New Relic Data Report Reveals Clear Link Between AIOps Usage and Increased Engineer Productivity

New Relic, the Intelligent Observability company, published its 2026 AI Impact Report, which found a significant connection between usage of AI-strengthened observability features and noise reduction, Mean Time to Close (MTTC) of issues and deployment velocity. This report draws on aggregated, anonymized data from 6.6 million users of the New Relic Intelligent Observability Platform over the course of 2025, comparing those with New Relic AI capabilities versus those without. New Relic AI refers to the platform’s generative AI and AIOps features, which use a mixture of ML, generative, and deterministic methodologies to create value.

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New Relic’s 2026 AI Impact Report found that users of its AI-strengthened observability capabilities in 2025 shipped code at an 80% higher frequency on average than non-AI users in 2025

Reducing the noise with higher correlation rates

Engineers spend 33% of their time firefighting or addressing system disruptions. In 2025 alone, New Relic customers managed 2.2 billion alert events, with nearly a billion originating in production. AI-strengthened observability cuts through this noise by automatically correlating signals into actionable incidents. According to the report, New Relic AI users achieved a 2X higher correlation rate and consistently generated 27% less alert noise than non-AI accounts, allowing them to spend less time firefighting and freeing them up to focus on other responsibilities, such as shipping new features.

Resolving issues nearly 2X faster with AI

With noise suppressed, success is measured by MTTC, which tracks the average duration issues remain open. MTTC bears a strong correlation to how quickly a system recovers from disruption. The data shows that New Relic AI users resolve issues on average 25% faster than their peers, a gap that widens significantly during high-pressure periods. In May 2025, AI-enabled accounts averaged 26.75 minutes per issue—nearly half the 50.23 minutes required by non-AI users. This 23-minute head start per incident preserves engineering momentum and prevents “investigation stall” when every second counts.

The deployment multiplier

The report draws a clear correlation between a reduction in the time spent on user toil, i.e., triaging and cutting through the noise, and code shipping velocity. While non-AI users averaged 87 deployments per day during peak periods, AI-empowered teams achieved up to 453 deployments per day—a 5X increase in the ability to ship new features and updates to market. The data shows that this considerable surplus of engineering hours may allow organizations to ship code at a much higher frequency, approximately 80% higher on average than New Relic’s non-AI users.

“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 New Relic Head of AI Camden Swita. “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.”