Three scenarios play out in organisations that lack a structured way to evaluate AI proposals. All three are expensive. None of them are visible in the quarterly report until it is too late.
A senior leader sees a demo. It is impressive. The vendor is credible. The use case sounds relevant. There is energy in the room. Someone says "let's pilot this" and nobody objects because nobody has a structured reason to object. They have instincts and reservations, but instincts do not survive a room full of enthusiasm.
The pilot launches. Three months later, it has consumed budget, IT resources, and the attention of a project manager. The results are inconclusive because the success criteria were never defined clearly. The tool works in isolation but does not integrate with existing systems the way the vendor suggested it would.
The pilot does not get cancelled. It gets extended. It continues to consume resources without producing outcomes and joins the growing list of "AI initiatives in progress" in the board deck.
AI proposals arrive from multiple directions. A vendor pitches contract review automation to legal. The operations team wants to pilot demand forecasting. Finance is exploring automated reconciliation. HR suggests an AI-powered onboarding tool.
Each proposal has merit. None of them have been evaluated against a common framework. So the decision comes down to who makes the strongest case in the steering committee. Not the strongest analytical case. The strongest political case.
The leaders whose proposals were rejected do not understand the decision criteria because there were no decision criteria. The next time an AI opportunity arises in their function, they are less likely to bring it forward.
This is the most common scenario, and the most difficult to detect. Proposals arrive. Nobody says no. Nobody says yes. The proposals enter a holding pattern of "further discussion needed" that never resolves into a decision.
This happens when the people responsible for AI decisions know they do not have the expertise to evaluate them confidently, but also know that making a bad decision is worse than making no decision. So they delay. They ask for more information. They commission a review. Each action feels productive but produces no outcome.
The cost is invisible but real. While the organisation deliberates, the processes that could have been improved continue running at the same level of efficiency. Delay is a decision. It is just not a documented or deliberate one.
The common thread
All three scenarios share the same root cause. The people in the room do not have a structured methodology for evaluating AI proposals. They have opinions. They have experience. They have instincts. But they do not have a repeatable framework that produces a documented, defensible verdict.
This is not a knowledge gap. These are experienced managers who evaluate complex proposals every day. Budget approvals have a process. Hiring has a process. Procurement has a process. AI decisions, in most organisations, have enthusiasm and a steering committee. That asymmetry is where the risk lives.
What changes when a framework exists
When managers have a structured evaluation methodology, the conversation changes in specific ways.
The vendor meeting becomes diagnostic rather than persuasive. The manager walks in with specific dimensions to evaluate. The vendor's answers either satisfy those dimensions or they do not. The evaluation is documented, not felt.
Competing proposals become comparable. When every AI proposal is evaluated against the same dimensions, the steering committee can compare them on equal terms. The decision about which one to pursue becomes analytical rather than political.
The holding pattern breaks. When a manager has a clear methodology, they can evaluate a proposal in a defined timeframe and produce a clear verdict. The verdict might be "no" or "not yet," but those are outcomes. They are better than indefinite delay, because they free up attention for proposals that are ready.
This is a capability problem, not a policy problem
Some organisations respond to these scenarios by writing AI policies. These documents are valuable. They are also insufficient.
A policy tells people what they should do. It does not teach them how to do it. An AI governance policy that says "all AI proposals must be evaluated for risk, feasibility, and strategic alignment" does not help a manager who does not know how to evaluate those things for AI specifically. The policy creates an obligation without building the capability to fulfil it.
Building that capability requires a programme that teaches managers a specific, repeatable methodology they can apply to any AI proposal that arrives. A methodology that produces a documented output they can share with stakeholders. A methodology that works whether the proposal comes from a vendor, an internal team, or leadership itself.
The organisations that invest in this capability early will make better AI decisions. Not because they know more about AI, but because they have a structured way to think about it. That structure is what separates a defensible decision from an expensive guess.
For Procurement and Supply Chain teams building that kind of practical capability, see the AI Training for Procurement & Supply Chain programme.
AI Training for Procurement & Supply Chain
A hands-on programme built around real Procurement and Supply Chain workflows. Real simulations. Bespoke organisational content.