The most expensive AI failures are usually not dramatic.

They do not look like a server outage or a broken login. They look like a workflow that quietly keeps running while leaking time, leads, or money in the background. That is the real tax on most AI-driven operations right now. Not model cost. Not prompt quality. Operational drift.

This matters because once you have even a handful of automations live, you stop dealing with single tasks and start managing a system. A system can "work" and still underperform. It can route the wrong person, skip a review step, publish without an asset, or fail silently in a way nobody notices until the damage is already done.

What has worked for us is not chasing perfect prompts. It is running a weekly AI ops audit with hard checks, owner assignments, and one rule: every important workflow must prove it is still doing the job it was hired to do.