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Start with one specific outcome, one bounded use context, and evidence that the proposed system can meet its requirements. A finishable AI engineering project defines who will use it, what it will and will not do, how people oversee it, what counts as success, and who owns the risks—before the team commits to building the full system.
What belongs in an AI project scope?
A useful scope is a set of decisions the team can use to build, evaluate, and operate a system—not just a feature list or model choice. Document the intended purpose and users, the task and deployment boundary, expected benefits and costs, requirements, risks, dependencies, available resources, evaluation criteria, and accountable owners.
NIST’s AI Risk Management Framework (AI RMF) Core organizes risk work around four connected functions: Govern, Map, Measure, and Manage. Its guidance is voluntary and context-dependent; use it to shape decisions for your organization and use case, not as a universal project plan. The framework landing page reports that AI RMF 1.0 is being revised, so check NIST’s AI RMF landing page for its current status. The Core material referenced here is from AI RMF 1.0, released January 26, 2023. NIST AI RMF Core | NIST AI RMF Development
How do you scope the project before building?
1. Define the outcome and use boundary
Describe the user, the situation in which the system will be used, the benefit it is meant to provide, and the decision or task it will support. Then draw the first-version boundary: which users, inputs, tasks, outputs, integrations, and operating conditions are included, and which are not.
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State the business or mission context, applicable requirements and norms, expected benefits and costs, and the organization’s risk tolerance. Be specific about what the system is not authorized or expected to do. For example, a tool that drafts internal support replies is not automatically in scope to send those replies, handle every customer issue, or make account decisions without review. NIST’s Map guidance calls for documenting context, intended task, target scope, requirements, potential impacts, benefits and costs, and human oversight.
2. Check capability, data, and dependencies
Write down the proposed model or method and the evidence that it can perform the task in this setting. Record known capability limits, failure modes, uncertainty, and conditions where a person must review, correct, or reject an output. Identify fallback behavior when the system is unavailable or its output cannot be trusted.
Inventory the data, software, hardware, services, and third-party components the project depends on. Check whether data is available, representative, suitable for the intended use, and usable under applicable legal and contractual terms. Map technical and legal dependencies, as well as how people will interpret and act on system outputs. Model performance alone does not establish that a larger workflow is ready to deploy.
Rank #2
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3. Make feasibility a scope decision
Compare the intended benefit with capability, data readiness, integration work, likely risks, operating and evaluation burden, cost, available skills, and the people and resources actually assigned. If the project depends on data or approvals that are not available, a model that cannot reliably perform the task, or oversight the organization cannot provide, reduce the scope or stop before committing to the broader ambition.
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4. Define “done” with evidence
Set acceptance criteria for the specific use context before implementation. Choose measures and benchmarks that reflect the intended outcome, and say how the team will assess reliability and uncertainty as well as safety, robustness, privacy, fairness, security, and human oversight where relevant. Record limitations and the conditions beyond which evaluation results should not be generalized.
Rank #3
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Plan testing before release and regular evaluation during operation. Assign someone to review results and decide what happens when acceptance criteria are missed or performance changes. NIST’s AI RMF Core calls for testing before deployment and regular testing while operating, supported by documentation and appropriate measures.
5. Assign risk and delivery ownership
Name the people responsible for requirements, data and dependency decisions, testing, approvals, deployment, monitoring, and incident handling. Prioritize risks in view of their potential impact, likelihood, and the resources available. For each material risk, decide whether to mitigate it, avoid the activity that creates it, transfer it where appropriate, or accept it through an accountable decision.
Keep those decisions connected to delivery. NIST presents Govern, Map, Measure, and Manage as related, iterative functions, with governance continuing across the AI lifecycle. Legal requirements depend on jurisdiction and use; the AI RMF is not a substitute for use-specific legal analysis.
Rank #4
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6. Gate expansion on results
Begin with a narrow, bounded pilot or deployment, then consider adding users, tasks, autonomy, or integrations only when evaluation results, risk controls, and available capacity support the change. Treat each expansion as a scope decision that may require new tests, oversight, or approvals. This is a practical application of NIST’s emphasis on target scope, capability, risk tolerance, and iterative evaluation; NIST does not prescribe a universal MVP process.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you know whether an AI approach is feasible?
Compare approaches against the same intended outcome and operating context. A rules-based workflow, conventional machine-learning system, and generative AI system can each be a candidate; none is universally preferable. Ask whether the approach can meet the acceptance criteria with available data, expertise, infrastructure, and oversight, at an operating and evaluation burden the organization can sustain.
| Decision axis | Questions to resolve |
|---|---|
| Outcome and capability | Can the approach perform the scoped task reliably enough for the intended use? What are its known limits and uncertainty? |
| Data and dependencies | Is suitable data available and usable? Which third-party services, software, hardware, or legal terms constrain the system? |
| Errors and oversight | What happens when the system is wrong? How much human review, fallback, or approval is needed? |
| Risk and requirements | What security, privacy, fairness, safety, legal, and operational concerns apply in this context? |
| Delivery and ongoing burden | Can the assigned team build, integrate, test, monitor, and maintain the approach within its resources and expertise? |
| Evidence of value | Which benchmarks and acceptance criteria will show whether the benefits justify the cost and remaining risk? |
For generative AI work, NIST’s Generative AI Profile discusses risks that can arise at different lifecycle stages and scales, including risks that may be unknown or difficult to evaluate. Consider those uncertainties in the scope and monitoring plan rather than assuming a successful demonstration settles the question. The profile, NIST AI 600-1, was published July 26, 2024. NIST AI 600-1, Generative Artificial Intelligence Profile
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Best Value
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What keeps the first version small enough to finish?
Keep the initial project focused on the smallest deployment boundary that can still test the intended benefit and its important risks. Narrow the use context before cutting evaluation, security work, or necessary oversight: a smaller task with clear acceptance criteria is more useful than a broad system whose results cannot be responsibly judged.
- Limit the initial user group and operating context.
- Choose a specific task instead of promising a general-purpose assistant or decision system.
- Exclude integrations, autonomy, and use cases that are not needed to test the outcome.
- State what the system must hand off to a person or decline to do.
- Make expansion conditional on evaluation results, risk decisions, and available staff.
Security work should be proportionate to the project’s risks and practical constraints. NIST’s SSDF explicitly says its intention is not to create a checklist, but “to provide a basis for planning and implementing a risk-based approach to adopting secure software development practices and continuously improving software development.” For generative AI and dual-use foundation model development, NIST published the SSDF Community Profile SP 800-218A on July 26, 2024. NIST SP 800-218A
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