High-Business Impact Outages Cost Financial Services and Insurance Sectors $2.2 Million Per Hour

Nearly half of respondents experience high-business-impact outages at least weekly
Learn more about New Relic

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic.

"Financial services and insurance organizations are navigating a fast-moving digital landscape where reliability, security, and operational efficiency are non-negotiable," said New Relic Chief Technical Strategist Nic Benders. "These businesses grapple with frequent high-impact outages, complex tool sprawl, and mounting regulatory pressures, all while striving to deliver seamless digital experiences. The report's findings demonstrate how critical observability is in helping businesses reduce costly downtime, leveraging AI, and modernizing legacy systems to meet rising customer expectations while maintaining compliance. Observability is no longer just a technical practice; it is mission critical."

Financial modernization and AI adoption are key priorities

Financial modernization was highlighted as a top priority of the research, with institutions migrating to the cloud, investing in digital-native subsidiaries, and adopting cutting-edge technologies like AI.

Observability plays a significant role in these transformations, with 34% of respondents citing AI-assisted troubleshooting as crucial to improving observability practices.

Additionally, 42% reported ambitions to consolidate tools in the coming year to address challenges like tool sprawl and data silos.

Organizations in the financial services and insurance sectors are also ahead of other industries in cloud-native application development (36% adoption compared to 31% across all industries) and containerized workloads (28% versus 23% overall). These modern technology strategies, combined with robust observability solutions, empower businesses to remain agile and competitive in an increasingly digital-first world.

AI adoption also accelerates observability adoption, with respondents highlighting automatic root cause analysis (32%) and AI-assisted remediation actions (32%) as key opportunities to strengthen their practice.

Financial and reputational outage risks require intelligent observability

Despite advances in technology adoption, financial services and insurance organizations face significant hurdles, including frequent outages, fragmented data, and the rising costs of downtime. The report reveals that these companies experience high-business-impact outages more often than most industries, with nearly half (48%) reporting at least one such incident weekly. The median cost of downtime for these outages in this sector is $2.2 million per hour; 16% higher than the average across all industries.

Detecting and resolving outages remains a challenge, with the median mean time to detection (MTTD) at 42 minutes, and mean time to resolution (MTTR) at 58 minutes; both higher than industry-wide averages.

However, those leveraging full-stack observability experience faster detection and resolution times, underscoring its value in mitigating the financial and reputational risks of outages.

Tool consolidation creates value, as observability ensures strong ROI and system uptime

Nearly half (49%) of respondents preferred a single observability platform to simplify operations and extract greater value from investments. By consolidating tools, businesses can overcome common barriers like data silos and achieve end-to-end visibility across their tech stack.

Financial services and insurance organizations report significant return on investment (ROI) from their observability investments, with a median annual return of 297%. These tools enable companies to reduce downtime, increase operational efficiencies, and enhance customer experiences by ensuring systems remain fast, reliable, and secure.

Nearly half of respondents (49%) say observability improves system uptime, while 42% point to operational efficiency gains. In particular, practitioners see observability as a productivity booster, which helps them troubleshoot faster and manage complex infrastructures with less guesswork.

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High-Business Impact Outages Cost Financial Services and Insurance Sectors $2.2 Million Per Hour

Nearly half of respondents experience high-business-impact outages at least weekly
Learn more about New Relic

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic.

"Financial services and insurance organizations are navigating a fast-moving digital landscape where reliability, security, and operational efficiency are non-negotiable," said New Relic Chief Technical Strategist Nic Benders. "These businesses grapple with frequent high-impact outages, complex tool sprawl, and mounting regulatory pressures, all while striving to deliver seamless digital experiences. The report's findings demonstrate how critical observability is in helping businesses reduce costly downtime, leveraging AI, and modernizing legacy systems to meet rising customer expectations while maintaining compliance. Observability is no longer just a technical practice; it is mission critical."

Financial modernization and AI adoption are key priorities

Financial modernization was highlighted as a top priority of the research, with institutions migrating to the cloud, investing in digital-native subsidiaries, and adopting cutting-edge technologies like AI.

Observability plays a significant role in these transformations, with 34% of respondents citing AI-assisted troubleshooting as crucial to improving observability practices.

Additionally, 42% reported ambitions to consolidate tools in the coming year to address challenges like tool sprawl and data silos.

Organizations in the financial services and insurance sectors are also ahead of other industries in cloud-native application development (36% adoption compared to 31% across all industries) and containerized workloads (28% versus 23% overall). These modern technology strategies, combined with robust observability solutions, empower businesses to remain agile and competitive in an increasingly digital-first world.

AI adoption also accelerates observability adoption, with respondents highlighting automatic root cause analysis (32%) and AI-assisted remediation actions (32%) as key opportunities to strengthen their practice.

Financial and reputational outage risks require intelligent observability

Despite advances in technology adoption, financial services and insurance organizations face significant hurdles, including frequent outages, fragmented data, and the rising costs of downtime. The report reveals that these companies experience high-business-impact outages more often than most industries, with nearly half (48%) reporting at least one such incident weekly. The median cost of downtime for these outages in this sector is $2.2 million per hour; 16% higher than the average across all industries.

Detecting and resolving outages remains a challenge, with the median mean time to detection (MTTD) at 42 minutes, and mean time to resolution (MTTR) at 58 minutes; both higher than industry-wide averages.

However, those leveraging full-stack observability experience faster detection and resolution times, underscoring its value in mitigating the financial and reputational risks of outages.

Tool consolidation creates value, as observability ensures strong ROI and system uptime

Nearly half (49%) of respondents preferred a single observability platform to simplify operations and extract greater value from investments. By consolidating tools, businesses can overcome common barriers like data silos and achieve end-to-end visibility across their tech stack.

Financial services and insurance organizations report significant return on investment (ROI) from their observability investments, with a median annual return of 297%. These tools enable companies to reduce downtime, increase operational efficiencies, and enhance customer experiences by ensuring systems remain fast, reliable, and secure.

Nearly half of respondents (49%) say observability improves system uptime, while 42% point to operational efficiency gains. In particular, practitioners see observability as a productivity booster, which helps them troubleshoot faster and manage complex infrastructures with less guesswork.

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Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

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Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

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If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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