Skip to main content

5 Key Challenges for Proactive Application Performance Management

TRAC's research shows that IT first learns about performance issues when business users bring it to their attention in 37% of cases. Additionally, many end-users never complain about issues with application performance as they simply abandon the application or a website.

The ability to prevent issues with application performance before they impact business users results in measurable operational and business benefits, but still many end-user organizations are not successful in meeting this goal. TRAC's research shows that only 41% of organizations have a 50% or better success rate in identifying performance issues before they impact business users.

Some of the key reasons why the prevention of performance problems is still a major issue for organizations include:

1. APM strategies predominantly focused on troubleshooting

Even though APM solutions that organizations are purchasing often include strong capabilities for proactive management, organizations tend to use these solutions for troubleshooting only. This comes as a result of:

- internal strategies for managing IT services that are predominantly focused on “firefighting”

- the lack of resources and time needed to support a learning curve and the provision of additional internal resources required to take advantage of features for proactive management

2. Baselining and alerting

Timeliness and accuracy of performance alerts play a significant role in enabling proactive APM. From a cost-savings perspective, there is a significant difference between being able to alert the IT team about issues as they happen as opposed to several minutes later.

The process for defining performance baselines determines how early organizations can detect potential performance issues and, therefore, define an organization's ability to mitigate the negative impact of application performance issues on their business.

3. Collaboration between production and pre-production teams

Production and pre-production teams must be able to speak a common language so organizations can identify potential performance bottlenecks before applications go to production thereby improving their success rate in preventing performance issues. Application transactions can serve as the common language between these groups. Organizations must deploy technology capabilities that will enable collaboration between production and pre-production teams.

4. Advanced APM analytics

Effective strategies for proactively managing application performance issues call for next-generation analytics that enable organizations to perform "what-if" analyses and to automatically detect performance anomalies. Not having these types of capabilities in place makes it more difficult to identify problems early and resolve them before they impact business users.

5. Monitoring the impact of change

Organizations are reporting that many performance problems are caused by changes – in usage patterns, application features, new infrastructure or service delivery type. However, many organizations do not have capabilities in place to analyze how these changes could impact application performance from the end-user's perspective.

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.

Related Links:

www.new.trac-research.com/

IT Performance Monitoring in 2013 – Key Market Trends

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 ...

5 Key Challenges for Proactive Application Performance Management

TRAC's research shows that IT first learns about performance issues when business users bring it to their attention in 37% of cases. Additionally, many end-users never complain about issues with application performance as they simply abandon the application or a website.

The ability to prevent issues with application performance before they impact business users results in measurable operational and business benefits, but still many end-user organizations are not successful in meeting this goal. TRAC's research shows that only 41% of organizations have a 50% or better success rate in identifying performance issues before they impact business users.

Some of the key reasons why the prevention of performance problems is still a major issue for organizations include:

1. APM strategies predominantly focused on troubleshooting

Even though APM solutions that organizations are purchasing often include strong capabilities for proactive management, organizations tend to use these solutions for troubleshooting only. This comes as a result of:

- internal strategies for managing IT services that are predominantly focused on “firefighting”

- the lack of resources and time needed to support a learning curve and the provision of additional internal resources required to take advantage of features for proactive management

2. Baselining and alerting

Timeliness and accuracy of performance alerts play a significant role in enabling proactive APM. From a cost-savings perspective, there is a significant difference between being able to alert the IT team about issues as they happen as opposed to several minutes later.

The process for defining performance baselines determines how early organizations can detect potential performance issues and, therefore, define an organization's ability to mitigate the negative impact of application performance issues on their business.

3. Collaboration between production and pre-production teams

Production and pre-production teams must be able to speak a common language so organizations can identify potential performance bottlenecks before applications go to production thereby improving their success rate in preventing performance issues. Application transactions can serve as the common language between these groups. Organizations must deploy technology capabilities that will enable collaboration between production and pre-production teams.

4. Advanced APM analytics

Effective strategies for proactively managing application performance issues call for next-generation analytics that enable organizations to perform "what-if" analyses and to automatically detect performance anomalies. Not having these types of capabilities in place makes it more difficult to identify problems early and resolve them before they impact business users.

5. Monitoring the impact of change

Organizations are reporting that many performance problems are caused by changes – in usage patterns, application features, new infrastructure or service delivery type. However, many organizations do not have capabilities in place to analyze how these changes could impact application performance from the end-user's perspective.

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.

Related Links:

www.new.trac-research.com/

IT Performance Monitoring in 2013 – Key Market Trends

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 ...