I learned a version of this lesson long before generative AI. In banking and payments environments I led, the most consequential risks were rarely contained within one application. They emerged where business rules, identity, workflow, vendor dependencies and operational exceptions met. A payment platform could be technically sound and still create exposure if decision rights were unclear during an exception, outage or recovery event. The control was not simply in the code. It was in knowing who could act, under what conditions and with whose accountability.

AI compresses those seams. It can traverse data, applications and organizational boundaries in seconds. If the enterprise has not made authority explicit, the system will inherit whatever permissions, defaults and informal practices already exist. Automation then turns ambiguity into scale.

This is why I believe consequence, not activity, should set the control boundary. The same technical action can carry very different enterprise consequences. An agent rescheduling an internal meeting is not equivalent to an agent changing a customer credit decision, releasing software into production or moving money. Governance that treats all AI activity alike will either obstruct low risk work or insufficiently control high-risk work.