New data from New Relic indicates a strong correlation between the use of AI-strengthened observability tools and improved engineering productivity, particularly in deployment velocity, alert management, and issue resolution times.
The findings are detailed in New Relic’s 2026 AI Impact Report, which analyzed aggregated, anonymized data from 6.6 million users of the New Relic Intelligent Observability Platform across 2025. The report compares organizations using New Relic’s AI capabilities—including generative AI and AIOps features—with those operating without AI assistance.
According to the report, teams using AI-enabled observability shipped code at an average frequency 80% higher than non-AI users during 2025. During peak periods, the gap widened significantly: while non-AI users averaged 87 deployments per day, AI-enabled teams reached up to 453 deployments per day, representing a nearly 5X difference in deployment capacity.
Alert Noise Reduction Emerges as a Key Differentiator
The report highlights alert fatigue as a persistent operational challenge. Engineers spend 33% of their time responding to system disruptions, and New Relic customers processed 2.2 billion alert events in 2025 alone, nearly 1 billion of them from production environments.
AI-strengthened observability was associated with a measurable reduction in this noise. New Relic AI users achieved a 2X higher signal correlation rate and generated 27% fewer alerts compared to non-AI users. By automatically correlating signals into actionable incidents, AI-enabled accounts spent less time on triage and more time on development work.
Faster Resolution Times Under Pressure
With alert noise reduced, the report points to Mean Time to Close (MTTC) as a key outcome metric. Across 2025, organizations using New Relic AI resolved issues approximately 25% faster than those without AI.
The difference became more pronounced during high-stress periods. In May 2025, AI-enabled accounts recorded an average MTTC of 26.75 minutes per incident, compared to 50.23 minutes for non-AI users—a gap of over 23 minutes per issue.
According to the report, this reduction helps prevent investigation delays during outages and preserves engineering momentum when response speed is critical.
Operational Efficiency Translates into Deployment Velocity
New Relic’s analysis draws a direct connection between reduced operational toil—such as alert triage and investigation overhead—and increased deployment frequency. By minimizing time spent on firefighting, AI-enabled teams appear able to redeploy engineering effort toward feature delivery and system improvements.
The report concludes that AI-strengthened observability is associated with both higher deployment throughput and faster recovery from incidents, suggesting a shift in how software operations scale under increasing system complexity.
A Changing Baseline for Software Operations
“AI is bringing a level of complexity to modern software operations that is moving beyond human ability to manage. In tandem, AI is also helping solve the problem it created,” said Camden Swita, Head of AI at New Relic.
“The data from our user base tells the story of a new operational baseline for businesses. Those using AI-strengthened observability reduce noise and accelerate resolution, unlocking more time to focus on building new features rather than maintaining existing ones. Ultimately, this is a measure of an organization’s ability to respond to the market. When the operational tax is minimized, the speed of the entire company accelerates.”