DevOps

This post was originally published on DevOps.com on April 28, 2026. AI has already arrived in the software development lifecycle; not as a pilot program or controlled experiment, but as an everyday reality. Developers are using AI coding assistants to generate functions, refactor modules, review pull requests, and accelerate delivery, often in direct tension with corporate policies meant to limit or control that use. While it’s tempting to consider this some kind of ‘Shadow AI’ or ‘Governance Failure’, it is a signal of things to come in this brave new world of AI-accelerated software engineering. ...

Introduction This document outlines an AIOps Maturity Model to help organizations assess and improve their Machine Learning Operations capabilities. It came from my own frustration that there weren’t any models that fit the real experience of end-to-end data science and operations relationships that covered both ‘conventional’ ML, and practically discussing LLM based systems and how completly differently you have to think about them. This was originally published internally around May ‘24 and then presented at NIDC as an ‘Eye Test Model’, and I promised that I’d eventualy publish it; this is it, dusted off and tidied up for public consumption. ...