Insurers are investing in cloud modernization to achieve greater business agility, but transformation isn’t instantaneous. Most organizations are still operating in hybrid environments, which present significant challenges and often lead to a fragmented view of system performance. This fragmentation can result in inefficiencies, security risks, and inconsistent customer experiences.

Achieving unified observability across hybrid infrastructures is becoming more critical than ever as systems are increasingly distributed and powered by AI. For instance, McKinsey predicts that more than 50% of insurance claims activities could be automated by 2030, intensifying the need for observability to ensure unified visibility, performance, security, and transparency.

Building the Foundation for Unified Observability

Before embarking on a unified observability initiative, insurers must first put a solid foundation in place. This begins with six core capabilities that span the full breadth of modern observability: instrumentation, efficient data processing, end-to-end tracing, visualization, integrations, and AI- and ML-driven insights.

These capabilities are not just technical requirements — they are the building blocks for real-time visibility, operational automation, and improved user experiences. However, many insurers still face challenges at this foundational level. In fact, 43% of financial and insurance organizations still consider their telemetry data to be siloed, a significant barrier to achieving full-stack observability.

Rather than treating these elements as standalone tools, insurers should approach them as an interconnected system. When unified under a single observability framework, these capabilities enable IT and business teams to identify issues, monitor performance, and resolve them at scale.

Advancing Through the Observability Maturity Model

As insurers modernize their IT landscapes to deliver always-on digital experiences, streamline operations, and reduce risk, they need a structured way to manage increasing complexity.

The unified observability maturity model not only helps eliminate blind spots, but it also provides faster problem-solving within a shorter time frame, a both insightful and comprehensive understanding of complex systems, and business agility over time.

This framework allows insurers to assess where they are today, identify key capability gaps, and prioritize future investments in line with their operational goals. Here are the stages of unified observability maturity:

Stage 1: Reactive (Monitoring)

At this first level, people are still heavily involved in taking action, even if they’re not the ones actively monitoring. For example, a legal policy administration system will send email-based alerts after outages, but support teams still have to manually check logs across different servers to pinpoint issues, slowing down recovery time and increasing customer frustration.

At the reactive stage, insurers rely on basic monitoring practices to identify system health issues. Data may be collected from infrastructure or applications, but it’s often siloed, and there’s minimal correlation between components. Dashboards are limited or nonexistent, alerts are basic, and incidents are handled manually after the fact. This stage offers limited visibility and slow response times, with troubleshooting largely dependent on human intervention. It’s a starting point, but one that exposes insurers to risk and inefficiency.

Stage 2: Proactive (Observability)

The proactive stage is where insurers begin correlating logs and metrics for more comprehensive visibility. Infrastructure, application performance, and user experience monitoring are automated, with alerts triggered by predefined thresholds. Teams can visualize system health across distributed services and respond more effectively using structured dashboards and incident workflows.

For instance, consider a customer portal that is equipped with synthetic monitoring and distributed tracing. When response times spike, the alerts are automatically routed to the IT service desk, generating an incident ticket and therefore speeding up the path to resolution. Though still reliant on manual root cause analysis, this level of maturity marks a shift from reactive troubleshooting to informed, data-driven action.

Stage 3: Predictive (Advanced Observability)

At this stage, observability becomes a predictive force. This could show up as an AI-powered observability platform detecting increased latency in an insurer’s claims processing system. The platform can then predict a potential performance bottleneck during peak hours, recommending an increase in personnel or resources to maintain any service-level agreements.

In stage three, AI and machine learning models analyze telemetry data to detect anomalies, forecast issues, and recommend responses. Synthetic monitoring simulates user behavior for critical systems, and distributed tracing is implemented across the full application and infrastructure stack. Insurers can identify patterns that signal impending outages or performance degradation, enabling them to take preventative action.

Stage 4: Autonomous (Intelligent Observability)

In the autonomous stage, observability systems not only detect and diagnose issues, but they can also resolve these issues automatically. Self-healing infrastructure, automated scaling, AI-driven root cause analysis, and intelligent notification routing are fully operational. Real-time data feeds into optimization engines that fine-tune infrastructure and application performance.

This stage allows insurers to run adaptive, resilient environments that minimize manual intervention while delivering seamless experiences for both customers and internal users. After a catastrophe, for instance, an insurer’s infrastructure automatically scales to accommodate surging traffic to its digital claims submission portal. If an anomaly is detected in the authentication process, the observability system self-corrects the impacted microservice without human intervention.

By progressing through these maturity levels, insurers can transform observability from a technical necessity into a strategic differentiator that enhances business agility, improves customer experience, and reduces operational overhead.

Charting a Clear Path to Intelligent Operations

Without unified observability, even the most ambitious cloud modernization efforts can fall short. The maturity model outlined above gives insurers a clear framework to assess their current capabilities and chart a practical path forward.

By investing in foundational capabilities and steadily advancing the maturity of their cloud observability, insurers can move from reactive problem-solving to intelligent, self-healing systems that drive real business value.

Interested in learning more about how the cloud can transform your organization? Read our whitepaper “Cloud Transformation in Insurance: A Practical Guide to Assess and Prepare for the Cloud.”