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Why APM is Valuable to Every Part of Your Business

Matt Watson

Virtually every business depends on mission-critical software to run their business. Any slight application slowdown or outage can lead to legions of unhappy employees or customers. Application Performance Management (APM) solutions can help monitor for performance issues, but they can also be used to gain insights to proactively improve performance as well.

APM is not just a tool for IT Operations. APM has grown into an essential tool that can be utilized by many departments within your business.

IT Operations

When you think of APM, you normally think about IT operations using it for monitoring mission-critical applications. Instead of only monitoring servers and infrastructure, APM solutions can help better track performance at the application level. Including overall performance, key transactions, and much, much more.

Development Teams

APM solutions collect a lot of data. Including code level performance, overall application usage and performance, metrics, log messages, errors, real user monitoring, and more. All of this data can be very valuable for developers when it comes to researching bugs in production. It can also be used to identify parts of an application that can be optimized and validating those performance optimizations.

Developers can also use APM in QA to test and validate the performance of their code before it gets to production.

QA

Traditionally, APM is thought to be used mostly in production. However, APM can be extremely valuable as part of your QA process to find problems before they get to production. It could be used to look for any overall change in performance, new application errors found, load testing validation and more.

Database Administrators

Most APM solutions track the performance of SQL queries. This can be useful information for your DBAs to augment other tools they may also have. They could potentially use APM for various monitoring capabilities. For example, monitoring how often a specific SQL query is taking or how often it is being called.

Product Owners and Executives

The product owner ultimately cares a lot about the application, its functionality, usage, service availability and performance. APM gives product owners visibility into the performance of their application and potentially into metrics around how much it is being used. APM dashboards are popular with product owners and other executives in a company.

Customer Service

When a customer calls and says your application is slow, what do you do? After a quick login test to your app, your customer service member would likely tell the customer that everything seems to be working fine, and the problem is likely on their end.

The problem is a user could be accessing your application on a different server, database, or even in a different data center. If your customer service team has access to basic APM dashboards, they could leverage those to better understand if any application problems may exist or not with more certainty. They also wouldn’t have to bug the IT department every time a customer complains.

Sales and Marketing

Major application outages are always a big PR problem for marketing teams, but hopefully they can use it to instead rave about how fast your application is! They could also use it to gather insights into how parts of your application are being used. Just like your customer service team, your sales team is going to get flooded with calls if your site is down.

Conclusion

Application performance is important to your entire business. APM solutions collect an amazing out of data and usually provide very flexible reporting options. I would encourage you to think of ways to leverage the value of it anywhere that you can.

Matt Watson is Founder and CEO of Stackify.

Hot Topics

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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 ...

Why APM is Valuable to Every Part of Your Business

Matt Watson

Virtually every business depends on mission-critical software to run their business. Any slight application slowdown or outage can lead to legions of unhappy employees or customers. Application Performance Management (APM) solutions can help monitor for performance issues, but they can also be used to gain insights to proactively improve performance as well.

APM is not just a tool for IT Operations. APM has grown into an essential tool that can be utilized by many departments within your business.

IT Operations

When you think of APM, you normally think about IT operations using it for monitoring mission-critical applications. Instead of only monitoring servers and infrastructure, APM solutions can help better track performance at the application level. Including overall performance, key transactions, and much, much more.

Development Teams

APM solutions collect a lot of data. Including code level performance, overall application usage and performance, metrics, log messages, errors, real user monitoring, and more. All of this data can be very valuable for developers when it comes to researching bugs in production. It can also be used to identify parts of an application that can be optimized and validating those performance optimizations.

Developers can also use APM in QA to test and validate the performance of their code before it gets to production.

QA

Traditionally, APM is thought to be used mostly in production. However, APM can be extremely valuable as part of your QA process to find problems before they get to production. It could be used to look for any overall change in performance, new application errors found, load testing validation and more.

Database Administrators

Most APM solutions track the performance of SQL queries. This can be useful information for your DBAs to augment other tools they may also have. They could potentially use APM for various monitoring capabilities. For example, monitoring how often a specific SQL query is taking or how often it is being called.

Product Owners and Executives

The product owner ultimately cares a lot about the application, its functionality, usage, service availability and performance. APM gives product owners visibility into the performance of their application and potentially into metrics around how much it is being used. APM dashboards are popular with product owners and other executives in a company.

Customer Service

When a customer calls and says your application is slow, what do you do? After a quick login test to your app, your customer service member would likely tell the customer that everything seems to be working fine, and the problem is likely on their end.

The problem is a user could be accessing your application on a different server, database, or even in a different data center. If your customer service team has access to basic APM dashboards, they could leverage those to better understand if any application problems may exist or not with more certainty. They also wouldn’t have to bug the IT department every time a customer complains.

Sales and Marketing

Major application outages are always a big PR problem for marketing teams, but hopefully they can use it to instead rave about how fast your application is! They could also use it to gather insights into how parts of your application are being used. Just like your customer service team, your sales team is going to get flooded with calls if your site is down.

Conclusion

Application performance is important to your entire business. APM solutions collect an amazing out of data and usually provide very flexible reporting options. I would encourage you to think of ways to leverage the value of it anywhere that you can.

Matt Watson is Founder and CEO of Stackify.

Hot Topics

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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 ...