The promise a few years ago was simple: an ML system that watches your metrics, learns what normal looks like, and alerts when something deviates. It worked for detection. Completely missed diagnosis. You'd get an alert saying "latency anomaly on checkout service" and then spend the next 30 minutes doing exactly what you did before this. Opening Datadog, checking deploys, reading logs, and connecting the dots manually. The ML powered system told you something was wrong. You still had to figure out why. This post breaks down what changed architecturally, why traditional ML hit a ceiling, and what LLMs genuinely unlocked for incident response. Key Takeaways The AIOps wave (2018-2022) solved detection but not diagnosis. Anomaly scoring on metrics could flag deviations but couldn't explain root cause across data types Traditional ML hit a fundamental architectural ceiling. It worked on structured numerical data.…