"AIOps" may be another new mashed-up term for the xOps lexicon, but it appears to have captured the attention of many an IT manager. A new survey finds a majority of IT managers, 68 percent, are working with or considering AIOps, or artificial intelligence for IT operations.

Photo: NASA Office of the CIO

AIOps may help IT managers "differentiate between legitimate signals and inconsequential noise," according to the authors of a recent survey from OpsRamp. The survey finds nearly three-quarters (73 percent) are taking advantage of AIOps capabilities to gain more meaningful insights related to system alerts.

So, where does AIOps fit into the scheme of things? In a recent Forbes post, Janakiram MSV outlined the following potential use cases for AIOps.

Capacity planning: "As enterprise workloads start to migrate to the cloud, cloud providers will continue to add new configurations, which will only increase the complexity. By applying AI, workloads can be mapped to the right configuration of servers and virtual machines. After running the workload in its peak state, AIOps can recommend the correct instance family type, storage choices, network configuration, and even the IO throughput of storage."

"As enterprise workloads start to migrate to the cloud, cloud providers will continue to add new configurations, which will only increase the complexity. By applying AI, workloads can be mapped to the right configuration of servers and virtual machines. After running the workload in its peak state, AIOps can recommend the correct instance family type, storage choices, network configuration, and even the IO throughput of storage." Resource utilization: "With AIOps, administrators will rely on predictive scaling in which the infrastructure intelligently adjusts itself based on historical data. It will learn how to reconfigure itself based on the current and anticipated utilization pattern."

"With AIOps, administrators will rely on predictive scaling in which the infrastructure intelligently adjusts itself based on historical data. It will learn how to reconfigure itself based on the current and anticipated utilization pattern." Storage management: "By bringing AI to storage ops, mundane tasks such as calibrating disks and reconfiguring them are automated. Through predictive analytics, storage capacity is automatically adjusted by adding new volumes proactively.AI can tackle fundamental to advanced operations related to storage management."

"By bringing AI to storage ops, mundane tasks such as calibrating disks and reconfiguring them are automated. Through predictive analytics, storage capacity is automatically adjusted by adding new volumes proactively.AI can tackle fundamental to advanced operations related to storage management." Anomaly detection: "AIOps can accurately highlight these outliers by pinpointing the actual source, which can help IT teams in performing efficient root cause analysis almost in real-time."

"AIOps can accurately highlight these outliers by pinpointing the actual source, which can help IT teams in performing efficient root cause analysis almost in real-time." Threat detection and analysis: "Advanced machine learning algorithms can be used to identify unexpected and potentially unauthorized and malicious activity within the infrastructure." The OpsRamp survey shows 58 percent of respondents noted that AIOps would help extract signal from noise in a complex organization. A majority (51 percent) said that up to half of all incidents were recurring. Meanwhile, 48 percent of respondents described their current alert volumes as too noisy, too high, or both.

The OpsRamp survey also finds AIOps is key to eliminating or reducing manual work. At least 74 percent of respondents wanted to use AIOps to automate tedious tasks. In addition, 66 percent of respondents indicated that AIOps would help improve root-cause analysis.