Microsoft Power BI AI: Choose and Check the Right Feature
Choose between conversational analysis and AI visuals, then check the data behind each finding before turning an interesting pattern into an action.
Updated 5 min read
Microsoft Power BI AI includes conversational assistance through Copilot and AI visuals that help investigate patterns in your data. Choose the feature that matches your question, then verify its measure, filters, and supporting records before treating the result as an explanation.
The answer also needs a durable record when someone changes the report or asks a different assistant tomorrow. fenbs can hold your agreed metric definitions, analysis decisions, and unresolved questions. It does not perform the visual analysis or read Power BI reports for you.
How do you choose a feature for your question?
- State one question in business language, such as which customer groups have a larger share of late deliveries.
- Confirm the approved measure and the fields that define the groups. Inspect the report period and data refresh before exploring.
- Use a conversational experience for a focused question or summary, a key influencers visual for associated factors, or a decomposition tree for exploring a measure across groups.
- Repeat the important result in a straightforward table. Check the population behind the pattern and look for a smaller or excluded group that changes its meaning.
- Save what was observed, what remains a hypothesis, and the next question that needs a person’s review.
An AI feature is useful when it helps you inspect something you can explain afterward. Start with a familiar measure and a question whose scope you can describe clearly. If you cannot say what a row represents, first resolve that with the report owner.
When should you use a conversational answer?
Use conversation when you want a summary, a bounded comparison, or help finding a result in the available report context. Microsoft’s Copilot overview (opens in a new tab) describes several experiences and their requirements. Confirm administrator settings, supported capacity, access, and region for the one you plan to use.
Ask “Compare the late-delivery percentage by US region for the last closed month” before asking “Why are deliveries late?” The first request has a measure and a checkable scope. The second invites explanations that may require operational evidence outside the report.
Where the experience provides supporting tables or queries, inspect them. Microsoft’s data question guide (opens in a new tab) explains available ways to examine an answer. Compare the response with the report using the same period and selections rather than accepting a sentence because it sounds plausible.
For a full question-to-reviewed-result process, use the Power BI analysis guide. The broader Microsoft AI tools guide helps when your task belongs outside the report.
What does a key influencers visual help you investigate?
The key influencers visual (opens in a new tab) analyzes factors associated with a selected metric or outcome. You supply the field to analyze and the fields that might help explain it. Use it to generate questions about groups worth examining more closely.
For example, you might investigate whether late deliveries are more common for certain shipment types. Start by checking what one record represents and whether the candidate fields were known at the time of shipment. A field added after a problem occurred might describe the outcome rather than help explain it.
Inspect the selected group’s size and comparison group. A striking pattern in a narrow segment may deserve follow-up without supporting a broad business rule. Repeat the view with sensible selections and confirm that missing fields or duplicate records are not driving the result.
Association alone does not establish cause. Express the result as an observed relationship in this data, then ask what operational check or study would test a causal explanation. Do not turn an interesting grouping into an automatic customer or employee judgment.
When is a decomposition tree more useful?
A decomposition tree (opens in a new tab) breaks an aggregated measure down across dimensions you provide. Its AI splits can suggest where to explore next using the selected criteria. This makes it useful when you want to follow a measure through several levels rather than read one fixed chart.
Begin with the approved late-delivery measure and useful dimensions such as region, carrier, and service level. Follow a branch, then record the path and the selections that produced it. The same-looking final group can mean something different after a page filter changes.
Check the root total first. If it does not match the report’s trusted total, exploring deeper branches only adds detail to a disputed starting point. Also check whether you are viewing a count, an amount, or a percentage; the largest count and the highest rate answer different questions.
How do you turn a pattern into a reviewed finding?
Write a short finding with four parts: the observed pattern, its scope, the check you ran, and what remains uncertain. For instance, say that one shipment group had a higher late-delivery share in the reviewed month, the report total matched the source check, and carrier assignment still needs investigation.
Keep the evidence separate from the action. A person may choose to review carrier operations without declaring that the carrier caused the difference. Preserve the accepted wording and the reason in Decisions and rules so another assistant does not turn a tentative explanation into a fact.
Keep the question open until the evidence closes it
Use fenbs to record which metric definition a person approved and what the visual actually established. Add a task for the unresolved explanation and keep the report reference with it. The next connected assistant can see the boundary between a pattern, a hypothesis, and an accepted decision.
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How to do this with fenbs
- Create a free account, then add a project for the report review.
- Add the report reference to Links and listings. Record metric definitions, exclusions, and useful field meanings in the glossary or context notes.
- Create tasks for the questions that need evidence. Include the selected visual, date range, filters, checks performed, and the uncertainty left to resolve.
- Record the person’s accepted interpretation in Decisions and rules. Save Where we left off so a connected assistant can continue with the next unanswered question.
fenbs does not create Power BI visuals or connect to Microsoft Power BI. Keep the actual report and its governed data in Microsoft’s tools. If your entire investigation stays in one report and needs no shared handoff, a maintained report note can be sufficient.
What should happen after the review?
Give every accepted action an owner and a clear check for completion. If the finding leads to product work, the guide to turning requests into tasks explains how to preserve the reason for the request. Keep unrelated questions out of the conclusion.
Use the Power BI AI Insights guide for data enrichment, the Power Automate AI guide for reviewed automation, and the Power BI assistant connection guide for connection questions. The AI at work hub covers the wider workflow around these tools.