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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How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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