How to Evaluate AI Vendors: A Practical Framework for Business Leaders

Why Most AI Evaluations Fail

A prototype that works in a demo still has to reach production. In McKinsey's 2024 survey, respondents most often reported that their organizations needed one to four months from project start to put generative AI into production — McKinsey, 2024. The gap between a convincing demo and a running system is where evaluation either earns its keep or fails.

Most organizations evaluate AI vendors the wrong way. They run a proof-of-concept on clean data, see impressive numbers, and sign a contract — only to find that production reality is very different.

Here's what to look for instead.

The Four Dimensions to Evaluate

1. Data Requirements

Every AI system needs data to function. The critical questions are: What data does this system need? Do we have it? In what format? Who owns it?

2. Integration Complexity

AI doesn't exist in isolation. It needs to plug into your existing systems. Ask vendors for a realistic integration timeline with your specific stack.

3. Total Cost of Ownership

The license fee is rarely the biggest cost. Factor in data preparation, integration engineering, monitoring, and retraining costs.

4. Explainability

For regulated industries (finance, healthcare), you need to explain why the AI made a decision. Not all models support this.

The Questions to Ask in Any Vendor Demo

Before signing anything: ask the vendor to run the demo on your data, not their sample dataset. Ask what happens when the model is wrong — who is liable and how is it corrected? Ask for a reference customer in your industry who has been live for 12+ months (not just launched). These three questions show how the vendor behaves when something goes wrong — before you are committed.

Red Flags in a Vendor Proposal

• An accuracy figure with no test set behind it. Ask what data it was measured on, and whether you can rerun the measurement yourself.
• No plan for monitoring. A model that works on day one drifts as your data changes. Someone has to watch it, and the proposal should say who.
• Pricing that covers only the licence. Integration, data preparation and support are the work; if they are missing, they will arrive as change requests.
• Your data or your prompts become theirs. Check who owns the trained model, the prompts and the logs, and what you can take with you if you leave.
• No human in the loop. For any decision that matters, there should be a documented way for a person to review, correct and override the output.

Run a Pilot on Your Own Data

A pilot answers one question: does this work here? Keep it narrow and decide the rules before it starts.

1. 01
   Pick one process

   with a baseline you already measure.

2. 02
   Agree the pass criteria in writing

   before the first test — the metric, the threshold and the test data.

3. 03
   Connect it to one real system

   , not a spreadsheet export, so integration problems surface now.

4. 04
   Review every failure

   , not only the average. The errors show you what production will look like.

After You Sign

Treat the system as a product you operate, not a purchase you made. Name an owner on your side, keep a log of wrong answers and how they were fixed, and keep an exit plan: the data, prompts and configuration you would need to move to another vendor. If you want a second opinion on a proposal, we review AI vendor proposals against your own data and systems.