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AIOps Use Cases

AIOps platforms combine machine learning, analytics and data at scale to deliver a superior digital experience. Read about challenges AIOps addresses and six common use cases for AIOps platforms.

Overview of AIOps

Delivering high-touch customer experiences requires IT operations and DevOps teams to:

  • manage large scale application and service environments
  • support continuous innovation

All while tackling increasing architectural complexity, disparate monitoring tools, multiple deployment environments and endless data.

AIOps simplifies this, by using machine-learning to achieve greater observability, predictability and visibility.

What is AIOps?

Key Challenges AIOps addresses

The monitoring gap is growing

Traditional monitoring solutions can’t keep up with the number and volume of data sources from rapidly expanding app and cloud environments. Enterprises are missing full the picture and lack visibility to connect the dots.

Too many tools, not enough answers

The number of different products in the monitoring stack is obstructing IT’s ability to understand the full impact of an issue. The consequence? Time kills and ‘Not my problem’ is the problem.

Humans are drowning in data and noise

It’s hard to find good talent, keep the lights on, let alone support rapid deployment of new technologies and environments. Manual analysis alone can’t stay on top of the volume of data and noise to make timely decisions.

6 Common Use Cases for AIOps

Use Case #1

A Unified Operational View

Data events, metrics, text strings, wire is scattered everywhere, in the cloud, on-premise, and in-between. Full-stack visibility enables analysts to understand what’s happening in these complex environments. AIOps allows for collection and monitoring of all raw event logs and contextualized time-series data in one spot, regardless of type, source, or time-horizon. This allows operators to see the entire monitoring surface and quickly get to the right answers.
Use Case #2

Real-time impact assessment to depict digital CX

Context is critical to developing a rich and accurate understanding of the situation. Traditional monitoring tools provide limited or incomplete insight into service issues meaning humans are still on the hook for gathering, correlating, and ultimately piecing together the full story. Intelligent monitoring solutions enable teams to more easily connect data across multiple layers, trace user interactions, determine the end-user impact, and effortlessly deliver business impact KPIs. 
Use Case #3

Detect problems before they happen, not after

Humans don’t have a sixth sense (yet!). In the meantime, anomaly detection is the process of identifying and alerting to abnormal behavior. When it comes to large-scale time-series or event data, machine learning algorithms are great at identifying anomalies, continuously filtering and prioritizing the most relevant alerts. Help IT operators sift through a barrage of false positives without losing focus of what’s important. 
Use Case #4

Fast Root Case Analysis (RCA)

Responding to an IT incident requires knowing the root of the problem, in order to quickly identify and deploy a solution. Root-cause analysis in application and service monitoring is intended to reveal the cause of an issue as soon as it’s discovered. In other words, the faster the mean time to investigation, the lower the mean time to resolution.

Use Case #5

Visualization to deliver meaning

IT ops and DevOps teams need to be able to interact with and experience their data in different ways to effectively “survey the scene” and define an appropriate resolution. Ease of use, intuitive interface, and point-and-click accessibility are the real differentiators when it comes to bringing data to life.

Use Case #6

Act quickly for intelligent remediation

Goodbye high-stress war rooms. Organizations need to turn insight into action – to be able to address incorporate streamlined remediation to more easily turn insights into action. AIOps solutions need to pave the way for proactive detection, automated triggers for known issues, and workflows for fast remediation.