Modern enterprises IT and Network environments generate an overwhelming volume of alerts. And what is worse, is that problem is growing while the size of the team managing them does not.
Every second, operations teams face a flood of signals from applications, infrastructure, and services—all demanding attention. The challenge isn't a lack of data; it’s making sense of it quickly enough to protect customer experience and business continuity.
This is exactly where Event Grouping in IBM Cloud Pak® for AIOps delivers transformative value. By using AI to intelligently cluster related alerts, the platform helps teams see the bigger picture behind issues—reducing noise, streamlining triage, and accelerating resolution.
Why Event Grouping Matters for Your Business
1. Reduce Alert Fatigue and Increase Focus
Instead of treating every alert as an isolated problem, IBM Cloud Pak for AIOps uses multiple AI-driven grouping methods to cluster alerts that are likely part of the same issue. This reduces the number of incidents and tickets teams must investigate, significantly diminishing operational noise.
Fewer, smarter incident views help teams spend more time solving problems and less time digging through noise.
2. Faster Root Cause Understanding
Grouped events present a unified incident story, helping site reliability engineers quickly see what’s related and what isn’t. This provides valuable context so teams can prioritize and act on what matters most. The approach aligns warnings, failures, and anomalies into coherent narratives, making it easier to understand the scope and impact of an issue.
3. AI That Learns From Your Environment
Event Grouping isn’t rule-based guesswork; it uses machine learning to understand how and when alerts tend to occur together. For example, temporal grouping studies historical event patterns to cluster alerts that frequently cooccur, improving accuracy over time. In another example, Shape Correlation discovers patterns in timeseries data and groups those that have a similar shape.
No complicated configuration is needed—teams benefit from AI that continuously adapts as environments evolve.
4. Align Teams Around What Truly Matters
When alerts are grouped by their relationships—whether by time, topology, or application scope—operations teams gain a shared view of incidents. This makes collaboration easier and reduces the number of teams chasing the same symptoms independently. The result: faster resolution and fewer escalations.
5. Significant Reduction in Tickets and Operational Load
Organizations using event grouping have reported major reductions in incident volume, sometimes reducing tickets by more than half simply by opening incidents based on groups rather than individual alerts. This directly reduces workload for operations and support teams and improves response times.
The value is straightforward: less time interpreting alerts means more time delivering resilient services.
AIOps Alert Grouping Methods:
IBM Cloud Pak for AIOps applies many complementary techniques:
When an Event is re-reported, the information from all the subsequent Events is amalgamated into one. The summarised information is an Alert. AIOps indicates an Event has been deduplicated by keeping track of the First Occurrence and Last Occurrence of the Alert, as well as how many times it has occurred. This helps Operations understand that the problem is recurring.
For Events that toggle between Active and Cleared, the logic can be changed to keep them open so that Operations understand their nature and resolve them. This also means one ticket is created and remains open as opposed to many short lived tickets.
Machine learning algorithm that clusters alerts that tend to occur around the same time, helping capture symptoms that occur around the same time of a single underlying issue. For example, a network disruption occurs a couple of times over the span of months, and the pattern change of network flow (some devices getting busier, others getting no traffic) is learned and used to correlate Alerts in the future.
Conditional Topological Grouping: Adding conditions to a Topology so that the Topological Grouping only occurs should the conditions be met. This allows users to have control over the events that are grouped together.
Bundles alerts that have common Alert Fields within a defined time window, dramatically reducing event volume. This could be the hostname, location, or any combination of fields. Many Scope Based rules can be created, but typically customers have just a few. In addition, Scope-Based allows customers to use correlations from external applications in AIOps by adding the Correlation Key to the Alerts, and grouping according to it.
Together, these methods ensure that teams see issues the way systems actually behave: as connected events, not isolated alarms.
🔹 Union Correlation (a.k.a. Super Grouping)
Using the discovered Alert Groups, if any groups contain common Alerts then the two groups will be combined into one holistic group. This allows small symptomatic problems to be grouped into one Alert Group that will be addressed by one person rather than multiple people.
What This Means for Your Organization
IBM Cloud Pak for AIOps turns millions of raw alerts into a manageable set of actionable incidents—giving teams the peace of mind that they’re working on the right things at the right time.
#community-stories1