AI can help answer business questions when it uses approved sources, agreed metric definitions and calculations that can be checked. Start with a limited use case and verified answers, and keep uncertain explanations as hypotheses.
A confident sentence is not a verified result
An AI system can produce a plausible explanation that is incorrect. It can also misunderstand “sales,” use the wrong period or overlook a missing cost category. Good writing cannot fix a calculation made on the wrong basis.
Make the question specific enough to test
Instead of a broad request to explain the business, start with a question the team can check. For example: compare the agreed contribution measure for one product group across two specified months, then show the components that changed.
- What period and business unit are included?
- Which definition of the measure is approved?
- Which sources contain the needed data?
- How will the calculation and answer be checked?
Agree on the output before building the agent
Decide whether the proposed agent should retrieve approved figures, calculate a comparison, suggest possible explanations or prepare a draft summary. These are different tasks with different checks. A possible explanation should remain a hypothesis when the data does not establish a cause.
Start with known answers and difficult cases
Use a small set of business questions with verified answers, alongside incomplete inputs and ambiguous wording. Check whether the proposed workflow handles them acceptably and makes uncertainty visible. Keep review responsibilities clear. The first goal is a useful analytical task with a traceable result, rather than an assistant that sounds certain about everything.
A useful first step with BI Forge.
BI Forge develops its own analytical AI agent. A useful project discussion defines the questions, approved sources, calculations and review process first. The initial pilot should be assessed against questions whose answers the team can verify.
Discuss an analytical AI use case