Operations | Monitoring | ITSM | DevOps | Cloud

There is a peculiar moment that happens when talking to AI. You ask it to rewrite an email, it does a good job, and you type, "Thanks!" Then, almost without thinking, you add, "Sorry, one more thing." It is software. It cannot be kept waiting, interrupted, or offended. Still, somehow, you have developed the manners. Then the questions get a little more personal.

Ask any operations team what question comes up most, and it isn’t about alert routing or escalation logic. It’s the one typed into a channel a dozen times a day: who’s on call for this right now? It’s also one of the slowest questions to answer. The schedule is set up correctly, the overrides are in place, and the group’s ownership is right.

September is associated with football, foliage, flannel and, for some, the Financial Plan. As we put pen to paper (or agents to harnesses), there’s a few core elements that have always driven the P&L outlook for the following year: rep productivity and new product releases driving new sales, expansion and contraction against the install base, employee roster changes, and discretionary spend.

Somewhere in your company, a team is three weeks into the AI project that’s going to matter. Somewhere else, a support pilot from the spring is still summarizing every ticket with a frontier model, and nobody has looked at it since it started working. On the invoice they’re identical, and the company has two moves: leave everything open, which funds the waste, or cap everyone, which kills the bet.

So you want your own pet LLM huh? Knowing where to start can be quite tricky, so luckily for you I’ve put together this end-to-end guide. It’ll get you not just started; you’ll end with a fully working chatbot that you’ve fine tuned on the dataset `nampdn-ai/tiny-webtext`, which is a training dataset designed to improve models’ critical thinking abilities. Buckle up, this is going to be both fun and deep.

Ask a simple question "who are our most profitable customers?" and things fall apart. The data lives in six systems, nobody agrees which table is canonical, the column called profit is actually revenue, and the business rules that matter live in someone's head or a Confluence page nobody's touched since 2023. Now point an AI agent at that same mess.

Everything that makes an app or agent real happens after it works on your machine. Locally coding an app is a joy: hot reload, a seeded database, a mocked API key. Then you go to ship it, and "deploy" quietly expands into a Dockerfile that behaves in CI, somewhere to actually run the container, a database it can reach, TLS, secrets wired into the environment, and a pipeline to hold it all together. The feature took an afternoon. The plumbing takes the rest of the week.

Software used to be built by engineers. Not any more. Low-code tools brought in domain teams. AI copilots brought in everyone else. And now agents are building and acting alongside humans; marketing, finance, HR and legal are all shipping the apps they used to file tickets for. The number of people (and things) building on your data has exploded, and it isn't slowing down. And there's no single, standardized way to build, there likely never will be.

AI is rapidly becoming a bigger part of how IT Operations teams detect incidents, interpret signals and decide what to do next.