How to Evaluate an AI Vendor Proposal: A Non-Technical Buyer's Checklist

Evaluation comes down to output quality and contract structure — both can be assessed rigorously with zero technical background.

The biggest anxiety for non-technical buyers evaluating AI proposals is "I don't understand this, I can only trust them." That anxiety can be resolved: evaluation centres on output quality and contract structure. Both can be assessed rigorously without any technical background.

Step 1: Refuse the Canned Demo. Demand Testing With Your Data.

A vendor's showcase demo uses curated data and ideal workflows, at a distance from your actual environment. The only effective test material is your own data: take three to five real documents (with sensitive content removed) and ask the vendor to run results on-site or within a set deadline.

A vendor willing to do this shows real capability. One who makes excuses — that signal itself is the answer.

Step 2: Evaluate Output on Three Dimensions

Once you have test output, check three things in sequence. Each requires only comparison and repetition — zero technical threshold.

Stability. Run the same input three times. The key content across all three outputs should be consistent: names, numbers, conclusions. A system whose output varies every time means colleagues will spend time verifying it every day after go-live.

Fidelity. Compare the output against the original document sentence by sentence: is there any "fact" in the output that cannot be found in the source? AI systems occasionally generate plausible-sounding but fabricated content — this is known as hallucination. If it appears during testing, ask the vendor: "What mechanism does the system use to reduce this, and how does it flag when it occurs?" A prepared vendor can name specific mechanisms. A vendor who answers "our model is very accurate" — be careful.

Format reliability. Does the output conform to the agreed format every single time? Occasional format deviation causes occasional downstream process interruption — these failures are the most costly to maintain.

Three dimensions, completable in one afternoon. That afternoon is worth more than any presentation.

Step 3: Eight Contract Questions You Must Ask

A proposal with concrete answers to all eight questions structurally outperforms most on the market.

Red Flag List

Promising "zero errors" or "100% accuracy" — contradicts technical reality. Refusing staged payment, requiring full upfront payment. Source code ownership ambiguous or explicitly staying with the vendor. Unable to explain API cost structure. Demo uses only curated data, refuses to test with your documents.

For common AI project failure patterns, see Why SME AI Projects Fail.

FAQ

Q: Is it enough to compare only on total price?

Total price must be compared together with the full four-layer cost structure (see The True Cost of AI Automation). Low quote + source code lock-in frequently exceeds high quote + full handover over three years.

Q: Do I need to hire a technical consultant to review it?

All the above checks can be self-executed. For large project amounts or core business data involvement, having an independent engineer audit the source code and architecture is reasonable insurance — the cost is typically small relative to the project amount.