AI changed the way many model and report developers create DAX code. It hasn't changed who's responsible when numbers are incorrect, though: the developer, a human being.

In this hands-on session we'll put AI-generated DAX through its paces against real semantic models. We'll see how relationships, grain, and modeling decisions silently shape what AI produces, where it shines, and where it confidently misleads.

You'll see the kinds of mistakes only an experienced eye catches, you will learn how to detect AI errors, and why semantic modeling is becoming more important as code generation gets easier.

We'll also talk honestly about what this means for teams: what to keep teaching, how code review changes, and how the role of the senior Power BI developer is shifting from author to reviewer and mentor and why version controlling your changes is ever so important.