A business case helps you decide whether an AI project is worth the cost and effort. It brings the problem, expected benefit, risks and plan into one place.
1. Define the problem and starting point
Describe the task, who does it and what happens today. Record a baseline, such as hours spent each week, error rate or response time.
2. Describe the proposed change
Explain what the tool would do, what still needs a person and which systems or information it needs. Compare AI with simpler automation and leaving the process as it is.
3. Estimate benefits and costs
Include setup, software, usage, testing, training and ongoing support. Estimate benefits over the same period and explain assumptions. Time freed is not necessarily a cash saving, and capacity and time should not be counted twice.
4. Plan for risk and delivery
Consider privacy, accuracy, access, failures and the effect on staff and customers. Name an owner, approval points, a realistic timeline and a way to stop or correct the process.
5. Measure the outcome
Set a target and compare results with the baseline. For example, a team spending 15 hours a week answering common questions could test whether an approved tool reduces that time without increasing errors or unresolved enquiries. Treat the target as an assumption until measured.
Keep assumptions visible
For an illustrative comparison, suppose a task takes ten hours a week and a trial reduces preparation to four hours plus two hours of checking. That leaves four hours of capacity in the sample, before setup and ongoing costs. It is not automatically a four-hour payroll saving.
Record the number of examples, correction effort and any effect on customers alongside the time. Compare the result over the same period as software, training and support costs. Update the case with measured results before deciding to expand.
See the hidden cost of manual admin for another way to establish a baseline.
