Featured Questions For Power BI Q and A - Making Natural Language Actually Useful

Power BI Q and A is one of those features that demos beautifully and then sits unused in production. You have probably seen the pitch: type a question in plain English, get a chart back, no need to know DAX or how the model is built. It looks like magic in the room. Then it ships, people try it once, get a confusing answer or an error, and never touch it again. The feature is not the problem. The empty question box is the problem, and featured questions are the fix that most teams skip.

I want to talk about featured questions specifically, because they are the difference between Q and A being a party trick and Q and A being something your finance team actually uses on a Monday morning. Microsoft's documentation on creating featured questions covers the how. I want to cover the why, and what we've learned putting this in front of real Australian business users who do not care about the technology and just want their number.

The blank box problem

Here is the thing about natural language interfaces. A blank box with a blinking cursor and "ask a question" is intimidating, not inviting. People stare at it and do not know what they are allowed to ask. Can I ask about revenue? By region? By month? Do I say "sales" or "revenue" or "turnover"? Will it understand "last quarter"? Rather than risk looking silly, most people just close it and go back to hunting through the report the old way.

This is not a Power BI failing, it is a universal truth about natural language systems. The freedom to ask anything is paralysing when you do not know what the system understands. Every good voice assistant and chatbot solved this the same way: by suggesting things you can say. Featured questions are exactly that. They are a curated set of example questions you put front and centre so that when someone opens Q and A, instead of a blank void they see "Show me sales by region this year" and "Top 10 products by revenue" sitting there ready to click.

That small change turns the whole experience around. Now the person is not inventing a question from nothing, they are picking one that looks close to what they want and tweaking it. Once they have seen a couple of questions that work, they get a feel for the phrasing and start asking their own. Featured questions are training wheels, and people need training wheels far more than the polished demo ever admits.

What featured questions actually do

Mechanically, featured questions are questions you as the report author pin to the top of the Q and A experience. You set them at the semantic model level, you write out the exact questions you want to showcase, and they appear as clickable suggestions when someone opens Q and A against that model.

The trick is that you are curating. You already know which questions your model answers well, because you built it. You know that "sales by region" returns a clean map and "profit margin by product category" gives a sensible bar chart. So you feature those. You steer people towards the questions you are confident will produce a good answer, and away from the ones that will confuse them or return an error. It is quiet quality control disguised as a convenience feature.

This matters because the fastest way to kill trust in Q and A is a bad first experience. If the first thing someone tries returns nonsense, they are done. They will not give it a second go, and they will tell their colleagues it does not work. Featured questions let you make sure the first thing they try is one you have hand-picked to work beautifully. You are managing the first impression on purpose.

Getting the model right is most of the battle

Now for the honest part. Featured questions sit on top of your semantic model, and they can only ever be as good as the model underneath. This is where a lot of Q and A rollouts quietly fall apart, and it is worth being blunt about it.

If your model has columns called "Col1," "Amt," and "FY_Rev_Net," Q and A has nothing sensible to work with. Natural language querying depends on the model speaking something close to human language. That means real field names that a person would actually say, tables and columns named the way the business talks about them, and synonyms configured so that when someone asks about "revenue" the model knows that maps to your "Sales Amount" column, and "clients" maps to "Customers."

That synonym work is the unglamorous secret behind good Q and A. Your finance team says "turnover," your sales team says "revenue," and your reports say "Sales Amount." Unless you tell the model these are the same thing, half your users ask questions the model cannot parse and conclude the feature is broken. Setting up synonyms and clean naming is genuinely a chunk of work, and it is the work that separates a Q and A experience people love from one they abandon. This is the sort of foundational modelling our Power BI consultants spend real time on, because it is what everything else stands on.

So my honest take: do not enable Q and A, slap a couple of featured questions on top, and expect magic. Get the model naming and synonyms right first, then use featured questions to showcase what the well-built model can do. In that order. The featured questions are the shop window. The model is the shop. A great window in front of an empty shop fools nobody twice.

Where Q and A shines and where it does not

Let me be even-handed about this, because Q and A is not right for every situation.

It shines when you have a well-modelled, reasonably simple subject area and a group of business users who want quick, ad-hoc answers without waiting for someone to build them a report. "What were sales in Queensland last month" is a perfect Q and A question. Fast, self-service, no ticket to the BI team. For a lot of everyday business questions, this is genuinely liberating and takes real load off your reporting team.

It struggles when the questions get complex or the analysis is subtle. Anything involving tangled multi-step logic, careful handling of business rules, or nuanced calculations is better served by a properly built report where the logic is baked in and tested. Q and A will give an answer to a complicated question, but whether it is the right answer is another matter, and a confidently wrong number is worse than no number. Know the boundary. Use Q and A for the fast, common, well-understood questions, and build proper reports for the ones where getting it exactly right matters.

The newer wave of AI and natural language features across the Microsoft stack, including the Copilot capabilities layering into Power BI and Fabric, are pushing this further, and they are genuinely improving. But the same fundamental truth holds: they are only as good as the data model and the naming underneath them. Better AI does not rescue a badly named model, it just fails more fluently. If you are thinking about where natural language and AI fit into your reporting, that is a strategy conversation as much as a technical one, and it is exactly what our business AI strategy work is for.

How I'd set it up

If a client wanted Q and A working properly, the order of operations would be clear. Fix the model naming first, so fields are named the way the business actually talks. Configure synonyms for the terms different teams use for the same thing. Test a good range of real questions the way real users phrase them, not the way you as the builder would phrase them, because those are different. Then pick the handful of questions that return genuinely good answers and feature them, so the first thing anyone sees is a set of examples you know will work.

And this is the bit teams forget: tell people it exists and show them how to use it. A feature nobody knows about gets no adoption regardless of how good it is. A two-minute walkthrough showing the team they can click a featured question and tweak it does more for uptake than any amount of behind-the-scenes polish.

Featured questions are a small feature. But they sit right at the point where natural language querying either becomes something people use or something they ignore, and that makes them worth getting right. The blank box loses. The curated set of "here is what you can ask" wins, every time.

If you want Power BI Q and A that people actually use rather than a feature that got switched on and forgotten, that comes down to the model underneath and the guidance on top, and both are very much what we do. Have a look at our data and analytics services or get in touch and we will take a look at what you've got.