GigaOm Radar for AIOpsv5.0

1. Executive Summary

In today’s rapidly evolving IT landscape, AIOps is revolutionizing how organizations manage and resolve complex IT issues. By harnessing the power of artificial intelligence, machine learning, and big data analytics, AIOps drives efficiency and innovation by automating the identification and resolution of common IT problems.

A significant advancement in AIOps is the integration of generative AI and large language models (LLMs), which serve as force multipliers. These technologies empower organizations to navigate the complexities of modern IT environments more effectively. AIOps extends its impact beyond traditional IT boundaries to encompass overall business operations by enabling businesses to query the IT organization and receive contextually relevant responses.

The journey to AIOps begins with monitoring, progresses through observability, and culminates in intelligence (see Figure 1). Monitoring has become a staple in ITOps, providing visibility into devices, applications, and infrastructure. Observability takes this a step further by consolidating data to derive meaningful insights, predict future states, and automatically remediate known issues.

Figure 1. From Monitoring to Intelligence

Intelligence represents the pinnacle of this evolution, reflecting the operational state of the entire company. It leverages comprehensive data from various departments—marketing, sales, legal, human resources, and manufacturing—to deliver on the promise of AIOps. This holistic approach allows organizations to answer critical questions about the company’s status, predict the impact of business initiatives, and adapt to planned and unplanned changes.

With the rise of cyberthreats, integrating security and compliance features within AIOps platforms has become paramount. Real-time threat detection and response, facilitated by SIEM and SOAR, are now integral to operational management, ensuring robust security measures.

The complexity of IT systems, characterized by multicloud infrastructures and microservices, necessitates advanced AIOps solutions to manage vast data volumes. However, seamless integration with existing IT tools and systems remains a challenge. Vendors that offer solutions requiring minimal customization and integration effort, as well as those that lower the expertise threshold in AI/ML and data analytics, will likely see greater adoption.

As we delve into the landscape of AIOps vendors, we will explore how they address these challenges and evaluate their capabilities in transforming IT operations.

This is our fifth year evaluating the AIOps space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report examines 29 of the top AIOps solutions and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading AIOps offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.