State of Production Reliability Report: 78% Hit Blind Spots
78% of teams hit failures their incident monitoring missed. See what the State of Production Reliability Report reveals about AI's role in reliability.
The State of Production Reliability Report finds 78% of engineering teams have hit a failure their monitoring stack missed entirely. Enterprise production ops has outgrown the way we run it.
Most engineering teams find out about failures one of two ways: an alert fires, or a customer tells them something is broken. NeuBird’s State of Production Reliability Report, published in 2026 as the State of Production Reliability and AI Adoption Report, surveyed more than 1,000 SRE, DevOps and IT professionals. 78% of the organizations surveyed have experienced at least one incident where no alert fired at all. Almost 40% of incidents are discovered by customers before the engineering team knows anything is wrong. These misses happen while organizations spend heavily on AI. VentureBeat’s coverage of the report found that leadership is writing checks for AI platforms while the technology often fails to reach the frontline. Engineering teams still spend around 40% of their time on incident management instead of building.
Why Is SRE Toil Increasing Despite More Monitoring Tools?
Engineering teams are spending a large amount of their time on incident management related tasks. That is time taken away from product development, from designing for reliability, from the work that actually moves systems forward. It goes to reactive work instead. And this figure holds consistent across company sizes. It is not a small-team problem; it is an industry-wide structural condition. MTTR is the number one KPI cited by 61% of organizations. It is a reasonable metric, but it only starts counting once resolution begins. By then, damage is already done. 93% of organizations pull in at least three engineers when a major incident fires. Each of them is context-switching away from whatever they were building, re-establishing context across a fragmented tooling environment spanning four to seven tools. There is also a slower-burning cost: burnout. Nearly 40% of organizations report more than a quarter of their on-call engineers are showing burnout symptoms tied to incident management.
When Does Incident Monitoring Become a Source of Noise Rather Than Signal?
Alert fatigue ranked as the top operational challenge in our survey. Above insufficient automation. Above difficulty identifying root causes. Alert fatigue used to be a morale problem. Now it is a reliability risk. When 70% of alerts don’t require action, engineers adapt and stop treating every alert as urgent. That is simply pattern recognition. The problem is what gets missed when the pattern breaks.
Source: 2026 State of Production Reliability and AI Adoption Report, NeuBird
44% of organizations experienced an outage in the past year directly linked to an ignored or suppressed alert. And 78% experienced at least one incident where no alert fired at all.
When customers are finding failures before your incident monitoring does, the monitoring system is no longer an early warning system. It is just generating noise.
Why Do Engineering Leaders and Practitioners See Reliability So Differently?
One of the clearest signals in the data is how consistently leadership and practitioners describe different realities. 74% of C-suite respondents say their organization actively uses AI for incident management. Only 39% of practitioners say the same. Executives evaluate what has been purchased. Practitioners evaluate what works during a production incident. On runbooks, the gap is just as sharp: 57% of C-suite describe their runbooks as comprehensive and widely used. Only 34% of practitioners agree. For engineers in the middle of an outage, an outdated runbook is often worse than no runbook at all. Practitioners are not resistant to change. In fact, more than half are actively evaluating AI solutions for incident management. They are waiting for AI to exist in their workflows, not just in their organization's software inventory.
What Does Production Operations Look Like When the Alert Cycle Breaks?
The more time teams spend on reactive incident work, the less time they have to design and build systems for better reliability. Unresolved root causes generate recurring failures, and each recurrence takes more time away from design work. Breaking the cycle requires a shift in how production operations are run. Teams move from responding after the fact to identifying risk before impact. That shift depends on autonomous systems that correlate, investigate and act. NeuBird customers catch degradations 30 to 60 minutes before alerts fire, isolate root cause in under 5 minutes at 94% accuracy, and see 80% fewer P1 war rooms. The latest release of NeuBird introduces Preventive Risk Insights, moving teams from incident resolution into continuous risk detection before failures affect production. Reactive work consumes 40% of engineering capacity. The systems teams rely on today, including their incident monitoring, were built for a lower level of infrastructure complexity. NeuBird replaces that model with a Production Ops Agent that prevents issues before they surface, resolves the ones that do in minutes, and continuously optimizes operations so that incident volume trends down over time. Falling incident volume returns time to design and reliability work. Check out the State of Production Reliability Report to see the complete findings, or book a demo to see the Production Ops Agent in practice.
