MAP-3.1: Potential benefits of intended AI system functionality and performance are examined and documented.

MAP-3.1 requires you to formally identify, validate, and document the expected benefits of an AI system’s intended functionality and performance, and to keep that documentation current as the system or its context changes. Operationally, this is a “benefits case” control: define benefit claims, link them to measurable success metrics, test them, and retain evidence. ¹

Key takeaways:

• Document benefit claims as testable statements tied to specific functionality and performance metrics. ¹
• Treat “benefits” as a governed artifact that must be reviewed when the model, data, use case, or user population changes. ¹
• Keep auditable proof: who approved the benefit claims, how they were evaluated, and what the results showed. ¹

MAP-3.1 sits in the NIST AI RMF “Map” function and forces discipline around a common failure mode: teams can describe what the AI does, but they cannot defensibly explain why it should exist, who benefits, and what “better” means in measurable terms. The requirement is narrowly worded, but examiners and internal risk committees will expect real operational outputs: benefit statements, success metrics, evaluation results, and approval records that match the system’s intended use.

For a Compliance Officer, CCO, or GRC lead, MAP-3.1 is easiest to run as a lightweight, repeatable control embedded into the AI intake and change-management process. Your job is not to prove the AI system is “good” in the abstract. Your job is to make benefit claims explicit, measurable, reviewed, and revalidated when conditions change. That creates a clean line of sight between business justification, system performance expectations, and downstream risk decisions like go/no-go approvals, human oversight design, monitoring, and customer communications. ¹

Regulatory text

Text (MAP-3.1): “Potential benefits of intended AI system functionality and performance are examined and documented.” ¹

What the operator must do:
You must (1) identify the potential benefits the AI is expected to deliver, (2) examine those benefits rather than assuming them, and (3) document the outputs in a way that is reviewable, approvable, and maintainable over time. “Examine” should be treated as “evaluate against defined criteria and evidence,” not “describe in a slide.” ¹

Plain-English interpretation (what MAP-3.1 really demands)

MAP-3.1 is a benefits governance requirement. Before you deploy or materially change an AI system, you need a written benefits case that answers:

• What beneficial outcomes are expected from the AI’s intended functionality (what it does) and performance (how well it does it)?
• How you checked those outcomes using reasonable evidence (testing, pilots, back-testing, simulation, benchmarking, or structured SME review).
• What you learned and approved, including limitations, tradeoffs, and any conditions needed for benefits to materialize (for example: user training, human review, data freshness).

If you cannot show this in documentation, you will struggle to defend why the system is appropriate, what success looks like, and whether risk controls are proportionate.

Who it applies to (entity and operational context)

MAP-3.1 applies to organizations that develop, procure, integrate, or deploy AI systems in production or in decision-support workflows. ¹

Operationally, it applies when:

• You are launching a new AI capability (internal build or third-party model).
• You are materially changing model behavior (retraining, prompt/system changes, feature additions, threshold updates).
• The use context changes (new user group, geography, product, customer segment, channel).
• The system affects regulated outcomes (eligibility, pricing, claims, employment, safety-critical operations) even if the model is “advisory.”

Third parties matter here: if a vendor claims benefits, you still own the duty to examine and document benefits in your context. The vendor’s marketing claims are not control evidence.

What you actually need to do (step-by-step)

Run MAP-3.1 as a gated deliverable in your AI system lifecycle (intake → evaluation → approval → monitoring).

Step 1: Define the “intended functionality and performance”

Create a short “intended use” statement and performance scope:

• Intended users and decisions supported
• Input data types and boundaries
• Output types (scores, labels, text, recommendations)
• Operating constraints (latency, uptime expectations, human review points)
• Known exclusions (what the model must not be used for)

This becomes the anchor for benefit claims. ¹

Step 2: Write benefit claims as testable statements

Convert vague goals into auditable claims. Use a standard template:

• Benefit claim: “The system will [improve outcome] for [population/workflow] by [mechanism].”
• Metric: Define how you will measure benefit (business KPI, quality metric, safety metric, user productivity metric).
• Baseline: What you compare against (current process, rules engine, human-only review).
• Acceptance criteria: What “good enough to proceed” means for each metric (your internal threshold).
• Assumptions: Dependencies that must be true (data freshness, user training, process adoption).

Examples (adapt to your use case):

• “Reduce manual review workload in triage while maintaining decision quality.”
• “Improve detection of anomalous transactions with acceptable false positive burden.”
• “Improve customer response completeness while meeting policy constraints.”

Step 3: Examine benefits using evidence, not narrative

Pick the evaluation method that fits the system and its risk:

• Offline testing against historical labeled data (where valid)
• Shadow mode / A-B testing against the current process
• Controlled pilot with monitoring and user feedback
• Benchmarking against a non-AI baseline
• Structured SME review with documented rubric (when labels are not available)

Your examination should explicitly connect functionality/performance to the benefit metrics you claimed. Keep the results, not just the conclusion. ¹

Step 4: Document tradeoffs and boundary conditions

Benefits often come with costs. Record:

• Where benefits do not hold (edge cases, low-confidence ranges, out-of-distribution data)
• Performance variance across segments relevant to your deployment context
• Human factors: required training, workflow changes, review burden
• Dependencies on third parties (data provider stability, vendor SLAs, model updates)

This is where many teams fail: they document “benefits” as upside only, then audits find the downside in incident tickets.

Step 5: Obtain accountable approval

Assign and document:

• Control owner (typically product owner + model risk/AI governance)
• Required approvers (risk, compliance, privacy/security, business owner)
• Approval decision (approve, approve with conditions, reject)
• Conditions and monitoring commitments

If you use Daydream to run third-party risk and GRC workflows, MAP-3.1 maps cleanly into an intake questionnaire + evidence checklist + approval workflow so benefit documentation is captured consistently across AI systems and vendors. ¹

Step 6: Operationalize re-examination triggers

Define triggers that require updating the benefits documentation:

• Material model or prompt changes
• Data source changes
• New user group or channel
• Drift signals or performance alerts
• Incident reports tied to benefit metrics (for example: increased rework, escalations)

Tie triggers to your change management process so the control runs without heroics. ¹

Required evidence and artifacts to retain

Auditors will look for a complete thread: claim → method → result → approval → ongoing review.

Minimum artifact set:

• Intended Use & Scope Document (versioned)
• Benefits Register (benefit claims, metrics, baselines, acceptance criteria, assumptions)
• Evaluation Plan (method, datasets, time period, tooling, roles)
• Evaluation Report (results, analysis, limitations, segment notes, tradeoffs)
• Approval Record (sign-offs, conditions, dates, version references)
• Change Log mapping changes to whether benefits were re-examined
• Monitoring Plan showing how benefit metrics are tracked post-deployment (even if lightweight)

Evidence quality rules (practical):

• Version-control the artifacts to the model/system release.
• Keep raw outputs or reproducible summaries where feasible.
• Ensure the doc clearly distinguishes “intended” benefits from “observed” benefits.

Common exam/audit questions and hangups

Expect these questions in internal audits, regulator inquiries, or customer due diligence:

1. “Show me the documented benefits and the evidence you used to examine them.”
   Hangup: teams provide a business case deck with no test plan or results.

2. “What is your baseline?”
   Hangup: teams measure only model metrics (accuracy) but not outcome metrics tied to workflow benefit.

3. “Who approved the benefit claims and why were the acceptance criteria chosen?”
   Hangup: no accountable owner; criteria look arbitrary.

4. “What happens when the model changes?”
   Hangup: benefits documentation is static and not linked to change control.

5. “How do vendor-provided claims translate to your environment?”
   Hangup: reliance on third-party claims without internal validation.

Frequent implementation mistakes (and how to avoid them)

• Mistake: Confusing model performance with business benefit.
  Fix: keep both, but explicitly tie them. For example, link precision/recall to downstream review burden and error costs.

• Mistake: Benefits stated as aspirations (“improve efficiency”) with no metric.
  Fix: require a metric and baseline for every benefit claim before approval.

• Mistake: Only documenting upside, ignoring tradeoffs.
  Fix: include a mandatory “limitations and boundary conditions” section in the benefits template.

• Mistake: One-time documentation at launch.
  Fix: define re-examination triggers and integrate them into change management tickets.

• Mistake: Treating vendor attestations as examination.
  Fix: require internal testing, pilot results, or structured SME validation in your context.

Risk implications (why this control matters in practice)

MAP-3.1 reduces two concrete enterprise risks:

• Governance risk: Without documented benefits, it is hard to justify why residual risk is acceptable, why certain controls were selected, or why an AI system should remain in service. ¹
• Misrepresentation risk: If product teams communicate benefits externally (customers, users, investors) without internal examination, you increase the chance of overstated claims and messy incident response when outcomes do not match expectations.

NIST AI RMF is a framework, not an enforcement regime by itself, but it is frequently used to demonstrate reasonable AI governance. Failing MAP-3.1 leaves a visible gap in “reasonable process” evidence. ²

Practical 30/60/90-day execution plan

First 30 days (foundation)

• Assign a MAP-3.1 control owner and define the approval path (RACI).
• Publish a one-page template: Intended Use + Benefits Register + Evaluation Summary.
• Update AI intake so no system progresses without benefit claims and metrics captured.

Days 31–60 (run it on real systems)

• Select one high-impact AI system and complete a full benefits examination and documentation package.
• Add change triggers to your SDLC or model ops process so updates route to GRC review.
• Start a lightweight benefits monitoring dashboard (even if manual) for the pilot system.

Days 61–90 (scale and audit-proof)

• Expand to the rest of the AI inventory, prioritizing customer-facing and high-impact systems.
• Standardize evidence collection and storage (central repository, consistent naming/versioning).
• Conduct an internal audit-style review: pick a system, trace from benefit claim to evaluation to approval to monitoring, and close gaps.

Daydream can reduce friction here by turning the Benefits Register and evidence list into a recurring collection workflow with reminders, versioning, and approval records, especially when systems depend on third parties. ¹

Frequently Asked Questions

Do we have to prove benefits before production, or is documenting expected benefits enough?

MAP-3.1 says benefits are “examined and documented,” so you need more than expectations. The level of proof can vary, but you should retain some form of evaluation evidence tied to the intended use. ¹

What if the AI system is only decision-support and humans make the final call?

You still need documented benefits, because the AI changes workflow, prioritization, and attention. Your benefits may focus on triage quality, speed, consistency, or coverage, but you still need an examination method and results. ¹

Can we rely on vendor testing reports to satisfy MAP-3.1?

Vendor materials can be inputs, but MAP-3.1 expects benefits examined for your intended functionality and performance in your context. Add internal validation, a pilot, or structured SME review, then document how vendor claims were confirmed or adjusted. ¹

What’s the minimum acceptable documentation package for a low-risk internal tool?

Keep it small but complete: intended use, 1–3 benefit claims with metrics and baseline, a short evaluation summary, and an approval record. “Low-risk” should not mean “no evidence.” ¹

How often do we need to re-examine benefits?

Re-examine when something relevant changes: model updates, data changes, new user populations, or monitoring signals that show benefit metrics degrading. Document the trigger and the decision, even if you determine no re-test is required. ¹

Who should own MAP-3.1 in a three-lines-of-defense model?

First line (product/model owner) drafts benefit claims and runs evaluations; second line (GRC/compliance/model risk) sets standards and reviews evidence; internal audit tests operating effectiveness. Document this in your RACI. ¹

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF program page