A new survey on IT production environments sheds light on the growing problem of alert fatigue. Analysis found that engineers spend 40% of their time putting out fires while customers discover outages before monitoring tools catch them, according to agentic AI pioneer NeuBird AI.

Current production environments outpace incident management practices built to support them, and this deficiency is now leading to measurable failures, the survey showed.

Based on a survey of 1,039 SRE, DevOps and IT operations professionals, the 2026 State of Production Reliability and AI Adoption Report reveals “an industry at an inflection point: reactive, alert-driven incident response is no longer sufficient for the scale and complexity of modern production environments, and the path forward requires autonomous systems that can prevent, resolve and optimize operations end to end.”

“This data highlights a gap in how today’s tools support modern production environments,” said Gou Rao, CEO and co-founder of NeuBird AI. “As systems grow more complex, alert-driven approaches alone can’t keep pace. Teams need AI that works alongside them to identify risks before they surface, resolve incidents faster, and continuously improve operations so reliability scales with the business.”

Almost half of organizations surveyed (44%) experienced an outage in the past year directly linked to suppressed or ignored alerts, and a vast majority (78%) experienced at least one incident where no alert fired at all, leaving engineers to discover failures only after customers were already affected. Meanwhile, 74% of executives say their organizations are actively using AI to address these problems, compared to just 39% of engineers.

The survey found that the majority of engineering teams spend 40% or more of their time on incident management rather than product development and innovation.

When a business-impacting incident strikes, almost all (93%) of organizations pull in three or more engineers to resolve it, and nearly 40% involve six to ten people, respondents reported.

Data showed 36% of teams spend five to ten hours every week on incident reports and post-mortems alone.

With 83% of teams navigating four or more tools during a live incident, every context switch adds time to an already costly response.

The resulting downtime financial exposure is significant.

Sixty-one percent of organizations estimate infrastructure downtime costs at least $50,000 per hour, and 34% put that figure at $100,000 or more.

Almost 60% of organizations report that their mean time to resolve a critical incident is between 30 minutes and two hours.

With almost 90% of companies handling up to 50 incidents per month, the cumulative cost of downtime is a material business risk.

Burnout is also a direct downstream consequence. Nearly 40% of organizations report that more than a quarter of their on-call engineers show burnout symptoms related to incident management.

“The math is stark. At a median downtime cost between $50,000 and $100,000 per hour, a one-to-two-hour resolution window for a critical incident represents $50,000 to $200,000 in direct exposure per event, not counting the engineering hours that disappear into diagnosis, root cause analysis and post-mortems,” continued Rao. “MTTR is the number one KPI organizations track for incident response, which reflects how central resolution speed is to operational performance, yet most organizations are still resolving incidents the same way they were five years ago.”

Once a morale problem, alert fatigue is now a reliability risk.

Respondents ranked alert fatigue and noise at the top of their list of concerns, followed by insufficient automation, knowledge silos and documentation gaps, difficulty identifying root causes, and integration challenges between tools.

Seventy-seven percent of on-call teams receive at least ten alerts per day, and 57% report that fewer than 30% of those alerts are actionable, the survey found.

Engineers have adapted accordingly, with 83% ignoring or dismissing alerts at least occasionally.

“Taken together, these findings describe an environment in which reactive, manual incident management has become the default, leaving little capacity for the preventive work, capacity planning, and reliability improvements that would reduce incident volume over time,” the report stated.

Interestingly, executives and practitioners differ when it comes to the use of AI in incident management.

A majority (74%) of C-suite respondents say their organization actively uses AI for incident management, while only 39% of practitioners report the same. Executives report what has been purchased or decided; practitioners report what is running in the environments where they work.

The divide is just as pronounced in the perceived impact of AI.

C-suite respondents overall were nearly three times as likely as practitioners to say AI has significantly reduced operational toil (35% vs. 12%).

Among practitioners who do use AI tools, 28% said the impact on their workload has been less than 10%.

Practitioners aren’t skeptical of AI; more than half say they’re actively evaluating AI solutions but are more realistic about what’s been deployed, not what’s been purchased or decided.

Among organizations that have deployed AI in incident management, automated root cause analysis is the leading use case, followed by anomaly detection and prediction, alert correlation, and noise reduction.

Budget constraints were cited as the top barrier to AI adoption, followed closely by concerns about AI increasing system complexity, security, and compliance concerns.

The survey of professionals at organizations with 100 or more employees, conducted in February 2026, included C-suite executives (20%); IT and engineering leadership (40%); and practitioners, including software engineers, system administrators, DevOps engineers, and SREs (40%).