Why Your AI Vendor Can't Answer the Seven Questions That Matter

A vendor walks into a conference room with a polished deck and a compelling demo. Twenty minutes later, someone in the room says "this looks promising." Forty minutes later, there's a verbal agreement to run a pilot. Nobody asked the seven questions that would have told them whether the pilot was a good idea.

This happens more often than anyone admits. Not because the people in the room are careless, but because they have no structured framework for evaluating AI proposals. They know how to evaluate budgets, timelines, and vendor credentials. They do not know how to evaluate whether a process is suitable for AI in the first place.

The vendor will not help. Vendors sell solutions. They are not in the business of helping you decide whether you need one. Their deck will show you what the tool does. It will not help you determine whether your process, your data, your regulatory environment, and your team's readiness make this a sensible decision.

That evaluation is your job. And most managers do not have a methodology for doing it.

The seven questions nobody asks

When I work with managers across enterprise organisations, I find the same pattern. The conversation about AI jumps from "this is interesting" to "let's try it" without passing through the stage that matters: structured evaluation. The questions that get skipped are not technical. They are governance questions.

1. 1

   Volume

   How much of this work actually exists? AI is expensive to implement and maintain. If the process handles twelve cases a month, the economics rarely justify automation. But volume alone does not decide anything. It is the first filter, not the verdict.

2. 2

   Error tolerance

   What happens when the AI gets it wrong? In some processes, errors are caught downstream with minimal cost. In others, a single mistake triggers regulatory exposure or reputational damage. The vendor will tell you the accuracy rate. They will not tell you what happens when the remaining percentage goes wrong in your specific context.

3. 3

   Regulatory and compliance rules

   Is this process governed by rules that require human judgment, audit trails, or explainability? The vendor's compliance section describes their platform's certifications. It will not map those certifications against your organisation's actual obligations.

4. 4

   Data readiness

   Is the data this process runs on clean, structured, accessible, and sufficient? Most AI failures are data failures. If the underlying data lives in thirty spreadsheets across four departments, you have a data project before you have an AI project.

5. 5

   Integration complexity

   How does this tool connect to your existing systems? A standalone AI tool that requires manual data export and import is not automation. It is a new step in the same manual process. Vendors describe integrations as "seamless." Ask your IT team whether they agree.

6. 6

   Change readiness

   Will the people who currently do this work accept the change? AI implementation is an organisational change project, not a technology deployment. The vendor's implementation timeline does not include the six months of change management that most organisations need.

7. 7

   Time to value

   How long before this investment produces measurable results? Not the vendor's projected ROI. Your actual timeline, given your data readiness, integration complexity, change management needs, and IT team capacity. Most organisations underestimate this by a factor of two or three.

Why vendors cannot answer these for you

This is not a criticism of AI vendors. They build products. Good ones build very good products. But their incentives and yours are structurally misaligned at the evaluation stage.

The vendor wants the pilot to happen. You need to know whether the pilot should happen. Those are different objectives, and they require different questions.

The evaluation is inherently internal. It requires knowledge of your processes, your data, your regulatory context, your team dynamics, and your organisational readiness. No external vendor has that knowledge. And no external vendor is incentivised to tell you that the answer might be "not yet."

What a structured evaluation looks like

The seven questions are not a checklist to run through quickly. Each one opens a line of enquiry that requires investigation, discussion, and documentation. A structured evaluation should produce a documented verdict that can be shared, challenged, and defended.

This is what governance looks like in practice. Not a policy document that sits in a shared drive. A repeatable methodology that managers can apply every time an AI proposal lands on their desk.

The cost of not asking

Organisations that skip structured evaluation do not save time. They spend it later, in less productive ways. Failed pilots consume budget and erode internal confidence in AI. Poorly scoped implementations create new problems instead of solving existing ones.

The seven questions are not difficult. They do not require technical expertise. They require a framework and the discipline to use it consistently. That is a capability most organisations need to build deliberately. It does not emerge on its own.

For Procurement and Supply Chain teams specifically, the AI Training for Procurement & Supply Chain programme is built around exactly this kind of structured, workflow-specific capability.