Every BI vendor now ships a chat box. Almost none will tell you their accuracy on a real schema.
Here's how to score them on the questions that actually break things.
The demo question vs the real ones
The demo is always single-table: "show me revenue by region." Every tool passes.
These are the ones your business actually asks:
- A metric requiring a join across three tables, one of which has a near-duplicate
- A time comparison where the fiscal calendar isn't the calendar year
- A question two people are entitled to different answers to
- The same question, asked twice, an hour apart
- A question with no valid answer
What each question reveals
| Test | What a failure tells you |
|---|---|
| Multi-table join | The tool guesses join paths |
| Fiscal calendar | Business rules aren't in the model |
| Two entitlements | Governance is applied after execution |
| Repeat question | The pipeline is probabilistic end to end |
| Unanswerable | It will invent rather than refuse |
Number five separates the field more than the other four combined. A tool that cannot say "I don't know" is a liability in any regulated workflow, because you can never identify which answer was the fabricated one.
The structural read
Conversational BI splits into two architectures dressed the same way. One infers meaning from a dataset model at query time. The other resolves intent against a typed semantic graph, proves the join, applies policy, then compiles SQL.
The first is faster to buy. The second is the only one that survives an audit — and on real enterprise schemas the accuracy gap between them is not marginal, it's the difference between the low teens and the high nineties.
The full breakdown — the scored comparison across the current field, with the governance and determinism criteria applied consistently — is here:
👉 Conversational BI Tools in 2026, Scored on Governance, Determinism, and Reach
Originally published at colrows.com/blogs/conversational-bi-tools
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