As organizations increasingly rely on Copilot for Power BI to accelerate insights and empower business users through natural language interaction, effectively "chatting with their data", the quality of your semantic model becomes the single biggest factor influencing Copilot’s accuracy, reliability, and usefulness. This session explores how to design and optimize semantic models specifically for AI‑driven consumption, ensuring Copilot understands your data the same way your business does.

We’ll demystify how synonyms, descriptions, and broader metadata of the semantic model shape Copilot’s grounding and reasoning. Drawing from lessons around model organization and naming conventions, we’ll walk through how thoughtful metadata design can dramatically enhance answer quality.

A major section will focus on AI Instructions, AI Data Schemas, and Verified Answers; three pillars that define how Copilot interprets intent, selects fields, and resolves ambiguous questions. We’ll explore how to structure these assets to reduce noise, improve consistency, and create a predictable experience.

To ensure attendees walk away with actionable techniques, we’ll close with prompting best practices tailored for Copilot for Power BI. You’ll learn how to craft clear prompts, set context effectively, and guide Copilot using structured requests. An approach emphasized in both AI instruction design patterns and end‑to‑end Copilot experiences requiring clarity for Q&A, narrative generation, and report creation.

Whether you are a model author, BI developer, or data leader, this session will equip you with the knowledge to prepare your semantic models for AI, improve Copilot’s reliability, and help your organization confidently adopt natural‑language data exploration.