Ask Better Questions Before Choosing Your Next AI Vendor

An AI vendor’s evaluation report can help you make a better buying decision. It can’t make the decision for you. Before treating a reassuring result as a reason to commit, find out what was tested, what the evaluator could see, and whether the findings apply to the work your team actually needs done.

That distinction matters as independent evaluation becomes part of the conversation around advanced AI. For a business leader, the useful question is straightforward. What evidence would justify putting this product into this particular workflow?

What changed in independent AI evaluation

On October 15, 2026, Anthropic announced that it had begun giving a METR team access comparable to its internal risk-assessment teams. This was an evaluation-access update, not a new model release or a completed assessment of every business use case. The announcement says METR can publish without Anthropic vetoing publication, while Anthropic can require redactions within predefined categories. Anthropic clarified the project terms on October 16. Read Anthropic’s announcement.

METR’s own October 14 explanation adds important context. It describes a commitment to disclose scope, access limitations, publication constraints, and relevant conflicts. It also acknowledges that voluntary engagement leaves evaluators dependent on companies providing information and access. The organization presents evidence to inform decisions, rather than claiming to settle every deployment question. Read METR’s approach.

For buyers, our recommendation is to use independent assessments as one part of the evidence. Then build a separate acceptance test around your own work.

Start with the buying decision you need to make

A vague goal produces a vague evaluation. “We need an AI platform” gives a sales team plenty of room to demonstrate impressive features without showing whether any of them solve your problem.

Write a short buying brief before the next demo. Name the recurring task, the people doing it, the expected output, and the reason the current approach needs improvement. Describe the information available when that task begins. Include the exceptions that make it difficult.

For example, a hypothetical customer-support team might need help preparing answers from current product documentation. Its buying decision should depend on whether the system produces useful, supported drafts when documentation is incomplete or conflicting. A polished response to a simple question won’t answer that.

The brief also gives the team permission to reject a technically impressive product that doesn’t fit. That is a valuable outcome of a pilot.

Ask for evidence you can examine

A vendor’s answer should point to something your team can review. Start with these questions in the next evaluation meeting:

• Which product version and configuration produced the reported result?

• Were the test cases chosen by the vendor, the evaluator, or the customer?

• Can we see examples of unsuccessful outputs as well as successful ones?

• What work remains for a person after the system generates its answer?

• Which parts of the proposed workflow were outside the test?

• What has changed in the product since the assessment?

Ask for the original report rather than relying on a slide quoting its headline. If the report is confidential, discuss an appropriate review arrangement. If the evidence isn’t available, record that as an unresolved question. Don’t quietly turn an unknown into a passing score.

Keep the distinction between a claim and its supporting material visible. “The vendor says this works” and “our team reproduced the result” are different entries in a buying decision.

Give frontline users a real role in the test

The people who do the work know where a convincing answer can still be wrong. Include them before the shortlist becomes a near-final purchase.

Ask a small group to select realistic cases and explain how they judge a good outcome. Include ordinary work, difficult exceptions, and cases where the right response is to ask for clarification. Use approved, sanitized examples where customer or employee information would otherwise be exposed.

In the support example, one case could contain an outdated article alongside a newer policy. Another could omit the product version entirely. These are hypothetical test designs, not claims about any named product’s behavior.

Give reviewers time to compare results against the existing process. Have them record the correction needed, not just a thumbs-up or thumbs-down. A wrong product version, an unsupported promise, and an awkward sentence create different kinds of work.

That feedback should influence the purchase. Asking employees to test a tool after the decision is effectively final turns their involvement into a formality.

Run a small acceptance test before a larger commitment

Use the following sequence as a practical starting point, adapting its scale to the consequence of a mistake.

1. Define what passing means. Agree on the required quality, acceptable review effort, and failures that would stop the pilot before seeing the vendor’s results.

2. Keep comparison conditions consistent. Use the same case set and available information for each shortlisted product. Record any extra setup or expert assistance provided.

3. Review the full output. Check supporting information and required corrections. Count an answer as incomplete if someone must reconstruct missing work to use it.

4. Repeat difficult cases. Look for inconsistent behavior across repeated attempts. Save the inputs, outputs, version, and settings so the team can explain what happened.

5. Make a bounded decision. Approve a defined use, continue testing a specific gap, or stop. Avoid turning a narrow success into permission for unrelated workflows.

A pilot should finish with a decision someone can explain. “The team liked the demo” is feedback. “The system met our agreed requirements for this task, with these remaining limits” is a basis for choosing.

Keep the buying decision human-led

Choose a business owner who can bring procurement, technical reviewers, and frontline users together. Their job is to weigh the evidence and make the tradeoffs explicit, including when the available evidence isn’t strong enough yet.

Where that leadership capacity is missing, HiVoltage’s fractional CAIO support can help connect AI selection to business priorities and the people responsible for the work.

Before the next vendor meeting, bring a one-page buying brief and three difficult examples from the workflow. Ask the vendor to show how its product handles them. You’ll leave with a more useful conversation than another tour of features, and a clearer basis for deciding what your team should use next.