AI governance: from policy to a control that runs
Nearly every large company now has an AI policy. Very few have a control that stops people ignoring it.
Nearly every large organization now has a written AI policy and governance framework. A document that articulates principles and states how people should use models. Nobody actually follows policies written as documents or PDFs.
A policy in a PDF or intranet wiki does not stop an engineer from pasting confidential client data into a public model or using an unapproved tool. Policies are aspirational and not enforced.
The controls that actually work are implemented in infrastructure: your own gateway that intercepts every model call, logging and observability on every request, an approved tool catalogue that is technically enforced.
That gateway is exactly where you later instrument cost tracking, quality signal measurement and audit trails for compliance and governance. Compliance pays for its own infrastructure installation.
The pattern repeats: policy defines aspiration, infrastructure implements control. Budget goes entirely into infrastructure.
Teams that lead with policy and add infrastructure later spend twice the budget. Teams that build the gateway first have control within weeks and can measure compliance.