A new industry report documents the true cost of reactive incident management — and a widening gap between what executives believe AI is doing and what engineers experience.
The alerts are not the problem. There are too many of them; most are not actionable, and the engineers responsible for responding to them have learned, out of necessity, to ignore them. That adaptation has now become a reliability crisis.
A new study from NeuBird AI, based on a survey of 1,039 SRE, DevOps, and IT operations professionals conducted in February 2026, finds that 44 percent of organizations experienced an outage in the past year directly caused by suppressed or ignored alerts. More striking still: 78 percent experienced at least one incident where no alert fired at all — leaving engineers to discover failures only after customers were already affected.
The picture the report assembles is of an industry whose incident management practices have not kept pace with the environments they are supposed to protect.
The Alert Fatigue Numbers
The scale of alert noise helps explain why engineers have stopped trusting the system.
Seventy-seven percent of on-call teams receive at least ten alerts per day. Fifty-seven percent report that fewer than 30 percent of those alerts are actionable. In that environment, 83 percent of engineers say they ignore or dismiss alerts at least occasionally — not out of negligence, but as a rational response to a signal that has lost its meaning.
Alert fatigue ranked as the top challenge among respondents, followed by insufficient automation, knowledge silos, difficulty identifying root causes, and integration friction between tools. Taken together, these findings describe an industry defaulting to reactive, manual incident management — leaving little capacity for the preventive work that would reduce incident volume over time.
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The Cost of Slow Resolution
When incidents do escalate, the organizational response is resource-intensive and expensive.
Ninety-three percent of organizations pull in three or more engineers to resolve a business-impacting incident. Nearly 40 percent involve six to ten people. Thirty-six percent of teams spend five to ten hours every week on incident reports and post-mortems alone. With 83 percent of teams navigating four or more tools during a live incident, every context switch adds time to an already costly response.
The financial exposure is significant. Sixty-one percent of organizations estimate infrastructure downtime costs at least $50,000 per hour, and 34 percent put that figure at $100,000 or more. Nearly 60 percent report a mean time to resolve critical incidents of between 30 minutes and two hours — a window that, at median downtime costs, represents $50,000 to $200,000 in direct exposure per event, before accounting for the engineering hours consumed by diagnosis, root cause analysis, and post-mortems.
With nearly 90 percent of companies handling up to 50 incidents per month, the cumulative cost is a material business risk, not an operational inconvenience.
Burnout is the downstream consequence that rarely appears in financial models. Nearly 40 percent of organizations report that more than a quarter of their on-call engineers show burnout symptoms directly linked to incident management.
“MTTR is the number one KPI organizations track for incident response, yet most organizations are still resolving incidents the same way they were five years ago,” said Gou Rao, CEO and co-founder of NeuBird AI.
The Executive-Engineer Disconnect
Perhaps the most consequential finding in the report is not about alerts or downtime. It is about perception.
Seventy-four percent of C-suite respondents say their organization actively uses AI for incident management. Only 39 percent of engineers say the same. Executives are reporting what has been purchased or decided. Engineers are reporting what is actually running in the environments where they work.
The gap in perceived impact is equally pronounced. C-suite respondents were nearly three times as likely as practitioners to say AI has significantly reduced operational toil — 35 percent versus 12 percent. Among practitioners who do use AI tools, 28 percent said the impact on their workload has been less than 10 percent.
Practitioners are not skeptical of AI. More than half say they are actively evaluating AI solutions. They are simply more clear-eyed about the difference between deployment and adoption — between a tool that has been purchased and one that is meaningfully changing how work gets done.
Among organizations that have deployed AI in incident management, automated root cause analysis is the leading use case, followed by anomaly detection and alert correlation. Budget constraints were cited as the top barrier to broader adoption, followed by concerns about AI increasing system complexity and security and compliance considerations.
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NeuBird AI’s Response
Alongside the report, NeuBird AI announced $19.3 million in new funding, led by Xora Innovation, and the launch of its autonomous production operations agent. The product, powered by NeuBird AI Falcon, is designed to provide continuous predictive intelligence across cloud, on-premises, and hybrid environments — moving from reactive alert response toward prevention, faster resolution, and ongoing operational optimization.
“As systems grow more complex, alert-driven approaches alone can’t keep pace,” said Rao. “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.”
