The Power of AI Observability – AIOps in IT Operations

Digital transformation has reshaped how businesses deliver value. Today, companies run on distributed systems, microservices, and cloud-native apps, while also increasingly leveraging artificial intelligence (AI) both in their products and to manage their complex operations. With all this innovation comes greater complexity and an urgent need to keep systems healthy, available, and high-performing.

Traditional monitoring often relies on static dashboards and manual checks, struggling to keep pace with this complexity. IT teams are often overwhelmed by alerts and buried under data, unable to spot problems before users are affected.

This is why observability and AI are now the foundation for successful, future-ready IT operations.

What is Observability?

Observability is the ability to understand the internal state of your IT systems by analyzing the data they produce. It goes far beyond basic monitoring. While monitoring tells you if something is wrong, observability helps you answer why it’s wrong and how to fix it.

Key components of observability include:

Logs: Text records that show what happened and when, providing details on system and application events.
Metrics: Numerical data that tracks system performance (e.g., CPU usage, memory consumption, request rates).
Traces: Maps of how requests move across different services, showing bottlenecks, dependencies, and failures in distributed systems.

Modern observability platforms aggregate all this data, making it possible to:

Quickly find the root cause of problems
Understand system health at a glance
Spot trends and patterns over time
Reduce downtime and improve user experiences

Here’s the problem, though: as you scale, the amount of system data skyrockets, and human teams simply can’t keep up. AIOps (AI for IT Operations) is becoming increasingly important precisely for this reason. It uses artificial intelligence to automatically analyze that massive wave of data, taking the pressure off your IT team and helping them work smarter, not harder.

AIOps - what is it and how it works

Artificial intelligence for IT operations means extending your DevOps practices with AI capabilities like machine learning. As IBM states, the estimated market size for AIOps is USD 1.5 billion, with a compound annual growth rate (CAGR) of around 15% between 2020 and 2025.

AIOps takes the rich data provided by observability and applies intelligent analysis to it:

Anomaly Detection: AIOps models automatically learn what “normal” looks like for your systems. They instantly flag unusual patterns or behaviors, like a sudden spike in error rates, before they cause major problems.
Event Correlation and Noise Reduction: Instead of generating hundreds of separate alerts, AIOps groups related incidents together. It filters out “noise” so teams can focus on the handful of issues that matter.
Root Cause Analysis: AIOps can analyze logs, metrics, and traces together, identifying the likely source of an outage or slowdown. This drastically shortens mean time to resolution (MTTR).
Automated Remediation: For common or well-understood problems, AIOps-powered systems can even take corrective action automatically, such as restarting a service or scaling up resources.
Predictive Insights: AIOps studies past incidents and usage patterns to forecast future issues, giving teams the chance to address risks before they become incidents.

AI in observability tools - use cases

AI is becoming a practical layer in observability tools, helping teams understand what’s happening in their systems without getting lost in data.

Spot performance issues

It can forecast resource usage, so teams see when capacity might become a problem and act before it does. It also helps detect performance issues earlier by spotting patterns that would be hard to catch manually.

On the user side, it improves experience monitoring by showing when real users start to feel slowdowns or errors, not just when a metric crosses a threshold. It also reduces alert noise by grouping related issues and filtering out what doesn’t matter, so teams can focus on what actually needs attention. Predictive maintenance is another step forward, allowing teams to fix things before they break instead of reacting after the fact.

AI agents

At the same time, AI is not only improving observability – it is also changing what needs to be observed. The rise of AI agents introduces a new layer to the system. These are not passive components anymore. They make decisions, take actions, use resources, and influence outcomes in ways that are often dynamic and hard to predict. Because of that, teams are starting to ask different questions. It’s no longer just about whether a system is working, but also how these agents behave inside it. Are they doing what they are supposed to do? Are they introducing risk or unexpected costs? Are they aligned with business goals?

This shifts observability beyond infrastructure and applications. It now also needs to provide visibility into decision-making processes that are no longer fully visible in traditional code.

At the same time, one thing hasn’t changed: the quality of insights still depends on the quality of data.

Many teams are experimenting with AI, but struggle to get real value because their data is inconsistent, fragmented, or hard to access. Poor telemetry limits what AI can actually do, while strong, well-structured data creates the foundation for long-term impact.

AI in Splunk

Splunk, the market leader in observability, has been introducing machine learning in his products long before the AI boom. According to the Splunk Artificial Intelligence for Observability whitepaper, today, we can enjoy a wide range of applications in various products.

Splunk Cloud Platform and Splunk Enterprise AI capabilities:

Detect anomalies, such as identifying outliers in the number of application errors.
Generate forecasts, for example forecasting resource utilization.
Make predictions, like predicting potential outages.
Cluster data into groups, for instance, clustering network activity to detect potentially misconfigured services.

Splunk also provides ML-powered experiences in the following products:

Out-of-the-box ML analytics in Enterprise Security
Workflows in IT Service Intelligence – an AIOps solution – for creating adaptive thresholds for key metrics, as well as predicting potential outages
Assistive wizards in Splunk Infrastructure Monitoring to detect outliers in metrics or predict when resource utilization thresholds will be crossed

Why Observability and AIOps Work Best Together

Observability and AIOps solve two sides of the same problem.

Observability gives you visibility – what’s happening across systems, where issues start, and how they impact users. AIOps builds on top of that by helping you make sense of it faster and act on it.

On its own, observability can still leave teams overwhelmed. Modern systems generate huge amounts of data, and even with good dashboards, it takes time to connect the dots. AIOps analyzes patterns, highlights what’s unusual, and helps detect issues earlier. This means you no longer have to rely so heavily on manual work, but rather on AI suggestions.

The combination is especially useful when it comes to reducing noise. Observability tools often generate many alerts, but AIOps can group related events, filter out false positives, and point to the likely root cause. This means fewer distractions and faster resolution.

From data overload to real-time action
Proactive, not reactive
Continuous learning and improvement

A study by Quinnox has revealed that companies using AIOps alongside observability see up to 45% fewer major incidents, resolve problems up to 90% faster, and roll out new features 10–15% quicker than those relying on traditional monitoring alone.

As AI agents and automation become more common, this partnership becomes even more important. Observability provides the data and context needed to understand how these systems behave, while AIOps helps interpret that behavior and keep it under control.

In the end, observability tells you what’s going on, and AIOps helps you understand what to do about it. Together, they turn raw data into useful insight and real action.

Conclusion

In conclusion, observability lays the foundation by providing the essential, high-fidelity data about system health and behavior. AIOps then intelligently processes this wealth of information, transforming raw telemetry into actionable insights, automated responses, and predictive capabilities.

Modern tools utilize both of these approaches to provide users with better insight into their systems and help prevent incidents.

How WeAre Can Help

WeAre Solutions Oy is a Finnish observability-focused consultancy and a leading Splunk Elite Partner in the Nordics. We specialize in observability and monitoring (using Splunk), Atlassian services (Jira), and software development. Founded in 2016 and headquartered in Helsinki, our mission is to turn observability into a competitive advantage for organizations.

At WeAre, we help organizations assess their Splunk environments, identify improvement opportunities, and align performance with real business needs. You can start with an observability assessment to understand your current state, or contact our team for a free consultation.