Below, we explore the practical use cases of AIOps and how it’s set to transform enterprise IT operations.
AIOps use cases include:
Cross-domain situational understanding
Proactive performance monitoring in real-time
Automate the identification of probable root causes
Not long ago, the role of IT was to support the business. But with digital services and applications becoming the primary way consumers engage with brands, IT is the business and must adapt quickly to market needs and changes.
Virtually every business today depends on the continuous performance and innovation of its digital services. But the sweet spot between performance and innovation becomes harder to find with each new wave of digital transformation.
DevOps has introduced faster release cycles than ever before. The adoption of cloud providers (now an average of eight per enterprise) has enabled the adoption of microservices, each with its own set of monitoring tools (an average of eleven or more).
But by producing terabytes of disparate data IT teams can’t make sense of, these innovative practices help as much as they hinder. Issues in data go undetected or take too long to resolve—driving up MTTR, risking SLA compliance and customer trust, and reducing the bandwidth to innovate. What’s frustrating is that answers lie right there in the data, waiting to be found. IT teams just need more support connecting those dots—and quickly.
Artificial intelligence for IT operations, or AIOps, is a way to address this. AIOps refers to the use of artificial intelligence (AI) and machine learning to ingest and analyze large volumes of data from every corner of the IT environment, reducing its complexity by bringing data silos together with the means to filter them, detecting patterns, and clustering meaningful information for more efficient actioning.
The long-term goal of AIOps is to allow IT teams to manage performance challenges proactively, in real-time, before they become system-wide issues. Along with the flexibility needed to find and fix issues faster, AIOps will eventually provide IT teams with predictive insights to prevent issues from happening in the first place.
Given the enormous potential for operational efficiency and self-healing, AIOps is gaining momentum. And it might be early days, but the hype is not unfounded. Early adopters, many of them leaders in their space, are already seeing significant benefits.
But what can it do for the enterprise? How is it used in practice and what is its future potential? Read on to explore key ways to leverage the power of AIOps.
Current AIOps use cases
AIOps can support a wide range of IT operations processes. According to AppDynamics, there are six key features taking root in leading enterprises’ APM strategies:
Intelligent alerting: By ingesting data from any part of the IT environment, AIOps filters and correlates the meaningful data into incidents. This prevents alert storms coming from domino effects—for example, a failure in System A triggers an alert, impacting system B, which also triggers an alert, and so on. Intelligent alerting also reduces alert fatigue and helps with prioritization based on user and business impact.
Cross-domain situational understanding: AIOps aggregates all the data and creates causality/relationships, providing IT with an overview of what’s at stake and enabling it to slice and dice the information as needed for a better understanding of the situation.
Automate the identification of probable root causes: Once alerted, IT is presented with the top suspected causes and evidence leading to AIOps' conclusions. This helps to build trust ("I can see why the AI engine came to that conclusion"), and provides an opportunity for feedback, enabling the AI engine to learn from human expertise.
Example: Reduce the hours of manpower required for routine troubleshooting.
Cohort analysis: AIOps shines brightest in areas where human struggle: the analysis of vast amounts of data. With modern, highly distributed architectures where tens of thousands of instances are running at the same time, identifying outliers in configuration or deployed application versions is an insurmountable task for humans. Not for an AI!
Automated remediation: AIOps helps automate closed-loop remediation for known issues. Once problems are identified—and based on historical data from past issues—AIOps suggests the best approach to accelerate remediation.
Example: Spin up additional instances of an application to combat slowdowns and address spikes in demand.
Future AIOps use cases
So what’s ahead for AIOps? Today, it’s mostly about:
Putting humans back in control of an unmanageable onslaught of data
Alerts to accelerate understanding and remediations
Aligning priorities based on user and business impacts
As the know-how and algorithms refine, we can plan for improved predictive capabilities
Advice for IT teams
Though AIOps is poised for a promising future, it's not so embryonic that you should hold off. In fact, given the urgency of today’s IT challenges, you can’t afford to wait. AIOps has become a necessity for every business to compete.
As today's business environment grows increasingly data-driven, as Gartner analyst Charlie Rich notes, AIOps can give IT leaders important insights to enhance business outcomes.
Identify your AIOps goals: A recent MIT Technology Review report found that IT leaders are already using AIOps solutions to retrieve, analyze, and extract value from IT ops data. With all the hoopla surrounding artificial intelligence and machine learning, it's easy to assume AIOps is a set-it-and-forget-it scenario. That's not the case, however, as AIOps requires strategic planning and thoughtful implementation. Be sure to have a solid technological framework in place to gather data from your whole IT environment and to check regularly to ensure your AIOps tools are working as intended.
Go step by step: Try a phased approach by starting with areas where you’ll see the most immediate results, and which you can use to evangelize AIOps down the road. Our customers have done just that.
Watch AIOps closely: AIOps is new and there’s still much work to be done to deliver on its promise. IT leaders should keep an eye on this space and start preparing for what’s next in monitoring. While many are interested in a more proactive approach to monitoring, our research shows that few have prioritized the development of an AIOps strategy. But the importance of doing so is clear. Get in there now.