Power BI AI Insights: Check the Feature Before You Begin

Power BI AI Insights can refer to an older data-enrichment feature or, more loosely, to AI-generated findings in reports. Check which meaning applies before following a tutorial: Microsoft retired the legacy Cognitive Services and Azure ML integrations in Power BI, so their old setup steps are not a current starting point.

The practical job is still useful: enrich source data, validate the added labels or predictions, and decide what the findings justify. fenbs can retain that analysis question, its source links, and the decisions people make from reviewed results while your data tools perform the processing.

How do you identify the workflow you actually need?

1. Write the desired output. Is it an added column such as a feedback category, a prediction from a trained model, or an answer about an existing report?
2. Check the current Microsoft documentation for that exact capability. Treat an old AI Insights ribbon screenshot as historical guidance until you confirm otherwise.
3. Ask the workspace owner which supported processing path and environment your organization uses. Confirm access before uploading or transforming source material.
4. Create a small test set with original record IDs and expected labels. Include difficult cases as well as clear examples.
5. Review the new output before loading it into the report. Keep the question, transformation settings, and review result together.

These steps prevent a common detour: changing licenses or searching menus to restore a retired feature. Microsoft’s retirement notice↗ (opens in a new tab) covers the legacy integrations and directs customers toward Fabric or Azure AI approaches. It does not mean every AI capability in Power BI has disappeared.

What can replace an old enrichment step?

Enrichment means adding information to an existing record, such as a category inferred from a written comment. Microsoft documents AI Functions in Fabric↗ (opens in a new tab) for tasks including classification, summarization, extraction, and sentiment analysis. Choose a supported route with the person responsible for your data platform.

That is a different setup from restoring the old Power BI button. Someone must decide where the transformation runs, where its output is stored, and how Power BI reads the resulting table. A generated category also needs a stable name and definition so it stays meaningful after refreshes.

Say you want to group support feedback into delivery, billing, and product issues. Keep the original text in an approved source location, assign a stable record ID, and add the proposed category separately. Do not overwrite the original comment or hide cases the processor could not classify.

For questions about numbers already in a report, use the Power BI analysis workflow; for choosing conversational features and report visuals, use the Microsoft Power BI feature guide. Those are separate jobs from creating enriched source columns.

Which prerequisites should you confirm first?

Confirm the selected service, workspace access, administrator settings, supported region, and the capacity or entitlement it requires. Read those requirements on the current feature page instead of borrowing them from a different Microsoft AI product. Availability for one experience does not establish availability for another.

• Data permission: the approved purpose, storage location, and people allowed to see the input and output.
• Technical owner: someone responsible for the processing step, refresh failures, and changes to its configuration.
• Output contract: the allowed labels, handling of missing values, and treatment of processing errors.
• Review standard: which cases need a person and what evidence is enough to accept the first version.

Use a small copy of approved test material before scheduling a full run. Include a record with no useful text, a mixed complaint, and language your reviewers expect to encounter. A test containing only obvious examples tells you little about the cases that will need attention.

How do you check the enriched data?

Compare each test output with the original record and the rule it should satisfy. Check both the label and whether it was attached to the correct record. A good classification linked to the wrong customer comment is still an incorrect result.

Count input records, completed outputs, missing outputs, and explicit errors. Keep “not processed” separate from “neutral” or “other.” Otherwise a failed transformation can quietly change the report’s apparent distribution of opinions.

Microsoft’s text classification tutorial↗ (opens in a new tab) shows a broader Fabric workflow with input preparation and validation. Your review still needs a person who understands the labels and the source language. Another AI’s agreement is useful as a signal to investigate, not proof of correctness.

When a rule changes, rerun the test set and compare differences. If “late delivery” previously included dispatch delays but now means arrival delays only, that is a definition change. Do not present a shift in category counts as a business trend until you separate it from the changed rule.

When is a finding strong enough for a decision?

Start with the narrowest supported conclusion. “Billing appeared in these reviewed comments” is different from “billing is the main cause of cancellations.” The second claim needs evidence about cancellations and a defensible comparison, not just category counts.

Save the source period, selection method, label definitions, test failures, and accepted limitations. Record which person approved the interpretation and what further evidence would change it. An AI brain is useful here as a durable work record rather than another generated summary.

Keep the question and the decision separate. The question may remain open even after you choose a practical next step, such as manually reviewing a set of complaints. fenbs’s Decisions and rules can preserve that distinction for the next connected assistant.

How to do this with fenbs

1. Create a free account, then add a project for the enrichment or analysis question.
2. Save references to the source data, transformation documentation, and report in Links and listings. Keep sensitive source material in its approved data system.
3. Record the person-approved category definitions and exception rules. Add tasks for the test set, failed records, and review of the resulting report.
4. Save the accepted finding, its limitations, and Where we left off. Ask the next connected assistant to read that context before suggesting a new interpretation.

fenbs does not enrich data, execute Fabric jobs, or connect to Power BI. Use your data platform for those jobs; use fenbs when rules and reviews need to survive a change of assistant. A one-time private experiment may need only a clear local analysis note.

How do you keep the next refresh understandable?

Before another run, confirm that the same source population, label definitions, and transformation configuration still apply. Review errors before sharing the refreshed report, and investigate unexpected category changes. Save the explanation for any intentional change so a later reader does not mistake it for a real-world event.

For separate connection questions, see the Power BI assistant connection guide. The AI at work hub includes other ways to organize analysis and review without treating every generated finding as a settled fact.