The Tool Desk
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Start with the task, not the tool
“Adopt AI” is too broad to evaluate. Name one workflow and the decision you need to make about it. For example, a business might assess whether an AI tool can sort incoming expense receipts into categories for an employee to review. That is a different evaluation from letting a system approve reimbursements or make tax decisions.
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- Workflow and users: What work is being done, by whom, and at what point in the process would the tool be used?
- Current method: How is the work handled now, including the time, cost, error checks, and handoffs involved?
- Desired outcome: What should improve—such as turnaround time, consistency, or staff capacity—and how will you measure it?
- Failure boundaries: Which errors are tolerable, which require correction, and which must stop the process or trigger escalation?
- Decision owner: Who can approve a trial, who owns the workflow, and who has authority to pause or end it?
Set the success measures and unacceptable outcomes before watching a polished demonstration. Otherwise, a compelling example can quietly redefine what counts as success. There is no universal test method for every AI use: NIST’s TEVV-Athlon announcement describes evaluation as adaptable to the application and its requirements.
Map the data, people, and consequences
Trace the proposed use from input to outcome. Identify what information enters the system, what it returns, where the output goes, who acts on it, and who may bear the cost of an error. Consider both normal operation and situations where the tool is unavailable, produces an uncertain answer, or returns something plausible but wrong.
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- Data: Does the tool receive personal, financial, confidential, regulated, or commercially sensitive information? Is each field necessary for the task?
- Workflow position: Is the output a draft, a recommendation, or an action that changes a record, sends a message, or affects a person?
- Impact: Who benefits if the tool works, and who might be disadvantaged by a mistaken or uneven result?
- Fallback: Can staff return to the existing process, and what happens to work already in progress if the service fails?
Risk depends on the use, not just on the model’s label. A tool drafting an internal summary may call for different controls from one influencing access to credit, pay, hiring, or a customer’s financial account. NIST advises considering trustworthiness throughout pre-design, development, deployment, use, and evaluation; its AI Risk Management Framework FAQs describe the framework’s lifecycle approach and trustworthiness characteristics.
Compare candidates against the same criteria
Use the same representative tasks, inputs, and review conditions for each candidate, including the current non-AI process where practical. Prioritize criteria according to the workflow’s consequences and document trade-offs. NIST identifies characteristics such as validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy, and fairness; it cautions that not every characteristic applies equally in every context.
| Comparison area | Questions to ask | Evidence to record |
|---|---|---|
| Task results | Does it complete the defined task? How severe are errors, and are results consistent? | Correctness against reviewed examples, error types, and consequences. |
| Reliability and resilience | Does performance hold across ordinary, unusual, and incomplete inputs? What happens during an outage or failure? | Results across test cases, failure behavior, availability commitments, and recovery process. |
| Data and privacy | What information is sent, retained, reused, or accessible to others? | Data flows, retention and reuse terms, access controls, and privacy review. |
| Security and supplier transparency | What security practices, dependencies, service commitments, and assurance evidence can the supplier explain? | Relevant security materials, contractual terms, software-component information, and assurance reports, when available. |
| Fairness and impacts | Who bears the cost of mistakes? Could meaningful differences in performance affect groups of people? | Relevant subgroup or impact checks, limits of the available evidence, and mitigation steps. |
| Explainability and accountability | Can users recognize limitations, challenge outputs, and identify who is responsible for the decision? | User guidance, escalation route, human decision owner, and records needed to review an outcome. |
| Operational fit | Can the tool fit the workflow with appropriate review, training, support, monitoring, and an exit option? | Integration needs, staff responsibilities, support arrangements, and replacement or rollback plan. |
| Overall decision value | Do expected benefits justify implementation, review, and risk-management effort compared with alternatives? | Documented benefits, costs and workload, risks, and reasons for the decision. |
This is a comparison framework, not a universal scoring formula. Do not hide a serious failure behind a high average score, and do not assign numerical weights unless your organization has a defensible reason for them.
Test with realistic work before a rollout
Build a test set that resembles the intended workflow rather than the vendor’s showcase examples. Include routine cases, edge cases, and cases designed to expose failure. For an expense-receipt example, that could mean clear receipts, blurry images, missing fields, unfamiliar merchants, and transactions that should be sent to a person instead of categorized automatically.
- Choose and document cases. Record where examples came from, what they represent, and any privacy steps taken before testing.
- Define the expected result. Decide what counts as correct, partially useful, wrong, or unsafe for each case before reviewing outputs.
- Run the same conditions. Use comparable settings and inputs across candidates. Note changes in prompts, configuration, or model version that might affect results.
- Review outputs and failures. Track error type and severity, consistency, handling of uncertainty, and whether a human reviewer could catch the problem.
- Write down limits and controls. State what the test does not establish, which work still needs human review, and what conditions would rule out use.
NIST’s Generative AI Profile, published July 26, 2024, recommends iterative, documented test, evaluation, verification, and validation (TEVV) early in the lifecycle and calls for input from representative AI actors. For a more structured evaluation approach, NIST’s TEVV-Athlon Framework for Evaluating AI Systems announcement is dated August 7, 2026. Its stated public-input deadline, October 6, 2026, has passed; the announcement alone does not establish what happened to the draft afterward.
Review the supplier, contract, and data terms
For a third-party tool—especially a generative AI service—evaluate the supplier as well as the model’s outputs. Product descriptions do not answer what happens to business data or what recourse you have when the service changes or fails.
- Data handling: Check what the supplier collects, how long it retains information, whether it uses submitted data to improve services, and how access is controlled.
- Privacy and security: Review the risks for the specific data and workflow, and ask what security practices and assurance evidence are available.
- Intellectual property: Consider what employees may submit and whether generated material or submitted content creates relevant ownership or use concerns.
- Service commitments: Understand availability, support, incident notification, change management, and what happens if terms or features change.
- Dependencies and assurance: Where proportionate, seek information about software components, service-level agreements, and assurance reports.
- Exit and portability: Check whether you can retrieve needed records, stop use, and move the workflow elsewhere without losing control of business information.
NIST’s Generative AI Profile report discusses third-party risks and possible procurement controls, including due diligence, service-level agreements, software bills of materials, and assurance reports. These are options to select in proportion to the system and context, not a checklist every business must apply identically.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Pilot with oversight and clear stop conditions
A bounded pilot is a practical way to learn how a tool behaves in the actual workflow; it is not a one-size-fits-all NIST requirement. Define the pilot before access expands, and keep consequential decisions with an accountable person until the evidence justifies any change.
- Limit the scope: Specify users, workflow, data, duration or review point, and what the tool is not authorized to do.
- Set human review: Identify which outputs require review, who performs it, and how reviewers can challenge, correct, or escalate results.
- Set stop conditions: Examples include a serious error, a privacy or security incident, repeated failure on a key task, or a supplier change that invalidates the assessment.
- Monitor in use: Track performance and incidents against the measures defined for the task, rather than relying only on user impressions.
- Reassess material changes: Review the decision if the model, supplier, data, or workflow changes in a way that could affect risk or performance.
NIST’s AI Risk Management Framework and Generative AI Profile address evaluation and risk management across the lifecycle, including monitoring and incident-response considerations. The AI Risk Management Framework is voluntary guidance released January 26, 2023, and NIST says it is being revised. It is not a legal certification or a substitute for organization-specific legal, security, and procurement review. Which laws apply depends on the business’s jurisdiction, sector, data, and intended use.
Record the decision so it can be revisited
Keep a concise decision record that another responsible person can understand without relying on a sales presentation or undocumented assumptions. The NIST AI RMF Playbook organizes suggested actions and documentation guidance around Govern, Map, Measure, and Manage.
- Use case, workflow owner, users, and affected people.
- Alternatives considered and the comparison criteria used.
- Test cases, methods, results, known limitations, and human-review needs.
- Supplier and data-term findings, identified risks, and chosen controls.
- Decision, approval conditions, pilot scope, stop conditions, and monitoring owner.
- Events or changes that will trigger reassessment.
The decision can be to proceed, proceed only under specified controls, gather more evidence, or decline adoption. The point is not to approve AI by default; it is to make the choice proportionate to the task, evidence, and consequences.
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