A full end-to-end masterclass on AI in Power BI. During this training day, we’ll cover every corner of AI in Power BI, from preparing data for AI to bringing your Power BI AI assets to life with MCP.

In these times, AI literacy is both an essential career skill and a powerful value-add for organizations. The ability to quickly and easily draw insights and get answers from our data is as important as ever. Furthermore, being able to interact with our data through conversational communication is increasingly in demand.

The training day will be a mix of presentation-style teaching, interactive exercises, and workshop labs. After attending this training day, you’ll be able to set up everything around AI in Power BI end-to-end. You’ll be up to date on the newest tools for AI-powered BI—ready to develop solutions or advise clients and colleagues on how to use AI with Power BI.

Module 1: Data Prep for AI – with AI

In this module, we’ll explore how to get your data ready for AI usage. We’ll cover semantic modeling best practices, context- and prompt engineering, and how to leverage AI itself to fine-tune semantic models for AI usage.

Module 2: AI-assisted Power BI development

In this module, we'll explore the various options for improving your development productivity with the available AI toolbox. We'll cover how the built-in Copilot may help improve low-code workflows and move on to full-fledged AI coding agents. At this stage, participants will learn how to make AI agents develop whole user stories with no human involvement.

Module 3: Data Agents

The third module covers everything about using semantic models as assets for AI agents. In this module, you'll learn how to set up, configure, and refine data agents so that your semantic model data is readily available for "chat with your data" scenarios, as well as for use outside of Power BI.

Module 4: External solutions

In the final module, we explore how Power BI data can be used with AI tools outside of Power BI itself. We'll cover how to use MCP to create custom AI agents and how to use the native MCPs for quick results. Furthermore, we'll explore how to integrate your data agents and semantic models with the rest of the Microsoft AI ecosystem, using AI Foundry and Copilot Studio. Finally, we'll cover how to move beyond the boundaries of Microsoft and use the data in cloud-agnostic use cases, using today's most talked-about AI tools.