AI ROI: build a business case you can measure

Begin with a decision, not a subscription

A finance team wants its monthly variance review to take less time. That is a useful starting point for an AI business case because the team can measure preparation time, review time and the quality of the final report. "Use more AI" provides no equivalent test.

Write down the task, who performs it and what happens after the output is ready. If nobody changes a decision or saves useful work, a convincing demonstration may still have little business value. This guide focuses on reporting and analysis with an AI data assistant.

Measure the work before changing it

Observe several normal reporting cycles. Record time spent gathering data, checking figures, explaining differences and correcting mistakes. Keep unusual periods visible rather than choosing the easiest month as the baseline.

Also record the outcome that matters. For a management report, that might be delivery before the review meeting with no unresolved material differences. For a project review, it could be whether owners investigate cost overruns while there is still time to act.

Agree what counts as an error and who decides whether an explanation is supported. AI-generated commentary can be wrong even when the underlying calculation is correct.

Include the whole cost

Estimate setup, source mapping, definition checks, training and ongoing support. Include the time needed to review generated results and maintain the process when a source changes. Use actual quotes from pricing and your suppliers rather than treating a model subscription as the whole project cost.

Avoid double counting. If a support contract includes monthly model checks, do not add the same work as a second external expense. Separate the one-time investment from recurring costs so the team can see both the first-year result and later operating costs.

Calculate a hypothetical example

Suppose monthly report preparation currently takes 14 hours. A pilot reduces preparation and review together to 4 hours. The net capacity released is 10 hours per month. At an internal planning rate of EUR 50 per hour, that represents EUR 500 of monthly capacity value.

Assume EUR 150 of monthly operating cost and EUR 600 of one-time setup effort. These are hypothetical inputs, not DigiData prices. Over twelve months, capacity value would be EUR 6,000 and total cost EUR 2,400. A capacity-based return would be 150%, calculated as the EUR 3,600 net benefit divided by EUR 2,400 cost.

That is not a cash return. If staff remain on the same payroll and no other expenditure changes, the organisation has released time rather than reduced spending. Show cash savings in a separate calculation, using only costs actually avoided. Do not count a hoped-for sale as realised revenue.

Decide what happens to the time

Give the task owner a specific plan for the released capacity. They might investigate more margin exceptions or spend more time reviewing supplier charges. Track whether that work actually happens before assigning it a monetary benefit.

Try a less optimistic scenario. If the pilot releases only four hours per month, the annual capacity value falls to EUR 2,400 at the same rate, matching the example costs. The decision now depends on whether the new process improves delivery or quality enough to justify it.

Set the next review before expanding

Use a short decision checklist:

• Confirm that required data exists in the supported integrations.
• Approve the calculation definitions and a set of expected results.
• Measure preparation plus review time, not generation time alone.
• Record unresolved errors and missed reporting deadlines.
• Compare actual costs and outcomes with the business case.

Continue, adjust or stop based on that evidence. The AI implementation plan turns the approved use case into a bounded pilot with owners and acceptance criteria.