Start with a recurring task, evidence someone can check and a person who needs the result. For AI in analysis and reporting, that is a more useful beginning than choosing a model first and then searching for something to do with it.
Consider an AI-assisted monthly management commentary as a possible first project. This is an illustrative approach, not a customer case. The questions about scope, data quality and ownership also apply to AI questions about business metrics.
1. Decide what the pilot should improve
Write a short brief: the finance owner should receive a reviewable draft covering revenue, costs and margin for one location. It must use the same period and measures as the existing report, refer to the underlying figures and flag incomplete information.
Record the current process: preparation time, difficult steps and usual corrections. This lets you compare work before and after the pilot. Generating more text achieves little if the recipient still has to redo the analysis.
2. Check access and agree the numbers
Select the sources the task actually requires. Monthly commentary might need financial actuals and budget. CRM, telephone and marketing data do not have to be included simply because they exist. A smaller scope makes both errors and benefits easier to assess.
Reconcile the measures with a report the finance owner recognises. Agree how credit notes, provisional postings and period boundaries work. Show the latest update and record who owns each definition. Resolve these issues before deciding whether the AI writes a useful commentary.
Also decide which information the model needs, where it is processed and how permissions apply. Provider selection, storage and access should be part of the proposed design. A user must not gain access to another location's figures simply by phrasing a question differently.
3. Write the tests before judging the demonstration
Collect real questions and representative reporting periods. Include an ordinary month and a difficult one. A period with late costs teaches you more than another example with perfectly complete data. Specify which evidence should be used and which claims the answer can legitimately make.
Review facts separately from wording. Are amounts, signs and periods correct? Is there a source? Then assess usefulness. Elegant wording can still fail the first check. Keep the tests to assess subsequent changes against the same expectations.
| Test case | Expected behaviour |
|---|---|
| An ordinary closed month | The commentary uses the correct period and traces back to defined measures. |
| A location has no budget | No budget variance is invented; the missing information is visible. |
| The previous period's margin is zero | The movement is described without a misleading percentage calculation. |
| A question about a restricted location | Information outside the user's access is not disclosed. |
| A question about why revenue fell | Observed movements are separated from explanations the data does not establish. |
4. Agree when to continue, change direction or stop
Set acceptance criteria with the person who will use the output. For a financial report, incorrect amounts, invented explanations or broken permissions might be release-blocking failures. Unhelpful wording might instead call for an adjustment and another evaluation. Decide the distinction before becoming attached to the demonstration.
Measure the whole task, including retrieving evidence, reviewing drafts and making corrections. Include maintenance when sources or definitions change. If review removes the benefit, revise the approach or solve the task another way. Preventing an unhelpful rollout is a useful pilot outcome.
- Continue when answers meet the agreed quality standard and the recipient gains a clear benefit.
- Narrow the scope when a particular source or question causes repeated failures.
- Pause when figures cannot be reconciled or nobody owns approval.
5. Give the solution an owner after the pilot
An accepted pilot begins an operating process. Someone must own the measures, receive error reports and decide when a change needs new tests. Keep a route back to the ordinary report if a source stops updating or the analysis needs to be paused.
PVBC helps connect the BI question, data integration, AI analysis and delivery. The initial proposal should define scope, quality checks and responsibility after handover. That makes implementation assessable as a business project.
Common questions
Must we collect all our data before implementing AI?
No. Start with the information needed for the selected task. It must be accessible, understandable and verifiable. Collecting every source first can enlarge the project without improving its first delivery.
How long does an AI implementation take?
Access, data quality, permissions and intended use affect scope. We examine these dependencies before proposing a pilot and schedule.
Who needs to be involved from our business?
Someone who uses the result, an owner for the metric definitions and someone who can arrange source-system access. IT or information owners may need to join decisions about hosting and data handling. One person can cover several of these roles.