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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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This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

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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.

Hot Topics

The Latest

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...