THE DECISION DESK
Build an AI business case your team can verify
Before approving an AI proposal, ask how saved time becomes an outcome the organization can measure. A useful business case makes adoption, review work and the cost of change visible.
Start with a comparable baseline
Select one recurring task. Record volume, time per task, required review, exceptions and quality using the same scope before and after. Include the work that moves to another person.
An illustrative example with explicit assumptions
Synthetic scenario for explanation. These are not client results, benchmarks or a savings promise.
| Input | Illustrative value |
|---|---|
| Comparable tasks per month | 600 |
| Baseline | 12 min |
| Human execution + review | 4 + 2 min |
| Additional exceptions assumed | 0 hours — validate in a pilot |
| Net time released | 60 hours/month |
| Assumed loaded hourly cost | USD 55 |
| Estimated capacity value | USD 3,300 / month |
| Tools + monthly implementation allocation | USD 800 + USD 400 |
The USD 400 assumes USD 4,800 in implementation costs allocated across 12 months. The modeled capacity value after these costs is USD 2,100 per month, before other costs. This does not demonstrate that payroll or another cash expense falls by that amount.
Identify how value will be realized
Agree who will use the released capacity and for what: faster response, more customer work, less overtime or a verified cost reduction. Select an observable outcome and a quality guardrail. Recalculate if adoption is lower, review is slower or exceptions increase.
Five questions for the next proposal
- What baseline and sample support the claimed benefit?
- Who performs review and handles exceptions?
- Which cost actually changes, or where will capacity be redeployed?
- What happens in a lower-adoption scenario?
- What evidence would make us revise or stop this plan?
Method and sources
The calculation and questions are an original educational synthesis. NIST AI RMF provides broader guidance on mapping, measuring and managing AI risk in context; it does not validate the illustrative savings figures above. NIST AI Risk Management Framework.
Editorial standard: distinguish verified facts, assumptions and interpretation; update when new evidence changes the conclusion. Send a correction or a question to vic@vicsol.org.
Use the free decision tools →