AI Procurement: Buying Smart When Every Vendor Claims to Be “AI-Powered”

Every software vendor now describes itself as AI-powered. The label has been applied so broadly that it has stopped meaning anything. For organisations trying to buy AI capability, this creates a real problem: the marketing no longer helps you tell good tools from bad ones.

This article offers a way to cut through the noise and evaluate AI tools on what actually matters.

Why “AI-powered” tells you nothing

The phrase covers everything from genuine, well-engineered AI systems to thin wrappers around a public model to features that were rebranded to ride the trend. A vendor saying “AI-powered” is telling you about their marketing, not their product.

This means procurement cannot rely on vendor claims. It has to test what the tool actually does, how well it does it, and whether it fits your context.

The questions that actually matter

Cut through the marketing by asking concrete questions.

What specifically does the AI do? Not “it uses AI” but “it does this task, in this way, with this output.” If the vendor cannot answer plainly, that tells you something. How well does it perform on our data? General benchmarks are less useful than performance on inputs that look like yours. Ask for a trial with real examples. What happens when it is wrong? Every AI system makes mistakes. A good vendor can tell you the failure modes, how often they occur, and what recourse you have. Where does our data go? Understand whether your data trains the vendor’s models, where it is stored, who can access it, and what happens when you leave. How does it handle governance and compliance? Ask about audit logs, explainability, access controls and the documentation you would need for regulators. And what is the real cost? Usage-based pricing can scale unpredictably. Understand the cost at the volume you actually expect.

Evaluating beyond the demo

Demos are designed to impress. Evaluation needs to go further. Run a pilot with real users doing real work, not a curated showcase. Test the edges - unusual inputs, high volume, the cases the demo carefully avoided. Talk to reference customers who use the tool the way you would. And involve the people who will actually use it, because their judgement of whether it helps is the judgement that matters.

Build versus buy versus wait

Not every AI need should be met by buying a tool. Sometimes existing tools you already own have added AI capability that covers the need. Sometimes the capability is core enough to your business that building or heavily customising makes sense. And sometimes the right answer is to wait - the category is moving fast, and a tool bought today may be outclassed in months. Procurement should weigh these options, not default to buying.

Avoiding lock-in

AI tools can be sticky. Your data, your workflows and your employees’ habits all accumulate inside them. Before committing, understand how hard it would be to leave: can you export your data, are the workflows portable, and how dependent would you become on this one vendor? Favour tools that make leaving possible, because that keeps the vendor honest and keeps you flexible as the market evolves.

What leaders should do

If you are responsible for AI procurement, stop letting “AI-powered” do any work in your evaluation. Ask what the tool specifically does, test it on your data with your users, understand the failure modes and the data handling, and weigh build, buy and wait honestly. Treat AI procurement as a discipline, not a reaction to vendor marketing.

The bottom line

When every vendor claims to be AI-powered, the claim is worthless as a signal. Smart AI procurement ignores the label and tests the substance: what the tool does, how it performs on your data, how it fails, where your data goes, and how hard it is to leave. Organisations that buy on evidence rather than marketing will build a genuinely useful AI stack. Those that buy on claims will accumulate expensive disappointments.