Inventory uses, not labels
Begin with a list of actual AI-enabled features, internal tools, suppliers, and experiments. Capture the purpose, users, input data, output, owner, and whether the result influences a person or business decision. This exposes work that a high-level AI strategy document usually misses.
Keep the inventory alive by connecting it to product intake, procurement, and change management. The question is not whether a system is called AI. The question is whether it creates a new decision, data flow, dependency, or failure mode that needs an accountable owner.
Classify impact before adding process
Use a small set of impact tiers based on factors such as sensitive data, customer effect, automation level, and reversibility. The purpose is to decide how much review a use case needs, not to create a false precision score. A drafting assistant and an automated eligibility decision deserve different scrutiny.
For each tier, state what must happen before release and what is monitored afterward. Higher-impact work may require explicit approval, documented testing, a fallback path, and more frequent review. Lower-impact work may need only inventory, data handling guidance, and an owner.
Define accountable decision rights
Name who can approve use, accept residual risk, pause a system, and respond when output is wrong. These roles should be understood before a customer complaint or incident makes the decision urgent. A cross-functional forum can advise, but a named owner must decide.
Document escalation triggers in plain language. Examples include unexpected sensitive data, a material model change, a safety concern, or a high-impact customer decision. Clear triggers help product teams move quickly without treating every experiment as a board matter.
Test the intended and unintended behavior
Testing should reflect the purpose of the system and the ways users may rely on it. Define expected behavior, unacceptable outcomes, known limitations, and the fallback when the system cannot perform reliably. Keep test examples relevant to the product, while protecting any sensitive data used in evaluation.
Review prompts, model settings, integrations, and downstream actions as part of the system rather than as isolated components. A sound model can still create harmful outcomes when it receives poor context or triggers an unbounded workflow.
Monitor change and learn from use
Set a review cadence that fits the impact tier. Look for changes in data sources, model versions, system permissions, customer use, and reported failures. The owner should know what signals lead to a pause, a remediation task, or a re-approval.
Record significant decisions and lessons in a form the team can revisit. Governance becomes credible when it changes how the next feature is designed, tested, and released. It is not a separate paperwork exercise performed after product decisions are final.
