Bottom line
Evaluate an AI vendor against your actual workflow rather than its most impressive demonstration. Test useful outputs, failure behavior, data handling, human controls, total costs, and exit options. Request evidence for important claims and involve the people responsible for security, procurement, and operational risk.
Choose representative test cases.
Select ordinary tasks, difficult cases, and examples with incomplete information. Define what a useful result looks like before running the evaluation. Compare candidates on the same inputs and record the review effort each requires. A polished answer that takes substantial correction may be less useful than a simpler output that is accurate and easy to verify.
Inspect how the product fails.
Check whether it admits missing information, distinguishes evidence from inference, and handles conflicting instructions safely. Ask what happens during service interruptions and model changes. Do not deliberately expose real customer data to test a tool before the data-handling review is complete. Use authorized, minimized, or synthetic examples for the initial evaluation.
Ask specific data questions.
Where is information processed and stored? Which subprocessors receive it? What retention, deletion, access control, and model-training terms apply to your chosen plan? Request the current contractual documents and security evidence. An AI summary of a policy is not a substitute for reading the applicable terms, and a vendor's broad compliance claim may not cover your intended use.
More on AI Strategy — every week.
Join 2,400+ leaders getting the decision intelligence briefing.
Verify human oversight and permissions.
Determine who can configure the system, approve sensitive actions, view records, and export work. Distinguish a feature shown in a demonstration from one included in the plan you are buying. If your workflow requires an approval boundary, test it rather than assuming that a label such as “enterprise-ready” guarantees the required control.
Compare full costs and operational fit.
Include usage charges, implementation, monitoring, training, human review, and support. Ask how prices change with volume and which capabilities require a higher plan. Consider the effect on existing tools and team responsibilities. Build a realistic small pilot before signing a commitment based on savings that have not yet been measured.
Plan the exit before the purchase.
Check export formats, deletion procedures, contract renewal terms, and dependencies on proprietary workflows. Name the decision owner and document why the chosen vendor meets the agreed requirements. Unresolved security or legal questions should go to qualified reviewers. A structured evaluation reduces avoidable uncertainty; it does not guarantee vendor reliability or eliminate the buyer's responsibility for how the tool is used.
Research context
Stanford's 2025 AI Index reports that 78% of organizations used AI in 2024, up from 55% in 2023.
Stanford Institute for Human-Centered AI: The 2025 AI Index Report (2025)This independently published statistic provides context for the topic; it does not substantiate every claim in this essay.
More on AI Strategy, weekly.
One essay. Every week. No noise.