This is a strong framing because most GenAI failures are not only model failures. They are operating model failures.
Enterprises usually rush into tools, pilots, copilots, cloud services, and agent workflows before defining ownership, approval paths, risk tiers, audit trails, and accountability. That is where governance becomes messy.
I like the GovOps idea because it treats governance as something that runs continuously, not as a one-time policy document. For GenAI and cloud platforms, that matters a lot because usage changes every week, new tools appear quickly, and teams often adopt them before security or compliance has full visibility.
The hard part will be keeping it practical. If GovOps becomes too heavy, teams will bypass it. If it is too loose, it becomes another document nobody follows.
The best version is probably lightweight, measurable, and tied directly to real workflows: who can use what, what data can move where, what needs approval, what gets logged, and who owns the risk when something goes wrong.