Getting started
How to prepare an AI data analyst pilot
Define scope, questions, and review criteria for a conversational BI pilot before extending it across the company.
Rumbleo team Updated
Choose a task with a clear finish
Testing AI against all company data leaves too many decisions open. Preparing a weekly sales review or investigating margin variances in one unit is a more concrete starting point. Someone should need the output and be able to judge its usefulness.
Write down what the pilot will deliver: for example, a reviewed account of changes by channel with follow-up questions. Avoid committing to hours saved before understanding the current effort and preparation required.
Bound the dataset
Select the analytical source, periods, and dimensions the task needs. Review freshness, missing values, and definitions with the data owner. Resolve metrics that disagree between reports before using them as references.
The connection should have access appropriate to the task. Rumbleo executes analytical queries in read-only mode; preparing access and definitions remains part of getting started.
Assign validation work
The business user brings questions and checks whether answers help complete the work. The data owner reviews calculations and filters. Someone should coordinate issues and decide when a correction requires changing a shared definition.
Keep representative questions and reference results. Include one requiring clarification and another that cannot be answered. Repeat the set after a fix to check that the change has not broken existing behavior.
Measure against the current workflow
Record how each question is answered today, including preparation, waiting, and review. Measure the same journey during the pilot. Distinguish questions users resolve independently from those still requiring help.
Record preparation and maintenance costs too. A pilot's value depends on how frequently the work recurs and the quality of the data; a fast answer in one session does not establish monthly savings.
| What to observe | Evidence |
|---|---|
| Consistency | Results reconciled to the reference |
| User independence | Questions resolved without intervention |
| Usefulness | Decision or task the answer supports |
| Effort | Recorded preparation and review |
| Limits | Unresolved questions and missing data |
End with a decision
Agree in advance on conditions for continuing, stopping, or preparing the data further. At the end, review successful questions, corrections, and outstanding limitations. You can learn where a tool fits without resolving every question.
If another department comes next, review its metrics and questions separately. Sharing a platform does not mean finance and operations need the same preparation.