Every vendor now slaps 'AIOps' on the box. Most of them just added a dashboard that says 'anomaly detected' and called it a day.
I want to tell you what AIOps actually changes, and what it doesn't.
What it actually changes
Correlation. Traditional monitoring alerts on a symptom CPU spike, 500 error, queue depth. AIOps correlates across signals and tells you one story: 'deploy 14a23 on payments-api broke the checkout flow, here are the 7 alerts it triggered.'
Noise reduction. On my old team we got 300 alerts/day. About 40 of them mattered. The rest were duplicates, known-flaky services, or transient spikes. A good AIOps layer suppresses 80% of that before a human sees it.
Root cause suggestions. Not answers suggestions. 'The top 3 likely causes based on historical incidents are...' Still needs a human to confirm. But it saves you 20 minutes of dashboard-hopping.
What it doesn't change
You still need good instrumentation. You still need runbooks. You still need someone on-call who can make a call.
AIOps is not a replacement for SRE. It is a force multiplier for SREs who already know what they're doing.
If you're drowning in alerts, the answer isn't more dashboards. It's letting AI do the triage so your humans can do the thinking.
Written by Dr. Samson Tanimawo
BSc · MSc · MBA · PhD
Founder & CEO, Nova AI Ops. https://novaaiops.com
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