What it means
AI use-case intake governance creates a controlled entry point where proposed AI uses are identified, classified, owned, and screened before procurement or development proceeds.
Why it matters
Early intake prevents unmanaged deployments and gives privacy, security, legal, architecture, and business stakeholders time to shape requirements.
Practical focus
- Define the system boundary, accountable owner, approved purpose, material risks, and decision authority for AI Use-Case Intake Governance
- Require a business purpose, owner, users, decisions affected, data types, model or vendor, autonomy level, affected parties, and preliminary risk tier.
- Collect and protect this evidence: intake forms, classification decisions, reviewer comments, approvals, rejected uses, conditions, and links to later assessments.
- Exercise this failure scenario as a planned test: a team begins a pilot with sensitive data because it was described as an experiment and never entered the normal governance process.
Common mistakes
- Using the label AI Use-Case Intake Governance without defining the exact systems, data, people, decisions, and time period in scope
- Treating a policy statement or tool demonstration as proof that AI Use-Case Intake Governance operates consistently in production
- Failing to retain intake forms, classification decisions, reviewer comments, approvals, rejected uses, conditions, and links to later assessments in a form that lets an independent reviewer reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when a team begins a pilot with sensitive data because it was described as an experiment and never entered the normal governance process.
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: