Overview
As cloud-native environments scale, operations teams are under growing pressure to maintain reliability, manage exploding telemetry volumes, and connect platform performance directly to business outcomes. theCUBE Research’s Day 2 Operations Survey Research Report examines how enterprises are evolving their observability practices, reliability strategies, and AI-powered operations. The findings point to strong momentum: monitoring and observability are now top priorities for nearly all organizations, SLO tracking is nearly universal, and AIOps is rapidly shifting from experimental capability to operational necessity.
At the same time, execution challenges remain. Cost, tool complexity, cultural resistance, and inconsistent visibility across containerized and serverless workloads continue to limit effectiveness. High-performing organizations are differentiating themselves by tying observability to business metrics, embedding operational intelligence into daily workflows, and investing in proactive, AI-enhanced operations rather than reactive monitoring. This report highlights where the industry is making progress, where friction persists, and how leaders can scale reliability and resilience as cloud-native delivery becomes the enterprise default.
Key Takeaways
- Observability is now a strategic priority, not a tactical tool: Nearly all organizations prioritize cloud and application monitoring, with SLO tracking applied to the vast majority of internally developed applications.
- AIOps is becoming table stakes: Most teams now view AI-powered operations as essential or a key differentiator for managing complexity, reducing noise, and accelerating root cause analysis.
- Visibility gaps still limit effectiveness: Many organizations report less than 50% coverage across containerized and serverless workloads, highlighting ongoing challenges with instrumentation and tooling sprawl.
- Investment momentum is strong: Over 85% of organizations plan near- or mid-term investment in observability and AI-driven operations, signaling a clear shift toward proactive, intelligence-driven reliability practices.
