The most common Power BI failure is not a technology failure. The reports work. The data refreshes on schedule. The dashboards load in under three seconds. Nobody opens them.

This is a design failure, not a technical one. And it happens in a recognizable pattern.

The report was built for the person who asked for it, not the person who will use it

Finance asked for a dashboard. A developer built what Finance described. Finance described what they thought they wanted, which was a digital version of the spreadsheet report they already had. The developer built exactly that. Nobody uses it because it answers the same questions the spreadsheet answered, just with a Power BI wrapper.

The problem starts in requirements. If the first question is "what data should go in this report?" instead of "what decision does this report need to support?" the output is almost always wrong.

The model doesn't reflect how the business actually thinks

A data model that reflects how the source system stores data is not the same as a model that reflects how the business thinks about its operations. When an analyst has to translate between the two every time they look at a report, they stop looking.

Measure names that mean something to a developer mean nothing to a sales manager. A date table that supports fiscal quarters in one region but calendar quarters in another creates confusion fast. The model is the interface. If the interface is wrong, adoption is wrong.

The report was launched, not adopted

A Power BI report launch is not the same as an adoption program. Sending a link with a one-paragraph email is a launch. An adoption program includes a short walkthrough of how the report connects to a decision the team makes regularly, a clear owner for questions, and a check-in at 30 and 90 days to see what is not working.

The difference in adoption rates between these two approaches is not marginal. It is the difference between a report that gets opened every Monday and a report that gets opened twice before everyone gives up and goes back to the spreadsheet.

What to do instead

Start requirements with the decision, not the data. Ask: "What are you going to do differently once you have this information?" If the answer is vague, the requirements are not ready.

Build the model to match business vocabulary. Measure names, table names, and hierarchy labels should come from the people who will use the report, not from the source system schema.

Plan an adoption phase before the project is considered done. Budget for it, schedule it, and put it in the statement of work. The most expensive Power BI deployment is the one nobody opens.