AI business cases can become optimistic very quickly. A demonstration saves someone ten minutes, that saving is multiplied across a workforce and a large benefit appears in a spreadsheet. The arithmetic may be correct, but the assumption behind it is often weak. A credible business case needs to show how the technology changes real work and how much of that benefit the organisation can actually capture.
We start by describing the current process. Who performs the task, how often does it happen and where is the effort concentrated? It is useful to separate time that can genuinely be removed from time that will simply move to review, exception handling or governance. AI often changes the shape of work rather than eliminating it completely.
Adoption also matters. A tool that could save an hour a week creates little value if only a small group uses it consistently. The business case should therefore include training, workflow redesign, data preparation and change activity. These are not side costs. They are often the difference between a successful deployment and an unused licence.
On the cost side, AI pricing can be variable. Usage may depend on transactions, tokens, documents or model consumption, so a single annual licence figure can hide future exposure. Procurement should model a realistic base case and a higher-use scenario. It should also consider implementation, integration, support and the internal effort needed to operate controls.
A strong AI business case is useful even when it recommends a smaller first step. It makes assumptions visible, gives finance and operational teams something concrete to challenge and creates measures that can be checked after launch. AIPVA focuses on business cases that are clear enough to govern and practical enough to revisit. The aim is to invest because the economics make sense, not because the technology is fashionable.
Benefits should also be expressed in more than one way. Time saved can matter, but so can shorter cycle times, better coverage, fewer missed obligations or improved consistency. Some benefits will be financial and others will be operational. Keeping them separate makes the case easier to challenge and reduces the temptation to convert every improvement into an inflated cash saving. A modest, measurable case is often more persuasive than a large theoretical one.
It also creates a useful baseline for post-implementation review, so promised benefits can be compared with what actually happened.