Use Case Intake

Why Use Case Intake Matters

Use case intake is the front door for AI governance. It turns an idea into a reviewable record, capturing the purpose, data, users, and expected outcome so the organisation can decide whether the proposed use is appropriate, who should review it, and what level of control is needed before any build or deployment starts.

That early record matters because many AI failures start as scope problems: a vague proposal can hide sensitive data use, unclear accountability, or a deployment context that was never assessed. A strong intake process creates the evidence base for triage, routing, and risk-based approval rather than relying on ad hoc judgment later in the lifecycle.

What Good Intake Should Capture

Good intake is not just a form, it is a structured governance checkpoint. The record should make the proposed use case understandable to non-builders, including what the system is meant to do, which business process it supports, what data it will touch, and whether the request is internal, customer-facing, or delegated to a vendor or platform.

It should also capture the risk signals that influence review depth. Those signals often include whether the use case affects regulated decisions, relies on personal or confidential data, creates external exposure, or changes the organisation’s tolerance for error, bias, automation, or human oversight.

For organisations building a broader AI programme, intake works best when it feeds a living register rather than an isolated ticket. NHIMG’s AI Governance Guide and AI Register and AI Inventory Template both reinforce the idea that intake becomes more valuable when it is tied to ownership, review triggers, and a durable inventory of approved systems.

How Intake Supports Classification and Routing

The main purpose of intake is to route the request to the right decision path. Some use cases need only lightweight review, while others need legal, privacy, security, model risk, procurement, or executive sign-off. Intake is the point where the governance team decides which path applies, based on the facts captured up front.

This is also where risk tiering becomes practical. The intake record should expose enough context to distinguish a low-impact experiment from a high-impact or externally facing use case, and enough detail to determine whether added controls, documentation, testing, or oversight are required before approval.

The related question is often not whether AI can be used, but whether it should be used in that form, with that data, for that audience. For that reason, intake should be written to support decision-making, not just record-keeping, and it should capture the minimum facts needed for review without forcing the reviewer to guess at intent.

NHIMG’s AI Use Case Risk Tiering Guide is a useful companion when intake needs to map proposed use cases into a clearer approval path, especially where impact, context, and deployment conditions affect the decision.

Controls, Oversight, and Lifecycle Consequences

Use case intake is not only about approval, it is also the first control point in the lifecycle. A weak intake process can let unsuitable use cases enter the environment with no clear owner, no documented purpose, and no reliable basis for future review, recertification, or decommissioning.

When intake is done well, downstream controls become easier to apply. Governance teams can connect the request to oversight requirements, assign responsibility, track changes in intended use, and later verify that the deployed system still matches the approved scope. That reduces drift between what was approved and what actually gets built or bought.

Intake also helps define the boundary between acceptable experimentation and governed production use. In practice, that boundary determines whether the organisation can measure the use case, monitor it, and hold a named owner accountable for ongoing compliance and risk treatment.

External guidance such as the NIST AI Risk Management Framework and the EU AI Act regulatory framework are especially relevant here because both depend on early identification, classification, and governance of AI use before deployment.

Risk and Threat Considerations

Use case intake is a control against invisible risk. If the organisation accepts a use case without understanding its purpose, data exposure, or user context, it can approve something that is mis-scoped, over-permissioned, or incompatible with the intended governance model.

Failure mechanism: Weak intake allows hidden sensitivity, unclear ownership, or unreviewed deployment context to slip past governance, which can lead to inappropriate data use, insufficient controls, or a model being introduced into the wrong business process.

Impact: The result can be privacy exposure, poor accountability, compliance failure, or a production AI system that is harder to monitor, justify, or retire safely once it is embedded in operations.

Practitioner Guidance

Governance implication: Treat intake as a decision gate, not a data collection step. The question is whether the organisation has enough structured evidence to classify the request, assign an owner, and choose the right review path before implementation begins.

What to watch for: Vague descriptions, missing data fields, undefined users, and “pilot” language without an exit condition are common signs that the intake record is too weak to support reliable governance.

Practitioner takeaway: The best intake forms do not just ask what the AI will do, they make it hard to approve a use case without first understanding its purpose, exposure, and accountability.

Related resources from NHI Mgmt Group

• AI Use Case Intake
• How do IAM teams decide whether an AI use case needs new controls or better NHI hygiene?
• How do organisations decide whether encrypted computation is enough for a use case?
• How do security teams decide whether biometrics are appropriate for a use case?