Artificial intelligence for IT operations (AIOps) is the use of machine learning and analytics on the data that monitoring tools produce, such as alerts, events, metrics, logs and traces, to help operations teams cut alert noise, group related events into incidents and find likely root causes faster. It can be a standalone platform or a set of features inside monitoring, observability and IT service management tools. The aim is to let people spend time fixing problems instead of sorting through alerts.
At a glance
- AIOps applies machine learning (ML) to IT operations data from many monitoring sources.
- Its common core is noise reduction, event correlation, anomaly detection and root-cause suggestions.
- Many platforms also automate responses, such as opening tickets or running fixes, depending on the product and what you allow.
- Results depend on the quality and coverage of the data fed in and an accurate map of how systems depend on each other.
- It supports operations teams and NOCs; it does not replace them.
What problem it solves
A typical mid-market environment has monitoring for the network, servers, cloud services, applications and security, often from different vendors. Each tool raises its own alerts. When something breaks, one root cause, such as a failed switch or an overloaded database, can trigger hundreds of alerts across those tools. The team spends the first part of every incident working out which alerts are symptoms and which is the cause, and real warnings get lost in the noise.
AIOps tackles that by pulling the data together, learning what normal looks like and grouping related signals. Instead of hundreds of alerts, the team sees a smaller number of incidents with a suggested cause. Done well, this shortens outages, reduces alert fatigue and can surface problems before users notice.
How it works
Ingest. The platform collects data from monitoring sources: network monitoring, servers, cloud platforms, application performance monitoring and observability (APM) tools, logs and change records from IT service management (ITSM).
Normalize and map. Data from different tools is put into a common format, and the platform builds or imports a map of dependencies: which applications run on which servers, over which network paths.
Analyze. Machine learning and rules work together to:
- detect anomalies against learned baselines rather than fixed thresholds;
- de-duplicate and suppress repetitive alerts;
- correlate related events by time, topology and pattern into a single incident;
- suggest a probable root cause, often linking it to a recent change.
Act. The platform routes incidents to the right team, opens or updates tickets and, in many products, runs automated runbooks for known problems. Teams usually start with suggestions and add automation as they gain confidence.
When it matters for buyers
- When alert volume overwhelms the team. If people ignore alerts because most are noise, AIOps techniques can help.
- After a painful outage. Long root-cause hunts across many tools are a common trigger for evaluation.
- When consolidating monitoring tools. Tool sprawl often goes hand in hand with alert noise; consolidation and AIOps are frequently evaluated together.
- When choosing a managed NOC. Many network operations center (NOC) providers use AIOps tooling; ask how it changes their response. Our network operations center overview covers outsourced options.
- When buying observability. Many observability platforms include AIOps features; compare what is included and what costs extra.
Questions to ask vendors
- Which data sources and monitoring tools do you integrate with out of the box?
- How do you build and maintain the dependency map, and how much of that falls to us?
- How much alert reduction do customers like us typically see, and how is it measured?
- How do you explain why alerts were grouped or a root cause was suggested?
- What automated actions can the platform take, and how are they approved and logged?
- How long is the learning and tuning period, and what professional services are needed?
- How is it priced: by data volume, devices, users or events?
How it differs from event correlation
Event correlation is the technique of linking related events, such as many alerts caused by one failure, into a single incident. It has existed for years using rules and topology. AIOps is a broader approach that includes event correlation but adds machine learning for anomaly detection, pattern recognition and root-cause suggestions across many data sources, and often automation of the response. In short, event correlation is one of the main jobs an AIOps platform does, not a synonym for it.