AIOops

The biggest challenge with AIOps isn't the technology - it's the expectations. I think it's finally time to hit the reset button (not the magic button).

Companies today throw around "AIOps" like it’s some mythical, all-knowing, all-fixing genie trapped inside their observability platform. With a wave of the reliability wand, suddenly all alerts are routed perfectly, incidents are resolved automatically, and on-call engineers can finally take that vacation they’ve been dreaming of. If only reality worked that way.

Let’s be honest—AIOps means wildly different things to different people. For some, it's advanced event correlation to cut down alert noise. For others, it's full-blown auto-remediation, where AI somehow understands the root cause of an incident (which we're getting quite close to achieving) and fixes it before humans even know something went wrong. And then there are those who think AIOps is just a fancy term for triggering a web-hook when a threshold is breached. (Spoiler: That’s automation, not AIOps, but hey, if calling it AI gets budget approval.... I'm not going to argue!)

Before implementing an AIOps strategy, it’s critical to define your objectives. Are you looking to improve incident triage through automated correlation? Do you want predictive capabilities to proactively identify risks? Or are you aiming for full auto-remediation with human-in-the-loop governance? Understanding the specific problems AIOps is solving ensures that the right tools and processes are put in place. AI can amplify efficiencies, but only when paired with well-architected observability and automation frameworks.

The problem isn’t that AIOps doesn’t provide value. It absolutely does—when implemented correctly, with realistic expectations. But far too often, enterprises think it’s an “out-of-the-box” solution that will instantly eliminate all toil. News flash: It won’t.

But just like any machine learning system, AIOps is only as good as the data feeding it. Poor-quality telemetry, inconsistent tagging, and alert storms can all degrade AIOps effectiveness. To see real value, engineers need to focus on data normalization & standardization (did someone say OTel?), defining clear incident workflows, and ensuring that AI-driven insights align with actual operational needsAIOps requires fine-tuning, solid data hygiene, and clear operational goals. Feeding garbage data into an AI model and expecting it to make intelligent decisions is like shoving a pizza into a printer and expecting it to come out as a Michelin-star meal.

So next time you hear someone say, "We need AIOps!" take a deep breath and ask them, "Great, what do you mean by that?" Their answer will tell you everything you need to know—mainly, whether you're about to have a productive conversation or if it's time to start preparing your best "AI doesn’t work that way" speech. Either way, grab some popcorn, because the AIOps confusion saga isn’t ending anytime soon.