Operations | Monitoring | ITSM | DevOps | Cloud

For IT professionals evaluating Observability for large, complex enterprise environments, the shortlist usually starts with technical depth and ends with operational reality.

Modern ops stacks are very good at detecting problems. From IT infrastructure and cloud platforms to industrial systems, cybersecurity tools, and IoT environments, monitoring technologies generate alerts the moment something goes wrong. But there is a critical problem modern operations teams still struggle with: Detection does not ensure response. And that gap is becoming one of the biggest operational risks organizations face today.

A major new study from Harvard Medical School and Beth Israel Deaconess Medical Center has found that a large language model (LLM) outperformed physicians across a wide range of clinical reasoning tasks, including making emergency-room triage decisions from messy, real-world patient data. The findings, published April 30 in Science, represent one of the largest comparisons yet between AI and physicians on clinical tasks.

Reducing alert noise involves drawing a line between incidents that need an immediate response and ones that do not. Get this distinction wrong and your team is either interrupted unnecessarily or misses something critical. In this guide, we’ll help you make that distinction clear. We’ll cover what counts as noise and how to reduce it without missing what matters.

In the evening of May 5th, 2026, large parts of the German web briefly went dark. For a few hours, anyone trying to load a.de address through a major DNS resolver got errors instead of websites. Bahn.de, Amazon.de, and Spiegel.de were among the affected. Major brands like Telekom, DHL, and Sparkassen felt it too, along with hosting providers Hetzner, Strato, and Ionos.

We’ll be rolling out new Slack capabilities to eliminate more manual toil from your incident workflow: click once to promote any alert to an incident, get dedicated channels created automatically, page responders without leaving Slack, and manage all your settings in one place. This is part of our path to autonomous operations: reducing toil, protecting your capacity, and letting you stay in flow. If you’re only using PagerDuty for on-call scheduling, you’re missing the full picture.

AI promise and AI capabilities often diverge, with developers often reporting much faster code production, but not enough change in how incidents are handled. When the rate of change is faster than ever, but the rate of recovery from incidents isn’t moving, developers wind up stuck in firefighting mode. And, when these systems fail, it’s costly. According to PagerDuty’s State of AI-First Operations, over a third of surveyed companies report losing $500K per hour of downtime.