AI Use Cases for Indirect Procurement — 15 Scored Use Cases, Vendor Diagnostic, Prompt Library, Governance

Description
Most AI-in-procurement material answers the wrong question. It asks what AI can do. The useful question, if you are the person who has to sign the business case, is where it can be trusted — and what happens when it is wrong.

Fifteen use cases across analyse, source, negotiate, manage and enable. Every one is stated with four things that are usually missing: the conditions under which it works, the specific way it fails, the measure that proves whether it is working, and the human control that has to sit around it. A use case without those four is a demonstration, not a plan.

The business case model includes the lines vendors never quote — data remediation, internal effort, integration, monitoring and re-training — which routinely dominate real cost. The vendor diagnostic is twenty-five scored questions to use live in a demo.

The prompt library gives ten working patterns for classification, contract extraction, bid comparison, clause review and more, each with the guardrail that makes it safe and the check that must happen before the output is used. No vendors are named.

Who it is for: procurement leaders asked to produce an AI plan, category managers evaluating a vendor claim, and anyone who has to justify the spend to a finance function that has already heard the pitch.

What it is not: it is not a technical guide, it names no vendors and it does not tell you which tool to buy. It is deliberately written to survive the next model release, because it is about where the work can be trusted rather than about what any particular system can do this month. All regulatory content is isolated in a single dated document so it can be updated without touching the rest.