AI use case inventory

A maintained repository or listing of an organization’s AI use cases, intended to support governance, transparency, and risk management by documenting where and how AI is designed, developed, procured, or used, and the purpose and outputs associated with those uses.

Source: AIEOG AI Lexicon (Feb 2026), adapted from 2024 Guidance for AI Use Case Inventories and DOJ AI Inventory

An AI use case inventory is a centralized record of every way your organization uses AI. It is the compliance team’s answer to the question: “What AI do we have, what does it do, and who is responsible for it?”

A well-maintained inventory captures key information for each AI use case, including its purpose, the AI models and systems involved, the data it uses, the decisions it informs, its risk classification, its current status, and the people accountable for its governance.

The inventory serves multiple audiences. Compliance teams use it to track governance obligations. Risk teams use it to assess and aggregate AI risk. Technology teams use it to manage dependencies and change management. Executives and boards use it for oversight reporting. Examiners use it to understand the institution’s AI footprint.

An effective inventory is not a static spreadsheet created once and forgotten. It is a living document that is updated as new use cases are added, existing ones change, and retired ones are decommissioned.

The AI use case inventory is rapidly becoming a baseline regulatory expectation. Federal agencies have published guidance specifically on AI inventories, and examiners are beginning to ask for them during supervisory activities. An institution that cannot produce a current, comprehensive AI inventory faces immediate credibility risk in an examination.

The inventory is also the foundation for every other AI governance activity. You cannot assess risk on use cases you have not identified. You cannot validate models you do not know exist. You cannot monitor performance on systems you have not documented. The inventory is the starting point.

Common pitfalls:

• Incomplete coverage. Inventories often miss AI embedded in vendor tools, shadow AI adopted by business units without IT involvement, and AI capabilities acquired through mergers or partnerships.
• Stale data. Inventories that are updated annually (or less) quickly become inaccurate. New deployments, changes, and retirements happen continuously.
• Insufficient detail. An inventory entry that says “AI for fraud detection” does not provide enough information for governance. Each entry needs specific detail about models, data, decisions, and risk.

1. Establish a mandatory registration process. Require all AI use cases to be registered in the inventory before deployment. Make inventory registration a gate in the AI lifecycle.
2. Define the minimum data fields. At minimum, capture: use case name, description, business owner, risk tier, AI models/systems involved, data sources, deployment status, last validation date, and next review date.
3. Conduct a discovery exercise. Perform an initial sweep across the organization to identify AI use cases that may not be formally documented. Survey business units, review vendor contracts, and audit technology systems.
4. Update continuously. Establish triggers for inventory updates: new deployments, model changes, data source changes, risk reclassification, and decommissioning.
5. Connect the inventory to governance workflows. The inventory should drive validation scheduling, risk assessment cycles, and regulatory reporting.
6. Report inventory metrics to leadership. Regular reporting should include total use cases, distribution by risk tier, validation status, and any gaps or overdue reviews.

AI use case, AI governance, AI risk assessment, Documentation, AI system, AI lifecycle