Operations | Monitoring | ITSM | DevOps | Cloud

Earlier this year, I had the chance to meet with one of our airline customers. During the meeting, we discussed how to use agentic technology to automate L1 workflows. As one of the largest global airlines, they have many applications and service teams focused on flight-critical, tier 1 environments. Any downtime can cause costly delays and unhappy customers.

New research from BigPanda reveals what enterprises spend on outsourced IT operations support, what they get in return, and why leaders are ready to rethink the model. Enterprises spend an average of $5.4 million a year on outsourced IT operations support. That’s a substantial investment. It should produce reliable frontline operations: incidents detected, understood, routed, and resolved with the speed and accuracy the business expects. The research shows a different picture.

Effective incident response depends on having all of the context surrounding what’s happening. You have to understand your systems, services, architecture, and teams deeply enough to correctly interpret whatever alert just fired. Too often, that context doesn’t arrive packaged neatly in one place. Gathering and interpreting context correctly under time pressure is one of the most difficult parts of the job.

Agentic IT operations have arrived. It’s no longer a question of if enterprise IT departments will adopt agentic ITOps, but how quickly. The question we hear most often at BigPanda isn’t “what are agentic ITOps,” it’s “what data do we actually need to get started?” That’s the right question to ask. Agentic AI is only as good as the data and context that feeds it. Real-time observability and telemetry data from machines. Structured ITSM and workflow records.

Picture a familiar scene: a critical application goes down during peak business hours, and your on-call engineers scramble to restore service. Two weeks later, the same application fails again, frustrating your teams with the same symptoms, the same scramble, and the same customer frustration. If this pattern feels familiar, your organization may be strong at IT incident management, but underinvested in IT problem management.

Enterprise IT has reached an inflection point. Your teams are responsible for hybrid cloud infrastructure, microservices, third-party dependencies, and shipping AI-generated code at unprecedented velocity. IT environments are becoming more complex faster than traditional tools and processes can keep pace. Alert volumes keep climbing. Institutional knowledge keeps walking out the door. And the pressure to do more with flat or shrinking budgets isn’t letting up.

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.