Business users want faster answers from enterprise data, but most self-service analytics still depends on dashboards, SQL requests, or long back-and-forth cycles with data teams. Conversational BI changes that experience, but only if the answers are trusted, governed, and grounded in the right business context.

This session shows how to design a practical conversational BI experience using Azure Databricks AI/BI Genie. We will walk through how to prepare curated datasets, define business-friendly metadata, configure a Genie space, guide users toward reliable questions, and generate accurate answers, summaries, and visualizations from governed enterprise data.

The session will focus on what actually makes conversational BI work in the enterprise: trusted tables, clear metric definitions, helpful synonyms, documented assumptions, row-level security, user feedback loops, and well-designed example questions. Using a demo scenario, attendees will see how non-technical users can ask natural language questions, explore trends, compare performance, and drill into business drivers without writing SQL.

We will also cover common implementation mistakes, including connecting Genie to unclear datasets, skipping semantic preparation, expecting AI to fix poor data quality, and launching without user training or governance review. Attendees will leave with a practical design checklist for building conversational BI that is useful, safe, and adoption-ready.