Operations | Monitoring | ITSM | DevOps | Cloud

Mean time to resolution (MTTR) measures the average duration to restore regular operation for an application, service, or infrastructure component. It’s a key performance indicator (KPI) for IT incident management. To tie MTTR directly to customer satisfaction, you first need to understand how it affects service and application reliability and availability. From there, you can make informed decisions, operate efficiently, and provide a seamless customer experience.

Agentic IT operations have arrived. It’s no longer a question of if enterprise IT departments will adopt agentic ITOps, but how quickly. Every year, IT environments grow more distributed, complex, and difficult to monitor with legacy tools and processes. At the same time, the pace of AI development is accelerating the volume of changes and incidents, straining teams that are still trying to manage them manually, reactively, and one alert at a time.

Enterprise IT operations leaders are realizing that legacy incident management processes cannot keep pace with today’s sprawling, hybrid-cloud enterprise environments. Enterprise IT doesn’t look anything like it did even five years ago. Hybrid cloud architectures, distributed microservices, and increasingly rapid CI/CD cycles have increased the speed and complexity of IT operations by orders of magnitude, leaving ITOps teams struggling to keep up.

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.

When an incident occurs, an L2/3 engineer or SRE can spend 20–30 minutes investigating across alert consoles, combing through change records, and pinging teams on Slack or Microsoft Teams. When you multiply that time spent across thousands of incidents per year by the cost of an IT outage at $14,056 per minute, the cost is staggering. Enterprises can’t afford to waste time searching across disparate tools.

We recently brought together IT operations leaders from across financial services, healthcare, airlines, media, and other industries for BigPanda 26, our annual customer event. The theme that emerged above all others during the event’s conversations is that our industry is no longer debating whether AI belongs in ITOps. The debate now is about how quickly it can be implemented, how to measure it, and who’s accountable when it acts. Here are some key learnings from BigPanda 26.

Imagine you’re in the middle of a critical project, and suddenly, your system crashes. Or it’s the middle of the night, and your server goes down, affecting countless users. While no enterprise can avoid all IT incidents, how you handle them can significantly reduce their impact. Fast, effective IT incident management is critical, as major incidents are increasingly costly.

Every enterprise IT leader facing the spiraling complexity of modern IT environments has a version of the same conversation. How can we manage the increasing complexity of more services, more dependencies, and more layers of observability and monitoring? Their answer would add headcount to the NOC, sign another Global System Integrator contract, and buy your organization another year.

IT incidents are rarely isolated. A network disruption can trigger degradations in infrastructure, which can ripple and cause application errors and end up causing a flood of user complaints. When an L1 operator looks at a single incident, they see only part of the story. Outside their immediate scope, other incidents are actively occurring that are either directly related or impacted by the same underlying cause. Without broader visibility, there is no way to know.

As enterprise ITOps teams monitor increasingly complex, cloud-based, containerized systems, traditional observability practices are struggling to keep up. As IT infrastructure complexity increases, the typical response is to layer on more monitoring, logging, and instrumentation.