Enabling Copilot on a semantic model is a single click. Making it consistently return answers you can trust is not.

Teams that turn Copilot on without preparing the underlying model tend to get confident-sounding responses built on the wrong measure, ambiguous field interpretations, and stakeholders who stop trusting the feature within a week.

This session walks through the full AI readiness pipeline: writing field and measure descriptions at scale, using verified answers as reliability anchors, structuring AI instructions files, inspecting prep settings programmatically from a Fabric Notebook, and choosing between Copilot for Power BI and Fabric Data Agents as distribution channels — including how we can use the Power BI Modelling MCP for efficiency.

Attendees leave with a concrete pipeline they can apply to their own models, and a clear view of when to choose each distribution channel.