What should an AI business case contain?
Begin with one workflow and its current baseline. Name the users, volume, cost, delay, error, or lost revenue the work creates today. Then define the expected result, the system needed to produce it, and who will own adoption. A business case is credible only when its value claim can be checked against a pre-existing baseline.
Include implementation, model usage, integrations, review time, support, and change-management costs. AI ROI measurement makes those costs visible after launch. AI use case prioritization compares the case against other opportunities instead of rewarding the most persuasive demo.
How does it prevent wasteful pilots?
Set assumptions and stop rules before a team starts. If data cannot be reached, users reject the workflow, cost per task grows, or outcomes do not improve, the next decision should be clear. A pilot without a decision rule is a demonstration, not an investment test.
The AI strategy explains why the work matters. The AI roadmap places it among dependencies and milestones. A build versus buy decision then tests whether an existing product can meet the case more cheaply than a custom system.