How AIOps Uses Machine Learning to Enable More Agile IT Operations

Digital transformation initiatives have shifted focus and accelerated in light of the COVID-19. Organizations are digitizing business processes to support work-from-home strategies that may become a permanent feature of their operations. IT teams are being forced to retool their processes as well to ensure that the IT infrastructure can deliver more information, faster, to a widely distributed user base.

AIOps may be the answer. AIOps platforms combine artificial intelligence techniques, big data analytics and automation to enhance IT operational processes. By analyzing data from multiple sources, AIOps helps IT teams to better monitor performance, detect anomalies and rapidly identify the root cause of problems. It can also provide insights that drive better IT decision-making.

A key value proposition of AIOps is the ability to automate monitoring, issue remediation and management. The machine learning capabilities of AIOps platforms enable more intelligent automation to enable a more agile and responsive environment.

How AIOps Works

Some tools categorized as AIOps simply apply AI and automation to a specific function such as application performance monitoring. AIOps platforms, on the other hand, are capable of ingesting data from monitoring tools, infrastructure components, network devices and many other sources. Machine learning is used to conduct real-time and historical analysis, reducing the “noise” that often results from high volumes of events and alerts.

According to a recent Gartner report, most organizations that have adopted AIOps are using it to improve monitoring, event correlation and anomaly detection. The research firm predicts that, by 2023, 40 percent of will augment their monitoring tools with AIOps capabilities.

However, monitoring is just one aspect of AIOps. It can also be used to support IT service management (ITSM) processes by automating tasks, assessing risk and enable virtual support assistants. In this capacity, AIOps can help IT teams keep up with the increasing velocity of change within the IT environment while improving SLAs and enhancing the user experience.

By applying machine learning to automation, AIOps helps create an environment that can learn and adapt without human intervention. AIOps tools can also understand device and application dependencies in order to handle more complex tasks and resolve issues quickly.

Getting AIOps Right

Gartner recommends that organizations take an incremental approach to AIOps, selecting platforms that allow IT teams to add use cases over time. AIOps should first be applied to less-critical applications, using event correlation and anomaly detection to drive manual processes. This allows IT teams to test the value of the AIOps analysis with minimal risk. AIOps can then be used to analyze larger volumes of data to assess the future impact of events. This provides the foundation for proactive alerting, root cause analysis and ITSM support.

Technologent can help you take advantage of AIOps through our Continuous Intelligence and Enterprise Monitoring solutions. CIEM leverages our expertise in application performance monitoring and big data analytics to help you gain greater insight across your IT environment. We can help you develop and implement a strategic plan for integrating AIOps tools into your operational processes.

According to data from Reportlinker, the AIOps market is expected to experience a compound annual growth rate of 32.9 percent through 2025. That’s not surprising given the accelerating pace of digital transformation initiatives that require a more agile and responsive approach to IT operations. If you’re exploring AIOps, we invite you to give us a call to discuss your options.

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