Evaluate an AI tool against a defined business task—not a polished demo. Set success criteria and unacceptable failure modes in advance, test candidates on representative work, examine data and supplier risks, and pilot with human oversight before expanding use. Keep a record of the evidence and the conditions for approval.
The NIST AI Risk Management Framework (AI RMF) offers a useful structure for this work: it is voluntary guidance for managing risk across the design, development, use and evaluation of AI systems. NIST released AI RMF 1.0 on January 26, 2023, and says it is being revised. It is not a legal certification or a replacement for your organization’s legal, security or procurement review. See NIST’s AI Risk Management Framework and AI RMF FAQs.
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1. Define the job before comparing tools
Start with the workflow and the decision you need to make. “Use AI to improve customer service” is too broad to evaluate; specify the task, such as drafting replies to a particular type of support request for an employee to review.
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- Workflow and users: Describe where the tool would fit, who would use it and who would rely on its output.
- Current method: Record how the work is done now, including important review steps or recurring problems.
- Desired outcome: Define what improvement means for this task, such as more complete drafts or less manual work, and how you will assess it.
- Failure boundaries: List outcomes that would make a tool unacceptable, such as a materially misleading answer reaching a customer or sensitive information being exposed.
- Decision owner: Name who is accountable for assessing the evidence and approving, limiting or rejecting the use.
Set the criteria before vendor demonstrations so a persuasive presentation does not become your test. There is no universal weighting or success threshold: choose measures that reflect the workflow, the consequences of error and the people affected.
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2. Map the data, people and consequences
Trace how information and decisions move through the proposed use. NIST advises considering trustworthiness throughout pre-design, design and development, deployment, use, and test and evaluation—not only at purchase time.
- What information goes into the system, and what does it produce?
- Where does the output enter the workflow, and who checks, acts on or is affected by it?
- What happens if the output is wrong, inconsistent, unavailable or difficult to challenge?
- Could errors or uneven performance affect particular groups differently?
- What changes if the tool is removed, replaced or temporarily unavailable?
Use the answers to determine how much testing, review and control the use needs. A tool that drafts internal meeting notes has different consequences from one whose outputs influence a consequential decision about a person.
3. Compare candidates against the same criteria
For each candidate, use the same representative tasks and operating conditions. Compare the dimensions that matter to your use case; NIST notes that trustworthiness characteristics do not apply equally in every setting and can involve tradeoffs. Its list includes validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed.
| Dimension | What to assess | Useful evidence or questions |
|---|---|---|
| Task results | Whether the tool completes the defined task, how consistent it is and how serious its errors are. | Compare outputs on the same cases; examine important errors, not just an overall impression. |
| Reliability and resilience | Behavior on ordinary, unusual and failed inputs; availability and recovery. | What happens when input is incomplete, ambiguous or outside the expected format? |
| Data and privacy | What information is sent, how it is retained or reused, who can access it and the privacy exposure. | Review data-use terms, retention settings and access controls for the proposed configuration. |
| Security and supplier transparency | Security practices, dependencies, contractual commitments and assurance material. | Ask what security documentation and service commitments are available and what they cover. |
| Fairness and impacts | Who benefits, who bears the cost of errors and whether outcomes differ meaningfully across affected groups. | Use cases that reflect the people and contexts involved in the actual workflow. |
| Explainability and accountability | Whether users can recognize limitations, challenge outputs and identify who owns the decision. | Can users tell when review is needed, and is responsibility for the final action clear? |
| Operational fit | Integration, training, human review, support, monitoring and the ability to exit or replace the tool. | Consider what must change in the workflow and what happens if the supplier or tool changes. |
| Total decision value | Expected benefit weighed against implementation, oversight and risk-management work. | Include the ongoing human and operational effort, not just the ease of a demonstration. |
Document tradeoffs rather than compressing them into an unexplained score. A candidate that performs well on routine cases may still be unsuitable if its failures are hard to detect or costly to correct.
4. Test with representative work, including failure cases
Build an evaluation set from realistic examples before choosing a winner. Include routine inputs, edge cases and cases designed to expose likely failure modes. If the tool will serve different teams or groups, include the contexts that matter to those users.
- Fix the test conditions. Use the same cases, instructions, settings and review rules for each candidate. Record any configuration that could affect outputs.
- Decide how results will be judged. Define the task-specific measures, who reviews outputs and how errors are classified by consequence.
- Run and record the tests. Preserve the test cases, methods, results and observed limitations so the decision can be revisited.
- Assess human review. Determine what reviewers must check, whether they can identify errors and what should be escalated rather than accepted.
- Compare against your current method. The relevant question is whether the tool improves the defined workflow under realistic conditions, not whether its output looks impressive in isolation.
NIST’s Generative AI Profile, published July 26, 2024, recommends iterative, documented testing, evaluation, verification and validation (TEVV) early in the lifecycle, with representative AI actors involved. NIST’s full Generative AI Profile report provides its detailed guidance. NIST also describes an adaptable assessment approach in its TEVV-Athlon Framework for Evaluating AI Systems announcement, dated August 7, 2026. The announcement’s public-input deadline was October 6, 2026; that deadline has passed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Review the supplier, service and data terms
For a third-party tool—especially a generative AI service—evaluate the actual product and configuration you intend to use, not just general statements about the supplier. Check how the terms and technical controls address the information and risks mapped for your use case.
- Data handling: What data is collected, retained, used or shared? What controls govern access and deletion?
- Privacy and security: What risks arise from sending your data to the service, and what security practices or assurance evidence can the supplier provide?
- Intellectual property: What exposure could result from submitting protected material or using generated outputs?
- Service commitments: What availability, support, change-notification or other commitments are stated in the applicable agreement?
- Dependencies and assurance: What software components and third parties are involved, and are materials such as a software bill of materials or assurance reports available?
- Exit options: What happens to your data and workflow if you stop using the service or switch providers?
NIST’s Generative AI Profile discusses acquisition and procurement due diligence, service-level agreements, software bills of materials and assurance reports as possible controls. Select controls in proportion to the system and its context; their mention does not mean every organization needs every item.
6. Pilot with oversight and monitor after adoption
A successful test is evidence for a bounded pilot, not proof that every team or situation is safe. Set the pilot’s scope and approval conditions before users begin relying on outputs.
- Specify permitted tasks, users, data and workflows; keep use outside that scope from becoming informal practice.
- Set human-review requirements and a route for escalating uncertain, harmful or unusual outputs.
- Define stop conditions, such as a serious failure or a change that invalidates the evidence used to approve the pilot.
- Track performance and incidents against the criteria established for the use case.
- Reassess when the model, supplier, data or workflow changes materially.
These are practical applications of NIST’s lifecycle and iterative-testing guidance, not a one-size-fits-all NIST mandate. The AI RMF covers evaluation across the lifecycle, and its Generative AI Profile includes monitoring and incident-response considerations.
7. Keep a decision record
Record enough to explain what was approved, why and under what conditions. NIST’s AI RMF Playbook organizes suggested actions and documentation guidance around Govern, Map, Measure and Manage; it is a resource for implementation, not a certification checklist.
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- Use case, workflow, affected people and accountable owner
- Risks identified and comparison criteria chosen
- Test cases, methods, results and known limits
- Supplier and data-term findings
- Approval scope, human-review requirements and stop conditions
- Monitoring measures, incident route and triggers for reassessment
Use the NIST AI RMF Playbook as a source of suggested actions. Which laws apply depends on the jurisdiction, sector, use case and data involved, so route the proposed deployment through organization-specific legal review rather than assuming a general framework settles compliance.
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