When we took over 24×7 reliability operations for a global ad tech platform, the first thing we did wasn’t switch on anything intelligent. We exported every monitor and every dashboard across every account and started deleting. More than 370 dashboards went, because nobody had opened them in months. Three separate uptime-monitoring setups, inherited from earlier acquisitions, became one. Monitoring costs fell by about 15%, and the team could finally see the signals that mattered.
That’s the part of AIOps vendors don’t put on the slide. Alert noise is a design problem, not a math problem, and you can’t machine-learn your way out of a bad design.
You can’t machine-learn your way out of a bad monitoring design.
Where noise actually comes from
In almost every environment I’ve looked at, noise comes from three places: alerts nobody owns, alerts on causes instead of symptoms, and the same problem reported by several tools at once. Fix those three and most of the noise disappears before any algorithm is involved.
Every page has to pass three questions
Before an alert is allowed to wake someone up, it should pass a simple test. Is it urgent? Is it actionable? Does it need a human? If the answer to any of these is no, it’s a ticket or a dashboard, not a page. Applying this test ruthlessly is the fastest noise reduction there is.
Alert on what customers feel
A server at 90% CPU for five minutes is a cause. Customers seeing errors on checkout is a symptom. Define service level objectives for the services that carry revenue and alert when the error budget starts burning unusually fast. A burn-rate alert fires less often than a static threshold and almost always means something real.
An alert without an owner is everyone’s and no one’s
Route every alert to the team that owns the service, with an escalation path if they don’t respond. Unowned alerts end up in a shared channel where everyone assumes someone else is looking.
Context beats speed
The difference between a five-minute fix and a fifty-minute one is rarely how fast someone was paged. It’s whether the incident arrived with its runbook, the last deployment, the state of its dependencies and the right dashboard already attached.
Now bring in the intelligence
With clean, owned, symptom-based signals, correlation and anomaly detection finally earn their keep. Grouping alerts by service, dependency and time turns twenty notifications into one incident with a likely cause. Anomaly detection on clean data finds real problems. On noisy data, it just finds new ways to page you.
Automate the 2 a.m. classics
Full disks, stuck services and exhausted connection pools are well understood. Automate the fix, with guardrails: a limit on how often it runs, a log of every action and a human escalation if the fix doesn’t hold.
Close the loop after every incident
Every post-incident review should answer one question: which alert should have told us first? Then tune, add or retire monitors accordingly. This is how noise reduction becomes permanent rather than a one-time cleanup.
What to measure
- Pages per on-call shift, the most honest measure of engineer burnout
- The share of pages that needed action
- Alerts per incident, which should fall as correlation improves
- Time to acknowledge and time to resolve
- Monitoring spend, which usually falls once the clutter goes
AIOps isn’t something you buy. It’s something you earn, one retired dashboard and one owned alert at a time.