6 AIOps hurdles to overcome

IT operations teams have a lot to juggle. They manage servers, networks, cloud infrastructure, user experience, application performance, and cybersecurity, often working independently of one another. Staffers are more often than not overworked, burdened with excessive alerts, and struggling to solve problems that involve multiple domains.

Enter AIOps, a burgeoning field of technologies and strategies that inject artificial intelligence into IT operations in an effort to solve challenges face by IT operations teams by reducing false positives, using machine learning to spot problems before they occur, automating remediation, and seeing a holistic view of the enterprise.

According to an October survey of IT leaders conducted by ZK Research and Masergy, 65% of companies are already using AIOps, and 94% say that AIOps is “important or very important” for managing network and cloud application performance. In addition, 84% see AIOps as a path to a fully automated network environment and 86% expect to have a fully automated network within the next five years.

Although AIOps is still new it is already proving its worth. According to a survey by Enterprise Management Associates released this summer, 62% of companies see “very high” or “high ROI from their AIOps investments, and the rest say they have broken even, or that it was too early to tell.

But the path to AIOps isn’t always smooth. More than half of the respondents to the EMA survey also said that AIOps was “challenging” or “very difficult” to implement. The most common obstacles companies reported include cost, data quality, conflicts within IT, distrust of AI, lack of skills, and integration challenges.

Read full article at CIO magazine.