Operations | Monitoring | ITSM | DevOps | Cloud

AI is changing incident management, but not in the way most people think. For years, operations teams focused on getting better at detecting problems. Monitoring improved. Observability improved. AI is now helping teams correlate signals, reduce noise, and identify issues faster than ever before. That’s all valuable, but many organizations are discovering that finding the problem is no longer the hardest part. The harder part is everything that happens next. Who owns the issue?

Most IT teams already know when something breaks. The real problem is making sure the right person responds fast enough. A server goes down. A customer-facing application crashes. A security alert triggers after hours. The monitoring system sends the notification. But nobody responds. The alert gets buried in Slack. The on-call engineer misses the push notification. The wrong person is scheduled. Everyone assumes somebody else is handling it. That is how small incidents become expensive outages.

When a production incident strikes, a sudden latency spike, a cascading API failure, a service returning 500s at scale, every minute of downtime has a cost. Root cause analysis (RCA) is the process that turns that chaos into a clear answer: what actually broke, and why. Not the symptom that triggered the alert. The underlying cause.

Our On-call product has a lot of great features: configuring escalation paths, viewing rotas and schedules, requesting cover, etc. However, when framing its reliability, we reduce it down to two critical pieces of functionality: It’s not that we’re happy if only these parts are working, but they are the most important parts. In this post, I'll go into more detail on how we think about their reliability.

Enterprise ITOps leaders are realizing that legacy incident management processes are collapsing under the weight of today’s sprawling, hybrid-cloud enterprise environments. Monitoring and observability tools generate a relentless flood of alerts across cloud platforms, infrastructure, applications, and services. The signals are there, the volume of noise makes it harder than ever to identify what’s urgent.