Skip to main content

5 Reasons Application Performance Management Fails

Jonah Kowall

After speaking to thousands of Application Performance Management (APM) users during my time with Gartner, I have seen the following 5 key issues that cause APM failures:

1. Organizational immaturity

The first cause of failure is the silos in many of today’s organizations. There are often too many stakeholders involved in APM decision-making, ranging from application support, server teams, network teams, database teams (DBAs), application developers, and various architects across the organization.

We’re also seeing more non-technical users, such as the business owner of an application interested in seeing usage and performance data on critical Business Transactions within the application. These business users will become a more central user of APM in the future.

It’s critical to identify the primary user of the product, and determine requirements focused on those primary users. Secondary users can have input but should not be the ones driving the key decision points. As products mature, they can sell into multiple areas or even cross sell through teams, but it shouldn’t be the focus of the initial implementation.

2. Ownership

Typically the excitement of an APM solution, the added visibility, and capabilities presented with an implementation provide immense value to operations, application support, and development. The implementation — if you select an easy to implement product — normally proceeds without many issues, and there is a clear owner or stakeholders of the product.

Over time, as roles and business direction changes, often APM loses its key owner. The result of this is that the product isn’t maintained or used day to day. The way to avoid this is to make the executives key stakeholders. As APM and Application Intelligence will become critical to business decision-making, changing what’s often been the fate of older APM products.

3. Application Complexity

APM tools are installed for two reasons. If APM is strategic it’s implemented during development or implementation of a project. The second driver for APM implementation is when the pain threshold gets too high, and something is needed to see the production environment to remediate issues. The lack of understanding or visibility in applications, both old and new, is normally the first benefit. You’ll often hear: “I didn’t know this was connecting to that?”

Issues occur within changing applications for two primary reasons. First, demands of business model changes driven by greater customer demand or higher volumes of data. The second reason for change is feature requests, requiring application changes. These two reasons for change can be distilled down to scale and complexity, making it harder to identify and correct issues (or passing this data to development to make corrective changes to the software).

4. Engineering Skills Required

With yesterday’s APM tools, the implementations were incredibly complex and time consuming. This was due to the amount of tuning and customization enterprises required. Companies which have failed in APM were normally due to having too heavy a services engagement. This has caused the likes of Optier to completely go out of business, and ITOM giants to rethink how they approach the market. Many of these companies even have staff members who would work full time at customer sites to keep the products up and running. These are often seen as benefits to the buyer, but eventually they become burdens.

Applications both in the enterprise and customer worlds have become easy to buy, implement, and show the value of the technology. This has permeated IT products as well. Buyers expect things to be easy and show value quickly. The APM winners today, and for the future build easy to implement products, and refuse to customize them or push a heavy services engagement.

The key is enabling customers, and not offloading the work of using the product or providing staff augmentation. If you are looking at managed services, select the right technology first, and then the managed services provider.

Many legacy APM tools are far too complex, with countless config files and GUI features to tune in order to get value out of the investment. You shouldn’t need to be a senior technologist to get results. Today’s modern tools are easy to understand, and often present information in a way that level-1 operations engineers get value from them.

5. Focus on the wrong thing

Selecting APM technology isn’t just about meeting the needs of your application today, but thinking about the future state of the applications and infrastructures. What is considered experimental and bleeding edge eventually become standard components of traditional enterprise applications. We’ve already seen this happen with PHP, and we’re beginning to see this with other languages. Today you may be a Java shop on VMware, and possibly even a PHP user on LAMP, but in the future you will likely be a node.js shop, possibly running on a public PaaS.

Most organizational leaders have a strategy for both private and public cloud, where areas of business innovation and differentiation tend to be built on public clouds. This is the reason Gartner states that “IT spending on public cloud services is growing more than five times faster than growth in IT spending across all categories.”

Similarly, your organization may not have a large mobile investment today, but I can assure you will in the future. In order to handle these shifts many applications are moving from a single programming language to being composed of multiple languages. These technology shifts are requiring people with new broader skills, or people who can learn new skills quickly. The path towards the full stack developer or IT operations generalist show many are evolving to meet these new challenges to meet business agility requirements.

Regardless whether these proof points or discussions match your organization, the ability to support past investments, existing investments, but most importantly future investments, is critical when selecting APM technologies. Areas of growth and innovation are critical to senior management, hence will provide the most value to the business. These challenges are being addressed by the APM innovators. Keep that in mind when selecting application management technology, keeping in mind the depth and context of the monitoring and analytics.

Hot Topics

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

5 Reasons Application Performance Management Fails

Jonah Kowall

After speaking to thousands of Application Performance Management (APM) users during my time with Gartner, I have seen the following 5 key issues that cause APM failures:

1. Organizational immaturity

The first cause of failure is the silos in many of today’s organizations. There are often too many stakeholders involved in APM decision-making, ranging from application support, server teams, network teams, database teams (DBAs), application developers, and various architects across the organization.

We’re also seeing more non-technical users, such as the business owner of an application interested in seeing usage and performance data on critical Business Transactions within the application. These business users will become a more central user of APM in the future.

It’s critical to identify the primary user of the product, and determine requirements focused on those primary users. Secondary users can have input but should not be the ones driving the key decision points. As products mature, they can sell into multiple areas or even cross sell through teams, but it shouldn’t be the focus of the initial implementation.

2. Ownership

Typically the excitement of an APM solution, the added visibility, and capabilities presented with an implementation provide immense value to operations, application support, and development. The implementation — if you select an easy to implement product — normally proceeds without many issues, and there is a clear owner or stakeholders of the product.

Over time, as roles and business direction changes, often APM loses its key owner. The result of this is that the product isn’t maintained or used day to day. The way to avoid this is to make the executives key stakeholders. As APM and Application Intelligence will become critical to business decision-making, changing what’s often been the fate of older APM products.

3. Application Complexity

APM tools are installed for two reasons. If APM is strategic it’s implemented during development or implementation of a project. The second driver for APM implementation is when the pain threshold gets too high, and something is needed to see the production environment to remediate issues. The lack of understanding or visibility in applications, both old and new, is normally the first benefit. You’ll often hear: “I didn’t know this was connecting to that?”

Issues occur within changing applications for two primary reasons. First, demands of business model changes driven by greater customer demand or higher volumes of data. The second reason for change is feature requests, requiring application changes. These two reasons for change can be distilled down to scale and complexity, making it harder to identify and correct issues (or passing this data to development to make corrective changes to the software).

4. Engineering Skills Required

With yesterday’s APM tools, the implementations were incredibly complex and time consuming. This was due to the amount of tuning and customization enterprises required. Companies which have failed in APM were normally due to having too heavy a services engagement. This has caused the likes of Optier to completely go out of business, and ITOM giants to rethink how they approach the market. Many of these companies even have staff members who would work full time at customer sites to keep the products up and running. These are often seen as benefits to the buyer, but eventually they become burdens.

Applications both in the enterprise and customer worlds have become easy to buy, implement, and show the value of the technology. This has permeated IT products as well. Buyers expect things to be easy and show value quickly. The APM winners today, and for the future build easy to implement products, and refuse to customize them or push a heavy services engagement.

The key is enabling customers, and not offloading the work of using the product or providing staff augmentation. If you are looking at managed services, select the right technology first, and then the managed services provider.

Many legacy APM tools are far too complex, with countless config files and GUI features to tune in order to get value out of the investment. You shouldn’t need to be a senior technologist to get results. Today’s modern tools are easy to understand, and often present information in a way that level-1 operations engineers get value from them.

5. Focus on the wrong thing

Selecting APM technology isn’t just about meeting the needs of your application today, but thinking about the future state of the applications and infrastructures. What is considered experimental and bleeding edge eventually become standard components of traditional enterprise applications. We’ve already seen this happen with PHP, and we’re beginning to see this with other languages. Today you may be a Java shop on VMware, and possibly even a PHP user on LAMP, but in the future you will likely be a node.js shop, possibly running on a public PaaS.

Most organizational leaders have a strategy for both private and public cloud, where areas of business innovation and differentiation tend to be built on public clouds. This is the reason Gartner states that “IT spending on public cloud services is growing more than five times faster than growth in IT spending across all categories.”

Similarly, your organization may not have a large mobile investment today, but I can assure you will in the future. In order to handle these shifts many applications are moving from a single programming language to being composed of multiple languages. These technology shifts are requiring people with new broader skills, or people who can learn new skills quickly. The path towards the full stack developer or IT operations generalist show many are evolving to meet these new challenges to meet business agility requirements.

Regardless whether these proof points or discussions match your organization, the ability to support past investments, existing investments, but most importantly future investments, is critical when selecting APM technologies. Areas of growth and innovation are critical to senior management, hence will provide the most value to the business. These challenges are being addressed by the APM innovators. Keep that in mind when selecting application management technology, keeping in mind the depth and context of the monitoring and analytics.

Hot Topics

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...