How to Evaluate an AI Analyst Before It Touches Production

Most AI-analytics evaluations begin with the wrong test: ask a few questions, inspect whether the charts look plausible, and compare response speed.

That tests the demo. It does not test what happens when a business term is ambiguous, the semantic layer has incomplete coverage, or a generated query binds to a plausible but incorrect field.

Before an AI analyst touches production, give it a question with more than one valid interpretation and evaluate the system around the model.

Start with a question your team has argued about

Use a real question such as *"Which customers are at risk?"* or *"Are we losing regular buyers?"* Do not define the ambiguous terms in advance. The purpose is to see whether the system notices that a business choice is required.

Then run this six-part evaluation.

1. Does it show the interpretation?

Before execution, the system should expose the intended metric, population, time window, comparison, grain, filters, and material assumptions in language the questioner can review.

Showing SQL only after generation is not the same test. The reviewer should be able to inspect the business interpretation without reverse-engineering joins and expressions.

2. Does it distinguish context from ambiguity?

The system should use the definitions, semantic models, metadata, and policies already available. But when two governed choices remain plausible, more retrieval is not a substitute for a decision.

Ask which source grounded each material choice. If no source resolves the ambiguity, the system should make that uncertainty visible.

3. Does it ask when the choice would change the analysis?

Not every missing detail deserves an interruption. The useful test is whether the system asks when a different answer would materially change the metric, scope, comparison, or result.

Record what triggers clarification and what the system is allowed to infer. That policy should be explainable, not a property of one successful prompt.

4. What happens when it cannot commit?

A production system needs an explicit behavior for unresolved meaning. Does it refuse, ask, mark the answer exploratory, or fall through to a looser generation path?

List every path that can emit SQL or a number. Verify that each path enforces the required semantic checks or is clearly fenced off from governed use.

5. Can you inspect what ran?

Capture the visible plan, clarification or acceptance event, generated query, selected bindings, semantic checks, data freshness, and relevant context versions.

Repeated runs do not need to produce identical SQL to pass this test. The requirement is enough evidence to explain what the system intended and what it executed.

6. Can it answer "why this, not the alternative?"

Point to a material choice: net rather than gross, same-store rather than all locations, or one customer definition rather than another. The answer should cite a phrase, policy, formula, approved definition, or recorded clarification.

A newly generated rationale is not provenance. The authorizing source should have been part of the interpretation when the plan was formed.

The evaluation artifact

For each question, leave with one packet:

1. question and relevant context;
2. competing valid interpretations;
3. visible plan;
4. clarification and acceptance record;
5. checked bindings and generated SQL;
6. result and provenance;
7. unresolved risks or exploratory paths.

If a vendor cannot produce that packet for one ambiguous question, a larger benchmark will not tell you how the system behaves when business meaning—not SQL syntax—is the hard part.

Bring one ambiguous question from your own business. That is where the evaluation should start.

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*See how Spotonix exposes the interpretation before execution: how it works · try the demo