How to evaluate an AI analytics tool with your own questions

A scripted demo introduces the interface. To evaluate the analysis, bring questions from your team's work and someone who knows the answers. Prepare a simple metric, a segment comparison, and an investigation with a follow-up.

Include an ambiguous term and a question requiring missing data. You can then observe whether the assistant clarifies context, acknowledges a limitation, or fills the gap with an unsupported explanation.

Save the reference result with its date and filters before testing. If the data changes during evaluation, distinguish freshness differences from calculation errors. Compare amounts and populations, as well as the quality of the writing.

Choose a business user to assess usefulness and someone familiar with the data to review calculations. Record disagreements and how they are resolved.

• Does the total match the reference?
• Do follow-ups retain filters?
• Does it distinguish percentage points from percentages?
• Does it acknowledge missing data?
• Can you review and retrieve the analysis?

Test what happens when a query takes too long, a source is unavailable, or the question is too broad. The response should help the user choose a next step. A screen that appears to work indefinitely has a cost for the team too.

Check access with test users against your company's intended behavior. Do not use another organization's data to test isolation.

Ask vendors to separate connecting data, defining metrics, training, and ongoing maintenance. Include usage and support terms. An isolated per-seat price cannot establish the full cost of two proposals.

Measure how long it takes a user to obtain a reviewed answer and how often the data team intervenes. Generating text is only part of the work.

Keep a record of each question, reference, answer, required correction, and the decision it supports. Repeated failures reveal preparation needed before expanding. A tool can suit one department while requiring further work for another.

A Rumbleo demo can start with a real question from your team. The purpose is to check fit and data requirements, rather than extrapolating from a generic demonstration.