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Start with the decision the pilot must support
A pilot is a bounded test of a specific use, not proof that AI is generally useful or production-ready. Before selecting a tool or setting a launch date, write down the decision the organization expects to make at the end: stop the work, revise the use case or controls, or expand it.
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Record the intended workflow, target users, problem to solve, and the current process that will serve as the baseline. Specify what the AI system may do, what it must not do, what data it can access, and which decisions require human review. Choose a comparison method that makes any claimed change in quality, time, cost, or service observable.
These are practical planning recommendations, not a verbatim checklist from NIST. They apply the NIST AI Risk Management Framework (AI RMF) Core approach of mapping context and impacts, aligning risk work with organizational goals and tolerance, and managing risks throughout the AI system lifecycle.
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Set goals and decision criteria before testing
Choose a small set of outcomes that matter to the use case. For each one, document the baseline, measurement method, review period, target or acceptable range, and what action the result will trigger. Possible outcomes include task completion, work quality, cycle time, cost, user experience, or access and service quality; include only measures relevant to the decision.
There is no universal accuracy, adoption, or ROI threshold established by the cited frameworks. Set thresholds for the specific task, baseline, consequences of failure, organizational risk tolerance, and applicable requirements. Predefine failure and escalation conditions as well: examples include unacceptable output errors, privacy or security incidents, policy violations, material harm to users, or a required human review that did not occur. The accountable organization must set the trigger levels for its context.
Use a balanced measurement plan
Adoption and time saved do not establish that a pilot is effective or safe. Pair the business outcome with task-level quality, system performance, user experience, operating cost where relevant, and evidence that controls worked.
| Measurement area | What to examine | Useful evidence |
|---|---|---|
| Business outcome | Whether the intended task or service improved against the existing-process baseline | Task completion, quality, cycle time, cost, or access measures tied to the stated goal |
| System quality and performance | Whether results are dependable and timely for the actual workflow | Error rates, accuracy scores, relevant performance benchmarks, latency, reliability, and—where applicable—request rates or token counts |
| Use and experience | Whether target users can use the system appropriately and whether it creates friction or workarounds | Meaningful usage or adoption data, surveys, interviews, stakeholder feedback, confusion reports, and qualitative concerns |
| Risk and controls | Whether relevant risks were found and required safeguards operated | Test results for harms and policy constraints, incident and escalation records, human-review effectiveness, and control checks |
| Cost and resources | Whether the approach is viable to operate for the intended use | Operational costs and staff effort when they affect the scale decision |
These are examples, not a mandatory metric set. Microsoft’s AI governance guidance recommends combining operational logging with surveys and interviews, documenting key metrics, findings, and anomalies, and choosing measurement frequency according to workload risk. Use quantitative measures such as accuracy or latency alongside qualitative evidence; neither alone gives a full account of outcomes.
Assign owners and governance before the pilot starts
Name a business owner who is accountable for the pilot and its stop, revise, or scale decision. Also identify who is responsible for technical operation, data, security, privacy, legal or compliance review, risk decisions, user communication, and incident response. One person may hold more than one role in a smaller organization, but responsibilities and escalation routes should still be explicit.
Document the intended use, risk assessment, applicable internal policies and legal obligations, data-handling boundaries, approvals, human oversight, review cadence, and reporting route. Provide relevant staff with risk and compliance training. Decide how incidents will be recorded, escalated, investigated, and communicated, and how the organization will preserve evidence for the final decision.
NIST’s AI RMF Core organizes its guidance around four functions: Govern, Map, Measure, and Manage. Governance is cross-cutting, and the functions are not a prescribed sequence or checklist; NIST says, “Actions do not constitute a checklist, nor are they necessarily an ordered set of steps.” The framework calls for multidisciplinary perspectives and ongoing risk work across the system lifecycle. Microsoft’s guidance also recommends systematic risk evaluation, documented reporting, periodic audits, and independent review where appropriate.
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Test generative AI risks that matter to the use case
For a generative AI pilot, tailor tests to the model, application, data, degree of access, intended task, and people affected. NIST AI 600-1, its cross-sectoral Generative AI Profile, highlights governance, content provenance, pre-deployment testing, and incident disclosure among its primary considerations. It identifies risks that may be novel or amplified in generative AI and offers suggested actions aligned to the AI RMF.
- Test representative normal cases and relevant edge cases; examine output errors and harmful or misleading content in the context of the task.
- Determine what provenance information users or reviewers need, and document how human oversight will work in practice.
- Check privacy and security risks associated with the data, system access, and intended use.
- Rehearse incident reporting and response, including who receives an escalation and what action can pause or constrain the pilot.
These are implementation examples, not a universal test protocol. The profile is a cross-sectoral resource, not a sector-specific legal compliance determination; tailor actions to the system and organizational risk tolerance.
Review evidence on a defined cadence and record the decision
Set the review frequency according to the workload’s risk and operating conditions. Specify who receives reports, what findings trigger immediate action, how anomalies and incidents are handled, and how results will be retained. NIST emphasizes iterative lifecycle risk management; Microsoft recommends using measurement feedback to update risk assessments and controls.
At the end of the pilot, compare results with the baseline and the criteria agreed in advance. The decision record should capture what worked, where evidence is weak, what risks remain, whether controls operated, and what changes are needed before broader deployment. If the pilot cannot produce reliable evidence for its decision, it is not evidence of production readiness.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsChoose pilot candidates by value and manageability
When comparing several possible use cases, assess them against the same practical dimensions rather than choosing solely on visibility or enthusiasm:
- Expected value and whether it can be measured against a credible baseline
- Data sensitivity and readiness
- Potential impact and reversibility of errors
- People affected and their ability to contest or correct outcomes
- Integration and ongoing operational burden
- Human oversight needs and ability to test that oversight
- Applicable regulatory, contractual, and internal constraints
This is a practical comparison approach informed by the NIST framework’s focus on context, impacts, and lifecycle risk; it is not an official NIST scoring rubric.
How to use NIST guidance without treating it as a compliance shortcut
NIST AI RMF 1.0 was released on January 26, 2023, and is voluntary guidance. NIST’s AI RMF Development page and AI RMF FAQs describe framework development and revision activity; confirm their current status when applying the framework because it can change. The NIST Generative AI Profile, AI 600-1, was approved July 25, 2024. Neither resource replaces a check of current legal, regulatory, contractual, or internal obligations for the pilot’s jurisdiction and industry.
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