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Define the use case before comparing vendors
A useful evaluation starts with a bounded description of the work. Write down who will use the system, what task or decision it supports, what inputs it receives, what outputs it produces, and who may be affected. Include the operating context: data sensitivity, expected volume, existing workflow steps, human checkpoints, and the consequences of an incorrect or unavailable result.
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Be specific about which errors matter. For one task, a false positive may waste staff time; for another, a false negative may expose a customer or organization to serious harm. That distinction should shape both the tests and the relative importance of accuracy, oversight, resilience, and cost of failure.
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NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance for incorporating trustworthiness into AI design, deployment, use, and evaluation; it is not a product certification or a legal-compliance determination. NIST says the framework is being revised, so check its current official resource when applying it.
How to test an AI vendor’s accuracy
Accuracy depends on the task, users, inputs, and conditions of use. A vendor-wide score or a result on a general benchmark does not establish how a system will perform on your cases. Ask for evidence tied to the intended use, then run a buyer-controlled evaluation against pre-agreed acceptance criteria.
Ask for the methods behind reported results
- The exact task and intended-use conditions covered by each score.
- The test set’s composition, size, date, and representativeness of your expected cases.
- Task-relevant error measures, such as false positives and false negatives where applicable.
- Variation across relevant user groups, languages, input conditions, document types, or operating conditions.
- Whether results include human review or correction, and what reviewers were expected to do.
- Known limitations, performance outside tested conditions, model or system version, and how updates are evaluated.
NIST’s guidance is direct: “Accuracy measurements should always be paired with clearly defined and realistic test sets – that are representative of conditions of expected use – and details about test methodology; these should be included in associated documentation.” See NIST AI Risks and Trustworthiness. NIST’s AI RMF core also calls for documenting limits to generalizability beyond development conditions; its technical testing, evaluation, verification, and validation resources are available through the NIST AI Resource Center.
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Run a representative evaluation
Use cases that resemble the data and operating conditions you expect, governed appropriately for your organization. Agree on what counts as acceptable before reviewing results, including which errors are tolerable, which require escalation, and which should stop deployment. Record the test method and limits so the result is not mistaken for proof of performance in situations you did not test. A demonstration can help explain a product, but it is not a substitute for this evidence.
Review security, privacy, and supplier risk
Assess the vendor as a supplier, not only the model as a technical component. Start by asking for a system and data-flow explanation: what information is sent, where it is processed and stored, which third parties can access it, how long it is retained, and whether customer data is used for training or product improvement.
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Request relevant security and privacy documentation and ask how access controls, encryption, vulnerability handling, incident response, resilience, recovery, and material changes are managed. Understand the supplier chain and component dependencies. Where appropriate, contracts should address data handling, incident notification, access to audit or evidence, and deletion or termination duties, and should support review of the vendor’s processes and standards.
NIST’s Generative AI Profile, NIST AI 600-1, published July 26, 2024, recommends use-case-based supplier assessment, third-party inventories, procurement due diligence covering privacy, security, and intellectual property, and contractual clauses that allow organizations to evaluate third-party processes and standards.
For ICT supplier due diligence, NIST SP 1326, published July 8, 2026, highlights provenance, resilience, foundational cybersecurity practices, foreign ownership, control or influence, and supply-chain tiers. These are prompts to consider in context—not a universal pass/fail checklist, nor evidence that every factor applies identically to every AI vendor.
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A system can perform well on a test set and still create friction or risk in daily operations. Map how it will fit into existing work, then verify the details in the intended environment: integrations, identity and permissions, data formats, handoffs, user training, exception paths, latency or throughput needs, and the work required to correct errors.
Use a pilot with representative users and cases. Include ordinary work as well as edge cases, uncertain outputs, unavailable dependencies, and cases that should trigger human escalation. Check that people can tell when to verify, override, or stop using an output. Document the manual fallback and recovery arrangements before the tool becomes operationally important. NIST’s Generative AI Profile recommends documenting value-chain risks and fallbacks for third-party generative AI systems, as well as contingency processes for failures in high-risk third-party systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare vendors on the same evidence
Keep test scenarios and evidence requests consistent across candidates, but set weights to match your use case and the consequences of failure. Preserve the underlying results; a single blended score can obscure important differences between, for example, strong task performance and weak recovery arrangements.
| Evaluation axis | Evidence to compare |
|---|---|
| Task accuracy and limits | Same buyer-defined cases, relevant error measures, methodology, edge cases, and generalization limits |
| Security and resilience | Data flows, controls, vulnerability and incident response, recovery, change handling, and evidence quality |
| Privacy and intellectual property | Data use and retention, third-party access, training use, provenance, and contract terms |
| Workflow fit | Integration work, permissions, handoffs, exception handling, user experience, and human review |
| Failure handling and oversight | Escalation, override, safe failure, fallback, audit trail, and responsibility allocation |
| Supplier and lifecycle | Subprocessors, dependencies, provenance, update notices, monitoring, and reassessment |
NIST recommends attention to validity and reliability, security and resilience, and third-party risks; it does not prescribe a universal vendor scorecard or weighting formula. Set priorities using your operating conditions, applicable sector requirements, and the harm or cost associated with failure.
Monitor the system after selection
Before launch, preserve a baseline: supplier and product version, model version if disclosed, configuration, test set, methods, test date, limitations, and acceptance decision. Establish reassessment triggers such as a material system or model change, a new subprocessor, changed data use, a security incident, or observed performance degradation.
Keep a route for users to report problems and maintain a practical fallback for important workflows. The NIST Generative AI Profile recommends monitoring, assessment, alerting, and dynamic evaluation of third-party generative AI risks. Reassessment should use the same discipline as procurement: identify what changed, determine whether it affects the original use case, and test where needed before relying on the changed system.
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