We've been testing Power BI Copilot in Fabric across a few client projects recently, and wanted to share an honest breakdown of what's actually reliable right now versus what still needs heavy manual intervention before it's production ready.

Where Copilot performs well

DAX generation from natural language prompts works reasonably well for straightforward measures, simple sums, averages, basic time-based calculations. If you're describing a metric in plain English and it maps cleanly to a single table or an existing relationship, Copilot gets you most of the way there quickly.

Report page generation is genuinely useful as a starting point. Describing the kind of report you want and getting a formatted first draft saves real time compared to building from a blank canvas.

Where it starts to break down

Once you move into anything involving nested CALCULATE statements, multiple active relationships, or context that needs to be manipulated across several tables, the generated DAX often needs significant rewriting. It's not wrong in an obviously broken way, it just doesn't always reflect the actual business logic you intended.

Formatting and layout from generated reports almost always need manual cleanup before they're client ready. Spacing, visual hierarchy, and brand consistency aren't something Copilot handles out of the box yet.

Open questions we're still working through

How consistent is Copilot's output when it's connected directly to a Fabric semantic model versus working in standalone Power BI Desktop? Does governance hold up when multiple people on a team are using Copilot to generate reports independently, especially around naming conventions and formatting standards? And how do you build a review process that catches subtle DAX logic errors without slowing the whole workflow back down to where it was before Copilot?

Why this matters going forward

The trajectory here is clear even if the tool isn't fully mature yet. Power Apps Copilot, Power Automate, and Power BI Copilot in Fabric are all part of the same shift toward natural language driven development across the Power Platform. Teams that figure out how to use this well now, with the right guardrails, are going to have a real head start as the tooling matures.

We wrote a more detailed breakdown of this shift, including a real case study and a practical framework for adoption, here at Dream IT Consulting Services

Would love to hear how others are handling the DAX accuracy and governance pieces specifically, since that seems to be where most teams are still figuring things out.