"AIOps" used to mean something. In 2026, it's the label vendors slap on a chart with a yellow band and call it a day. If you're evaluating an AIOps platform this year, the question isn't does it have AI. The question is what kind, trained on what, and capable of what.
What's getting called AIOps that isn't
Five things vendors call AIOps that mostly aren't:
- Static thresholds with seasonality. "It auto-adjusts based on time of day" is a calendar, not AI.
- Anomaly detection with hardcoded sensitivities. If the model isn't retraining on your stack's specific drift patterns, it's not learning. It's a rule with a softer name.
- NLP wrappers on dashboards. "Ask your data a question" is a query parser. It doesn't predict, correlate, or remediate anything.
- Predefined runbooks with a robot icon. Automation isn't AI. Triggering a script when a threshold trips is exactly what we've been doing for 20 years.
- "AI-generated" reports. Letting an LLM rewrite your dashboard summaries doesn't change what the dashboards measure.
What real AIOps actually does
The features that justify the AI label all share one trait: they require a model that's trained on your specific stack, not on a generic baseline. Predictive forecasts that anticipate CPU exhaustion in 4 days. Auto-correlation that collapses 12 alerts into 1 incident with the right root cause. Adaptive thresholds that learn what "normal" looks like for each individual host, not for an industry average.
Closed-loop automation is the test most platforms fail. If the AI surfaces an insight but a human always has to interpret it before action, the operational value is incremental. If the AI can fire a runbook on a high-confidence signal and report back what it did — that's where MTTR actually drops.
How to evaluate an AIOps platform
Three questions cut through most of the hype. Ask vendors: What data does the model train on, and how often does it retrain? If the answer is "we trained it on aggregated customer data once" — that's not your AIOps, that's their AIOps applied to your stack.
Show me how the model explains a prediction. Black-box predictions don't get adopted. The team has to trust why the model thinks node-12 will exhaust CPU in 4 days, or they'll override every alert.
What happens when the AI is wrong? Real platforms have a feedback loop — correct the prediction, retrain, get smarter. Lipstick-on-AI products just get the same answer wrong again next time.
Get clear answers on those three and you'll know if you're buying AIOps or buying marketing.