Using AI with Fabric goes well beyond natural language queries on your lakehouse. For production AI success, you need a data estate that works across Fabric Data Agents, Fabric Copilots, Microsoft 365 Copilot, Copilot Studio, Foundry, and external agents too. From harnessing schema design, data federation, semantic models, and ontologies, to leveraging MCP technology and Skills, we'll cover how to make your data estate not just ready for AI, but a true catalyst of AI transformation.

To get there, you need data that is consistent, discoverable, and enriched with the context that AI systems rely on. That means aligning your semantic models, enforcing shared ontologies, and ensuring your schemas and federated sources expose the signals copilots and agents can interpret. We’ll look at how OneLake Storage and the OneLake Catalog, together with Fabric’s diverse compute engines, support this consistency and context flow, and how external agents can safely consume Fabric data through MCP Skills. The result is a data estate that allows AI systems to generate richer insights, adapt to business context, and be reused reliably across a range of tools and surfaces.

You will learn:

• How to make your data AI‑ready
• Why semantic modeling and ontologies matter
• How to use Fabric’s AI capabilities effectively
• How external AI tools can safely and effectively use your Fabric data