Governance

Fourteenth post in the series. In the previous one, we used AI for our own infrastructure work. This time the scope is bigger: how to take an entire organization from “let’s use AI” to a governed platform that can survive contact with finance, security, and production support. tl;dr AI adoption fails when teams skip readiness, guardrails, and cost controls. A workable path is assessment, enablement, platform prep, controlled experimentation, production governance, and continuous review. Treat AI as an operating capability with budgets, runbooks, and policies from day 1. Best intentions, worst outcomes Your CTO walks into the all-hands and says: “We’re going all-in on AI.” The room buzzes. Teams start brainstorming use cases before the meeting ends. Within two weeks, Slack is full of threads about GPU availability. ...

…and why that’s not an accident. If you have worked with both Azure and AWS long enough, you have probably felt it. AWS feels straightforward. Azure feels… heavier. Not worse. Not broken. Just harder to reason about. The console feels denser. The mental model feels less obvious. The number of “extra” concepts feels higher. This is not a beginner problem. Senior engineers feel it too. And the most interesting part is this: that friction is not accidental. ...