In today’s business environment, IT operations teams work under constant pressure. Meeting service-level objectives, handling incidents, preventing outages, troubleshooting issues, and managing tickets all happen in parallel—often within complex, rapidly changing infrastructures. This type of “complexity management” frequently consumes resources that could otherwise be dedicated to value creation. AIOps is designed to address this challenge.
What Is AIOps?
AIOps, or Artificial Intelligence for IT Operations, is an approach that applies artificial intelligence and machine learning to support and automate IT operations processes.
The goal of AIOps is to make the large volumes of data generated by IT systems, applications, and infrastructure actionable by uncovering relationships between events and supporting the identification of incident root causes.
According to Gartner, AIOps combines big data and machine learning to automate, among other capabilities:
- event correlation,
- anomaly detection,
- and the identification of causal relationships.
An important distinction between observability and AIOps is that while observability primarily focuses on making system behavior transparent and understandable, AIOps uses this understanding to support operational decision-making and, in some cases, automation. Observability shows what is happening within a system and how issues emerge across dependencies. Building on this foundation, AIOps prioritizes signals, anticipates potential issues, and provides recommendations for the necessary operational actions.
While AIOps can deliver value on its own, its sustained and reliable effectiveness depends on comprehensive, high-quality data interpreted in context—capabilities provided by observability.
AIOps Practices and Typical Use Cases
The purpose of AIOps is to help IT operations teams manage the growing complexity of modern IT environments without becoming reactive or overwhelmed. To achieve this, AIOps supports several well-defined practices:
Proactive Incident Detection and Prevention
AIOps does not focus solely on the current system state. By analyzing historical and real-time data, it can identify patterns and deviations that typically lead to incidents or service degradation.
This proactive approach enables IT teams to intervene before issues have a noticeable impact on users or business processes.
Alert Noise Reduction
Monitoring tools often generate a high volume of alerts, many of which stem from related events. Using event correlation, AIOps connects these signals, filters out false positives, and places alerts into operational context.
As a result, IT operations teams receive fewer—but significantly more relevant—alerts, allowing faster and more effective response.
MTTR (Mean Time to Resolution) Reduction
One of the most important and measurable business benefits of AIOps is the reduction of MTTR (Mean Time to Resolution). By uncovering relationships between events and supporting root cause identification, AIOps helps avoid parallel, disconnected troubleshooting efforts.
This shifts the focus toward resolving the underlying problem, typically resulting in faster incident resolution and shorter service outages.
Scalability in IT Operations
Modern IT environments are not only larger but also more complex. More services, more data, and more events must be handled—far beyond what manual processes can efficiently manage.
With appropriate data quality and ongoing model maintenance, AIOps can scale alongside the environment and continue to deliver relevant insights even as data volumes and event rates grow. This ensures that IT operations remain sustainable over the long term.
Cross-Domain Visibility and Collaboration
In more mature implementations, AIOps integrates data from multiple domains, such as infrastructure, applications, and networks. By correlating these sources, it provides a unified view of the current state of the IT environment.
Cross-domain visibility establishes a shared foundation for IT operations, DevOps, and SRE teams and supports collaboration when handling complex incidents.
How Does AIOps Work?
AIOps does not operate as a standalone system. Instead, it works closely with existing monitoring and observability platforms. Its role is to analyze, correlate, and transform the data generated by these tools into insights that are actionable for IT operations.
The operation of AIOps can be divided into several distinct stages.
Data Ingestion
The first step is data collection. AIOps ingests data from various sources, including:
- infrastructure and application metrics,
- logs,
- events and alerts,
- performance data from different systems and services.
The effectiveness of AIOps depends heavily on the volume and quality of available data. Comprehensive coverage of the environment is a prerequisite for AI models to produce accurate analyses.
Anomaly Detection
AIOps analyzes the collected data using machine learning and AI models. Based on historical and real-time data, these models establish what constitutes normal behavior in a given environment.
When deviations occur, AIOps identifies anomalies and outliers. This capability enables early detection of issues before they escalate into incidents or service degradation.
Event Correlation
In modern IT environments, a single issue often manifests as multiple related events and alerts. AIOps correlates these signals to:
- identify which events belong to the same incident,
- reduce duplicated and repetitive alerts,
- filter out false alarms.
This significantly reduces alert noise and supports more efficient IT operations.
Contextual Analysis and Root Cause Insight
AIOps goes beyond simple event correlation. By incorporating environmental and topological data, it places events into operational context and supports root cause identification.
At this stage, AIOps highlights:
- which components are affected,
- how events are related,
- where the issue originated,
- and how the incident impacts business operations.
This step accelerates the incident investigation process and reduces the Mean Time to Resolution (MTTR).
Response and Automation
The final stage of AIOps focuses on response and automation support. The system can:
- provide remediation recommendations to IT teams,
- create and assign tasks,
- trigger automation workflows based on predefined rules and runbooks.
These capabilities reduce the need for manual intervention and help ensure that IT operations remain scalable and sustainable.
What Are the Benefits of AIOps?
The primary value of AIOps lies in its ability to help IT operations teams prevent issues, prioritize effectively, and resolve problems faster in increasingly complex environments.
Proactive Operations and Reduced Downtime
By analyzing real-time and historical data, AIOps can detect patterns and anomalies that would otherwise lead to incidents or service degradation, enabling intervention before business operations are affected.
Fewer Alerts, Better Focus
Through event correlation and contextual analysis, AIOps significantly reduces alert noise. IT teams receive fewer, more relevant alerts, helping prevent operational overload and accelerating incident triage.
Faster Troubleshooting and Lower MTTR
By supporting rapid root cause identification through correlated events and environmental context, AIOps shortens investigation time, reduces MTTR, and lowers business risk.
Automation and Scalable IT Operations
Using predefined runbooks and automation workflows, AIOps enables faster responses—sometimes without manual intervention. This is especially critical in large, dynamically changing environments where traditional operating models no longer scale.
What Challenges Come with Implementing AIOps?
Many organizations make the mistake of treating AIOps purely as a technology problem. They purchase an AIOps platform, integrate a few data sources, and expect immediate results. Disappointment often follows, leading to the conclusion that “AIOps doesn’t work”.
In reality, the issue is not that AIOps fails, but that the environment is not ready for it.
The first obstacle is almost always data quality. AIOps can only work with the data it can see. If observability data is fragmented, incomplete, poorly tagged, or spread across isolated tools, AI models lack sufficient context. In such cases, anomaly detection becomes inaccurate, event correlation misleading, and conclusions unreliable. AIOps is then forced to interpret noise.
Another common challenge is the lack of topology awareness. Many environments lack a clear understanding of which services depend on which databases, how applications are interconnected, and where components actually run. From an AIOps perspective, this is critical. Without topology, event relationships cannot be interpreted accurately, root cause analysis remains superficial, and automation becomes risky.
Integration is another key challenge. AIOps performs best when it has fast, reliable access to relevant monitoring, observability, and security data, as well as tight integration with ITSM systems. If AIOps does not align with existing incident management processes, it creates friction on the operations side rather than providing meaningful support.
Finally, many organizations face operational and cultural barriers rather than technical ones. Implementing AIOps requires a transformation in how IT operations function. The focus gradually shifts from manual triage and ad hoc interventions to continuous, data-driven decision support. This approach requires IT operations, DevOps, and SRE teams to rely on a shared data foundation and a common interpretive framework. It also demands trust in data quality, model-driven insights, and automated recommendations—along with clearly defined responsibilities for automation.
Summary
AIOps addresses the challenge of managing increasingly complex IT environments without turning operations into constant firefighting. By correlating events, accelerating problem understanding, and supporting automation, AIOps enables more focused and predictable operations.
Effective AIOps is built on high-quality observability data interpreted across dependencies. Without complete and reliable data, AI cannot produce trustworthy insights. Organizations that truly realize the benefits of AIOps are those that first make their systems transparent and then build automation on that foundation.
Telvice Zrt. supports its customers in ensuring that AIOps and observability become not isolated technologies, but deliberately designed operational capabilities.
Prepare for growing complexity and AI-driven IT operations with Telvice’s expertise. Contact us to request a free consultation or demo.
Sources:
Dynatrace – How an AIOps strategy unlocks new possibilities for automation and customer satisfaction (Dynatrace blog)
Datadog – What is AIOps? (Datadog Knowledge Center)Dynatrace – AIOps platform overview
