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A New Look at AIOps

Dennis Drogseth

On March 26, EMA will be presenting a webinar with some surprising facts based on our Radar — AIOps: A Guide to Investing in Innovation.

In the course of EMA research over the last twelve years, the message for IT organizations looking to pursue a forward path in AIOps adoption is overall a strongly positive one. The benefits achieved are growing in diversity and value. The obstacles do remain similar, as they reflect not only on a technology purchase, but also on processes, organizations, and cultural realities.

In selecting and then evaluating the thirteen vendors included in this Radar report, our key criteria included:

■ Capabilities for self-learning to deliver predictive, prescriptive, preventative, and if/then actionable insights

■ Support for a wide range of advanced heuristics, such as multivariate analysis, machine learning, streaming data, tiered analytics, cognitive analytics, and generative AI

■ Potential use as a strategic overlay to assimilate or consolidate multiple monitoring and other toolset investments

■ Advanced levels of integrated automation to facilitate communication and action

■ Discovery and dependency mapping for enhanced analytic context

■ Support for private and public cloud, as well as hybrid and legacy environments

■ Assimilation of data from cross-domain sources in high data volumes for real-time and historical cross-domain awareness.

■ With an eye on observability, we also examined a breadth of data types (e.g., events, metrics, logs, flow, traces, configurations, etc.) with a growing move toward open source data and OpenTelemetry.

Our methodology for the Radar required that EMA complete the following steps with each of the thirteen vendors in this report:

■ Finalizing a questionnaire and sharing it with vendor – with key categories: deployment and administration, cost advantage, architecture, functionality, and vendor strength

■ Reviewing vendor inputs in a series of digital and conversational interactions

■ Interviewing customers to validate vendor claims — with 21 interviews in total

■ Analyzing the results in December 2023 and developing Radar Chart positioning and the profiles in January 2024

■ Final reviews and report generation in February/March 2024

In this webinar you'll see how and where each of the thirteen vendor positions based overall product strength (the vertical axis) and cost and administrative effectiveness (the horizontal axis).

The AIOps marketplace is clearly evolving at an accelerated rate, with an average of 100% growth in AIOps-related revenue across the thirteen vendors since 2020, with customer bases sometimes tripling or more. Both OpenTelemetry and generative AI have redefined the market in creative and positive ways. Deployment time is accelerating, along with time to achieve ROI. Volume and quality of data breadth has been substantially on the rise. And the ability to promote more informed collaboration across IT, as well as between IT and the business, is also accelerating at AIOps pace.

And indeed, 2023 was an explosive year for generative AI, with the momentum very much moving into the present. Eleven of the thirteen vendors introduced new generative AI capabilities. Some of the key areas of focus were:

■ Troubleshooting and/or analytics summarization

■ Recommendations for taking action

■ Action/automation (e.g., configuration automation, patch management, or accelerating workflow development)

■ Generating trouble ticket summaries, or more broadly improving ITSM efficiencies

■ Post-mortem analysis and recommendations for improvement

In customer interviews we looked at vendor selection, deployment, and benefits. The two following quotes are telling examples:

"We had monitoring systems all over the place, but nothing to bring them together. Our AIOps platform took all the puzzle pieces for root causes and alerts and delivered a common analysis across the broader spectrum."

"They've helped us build a bridge between the business and operations, providing tailored dashboard views driven from the same event and enrichment data, avoiding conflicts between the varied support and business areas."

AIOps can and should be transformative in enabling more effective decision-making, data sharing, and analytics-driven automation. But which vendor can most effectively address your top prioritized near-term and long-term goals?

Which vendor is a most natural fit for your current technology environment?

What roles need to be supported across Operations, ITSM, DevOps, Security, and business stakeholders?

This Radar helps to provide answers to all these questions and more in a multidimensional manner.

Hot Topics

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Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

A New Look at AIOps

Dennis Drogseth

On March 26, EMA will be presenting a webinar with some surprising facts based on our Radar — AIOps: A Guide to Investing in Innovation.

In the course of EMA research over the last twelve years, the message for IT organizations looking to pursue a forward path in AIOps adoption is overall a strongly positive one. The benefits achieved are growing in diversity and value. The obstacles do remain similar, as they reflect not only on a technology purchase, but also on processes, organizations, and cultural realities.

In selecting and then evaluating the thirteen vendors included in this Radar report, our key criteria included:

■ Capabilities for self-learning to deliver predictive, prescriptive, preventative, and if/then actionable insights

■ Support for a wide range of advanced heuristics, such as multivariate analysis, machine learning, streaming data, tiered analytics, cognitive analytics, and generative AI

■ Potential use as a strategic overlay to assimilate or consolidate multiple monitoring and other toolset investments

■ Advanced levels of integrated automation to facilitate communication and action

■ Discovery and dependency mapping for enhanced analytic context

■ Support for private and public cloud, as well as hybrid and legacy environments

■ Assimilation of data from cross-domain sources in high data volumes for real-time and historical cross-domain awareness.

■ With an eye on observability, we also examined a breadth of data types (e.g., events, metrics, logs, flow, traces, configurations, etc.) with a growing move toward open source data and OpenTelemetry.

Our methodology for the Radar required that EMA complete the following steps with each of the thirteen vendors in this report:

■ Finalizing a questionnaire and sharing it with vendor – with key categories: deployment and administration, cost advantage, architecture, functionality, and vendor strength

■ Reviewing vendor inputs in a series of digital and conversational interactions

■ Interviewing customers to validate vendor claims — with 21 interviews in total

■ Analyzing the results in December 2023 and developing Radar Chart positioning and the profiles in January 2024

■ Final reviews and report generation in February/March 2024

In this webinar you'll see how and where each of the thirteen vendor positions based overall product strength (the vertical axis) and cost and administrative effectiveness (the horizontal axis).

The AIOps marketplace is clearly evolving at an accelerated rate, with an average of 100% growth in AIOps-related revenue across the thirteen vendors since 2020, with customer bases sometimes tripling or more. Both OpenTelemetry and generative AI have redefined the market in creative and positive ways. Deployment time is accelerating, along with time to achieve ROI. Volume and quality of data breadth has been substantially on the rise. And the ability to promote more informed collaboration across IT, as well as between IT and the business, is also accelerating at AIOps pace.

And indeed, 2023 was an explosive year for generative AI, with the momentum very much moving into the present. Eleven of the thirteen vendors introduced new generative AI capabilities. Some of the key areas of focus were:

■ Troubleshooting and/or analytics summarization

■ Recommendations for taking action

■ Action/automation (e.g., configuration automation, patch management, or accelerating workflow development)

■ Generating trouble ticket summaries, or more broadly improving ITSM efficiencies

■ Post-mortem analysis and recommendations for improvement

In customer interviews we looked at vendor selection, deployment, and benefits. The two following quotes are telling examples:

"We had monitoring systems all over the place, but nothing to bring them together. Our AIOps platform took all the puzzle pieces for root causes and alerts and delivered a common analysis across the broader spectrum."

"They've helped us build a bridge between the business and operations, providing tailored dashboard views driven from the same event and enrichment data, avoiding conflicts between the varied support and business areas."

AIOps can and should be transformative in enabling more effective decision-making, data sharing, and analytics-driven automation. But which vendor can most effectively address your top prioritized near-term and long-term goals?

Which vendor is a most natural fit for your current technology environment?

What roles need to be supported across Operations, ITSM, DevOps, Security, and business stakeholders?

This Radar helps to provide answers to all these questions and more in a multidimensional manner.

Hot Topics

The Latest

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...