The demonstration is quick, but the business case counts every saved minute as money in the bank. An AI business case should connect a bounded workflow change to measured effort, quality, costs and adoption assumptions. Show uncertainty instead of hiding it inside a single confident return figure.
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Establish what is being compared
Define the current task from input to an accepted result. Measure active handling, checking, corrections and exceptions. Describe the proposed assisted task using the same finish condition. Comparing a checked manual result with an unchecked AI draft is not a fair comparison.
Use a one-page case
Problem, users and accountable owner.
Current volume and effort, with evidence period.
Proposed change and required controls.
Setup, integration, subscriptions, review and maintenance costs.
Expected capacity effect and other evidenced benefits.
Base, cautious and optimistic assumptions.
Pilot criteria and the next investment decision.
An illustrative sensitivity check
Suppose the current task takes 15 minutes and the assisted task is estimated at eight minutes including review. If review takes another five minutes, the net reduction becomes two minutes rather than seven. That does not settle the decision, but it identifies review effort as an assumption the pilot must measure carefully.
Keep released capacity separate from avoided expenditure. Time can support more delivery without reducing payroll. Give quality or reliability benefits their own evidence rather than assigning an invented financial value.
Ask for a decision proportional to the evidence
Keep an assumptions register alongside the calculation. For each estimate, record its source, plausible range, importance to the decision and how the pilot will check it. In the example, review time deserves priority because a small change substantially reduces the expected capacity benefit. A subscription price alone is easier to verify but does not settle whether the new workflow is worthwhile.
Related guidance: guide to mapping the work before changing it.
An early case may justify a limited evaluation, not full implementation. Specify the evaluation budget, owner, stop conditions and evidence needed before expansion. Keep unresolved data, access or review concerns visible even if the arithmetic looks attractive. Hili Consulting can help turn a promising AI idea into a business case that can be tested.
