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5 Top Findings from TRAC Research's APM Spectrum

The following are five interesting findings from TRAC Research's APM Spectrum report, based on more than 400 survey participants and more than 120 live interviews:

1. Differences in the value of APM capabilities for specific job roles

Research example: IT operations roles are nearly four times less likely to be interested in deploying application instrumentation techniques. The study includes sections that cover the need for APM solutions for IT operations, application developers/QA, CIO and business users.

2. It is not about selecting the best solution, it is about the right mix of products that can close all visibility gaps

Research example: 71% of user organizations are using more than 1 tool for APM. The study includes nine APM submarkets that include between 8 and 23 technology vendors based on their ability to support specific use cases and job roles, and very often using a mix of different underlining technologies to address different aspects of application performance monitoring.

3. Beyond features and functionalities

Research example: CIOs reported that 72% of IT budgets is being spent on operating and maintaining existing IT services. Most of the purchases are driven, not by features and functionalities, but by the ability to solve real business problems. These problems range from reducing the amount of IT resources allocated to firefighting and troubleshooting problems to ensure that performance issues do not cause revenue loss.

4. Supporting different IT initiatives

Research example: 49% of organizations reported that lack of SLAs for user experience is the key challenge for managing application performance in the cloud. Organizations are reporting that their APM capabilities are often defined based on IT initiatives that they are looking to support, such as virtualization, cloud, SaaS, Big Data, Web Services, or mobility.

5. Capabilities relevant for different industry sectors

Research example: Organizations in the Finance sector are 59% less likely to deploy, or are interested in deploying, APM solutions that are delivered as SaaS. When it comes to specific APM capabilities, the research shows major differences across organizations from different industry sectors.

ABOUT Bojan Simic

Bojan Simic is President and Principal Analyst at TRAC Research, a market research and analyst firm that specializes in IT performance management. As an industry analyst, Simic has interviewed more than 2,000 IT and business professionals from end-user organizations and has published more than 50 research reports. His domain knowledge includes insights into end user experiences, best-practices in deploying solutions for IT performance management, and strategies of related solution providers.

Prior to joining TRAC Research, Simic was a lead analyst for Network and Application Performance Management research at Aberdeen Group. He is frequently quoted in leading industry publications and has presented his research findings at more than 30 market facing events.

Simic's coverage area at TRAC Research includes application and network monitoring, WAN management and acceleration, cloud and virtualization management, business service management and managed services.

Bojan holds a BA in Economics from Belgrade University in Belgrade, Serbia and an MBA from McCallum Graduate School of Business at Bentley University.

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For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

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 ...

5 Top Findings from TRAC Research's APM Spectrum

The following are five interesting findings from TRAC Research's APM Spectrum report, based on more than 400 survey participants and more than 120 live interviews:

1. Differences in the value of APM capabilities for specific job roles

Research example: IT operations roles are nearly four times less likely to be interested in deploying application instrumentation techniques. The study includes sections that cover the need for APM solutions for IT operations, application developers/QA, CIO and business users.

2. It is not about selecting the best solution, it is about the right mix of products that can close all visibility gaps

Research example: 71% of user organizations are using more than 1 tool for APM. The study includes nine APM submarkets that include between 8 and 23 technology vendors based on their ability to support specific use cases and job roles, and very often using a mix of different underlining technologies to address different aspects of application performance monitoring.

3. Beyond features and functionalities

Research example: CIOs reported that 72% of IT budgets is being spent on operating and maintaining existing IT services. Most of the purchases are driven, not by features and functionalities, but by the ability to solve real business problems. These problems range from reducing the amount of IT resources allocated to firefighting and troubleshooting problems to ensure that performance issues do not cause revenue loss.

4. Supporting different IT initiatives

Research example: 49% of organizations reported that lack of SLAs for user experience is the key challenge for managing application performance in the cloud. Organizations are reporting that their APM capabilities are often defined based on IT initiatives that they are looking to support, such as virtualization, cloud, SaaS, Big Data, Web Services, or mobility.

5. Capabilities relevant for different industry sectors

Research example: Organizations in the Finance sector are 59% less likely to deploy, or are interested in deploying, APM solutions that are delivered as SaaS. When it comes to specific APM capabilities, the research shows major differences across organizations from different industry sectors.

ABOUT Bojan Simic

Bojan Simic is President and Principal Analyst at TRAC Research, a market research and analyst firm that specializes in IT performance management. As an industry analyst, Simic has interviewed more than 2,000 IT and business professionals from end-user organizations and has published more than 50 research reports. His domain knowledge includes insights into end user experiences, best-practices in deploying solutions for IT performance management, and strategies of related solution providers.

Prior to joining TRAC Research, Simic was a lead analyst for Network and Application Performance Management research at Aberdeen Group. He is frequently quoted in leading industry publications and has presented his research findings at more than 30 market facing events.

Simic's coverage area at TRAC Research includes application and network monitoring, WAN management and acceleration, cloud and virtualization management, business service management and managed services.

Bojan holds a BA in Economics from Belgrade University in Belgrade, Serbia and an MBA from McCallum Graduate School of Business at Bentley University.

Hot Topics

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

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 ...