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EMA Publishes New Radar Report on AIOps

Enterprise Management Associates (EMA) announced the release of its newest EMA Radar Report, titled EMA Radar Report: AIOps – A Guide for Investing in Innovation.

Download a complimentary copy of the report

Created to assist IT professionals in selecting the right solutions for their specific needs, EMA identifies the leading vendors in this space based on key criteria defined by EMA VP of Research, Dennis Drogseth.

“All indications are that this is groundbreaking research,” said Drogseth. “So far, the industry has been struggling to define and understand AIOps, including its benefits, requirements, and challenges. Our extensive data gathering, vendor dialogues, and 31 supplemental deployment interviews have brought AIOps into a new level of clarity—one that underscores both its diversity and its richly beneficial common ground.”

The fact that AIOps is a market showing strong growth in value has been borne out time and again in EMA research over the past decade and remains true in this newest iteration of research on the topic. 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 remain similar, as they reflect not only on a technology purchase, but also on processes, organizations, and cultural realities.

To assist IT organizations pursuing this path, EMA evaluated 17 vendors providing AIOps solutions. Any of the 17 vendors represented in the report might be the best choice for an IT organization depending on what tools and solutions they currently have, their level of process and organizational maturity, their goals and priorities, and what advanced technologies they already have deployed.

The following criteria were used for market inclusion:

- Assimilation of data from cross-domain sources in high data volumes for cross-domain insights.

- The ability to access critical data types, e.g., events, KPIs, logs, flow, configuration data, etc.

- Capabilities for self-learning to deliver predictive and/or prescriptive and/or if/then actionable insights.

- Support for a wide range of advanced heuristics, such as multivariate analysis, machine learning, streaming data, tiered analytics, cognitive analytics, etc.

- Potential use as a strategic overlay that may assimilate or consolidate multiple monitoring investments.

- Support for private cloud and public cloud, as well as hybrid/legacy environments.

- The ability to support multiple use cases, including but not limited to application/infrastructure performance and availability.

Two areas of primary interest not on this list, but examined closely in this Radar, were support for automation to accelerate action and how platforms leverage discovery and dependency mapping for improved context.

A detailed, comparative study of solutions from the following vendors is provided in the report:

Aisera
BigPanda
BMC Software
Broadcom
Centerity
CloudFabrix
Digital.ai
Digitate
IBM
Interlink Software
Micro Focus
Moogsoft
Resolve Systems
ScienceLogic
ServiceNow
Splunk
Virtana

The objective of the Radar was not to pick a single winner but, instead, to provide IT organizations with use case descriptions relevant to purchase. The three use cases evaluated were:

Incident, performance, and availability management. This used case focused on optimizing the resiliency of critical application and business services—including microservices, VoIP, and rich media—in cloud (public/private) as well as non-cloud environments with a strong focus on triage, diagnostics, roles supported, self-learning capabilities, and associated automation.

Change impact and capacity optimization. These are admittedly two use cases combined into one but share requirements in terms of understanding interdependencies across the application/service infrastructure as volumes increase, changes are made, configuration issues arise, and automated actions are required.

Business impact and IT-to-business alignment. This use case includes user experience, customer experience, customer management, business process impacts, and other relevant data, with an eye to supporting business initiatives, such as digital transformation through superior IT-to-business alignment.

The Radar also looked at DevOps support, integrated SecOps capabilities, and IoT support, which could variously play to each, or all, of the use cases listed depending on the platform’s design and the vendor’s focus.

AIOps can and should be transformative in enabling more effective decision-making, data sharing, and analytics-driven automation. The recommendation from EMA remains that buyers should consider their own realities, then begin a search for the AIOps platform that most fits their requirements. Which vendor can most effectively address top prioritized long-term goals? Which vendor is a most natural fit for the current technology environment? Which vendor is likely to bring the fastest near-term wins? The answer could be any one of the 17 presented in this Radar, depending on the answers to these and other questions.

The Latest

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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

EMA Publishes New Radar Report on AIOps

Enterprise Management Associates (EMA) announced the release of its newest EMA Radar Report, titled EMA Radar Report: AIOps – A Guide for Investing in Innovation.

Download a complimentary copy of the report

Created to assist IT professionals in selecting the right solutions for their specific needs, EMA identifies the leading vendors in this space based on key criteria defined by EMA VP of Research, Dennis Drogseth.

“All indications are that this is groundbreaking research,” said Drogseth. “So far, the industry has been struggling to define and understand AIOps, including its benefits, requirements, and challenges. Our extensive data gathering, vendor dialogues, and 31 supplemental deployment interviews have brought AIOps into a new level of clarity—one that underscores both its diversity and its richly beneficial common ground.”

The fact that AIOps is a market showing strong growth in value has been borne out time and again in EMA research over the past decade and remains true in this newest iteration of research on the topic. 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 remain similar, as they reflect not only on a technology purchase, but also on processes, organizations, and cultural realities.

To assist IT organizations pursuing this path, EMA evaluated 17 vendors providing AIOps solutions. Any of the 17 vendors represented in the report might be the best choice for an IT organization depending on what tools and solutions they currently have, their level of process and organizational maturity, their goals and priorities, and what advanced technologies they already have deployed.

The following criteria were used for market inclusion:

- Assimilation of data from cross-domain sources in high data volumes for cross-domain insights.

- The ability to access critical data types, e.g., events, KPIs, logs, flow, configuration data, etc.

- Capabilities for self-learning to deliver predictive and/or prescriptive and/or if/then actionable insights.

- Support for a wide range of advanced heuristics, such as multivariate analysis, machine learning, streaming data, tiered analytics, cognitive analytics, etc.

- Potential use as a strategic overlay that may assimilate or consolidate multiple monitoring investments.

- Support for private cloud and public cloud, as well as hybrid/legacy environments.

- The ability to support multiple use cases, including but not limited to application/infrastructure performance and availability.

Two areas of primary interest not on this list, but examined closely in this Radar, were support for automation to accelerate action and how platforms leverage discovery and dependency mapping for improved context.

A detailed, comparative study of solutions from the following vendors is provided in the report:

Aisera
BigPanda
BMC Software
Broadcom
Centerity
CloudFabrix
Digital.ai
Digitate
IBM
Interlink Software
Micro Focus
Moogsoft
Resolve Systems
ScienceLogic
ServiceNow
Splunk
Virtana

The objective of the Radar was not to pick a single winner but, instead, to provide IT organizations with use case descriptions relevant to purchase. The three use cases evaluated were:

Incident, performance, and availability management. This used case focused on optimizing the resiliency of critical application and business services—including microservices, VoIP, and rich media—in cloud (public/private) as well as non-cloud environments with a strong focus on triage, diagnostics, roles supported, self-learning capabilities, and associated automation.

Change impact and capacity optimization. These are admittedly two use cases combined into one but share requirements in terms of understanding interdependencies across the application/service infrastructure as volumes increase, changes are made, configuration issues arise, and automated actions are required.

Business impact and IT-to-business alignment. This use case includes user experience, customer experience, customer management, business process impacts, and other relevant data, with an eye to supporting business initiatives, such as digital transformation through superior IT-to-business alignment.

The Radar also looked at DevOps support, integrated SecOps capabilities, and IoT support, which could variously play to each, or all, of the use cases listed depending on the platform’s design and the vendor’s focus.

AIOps can and should be transformative in enabling more effective decision-making, data sharing, and analytics-driven automation. The recommendation from EMA remains that buyers should consider their own realities, then begin a search for the AIOps platform that most fits their requirements. Which vendor can most effectively address top prioritized long-term goals? Which vendor is a most natural fit for the current technology environment? Which vendor is likely to bring the fastest near-term wins? The answer could be any one of the 17 presented in this Radar, depending on the answers to these and other questions.

The Latest

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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...