What is observability in AIOps (AIOps observability)?

AIOps is the application of AI capabilities—such as natural language processing and machine learning models—to automate IT service management and operational workflows. Observability is the ability to understand the internal state or condition of a complex system based solely on knowledge of its external outputs, specifically its telemetry. Combining these practices provides powerful tools for optimizing, troubleshooting and automating in complex multicloud IT environments.

AIOps observability uses AI and ML techniques to analyze a system’s logs, metrics and traces and perform operations including:

• Anomaly detection, where algorithms analyze large volumes of data to determine baseline system performance and identify deviations.
  
  
• Root cause analysis (RCA), which moves beyond correlation to identify actionable insights into system issues.
  
  
• Predictive analytics, which helps predict future system workloads and scale resources up or down accordingly.

To combine AIOps and observability, most organizations use observability platforms with built-in AI features. Modern observability platforms often include generative AI features, such as text interfaces that can answer questions about network status or real-time data visualization tools built into the platform’s dashboard. IT teams can use these gen AI tools—alongside the observability platform’s own AI-powered automated remediation tools—to forecast downtime, increase operational efficiency and improve application performance.

Here is an example of how AIOps solutions can be used in observability. Say that an observability platform surfaces a correlation between a sudden influx of alerts about applications slowing down and latency in a core router.

The platform can, using an established baseline of network behavior, identify anomalous activity that preceded the latency—for example, an unscheduled change to that router’s configuration. Then, it can perform an automated root cause analysis to identify how, when and where the change was made. After that, the platform can consult preapproved workflows to apply a fix (such as rolling the router firmware back to a previous version). Finally, it can present the IT team with an incident report, helping prevent further disruptions.

Generative AI, hybrid cloud operations and observability are deeply intertwined. A 2025 report from research firm Gartner¹ describes observability as a key capability of gen AI-powered CloudOps (cloud operations). According to a 2025 report from S&P Global Market Intelligence² 71% of organizations that use observability solutions are using their AI features, an increase from 2024 of 26%.

AIOps observability works by collecting traditional observability data such as logs, traces and metrics. It then uses AI and machine learning to perform core observability functions with this data—such as root cause analysis and anomaly detection—and establish automated workflows to help optimize IT infrastructure.

AIOps observability relies on the three traditional pillars of observability: logs, traces and metrics.

• Logs are granular, time-stamped, complete and immutable records of application events.
  
  
• Traces record the end-to-end journey of every user request, from the user interface, through the entire architecture, and back to the user.
  
  
• Metrics are fundamental measures of application and system health over time, such as CPU usage and latency measurements. 

The use of powerful artificial intelligence and machine learning capabilities differentiates AIOps observability from traditional observability. AIOps observability entails using these tools to perform root cause analyses, anomaly detection and predictive analytics, among other capabilities.

Root cause analysis is the quality management process by which an organization searches for the root of a problem, issue or incident after it occurs. This analysis is often enhanced by causal AI, which can identify root causes of issues by joining together observability data. It can then demonstrate how and why certain entities were identified as a probable cause of the issue, allowing IT professionals to identify and fix them.

Anomaly detection is the identification of data points that deviate from what is usual, standard or expected, making them inconsistent with the rest of a data set. AI and ML capabilities can automatically identify unexpected changes in a data set’s normal behavior by using the telemetry collected by observability tools to flag deviations from the baseline. These deviations help detect issues with application performance, cybersecurity and ecommerce platforms, among other uses.

Predictive analytics is the practice of making predictions about future outcomes by using historical data combined with statistical modeling, data mining techniques and machine learning. In the context of AIOps observability, AI models can use telemetry to predict future workloads and scale network resources up or down accordingly, reducing latency and improving user experience.

When observability is combined with AIOps, ML and automation capabilities, IT teams can predict issues based on system outputs and resolve them with minimal human intervention.

AIOps software can use root cause analysis, anomaly detection, predictive analytics and other AI and ML capabilities to speed up troubleshooting. Faster troubleshooting helps to prevent future outages by increasing system performance and the pace of incident resolution. It can also free up DevOps engineers for other critical tasks.

When implemented, AIOps observability establishes a sort of beneficial “loop.” The deluge of telemetry data generated by a system becomes a resource that IT professionals, with the help of the platform’s automation capabilities, can use to identify weak points and automatically develop fixes.

For example, an observability platform with AIOps capabilities might notice through correlated metrics that the CPU utilization within a Kubernetes cluster has exceeded the threshold set by the organization, increasing latency.

After identifying that the problem stems from one overworked microservice, the AI might suggest the network should scale horizontally by increasing the number of server instances. It can then set a rule to automatically perform these actions whenever the microservice in question is taxed and revert when traffic returns to normal, preventing the bottleneck in the future.

AIOps observability can improve an organization’s mean time to repair (MTTR), the efficiency of its DevOps workflow and its security practices.

AIOps observability can vastly reduce recovery and repair time by speeding up root cause analysis.

Automated analysis can be the difference between triaging an incident for hours and resolving an impending issue before it happens, reducing downtime and freeing up DevOps teams for other tasks.

AIOps observability can make DevOps more efficient by identifying opportunities to streamline and automate administrative tasks.

For example, say that an AIOps platform identifies through root cause analysis that a certain cache needs to be cleared before a connected application can function properly. Site reliability engineers can use this information to create an automated workflow that detects the condition in real time and automatically clears the cache when it reaches a certain volume. The AIOps platform can also produce a visualization of areas on the network at greatest risk of similar congestion. This visualization can help the DevOps team and others make more informed decisions when writing organization-wide policies.

Some observability platforms with AI capabilities can automatically perform risk assessments, scan systems or malware and generate audit trails and reports. When incidents occur, AI-powered platforms can use relevant telemetry data to automatically identify attack vectors, assess impact and remediate vulnerabilities faster than traditional incident response.

AIOps can also support compliance requirements by automatically compiling and maintaining detailed audit trails of system access and data flows.

Administrators can use the telemetry data gathered through AIOps observability to suppress excessive or irrelevant alerts, plan organizational capacity and prevent performance degradation before it begins.

Excessive alerts can cause alert fatigue, a state of mental and operational exhaustion caused by an overwhelming number of alerts that are low priority, false positives or otherwise non-actionable.

AI-powered observability platforms can sift through high volumes of alerts by using ML-driven triage. This triage can significantly reduce manual labor and error rates by identifying patterns, reducing duplicates and correlating related alerts to lighten the human workload. 

Capacity planning is the strategic process that examines the production capacity and resources an organization needs to meet current and future demand. AIOps observability can improve this process by feeding application performance metrics and other telemetry data into predictive algorithms. Some AI-enabled observability platforms can also trigger workflows to expand and contract capacity as network conditions demand.

AIOps observability helps prevent performance degradation, the natural entropy of a network as new patches, applications and configurations are applied. By processing the large volumes of data a network produces and establishing baseline behavior, it can proactively alert IT teams when a change might cause an issue. If given the appropriate playbook, it can also automatically act to prevent the issue before it occurs.

Generative AI features are increasingly important to AIOps and observability, with many tools featuring chatbot assistants that can provide direct, natural-language feedback and troubleshooting to engineers.

Given the vast scope of both the telemetry data collected by observability platforms and the platforms’ own AI-driven capabilities, a streamlined generative AI interface allows site reliability engineers to quickly and directly find answers to a question like “Why has service slowed for users in Europe?”

Generative AI features also assist with writing straightforward summaries of network events for administrators and creating data visualizations of network health and event correlation.