Power BI Copilot rarely says "I don't know."
It says a number. The number looks right. That's the expensive part.
The failure isn't syntax
The generated DAX or SQL usually runs fine. The failure is semantic — the right query shape over the wrong interpretation.
| Failure | What happens |
|---|---|
| Ambiguous column | Two tables have revenue; it picks one |
| Misread relationship | Exists in the model, means something else |
| Lost filter context | Visual-level filters don't survive into the generated logic |
| No provenance | Nobody can tell afterwards which definition produced the number |
An answer you can't reproduce isn't an answer. It's a suggestion with a decimal point.
Why prompting doesn't fix it
Copilot inherits the Power BI semantic model, which means it inherits everything that model leaves ambiguous. If two measures could plausibly answer the question, no amount of prompt engineering tells it which one your CFO means — because that information was never in the model.
You cannot prompt your way past a missing definition. You can only supply the definition.
What closes the gap
- Entities and metrics resolved against a typed, versioned graph before any query exists
- A proven join path — no valid path, no query, rather than a plausible guess
- Policy compiled into the query per person, so different entitlements produce different SQL
- An audit trail that reproduces the answer exactly, months later
On real enterprise schemas that difference is not marginal. Raw schema access scored 14.5% in our benchmark; the same model with compiled, governed context scored 98.2%.
The model was never the variable.
The full breakdown — the specific failure modes with examples, what a wrong number costs, and the architectural fix — is here:
👉 Why Power BI Copilot Delivers Wrong Answers (and What It Costs You)
Originally published at colrows.com/blogs/power-bi-copilot-wrong-answers
