Can you trust AIOps tools not to make mistakes? Is there an implementation standard for AIOps? What’s the risk of not adopting AIOps?

Since the term ‘AIOps’ came into use in the monitoring sector a couple of years ago, there has been much confusion about what it means. We hear from users asking if they need it – a difficult question given that the answer depends on how you define it. Since there isn’t a broadly accepted definition, a range of vendors now market their products as AIOps offerings, even though these products cross subsectors and may not be directly competitive.

Imagine you’ve flown into an unfamiliar city. You may have no idea how to get to your hotel or where to grab lunch. But no worries, right? You just open up your trusty Google Maps, and instantly you know where to go and what’s available along the way. Google Maps even warns you about traffic conditions, so you can steer clear of trouble. Now compare that situation to managing your hybrid IT environment.

DevOps is fast, glamorous and agile. It is key to keeping modern, fast-moving IT environments up and running. And it is no stranger to automation: DevOps has been relying on automation for many years now to ensure the rapid delivery of applications in this ever-changing landscape. Yet even the most agile and advanced DevOps teams cannot escape the growing complexity, scale and pace of the modern IT stack.

This article originally appeared in TechBeacon. Gartner first coined the term "AIOps" a few years ago to describe "artificial intelligence for IT operations," and over the last few years, IT operations monitoring tool vendors have begun incorporating AIOps features into their products. Now AIOps tools are commonplace, but many IT leaders remain cautious about using these relatively new capabilities.

AIOps helps DevOps teams in multiple ways, including by boosting developer productivity, improving the customer experience, and increasing the CI/CD cycle frequency.

If your end users regularly report issues before your Operations team discovers them, you need AIOps for earlier detection, faster action, and more precise diagnostics.

Curious about AIOps these days? You’re not alone. AIOps (Artificial Intelligence for IT Operations) is all about analyzing and automating your IT operations using artificial intelligence and machine learning algorithms. These operations include end-to-end workflows that bring monitoring, analytics, incident management, and automation systems together with a common goal of optimizing and automating operational tasks.

The volume and ambiguity of log files makes them impossible for humans to process. The promise of logs is revealed when AIOps is applied to analyze their deep structure.

The hype has gone off the charts for AI and machine learning tools' potential in IT organizations. It’s time to temper expectations and move towards a sensible adoption path. Artificial intelligence is not fairy dust sprinkled on a data center, despite the enthusiastic proclamations of many IT operations vendors today. A couple of years ago, AI was the bride atop the wedding cake. It was perfect, with promises to render obsolete errors and out-of-control performance issues.