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An enterprise AI pilot should produce evidence for a decision—not just demonstrate that a model can run. Define the business outcome and baseline, intended users and use boundaries, success and stop criteria, measures for value and risk, accountable owners, and a review process. Before launch, agree who can stop, revise, or scale the pilot and what evidence that decision will require.
What should an enterprise AI pilot be designed to decide?
Start with the decision the pilot will inform. It might determine whether to stop the work, change the workflow or controls, or expand to a larger group. Without that decision in view, teams can collect activity data—such as usage or time spent—without learning whether the system improves the business process safely and reliably.
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Write down the workflow, problem, intended users, and expected business outcome. Describe what the AI system may do, what it must not do, what data it can access, and which decisions require human review. Record how the current process performs so that any claimed improvement has a meaningful comparison. NIST’s AI Risk Management Framework (AI RMF) calls for understanding context and impacts and aligning risk work with organizational goals and risk tolerance; this scope-setting approach is a practical application, not a verbatim NIST checklist. NIST AI RMF Core
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Keep the pilot narrow enough to evaluate. If several use cases are competing for attention, compare them on expected value and measurability, data sensitivity and readiness, the impact and reversibility of errors, affected users, integration and operational burden, baseline availability, human-oversight needs, and applicable regulatory or contractual constraints. These are decision factors, not an official NIST scoring rubric. NIST AI RMF Core NIST Generative AI Profile
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How should goals and decision criteria be set?
Choose a small number of outcomes that reflect the actual task. For every goal, record the baseline, measurement method, review period, acceptable range or target, and what decision follows from the result. Possible outcome categories include task completion, work quality, cycle time, cost, user experience, and access or service quality. Select only those relevant to the use case: the cited frameworks do not establish universal pilot targets or thresholds.
Also define failure conditions before the pilot starts. Potential triggers include unacceptable output errors, a privacy or security incident, a policy violation, material user harm, or a failure of required human review. The accountable organization must set trigger levels for the specific task, consequences of error, risk tolerance, and applicable requirements. NIST and Microsoft both emphasize context-sensitive risk management rather than a single threshold that works for every deployment. NIST AI RMF Core Microsoft AI governance guidance
| Goal element | What to record |
|---|---|
| Outcome | The business or user result the pilot is intended to improve. |
| Baseline | How the existing workflow performs and how the comparison will be made. |
| Measure and review period | How evidence will be collected and how often it will be reviewed. |
| Acceptance criteria | The use-case-specific target or acceptable range, set by the accountable organization. |
| Decision consequence | What result leads to stopping, revising, or expanding the pilot, and who makes that decision. |
Which metrics belong in the evaluation plan?
Measure business outcomes alongside task quality, system operation, user experience, cost where relevant, and risk controls. Adoption or time saved alone cannot show whether the system produces acceptable work, operates reliably, or introduces harms. Microsoft’s examples include error rates, accuracy scores, performance benchmarks, qualitative feedback, latency, token counts, and request rates; they are examples to tailor, not a required metric set for every pilot. Microsoft AI governance guidance
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| Evidence area | Examples to consider | Question it helps answer |
|---|---|---|
| Business outcome | Task completion, quality against the current process, cycle time, cost, or service quality. | Did the workflow improve in the way the pilot intended? |
| System performance | Accuracy or error rates, latency, reliability, and task-relevant performance benchmarks. | Does the system perform acceptably for this task under the conditions tested? |
| Use and experience | Usage where meaningful, user feedback, stakeholder satisfaction, confusion, and workarounds. | Can intended users work with the system effectively, and where does it create friction? |
| Risk and controls | Relevant harm and policy tests, incidents, escalations, human-review effectiveness, and whether required controls operated. | Did the safeguards work, and what problems or residual risks remain? |
| Cost and resources | Operating costs and staff effort when they affect the decision. | What resources does the workflow require relative to its outcomes? |
Pair automated operational logs with human evidence such as surveys and interviews. Set measurement frequency according to workload risk, document findings and anomalies, and make sure each metric has an owner and a clear connection to the decision criteria. Microsoft recommends this combination of measurement methods and risk-based review frequency. Microsoft AI governance guidance
Who owns governance during the pilot?
Name accountable people before launch; the roles may be combined or distributed differently across organizations. At minimum, identify who owns the business outcome and who is responsible for technical operation, data, security, privacy, legal or compliance review, risk decisions, user communication, and incident response. Document the intended use, relevant policies and obligations, data-handling boundaries, approvals, human oversight, review cadence, and escalation route.
Governance is not a launch approval that ends once the pilot goes live. NIST’s AI RMF Core describes four functions—Govern, Map, Measure, and Manage—and treats Govern as cross-cutting. It also says the functions are not a prescribed ordered checklist and that risk work should continue across the AI system lifecycle. The framework calls for multidisciplinary perspectives. NIST AI RMF Core
In operational terms, establish how the team will evaluate risk on an ongoing basis, report findings, train relevant staff on risk and compliance responsibilities, audit the pilot, and obtain independent review when appropriate. Microsoft’s governance guidance recommends these practices alongside measurement and documentation. Microsoft AI governance guidance
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What additional checks does a generative AI pilot need?
For generative AI, tailor testing to the model, application, data, level of system access, intended task, and people affected. NIST’s cross-sectoral Generative AI Profile identifies risks that are novel or amplified in generative AI and highlights governance, content provenance, pre-deployment testing, and incident disclosure as primary considerations. It is a companion resource, not a sector-specific legal compliance determination. NIST AI 600-1
Turn those areas into tests and controls appropriate to the use case. For example, evaluate representative routine and edge cases, check for inaccurate, harmful, or misleading outputs, establish provenance needs, verify that human review works as intended, and rehearse incident reporting and response. These are implementation examples; the profile’s suggested actions must be adapted to the system and the organization’s risk tolerance.
How should teams review results and make the scale decision?
Agree in advance how often the team will review metrics and risks, who receives reports, which findings require action, and how evidence will be retained. Use new findings to update risk assessments and controls. Microsoft recommends tailoring measurement frequency to workload risk; NIST emphasizes iterative risk management throughout the lifecycle. Microsoft AI governance guidance NIST AI RMF Core
At the decision point, compare observed results with the baseline and criteria set before launch. Record what worked, where evidence is weak, which risks remain, whether controls operated, and what must change before any broader deployment. A pilot that cannot establish reliable evidence is not proof of production readiness.
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Which framework guidance applies, and what does it not decide?
NIST AI RMF 1.0 is voluntary guidance. NIST says the framework is being updated; check its current resources for the latest status. The framework can help structure context, evaluation, governance, and ongoing risk management, but it does not settle every local legal or internal requirement. Confirm the obligations that apply to the pilot’s industry, jurisdiction, contracts, and organization. NIST AI RMF FAQs NIST AI RMF Development
NIST says AI RMF 1.0 was released on January 26, 2023, and its Generative AI Profile, NIST AI 600-1, was approved on July 25, 2024. Those dates identify the resources; neither document supplies a universal numerical success threshold for an enterprise pilot. NIST AI RMF Development NIST AI 600-1
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