AI Use-Case Intake and the Model Lifecycle: Govern Before the Pilot
A disciplined intake makes the proposed purpose, data, affected users, decision authority, and failure modes visible before experimentation hardens into production. Governance then follows the use case through change and retirement.
Intake is a decision, not paperwork
AI proposals frequently begin as a small pilot, a vendor demonstration, or a productivity experiment. If teams defer governance until deployment, they may discover late that the data is unsuitable, the intended use has shifted, or the output influences a consequential decision. Intake should be light enough to encourage early disclosure and structured enough to route proposals to informed reviewers.
Begin by describing the problem, intended users, affected people or processes, expected benefit, and what the system will and will not do. Identify whether it generates recommendations, content, rankings, or actions; name its data sources and external services; and say what human decision remains. Uncertainty is a valid intake answer when it is made explicit and assigned for investigation.
Carry governance through lifecycle gates
- Assess: Triage by purpose, impact, data sensitivity, autonomy, third-party reliance, and reversibility. Route for privacy, security, legal, conduct, model, or operational review as appropriate to the use case.
- Design and test: Define intended-use limits, performance measures, representative test conditions, known limitations, fallback behavior, and accountable owners before a live pilot.
- Approve and deploy: Record unresolved risks, safeguards, approval scope, permitted users, monitoring signals, and rollback or pause authority. Do not let a pilot imply general approval.
- Change and retire: Reassess when data, model, prompt, provider, workflow, or user population changes. At retirement, address access, records, dependencies, and any continuing decisions or obligations.
Evidence and reviewer challenge
Maintain a linked record of the use case, classification rationale, system and version, owners, assessments, test results, decisions, conditions, incidents, and material changes. Keep the record current enough to distinguish the proposed design from what is actually operating.
- What decision or task changes because this system is used, and for whom?
- Could the same objective be met with a simpler or less data-intensive approach?
- What evidence supports fitness for this purpose, and what remains untested?
- Who can stop use, notify affected teams, and authorize a changed scope?
Lifecycle governance is not a one-time gate. It is a sequence of explicit decisions with named owners, proportionate evidence, and a clear route back to review when reality differs from the original case.