Power BI Copilot for Exploring Data - What It Actually Does and Where It Trips Up
Most people do not want a dashboard. They want an answer. The dashboard is just the thing we built because, until recently, there was no way to ask a database a question in plain English and get something sensible back. Copilot in Power BI is Microsoft's attempt to close that gap, and it is one of the more interesting bits of the whole Fabric and Power BI push because it changes who gets to ask questions of the data.
I have watched a finance manager who has never written a line of DAX type "which products dropped in margin last quarter and why" into a report and get a genuinely useful starting point back in about four seconds. I have also watched the same feature confidently summarise a chart in a way that was subtly, dangerously wrong. Both of those things are true at once, and that is what makes this feature worth writing about honestly rather than just cheering for it. Microsoft's own walkthrough on exploring data and getting insights with Copilot is a fine starting point. What follows is the version I would give a client over coffee.
What "explore data" actually means here
When people hear "AI in Power BI" they imagine something magical building reports for them. The reality is more grounded and, honestly, more useful once you understand it. Copilot in a Power BI report sits on top of the data model you already have and lets a report consumer do a few specific things without touching the authoring tools.
The first is asking a question in normal language and getting a visual back. You type "show me sales by region for this financial year" and Copilot builds the chart. The second is summarising what is already on the page. You open a report someone else built and ask Copilot to explain what the visuals are telling you, which is handy when you have inherited a dashboard with fourteen tiles and no documentation. The third, and the one I find most valuable, is the follow-up. You ask a question, get an answer, then ask "now break that down by product category" and it keeps the context. That back-and-forth is where it starts to feel less like a search box and more like a colleague.
The thing to hold onto is that Copilot is answering from your semantic model. It is not going out to the internet, it is not making up numbers from thin air, and it is not smarter than the model you gave it. If your model is a mess, Copilot inherits the mess. This is the single most important thing to understand before you switch it on for anyone.
Why the model underneath matters more than the AI
Here is the uncomfortable truth we have learned on client engagements. The quality of Copilot's answers is almost entirely a function of how well your semantic model is built. The AI is the easy part. Microsoft ships it. The hard part is the same hard part it has always been in Power BI, which is a clean, well-named, properly related data model with measures that mean what they say.
A concrete example. We had a client whose model had a column called "Amount" that was sometimes revenue, sometimes cost, depending on a "Type" column sitting next to it. A human analyst knew to filter it. Copilot did not. When a user asked "what was our revenue in July", Copilot happily summed the whole Amount column and returned a number that mixed revenue and cost together. It was not lying. It was doing exactly what the model let it do. The number looked plausible, which is the worst kind of wrong.
The fix was not an AI fix. It was renaming fields to be unambiguous, splitting revenue and cost into separate measures, adding descriptions to the tables and measures so Copilot had context, and hiding the columns that should never be aggregated directly. Once the model was honest, Copilot got honest with it. This is why we spend so much time on the modelling layer when a client wants Copilot to work, and it is a big part of what our Power BI consultants actually do day to day. The interesting AI feature is downstream of unglamorous data work.
Where it genuinely earns its keep
I do not want to sound like I am talking anyone out of this, because when the model is good, Copilot is a real productivity win. A few places it has clearly paid off for clients.
Onboarding new people to a report is faster. Instead of a training session on where every measure lives, someone can ask the report what it means and get moving. Ad hoc questions that used to become a ticket for the BI team now get answered by the person who had the question. That alone takes real pressure off analytics teams who are drowning in "can you just quickly pull" requests.
The summarisation of narrative insights is also better than I expected. Copilot can look at a chart and write a short paragraph describing the trend, the outliers, and the notable movements. For someone assembling a monthly board pack, that is a genuine time saver, provided you read what it wrote rather than pasting it in blind. I will come back to that "provided" because it is doing a lot of work.
Where it is still rough
Now the honest part. Copilot will sometimes summarise a visual and get the direction of a trend wrong, or attribute a change to the wrong driver. It is confident when it does this, which is the problem. There is no little wobble in its voice to tell you it is guessing. We treat every Copilot narrative as a draft that a human needs to sanity check against the actual numbers, and we tell clients to do the same. If your board pack goes out with a Copilot summary that says revenue grew when it actually fell, "the AI wrote it" is not a defence anyone wants to give.
It also struggles with genuinely ambiguous questions. "How are we doing" means nothing to a model. The more precise the question and the better the field names, the better the answer. Users need a small amount of coaching on this, the same way people had to learn how to phrase a Google search fifteen years ago. It is not hard, but it is not zero.
Language and regional quirks matter too. If your users write in Australian English and your fields are labelled in American terms, or your fiscal year runs July to June and the model does not encode that clearly, Copilot can get confused about what "this year" means. We have seen it default to a calendar year when the business runs on a financial year, which quietly throws every time-based answer off. Worth checking before you roll it out.
Licensing and the practical bits
Copilot in Power BI needs the right capacity behind it, which in practice means Fabric or Premium capacity rather than the cheapest Pro licence. This surprises people, so it is worth raising early in any planning conversation rather than after someone has fallen in love with the demo. The costs are real and they scale with usage, so it pays to think about who actually needs this rather than switching it on for the whole organisation on day one.
There is also a governance angle. Copilot can only answer from data a user already has access to, which is good, it respects your row-level security. But it also means your security had better be right, because Copilot makes it much easier for a curious person to go poking around a report and asking questions. If your row-level security is sloppy, Copilot will help someone find the gap faster than they would have clicking through visuals. Get the security layer sorted first.
How we would roll it out
If a client asked me where to start, I would not start with Copilot. I would start with one important report and its model, get that model genuinely clean and well described, and only then switch Copilot on for that one report with a small group of users. Let them use it, collect the questions it got wrong, and use those to keep improving the model. Copilot is a mirror. It shows you where your data model is weak, which is uncomfortable but useful.
Then expand from there, report by report, rather than flipping it on across the tenant and hoping. The organisations getting value from this are the ones treating it as a data quality project with an AI feature on top, not as a magic button. That framing tends to come out of a proper strategy conversation, which is why we usually fold it into a broader AI strategy piece of work rather than a bolt-on. And if the goal is getting your people comfortable actually using these tools well, structured Copilot training does more good than any amount of documentation.
Copilot in Power BI is a real step forward for letting more people ask questions of their data. It is not a replacement for building good models, it is a reward for having built them. Get the foundations right and it is genuinely useful. Skip them and it will confidently tell your executives the wrong thing. Choose accordingly.
If you want a hand getting your semantic models into a state where Copilot actually helps rather than embarrasses you, get in touch. We do this a lot.
Reference: Explore data and get insights with Copilot in Power BI, Microsoft Learn.