AI is changing how users interact with analytics, but reliable AI experiences still depend on well-designed data, semantic models, and governance. This session explores how Microsoft Fabric and Power BI can provide the foundation for AI-ready analytics. We will follow data from OneLake and Fabric engineering workloads through semantic modeling and Power BI, then examine how AI-assisted experiences can consume that trusted context. Along the way, we will discuss data quality, metadata, business definitions, security, and why simply connecting an LLM to enterprise data is not enough. Attendees will leave with a practical architecture for preparing analytics platforms for the AI era.

Focus: How Fabric, semantic models, Power BI, and AI work together to turn governed enterprise data into AI-ready analytics.

Objectives:
1. Design an AI-ready analytics architecture connecting Fabric, OneLake, semantic models, and Power BI.
2. Understand how data quality, metadata, business semantics, and governance influence the reliability of AI-generated insights.
3. Identify where AI adds value to analytics workflows and where traditional BI remains the better approach.