By now, you’ve likely heard of AIOps, a technique that promises to inject new levels of efficiency into IT operations with the help of AI and machine learning.
But what, exactly, does AIOps mean in practice? Which specific use cases can IT organizations enable or improve with the help of AIOps? Those may be more difficult questions to answer if you have yet to see AIOps at work in your organization.
To provide clarity on what AIOps looks like in practice, let’s walk through five of the top use cases for AIOps in the modern enterprise.
Cost-Effective Data Analysis
It’s no secret that businesses generate more and more data – a trend that is not likely to cease anytime soon. On the contrary, the rollout of larger, more complex IT infrastructures that include technologies like IoT devices or edge computing will only increase the reams of operational data that businesses need to process in order to understand what is happening within their IT estates and make effective decisions.
Faced with ever-growing volumes of data (not to mention data that is becoming increasingly diverse in structure and form), businesses need a way to analyze it all without breaking the bank.
That’s where AIOps comes in. By using AI and ML to interpret vast quantities of data, AIOps allows businesses to make informed decisions about complex IT processes without having to increase the size of their teams. In other words, AIOps ensures that IT decision-making can scale and remain cost-effective even as IT estates continue to grow in size and complexity.
Aligning IT Operations with Business Outcomes
It’s one thing to run a stable IT organization. It’s another to run an IT organization that actually delivers business value. Just because your servers are up and your applications are responsive doesn’t necessarily mean that IT operations are reinforcing business outcomes.
To achieve alignment between IT and business, organizations need a means of assessing the business impact of every IT initiative or investment. That can be very difficult to do manually, given the wide range of variables at play. How does a new software feature impact customer engagement? How does migrating an application from VMs to containers impact the bottom line? Those are questions that can be answered only by assessing vast quantities of data – something that AIOps is well-positioned to do, no matter how complex or voluminous the data involved.
Real-Time, Actionable Observability
Today’s systems require performance management strategies that are capable not just of detecting problems, but detecting and solving them in real time – hence the shift from monitoring (which merely alerts you to problems) to observability (which enables rapid understanding and resolution of problems).
AIOps plays a central role in observability. Not only does AIOps make it possible to interpret vast amounts of monitoring data quickly, but it also allows teams to correlate disparate data sources (like infrastructure metrics, application logs, and logs from CI/CD processes) together in order to gain complete context on complex issues.
Efficient Event Management
One of the major risks that IT organizations face in an age of ever-increasing volumes of data is what’s known as alarm fatigue. Alarm fatigue happens when engineers face so many alerts that they become overwhelmed and struggle to determine which issues to respond to first.
AIOps is part of the solution to alarm fatigue. AIOps tools are capable not only of detecting and interpreting problems, but also of assessing their severity level in order to help teams make effective decisions about which alarms to prioritize. Likewise, AIOps can help engineers understand the relationships between disparate issues, which in turn enables them to determine which alerts stem from the same root cause.
Network Performance Management
While application management was originally a main use case for AIOps, the growing complexity of network architectures has made network performance management a key AIOps use case, too.
The explosive growth of software-defined networks, intent-based networking, and 5G networks that demand ultra-low latency has raised the bar when it comes to network performance. Faced with so much networking data to analyze, as well as pressure to resolve networking issues in true real time, IT teams are increasingly turning to AIOps as a solution for ensuring a smooth user experience even in situations where a few milliseconds’ delay is unacceptable.
Conclusion: AIOps as the Driver of Business Innovation
Broadly speaking, all of these AIOps use cases fall into one overarching category: business innovation. From 5G networking to optimizing alignment between IT and business to managing large-scale systems and beyond, AIOps plays a central role in allowing businesses to continue adopting high-value technologies that would be unmanageable without the efficiency and velocity that AIOps brings to the table.
