There are many benefits to using Artificial Intelligence for IT Operations (AIOps), including its ability to efficiently scan massive amounts of data from countless sources, identify the most important alerts or underlying trends, and instantly glean insights into complex IT systems and services. Given this, it should come as no surprise that companies are increasingly investing in AIOps to enhance decision-making and drive competitive advantage. 

With that in mind, the following are a few areas where organizations can implement AIOps to reap the most benefits. 

Identifying Patterns 

AIOps can analyze operational data to identify common patterns and then determine which are normal and which might be indicative of a problem. The technology can also identify recurring issues and help companies address them before they become more serious, like flagging when servers are overloaded before any user impact is detected. This speeds the root cause analysis of issues and helps reduce the load on IT staff as multiple alerts are correlated to a single underlying problem without any employee involvement.   

Monitoring and Tracking 

AIOps also makes it easier for operations staff to identify and track changes in the IT environment, monitor its performance, and manage the entire IT landscape more cost-effectively. In some cases, these monitoring and tracking capabilities can also be extended externally to partners, customers, or other third parties. AIOps can also help organizations observe changes to the IT environment and understand the reasons behind the availability and reliability of their systems. 

Understanding the Root Cause 

AIOps can also cut through the noise and help companies get to the root cause of problems faster. This can save significant manpower as teams can instantly determine at what layer of service the problem exists—whether it’s in the browser, the database, the code, or on-premises within the network. In addition, identifying the cause of the issue more efficiently results in improved performance for customers and users. 

For more on the above—and a few examples of where AIOps currently falls short—take a look at this recent CIO article