Operations | Monitoring | ITSM | DevOps | Cloud

AIOps is a term Gartner invented to describe a general trend of applying AI techniques to IT Operations data sources to provide additional insights and scale to the teams operating today’s complex software system. AIOps is essentially a feature or set of features to analyze, combine, and collect data. Unfortunately, the lack of AI in these solutions often turns many people off, but this promise is still possible.

What drives an enterprise’s decision to adopt an AIOps platform? Which key problems are they trying to solve? Once it’s deployed, how can they track its value, and explain it to non-IT business leaders? What cultural and technology changes does an AIOps deployment trigger?

In recent years, digital transformation projects have dominated the tech priorities of most IT departments – and rightfully so given that they are tasked with ensuring their organizations stay relevant in a fast-changing world where customer expectations are soaring, and agility is everything. However, the COVID-19 pandemic has thrown a curveball to businesses around the globe.

Network/Security Operations Center (NOC/SOC) engineers and service desk personnel are tasked to process numerous incidents as quickly as possible. However, to resolve an incident they are required to to perform various activities including collecting various operations data including metrics, logs, traces and more from different tools. In many cases, the process also involves coordinating with other IT personnel or creating a war room to bring the incident to closure.

Based on our interactions with buyers evaluating vendors in the AIOps market, much of what we’re hearing chimes with this quote - “What will AI allow us to automate? We'll be able to automate everything that we can describe. The problem is: it's not clear what we can describe.” Stephen Wolfram, computer scientist and physicist.

Alert management is no longer a manageable task, given the growth in applications, cloud environments and point monitoring tools. Too much time is spent filtering and making sense of alert data and determining where to route incidents. All of these steps slow down critical issue identification and resolution. In this article, I want to discuss a more sensible, modern way to deal with IT alerts, through machine learning intelligence and automation.