Is AIOps About Automation or Insight?

For more than a year, it seems, there has been some debate about whether AIOPs fundamentally is about automation or insight. It can be hard to discern, as a fully-developed AIOps solution, capable of providing insights, also often also is said to include the ability to proactively diagnose and resolve issues. 

Part of the problem is that the approach is so new. Right now, developers are creating new capabilities around the ingestion of data, correlation of incidents and resolution of issues. AIOps is “about making sense of large volumes of information,” says Ciaran Byrne, OpsRamp VP. 

There is an element of remediation and automation, though, he notes. 

At the current stage of development, nobody is willing to entrust AI-enabled systems to reconfigure networks, servers and software autonomously. Beyond that, though, the fundamental value of AIOps arguably is not the automated response but the move “from hindsight to insight to foresight,” says Joris DeWinne, StackState solutions architect. 

Today, enterprises monitor the health of components and systems,  but the problem has been too many notifications, he says. At the next level, monitoring adds logs and metrics. Next-generation monitoring has been added by firms such as ServiceNow, AWS and Azure, using Kubernetes, for example.

AIOps will represent the next level after that, where prediction starts to be feasible, he argues.