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Map each AI-enabled use case as an owned, risk-tiered inventory entry that shows where AI fits in a business process, what it depends on, who is accountable, what its outputs influence, and how it is evaluated, monitored, changed, and retired. The documentation should be detailed enough to manage the use case’s risks, but proportionate to its impact and the institution’s size, complexity, and use of models.
Start with the workflow, not just the model
A model register alone may identify a model without showing how it affects a customer, employee, decision, or operational process. Map the full workflow from the point where the use case begins to the point where its output is acted on. Include the model or AI service, the surrounding systems and people, and the controls applied before and after an output is used.
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Treat each distinct business use as an inventory entry, even when multiple uses share the same model or vendor. A single AI service may support workflows with different users, data, consequences, human review, and fallback options. The inventory should make those differences visible and support oversight of both individual use cases and aggregate exposure.
What to record for each AI use case
The following fields are a practical documentation structure, not a verbatim regulatory schema or a universally mandatory checklist. Tailor the detail to the use case and applicable supervisory, legal, and internal requirements.
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| Layer | Record | Why it matters |
|---|---|---|
| Business context | Purpose; product or service; affected customers or employees; workflow entry and exit points; and the outcome the AI supports. | Shows what the system is for and where its influence begins and ends. |
| System boundary and dependencies | Model or AI service; version and deployment; whether it is internally developed or third-party; upstream and downstream systems; data stores; APIs; and material vendor dependencies. | Defines what is included in the use case and where a change or failure could affect it. |
| Inputs and outputs | Data categories and sources; transformations; prompts, rules, or other relevant configuration; generated scores or content; and where outputs flow into decisions or customer communications. | Supports traceability from source information to the system output and its use. |
| People and accountability | Business and technical owners; risk and control owners; vendor contact; approvers; human reviewers; escalation route; and, where relevant, separation of development, validation, and audit roles. | Clarifies who operates, challenges, approves, and responds to the use case. |
| Risk and controls | Risk tier and rationale; consumer, operational, privacy, security, conduct, and model risks considered; access and use restrictions; human oversight; fallback and incident arrangements; and evidence of controls. | Connects identified risks to safeguards and shows how the use case should be handled when something goes wrong. |
| Evaluation and monitoring | Testing or validation performed; limitations and assumptions; outcome monitoring; quality or drift triggers; review frequency; incident thresholds; remediation owner; and exception handling. | Shows what supports confidence in the use case and how problems are detected and addressed. |
| Change and lifecycle | Development, approval, and release history; material model, data, vendor, prompt, or workflow changes; ongoing review; retirement; and evidence-retention arrangements. | Preserves the history needed to understand how the use case evolved and whether an earlier assessment still applies. |
Build the map in a repeatable sequence
- Define the use case. State the business purpose, affected people, intended outcome, and workflow boundaries. Avoid broad labels such as “customer service AI” if they combine distinct activities or levels of impact.
- Trace the data and system path. Follow relevant information into the model or service, through any transformations or prompts, and onward into downstream systems, decisions, or communications. Note external services and material vendor dependencies.
- Locate decision points and human review. Record whether an output is advisory, reviewed before use, or acted on automatically. Identify the reviewer, what they are expected to check, how they can override or escalate, and what happens when review is unavailable or the system cannot be used.
- Assign owners and controls. Name accountable business, technical, and risk or control owners, as well as approvers and escalation contacts. Link each material risk to the relevant restriction, oversight, fallback, or other control and retain evidence that the control operates.
- Document evaluation and monitoring. Record the tests or validation performed, the assumptions and limitations, how outcomes and quality are monitored, and what triggers review, incident response, or remediation. Give exceptions and remediation an owner.
- Set change and retirement arrangements. Define which changes require review or approval, including material changes to the model, data, vendor, prompts, or workflow. Keep an intelligible record of approvals, ongoing reviews, and retirement decisions.
For each entry, the result should let a reviewer answer: what does this use case do, where can its output matter, who is accountable, what could go wrong, what evidence supports the controls, and what would prompt action?
Use NIST AI RMF to organize governance work
NIST’s AI Risk Management Framework (AI RMF) provides four functions that can organize an institution’s process without replacing its existing risk governance. NIST’s AI RMF Playbook’s Govern resource also calls for policies for a model-documentation inventory system and regular review of its completeness, usability, and efficacy.
| Function | How it helps organize workflow documentation |
|---|---|
| Govern | Set policy, ownership, accountability, documentation expectations, and oversight. |
| Map | Describe intended context, users, workflow, system boundaries, dependencies, and potential impacts. |
| Measure | Record evaluation and evidence about risks and trustworthiness characteristics. |
| Manage | Prioritize, treat, monitor, respond to, and improve risks over the use case’s lifecycle. |
The U.S. Treasury has released a financial-services AI risk-management framework that adapts NIST AI RMF to sector-specific operational, regulatory, and consumer-protection considerations. NIST’s Generative AI Profile, published July 26, 2024, is a cross-sector companion to AI RMF 1.0; it identifies contexts such as large-language-model use, cloud services, and acquisition. Neither framework by itself establishes that an institution has met every applicable legal or supervisory obligation.
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When capacity is limited, start with workflows where an error could materially affect customers, financial decisions, reporting, safety and soundness, or important operations. Then map lower-impact uses with a level of detail proportionate to their circumstances. Apply the same prioritization factors consistently, and record the reason for a risk tier rather than relying on an unexplained label.
- Potential customer or financial impact.
- Decision criticality and degree of automation.
- Sensitivity and provenance of the data.
- Degree and effectiveness of human review.
- Dependence on systems, vendors, or external services.
- Strength of evaluation and monitoring evidence.
- Frequency of change and ability to trace decisions, exceptions, incidents, and remediation.
These factors are a practical comparison aid, not an official scoring formula. A useful inventory should let leadership see risk at the individual-use-case level as well as combined exposure across the institution.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the April 2026 U.S. bank guidance covers—and does not
On April 17, 2026, the OCC, Federal Reserve Board, and FDIC issued revised interagency model-risk guidance. Federal Reserve SR 26-2 says the revised guidance supersedes SR 11-7 and the 2021 BSA/AML model-risk statement. The agencies describe a risk-based approach tailored to an institution’s risk profile, size, complexity, and model use; the guidance expressly says it is not prescriptive or enforceable.
The guidance is expected to be most relevant to Federal Reserve-regulated banking organizations with more than $30 billion in assets. It may also be relevant to smaller banks with significant model-risk exposure because of the prevalence or complexity of their models or activities beyond traditional community banking. Applicability and relevance depend on the institution and its circumstances.
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Its scope covers traditional statistical and quantitative models and non-generative, non-agentic AI models. OCC Bulletin 2026-13, issued April 17, 2026, states: “Generative AI and agentic AI models are novel and rapidly evolving. As such, they are not within the scope of this guidance.” The bulletin also points institutions to their general risk-management and governance practices when determining appropriate controls for tools, processes, and systems outside the document’s scope.
That boundary is not a general exemption from governance or from other applicable consumer-protection, privacy, security, legal, or supervisory duties. For generative or agentic systems, use the institution’s broader governance to determine controls appropriate to their purpose, data, autonomy, and consequences. Institutions operating across jurisdictions, or using AI in high-impact settings, should have legal and compliance teams identify additional requirements for their specific products, customers, and locations.
Maintain the map as a living record
An inventory loses value if it becomes a one-time catalog. NIST’s AI RMF Playbook calls for regular review of the inventory system’s completeness, usability, and efficacy. In practice, assign responsibility for updating entries when material system or workflow changes occur, when monitoring raises a concern, or when an incident or exception reveals that the documented boundary or controls need attention. Preserve enough documentation to support continuity, track recommendations and exceptions, and follow remediation.
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