Operations | Monitoring | ITSM | DevOps | Cloud

Only 14% of CFOs can prove AI ROI. OpenAI’s gross margin fell from 40% to 33% in 2025, well below its 46% target. Even the AI providers cannot reliably predict what AI will cost. Companies are scaling AI faster than they can measure it: more tokens, more agents, more model calls, more spend moving through systems finance cannot yet see. Every board is asking the same question: What is this AI investment returning? Most companies cannot answer it. The ones that can will compound their advantage.

Is your monitoring stack really giving you clarity, or just more alerts? Your monitoring stack is probably working exactly as designed. That is the problem. As systems grow, most IT and platform teams start to see the same patterns: At this point, traditional monitoring starts to feel limited. This is where teams begin exploring AI in observability. In this guide, we will explain what AI-powered observability actually means, how it works, and when it is useful.

A critical production alert wakes you up: p99 latency just hit 4 seconds. You drag yourself to a terminal, open five dashboards, start correlating log timestamps with trace IDs, dig through 47,000 log lines across eight services, and 90 minutes later, you finally find the culprit: an N+1 database query introduced in a deployment that shipped four minutes before the spike started. An Atatus AI SRE Agent would have identified that root cause and drafted a remediation plan in 28 seconds. Not approximation.