Enterprise AI needs a decision boundary.
Define what may be recommended, approved and automated before connecting AI to live operations.
Start with advisory mode
Private deployment alone does not establish decision governance. Identify the business owner, the data available to the system and the actions it may propose. In an initial shadow period, recommendations are reviewed alongside existing decisions without triggering changes to live systems.
Make approval meaningful
A reviewer needs the recommendation, its assumptions, binding constraints and plausible alternatives. Record who approved the action and which model and data version informed it. Escalate unusual inputs and infeasible solutions instead of silently forcing a recommendation through an approval screen.
Automate within agreed limits
Automation should follow demonstrated reliability for a specific workflow, with limits, monitoring and a defined way to pause or revert. Review exceptions with the operating team. Expand the permitted scope only when the evidence and accountable owners support it; a successful demo is not sufficient proof.