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AIOps Across 17 Vendors: What the Data Shows

Dennis Drogseth

One of the benefits of doing the EMA Radar Report: AIOps- A Guide for Investing in Innovation was getting data from all 17 vendors on critical areas ranging from deployment and adoption challenges, to cost and pricing, to architectural and functionality insights across everything from heuristics, to automation, and data assimilation.

Listen to EMA's Dennis Drogseth on the AI+ITOPS Podcast


Administration and Deployment

In the area of deployment and administration, EMA found that on average AIOps vendors indicated between 1-1.5 full-time employees (FTE) were required for ongoing administration in an enterprise with about 10,000 employees. This didn’t include initial deployment or any significant extension in breadth of coverage or functionality.

In 31 interviews, these estimates were generally borne out. Three vendors at the high end estimated between 2.5 and 3 FTEs, whereas the three vendors at the low end estimated between less than 0.5 FTEs.

Heuristics

The great majority of AIOps platforms have heuristics that can "learn" their environments dynamically, without added administrative intervention. On average, they can do this in a little more than one week for 5,000 managed entities.

EMA then asked vendors to weight their AI/ML heuristics on a scale from 0-2, with 2 being a featured heuristic value, 1 being present, and 0 being absent. The top 10 heuristics getting a 2 weighting were:

1. Correlators

2. Anomaly detection

3. Machine learning and baselining for event pattern recognition

4. Topology-based analytics

5. Prescriptive analytics

6. Predictive algorithms

7. Comparators

8. Streaming analytics

9. Optimization algorithms

10. Object-based modeling

Data Assimilation

On average, AIOps vendors could assimilate between 1 million and 10 million metrics within five minutes. When we asked about what data types were in play, we saw:

1. Events (performance related)

2. Time Series

3. Log files

4. Events/ Time Series (security related)

5. Transaction (application performance)

6. Configuration/topology

7. Unstructured data

8. Agent data (systems)

9. Byte code instrumentation

10. Comma delimited files /CSV files

Third-party toolset integration

Significantly, all 17 vendors have some level of third-party toolset integration out of the box, or in parallel, none claim to do "all their own monitoring." In fact, the average AIOps platform has supported integrations for more than 50 different third-party toolsets, with four vendors indicating 100 or more.

These integrations can have powerful political and practical advantages, easing stakeholder reluctance by eliminating the need to break away from their existing tools completely. Additional values include toolset consolidation as IT organizations begin to observe redundancies while also realizing which toolsets are most valuable.

The most common toolset integrations were application performance monitoring (APM) tools tied with CMDBs or extended configuration management systems. Service desk integration for trouble ticketing followed and third-party event management systems came in fourth. Automation integrations were also key, with IT process automation (runbook), and workflow across IT in the lead.

A few use-case views

We had three use-case scenarios. And for each use case we examined a number of factors ranging from domain reach, stakeholders supported, real-time data currency, and heuristics to enable not only awareness of anomalies, but predictive and prescriptive recommendations. Vendors were positioned separately on a per-use-case basis.

When we asked about the top benefits for incident, availability and performance management all vendors led with the following six items, which were also born out in deployment interviews:

■ Faster time to repair problems

■ Proactive ability to prevent problems

■ Improved OpEx efficiencies within IT

■ Less time spent writing rules

■ Real-time insights and historical trends on IT services

■ Reduction/consolidation, minimalization of tools

When we asked what changes each vendor could trace for change impact and capacity optimization, we got the following top five:

■ System configuration service impact analysis

■ Application release changes

■ Service impact analysis (in general)

■ Virtualized infrastructure service impact analysis

■ Containers and microservices service impact analysis

For business impact and IT-to-business alignment, we asked about relevant data sources and saw these as the top five:

■ Enterprise operations data

■ IT warehouse for advanced trending

■ Business application owner data

■ Executive dashboard

■ Security/audit compliance systems

To wrap up

This is just a taste of the data that emerged from our AIOps Radar research. The report contains considerably more detail, while still being a condensation of 105 data-rich slides.

Doing this has been an adventure for me, for EMA as a whole, and I believe for the vendors involved, as well. I do hope you can check out the report and see for yourself as to why.

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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 ...

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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 ...

AIOps Across 17 Vendors: What the Data Shows

Dennis Drogseth

One of the benefits of doing the EMA Radar Report: AIOps- A Guide for Investing in Innovation was getting data from all 17 vendors on critical areas ranging from deployment and adoption challenges, to cost and pricing, to architectural and functionality insights across everything from heuristics, to automation, and data assimilation.

Listen to EMA's Dennis Drogseth on the AI+ITOPS Podcast


Administration and Deployment

In the area of deployment and administration, EMA found that on average AIOps vendors indicated between 1-1.5 full-time employees (FTE) were required for ongoing administration in an enterprise with about 10,000 employees. This didn’t include initial deployment or any significant extension in breadth of coverage or functionality.

In 31 interviews, these estimates were generally borne out. Three vendors at the high end estimated between 2.5 and 3 FTEs, whereas the three vendors at the low end estimated between less than 0.5 FTEs.

Heuristics

The great majority of AIOps platforms have heuristics that can "learn" their environments dynamically, without added administrative intervention. On average, they can do this in a little more than one week for 5,000 managed entities.

EMA then asked vendors to weight their AI/ML heuristics on a scale from 0-2, with 2 being a featured heuristic value, 1 being present, and 0 being absent. The top 10 heuristics getting a 2 weighting were:

1. Correlators

2. Anomaly detection

3. Machine learning and baselining for event pattern recognition

4. Topology-based analytics

5. Prescriptive analytics

6. Predictive algorithms

7. Comparators

8. Streaming analytics

9. Optimization algorithms

10. Object-based modeling

Data Assimilation

On average, AIOps vendors could assimilate between 1 million and 10 million metrics within five minutes. When we asked about what data types were in play, we saw:

1. Events (performance related)

2. Time Series

3. Log files

4. Events/ Time Series (security related)

5. Transaction (application performance)

6. Configuration/topology

7. Unstructured data

8. Agent data (systems)

9. Byte code instrumentation

10. Comma delimited files /CSV files

Third-party toolset integration

Significantly, all 17 vendors have some level of third-party toolset integration out of the box, or in parallel, none claim to do "all their own monitoring." In fact, the average AIOps platform has supported integrations for more than 50 different third-party toolsets, with four vendors indicating 100 or more.

These integrations can have powerful political and practical advantages, easing stakeholder reluctance by eliminating the need to break away from their existing tools completely. Additional values include toolset consolidation as IT organizations begin to observe redundancies while also realizing which toolsets are most valuable.

The most common toolset integrations were application performance monitoring (APM) tools tied with CMDBs or extended configuration management systems. Service desk integration for trouble ticketing followed and third-party event management systems came in fourth. Automation integrations were also key, with IT process automation (runbook), and workflow across IT in the lead.

A few use-case views

We had three use-case scenarios. And for each use case we examined a number of factors ranging from domain reach, stakeholders supported, real-time data currency, and heuristics to enable not only awareness of anomalies, but predictive and prescriptive recommendations. Vendors were positioned separately on a per-use-case basis.

When we asked about the top benefits for incident, availability and performance management all vendors led with the following six items, which were also born out in deployment interviews:

■ Faster time to repair problems

■ Proactive ability to prevent problems

■ Improved OpEx efficiencies within IT

■ Less time spent writing rules

■ Real-time insights and historical trends on IT services

■ Reduction/consolidation, minimalization of tools

When we asked what changes each vendor could trace for change impact and capacity optimization, we got the following top five:

■ System configuration service impact analysis

■ Application release changes

■ Service impact analysis (in general)

■ Virtualized infrastructure service impact analysis

■ Containers and microservices service impact analysis

For business impact and IT-to-business alignment, we asked about relevant data sources and saw these as the top five:

■ Enterprise operations data

■ IT warehouse for advanced trending

■ Business application owner data

■ Executive dashboard

■ Security/audit compliance systems

To wrap up

This is just a taste of the data that emerged from our AIOps Radar research. The report contains considerably more detail, while still being a condensation of 105 data-rich slides.

Doing this has been an adventure for me, for EMA as a whole, and I believe for the vendors involved, as well. I do hope you can check out the report and see for yourself as to why.

Hot Topics

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 ...