Key Criteria for Evaluating AIOps Solutionsv4.0

1. Summary

The proliferation of both performance management tools and observability solutions has made artificial intelligence for IT operations (AIOps) the unifying force multiplier for operations staff.

AIOps addresses the volume of noisy and often redundant alerts, events, and messages that can easily overwhelm IT operations staff, and it enables faster incident response. Using machine learning (ML) and AI to scan large volumes of data, AIOps solutions deduplicate data, correlate it, and find patterns far beyond the human capacity to do so. This in turn reduces the number of operational personnel needed to address incidents (or eliminates the need for additional headcount) and frees up operations teams to focus on higher-level tasks requiring judgment and creativity.

Additionally, AIOps is a key component of operational awareness, and thus awareness of the state of the entire organization. AIOps solutions can respond to change quickly, easily adapting to fluctuations in application requirements, finances, and performance. Without an AIOps solution, large organizations risk flying blind.

This is the fourth year that GigaOm has reported on the AIOps space, and the need to improve incident response and operational awareness has only continued to grow. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Key Criteria report details the capabilities (table stakes, key criteria, and emerging technologies) and non-functional requirements (evaluation metrics) for selecting an effective AIOps solution. The companion GigaOm Radar report identifies vendors and products that excel in those capabilities and metrics. Together, these reports provide an overview of the category and its underlying technology, identify leading AIOps offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.