New Gartner AIOps Platform Market Guide Shows More Use Cases for Ops and Dev Teams
Richard Whitehead | April 23, 2021

In Gartner’s new Market Guide for AIOps Platforms, analysts offer a fresh look at AIOps platforms and how organizations are using them in new ways to improve analysis and insights across the application lifecycle.

In Gartner’s new Market Guide for AIOps Platforms, analysts offer a fresh look at AIOps platforms and how organizations are using them in new ways to improve analysis and insights across the application lifecycle.

Gartner jumps right into it, describing a reorientation of a tool that has previously focused on IT service management and automation. AIOps is now also enabling a variety of new observability use cases for DevOps and Site Reliability Engineering (SRE) teams. This blog presents the guide’s major findings and a link so you can read the report for more details.

About the AIOps Platform Market

Digital transformation is the main driver for using an Artificial Intelligence for IT Operations (AIOps) platform, especially with multi-cloud environments. Transformation enables an agile enterprise, delivering business benefits of rapid development and deployment of apps for the organization, partners, customers, and the extended value chain. With these benefits also comes an exponential surge in operational data. The volume, velocity, and variety of data dwarf the abilities of legacy rules- based tools and manual processes to ensure performance and continuity of operations.

An AIOps platform turns this problem on its head by using algorithmic analysis of the data to automatically discover issues and pinpoint a sure path to quick remediation. Gartner characterizes central functions of AIOps platforms to include ingestion of all events, telemetry, and streaming analytics for intelligent observability anywhere in the enterprise. The data algorithmically presents a physical and logical topology to provide context to issues. Algorithms automatically correlate data by combining time and topology to group related events and exclude irrelevant noise. A platform should have the ability to detect or predict important issues, and point teams to the probable root cause, offer remediation advice, or even trigger an automated remediation playbook.



“There is no future of IT operations that does not include AIOps. This is due to the rapid growth in data volumes and pace of change … that cannot wait on humans to derive insights.” – Gartner


How the AIOps Platform Market is Changing

AIOps platforms have traditionally been domain-centric, which are built for a single use case, narrow data type, and often placed within a legacy monitoring tool. Gartner says domain-centric tools preclude the flexibility to ingest diverse datasets from multi-vendor, multi-cloud environments and progressive, evolving roadmaps stretching years ahead. Organizations also need to be more proactive with this data in a digitally transformed scenario.

Gartner says I&O leaders are looking for domain-agnostic tools to solve these issues – including KPIs and dashboards for at-a-glance sharing with executives. Domain-agnostic tools are built as a standalone solution. They provide flexibility across multiple data types and enable future use cases without having to add a new tool.

Gartner sees domain-agnostic tools as the new imperative for digital transformation. “AIOps has become a defining feature in many domain-centric markets,” according to Gartner. “As organizations mature in AIOps adoption, they require a single domain-agnostic platform across I&O, DevOps, SRE, and in some cases, security practices.”

Market Analysis for Domain-Agnostic Platforms

AIOps has traditionally focused on IT operations and service management. Domain-agnostic platforms enable many use cases beyond ITOM and ITSM based on an enormous range of data ingestion. Gartner lists the following example of data sources for domain-agnostic AIOps platforms:

  • API
  • Application logs
  • CRM data
  • Customer data
  • Events
  • Graph
  • ITSM
  • Metadata
  • Metrics
  • Social
  • Traces
  • Wire

Gartner suggests organizations consider AIOps platform capabilities by comparing three key attributes:

Data Ingestion and Handling. Modern requirements for observability include ingestion of historical data at rest and real-time streaming data in motion. Distributed analytics is a must, where data is analyzed in real-time at the point of ingestion with correlated analysis across multiple streams of data.

Machine Learning Analytics. AIOps platforms must use a variety of ML analytic approaches. Gartner suggests the following: Statistical, probabilistic analysis; automated pattern discovery and prediction; anomaly detection; probable cause determination; topological analysis; and prescriptive advice.

Remediation. Achieving observability with AIOps is not the end goal. Operations teams must be able to immediately leverage prescriptive direction provided by the platform to triage and remediate issues. Ideally, the platform should provide an automated, closed-loop process that Gartner calls “self-driving ITOM.”

A common complaint by Gartner clients is that AIOps platforms can take too long to deploy and derive value. The report advises organizations to select a platform that provides rapid, SaaS-based deployment, out-of-the-box integrations for common interfaces, repeatable workflows, and lower false positives generated by the system.

“Shifting Left” for DevOps and SREs

A major new use case for AIOps is improving the application development lifecycle. With digital transformation, apps undergo constant change with new iterations appearing multiple times a day. A domain-agnostic solution can “shift left” in the lifecycle to enable DevOps and SRE teams with observability and control that was previously available only to ITOM and ITSM teams.


Gartner Market Guide


Gartner’s Recommendations

The report provides two vendor lists: one is 17 representative vendors in the domain-centric AIOps platform market; the other is 11 vendors in the domain-agnostic market. Moogsoft is a leader in the domain-agnostic market.

In considering options from these vendors, Gartner provides three recommendations.

Use a Top-down AIOps Framework. Create a roadmap with an end-goal objective to be achieved with an AIOps platform.

Use Automation for Insights. Automated actions for remediation can be inhibited by a lack of standardization. For now, Gartner suggests a more achievable goal of prioritizing tools that reduce “visual overload” on operators. Tools should automatically highlight areas needing human attention.

Seek Relevance for Diverse Personas. AIOps can serve the requirements of many personas. Gartner advises the selection of a platform that “offer the flexibility to ingest events, logs, and metrics and offer out-of-the-box capabilities for at least one prioritized use case for I&O.”

Finally, we note this conclusion by Gartner: “Enterprises have started adopting AIOps platforms to compete with and replace some traditional monitoring tool categories. For example, monitoring IaaS and observability is being done entirely within AIOps platforms, especially if the enterprise has its entire IT footprint in the cloud.” This is the approach provided by Moogsoft. To learn more about AIOps trends, click here to download your copy of the Gartner report.

Moogsoft is the AI-driven observability leader that provides intelligent monitoring solutions for smart DevOps. Moogsoft delivers the most advanced cloud-native, self-service platform for software engineers, developers and operators to instantly see everything, know what’s wrong and fix things faster.

About the author


Richard Whitehead

As Moogsoft's Chief Evangelist, Richard brings a keen sense of what is required to build transformational solutions. A former CTO and Technology VP, Richard brought new technologies to market, and was responsible for strategy, partnerships and product research. Richard served on Splunk’s Technology Advisory Board through their Series A, providing product and market guidance. He served on the Advisory Boards of RedSeal and Meriton Networks, was a charter member of the TMF NGOSS architecture committee, chaired a DMTF Working Group, and recently co-chaired the ONUG Monitoring & Observability Working Group. Richard holds three patents, and is considered dangerous with JavaScript.

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