cancellations due to the new pricing model." That is insight, not just data.