Skip to main content

The Business Cases for Application Performance Management

Gabriel Lowy

The business cases for Application Performance Management (APM) are customer satisfaction and operational efficiency. When applications run well, companies are better positioned to achieve return on investment (ROI) and risk management objectives.

Business processes can be streamlined or automated. They can be optimized around how to improve employee engagement and customer experience. Meeting or exceeding customer expectations – either through more responsive employees or excellent website interactions – is the key to strong customer relationships and loyalty.

Conversely, poor application performance negatively impacts the business. Employee productivity and morale sinks. They are unable to get their work done in a timely fashion or are hindered in their ability to provide high quality customer service. Trading partners become frustrated and look for alternatives. Most importantly, customer satisfaction plummets, undermining loyalty. The result is missed opportunities, competitive disadvantage and reputational damage.

IT teams that lack sufficient information about the root cause of application issues and poor user experience are plagued by inefficiency. Too much time is spent tracking down the source of performance problems rather than supporting the business.

Cutting Through Complexity to Deliver Consistent End User Experience

Companies are increasingly relying on applications to run all facets of their business. This has made it more important than ever for enterprises to monitor and manage the end user experience across all environments – physical, virtual, cloud, mobile and mainframe.

The greatest barrier to consistently high performance is complexity. Because modern applications have so many connection points between the end user and the data center, performance issues can arise anywhere along the application delivery chain. This becomes more pronounced as users increasingly engage with cloud-based applications that are controlled by service providers. And as more of these apps are accessed by mobile devices that the end user owns, complexity rises further.

The more business processes come to depend on multiple applications and the underlying infrastructure, the more susceptible they are to performance degradation. Failure at any point can turn a satisfied user into a frustrated one. If that user is an employee, productivity drops and so does their engagement. If the end user is a customer, the cost can be much higher in the form of eroded loyalty or lost business.

When application performance or availability issues do arise, end users expect a quick response time to problem resolution from IT, more frequently within minutes. And users will hold IT responsible for application performance – regardless of whether the application resides on premise or in the cloud. It’s no longer good enough for an application to work; it now needs to work to end user expectations.

IT can avoid the pressures of this guessing game by understanding their users and prioritizing the performance of their apps and websites accordingly. They can make sure that the apps that drive the business have the highest availability and reliability. This is the path to consistently meeting or exceeding user expectations.

Application Performance Drives Financial Metrics

As applications increasingly drive the business, APM is strategic and key to end-user engagement and loyalty – both within the enterprise and with customers. A clear linkage has emerged with how improvements in application performance and customer experiences are driving financial benefits. These include reduced costs, higher productivity and new revenue streams. But in order to realize the benefits of satisfied customers, application performance must been stellar – consistently.

CIOs can more closely align with end user objectives and corporate strategy by recognizing their role in employee engagement and customer satisfaction. The right tools can identify root cause of application issues and perform real-time triage to optimize user experience. A proactive approach improves their company’s employee responsiveness to build customer loyalty, increase revenues, and drive operational efficiency.

A more efficient IT team enables businesses to act on operational intelligence gained from a unified APM platform. Improvements have a domino effect across all functional areas of the organization – from sales and marketing to product development, manufacturing and supply chain management. They also help companies strengthen financial management, reduce risk and ensure adherence with governance, regulatory and compliance requirements.

To meet end user expectations, IT teams need to adapt a more holistic approach to performance management and decision analytics. Through best practices, they can help their companies leverage IT investments to discover, interpret and respond to the myriad events that impact their operations, security, compliance and competitiveness.

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 ...

The Business Cases for Application Performance Management

Gabriel Lowy

The business cases for Application Performance Management (APM) are customer satisfaction and operational efficiency. When applications run well, companies are better positioned to achieve return on investment (ROI) and risk management objectives.

Business processes can be streamlined or automated. They can be optimized around how to improve employee engagement and customer experience. Meeting or exceeding customer expectations – either through more responsive employees or excellent website interactions – is the key to strong customer relationships and loyalty.

Conversely, poor application performance negatively impacts the business. Employee productivity and morale sinks. They are unable to get their work done in a timely fashion or are hindered in their ability to provide high quality customer service. Trading partners become frustrated and look for alternatives. Most importantly, customer satisfaction plummets, undermining loyalty. The result is missed opportunities, competitive disadvantage and reputational damage.

IT teams that lack sufficient information about the root cause of application issues and poor user experience are plagued by inefficiency. Too much time is spent tracking down the source of performance problems rather than supporting the business.

Cutting Through Complexity to Deliver Consistent End User Experience

Companies are increasingly relying on applications to run all facets of their business. This has made it more important than ever for enterprises to monitor and manage the end user experience across all environments – physical, virtual, cloud, mobile and mainframe.

The greatest barrier to consistently high performance is complexity. Because modern applications have so many connection points between the end user and the data center, performance issues can arise anywhere along the application delivery chain. This becomes more pronounced as users increasingly engage with cloud-based applications that are controlled by service providers. And as more of these apps are accessed by mobile devices that the end user owns, complexity rises further.

The more business processes come to depend on multiple applications and the underlying infrastructure, the more susceptible they are to performance degradation. Failure at any point can turn a satisfied user into a frustrated one. If that user is an employee, productivity drops and so does their engagement. If the end user is a customer, the cost can be much higher in the form of eroded loyalty or lost business.

When application performance or availability issues do arise, end users expect a quick response time to problem resolution from IT, more frequently within minutes. And users will hold IT responsible for application performance – regardless of whether the application resides on premise or in the cloud. It’s no longer good enough for an application to work; it now needs to work to end user expectations.

IT can avoid the pressures of this guessing game by understanding their users and prioritizing the performance of their apps and websites accordingly. They can make sure that the apps that drive the business have the highest availability and reliability. This is the path to consistently meeting or exceeding user expectations.

Application Performance Drives Financial Metrics

As applications increasingly drive the business, APM is strategic and key to end-user engagement and loyalty – both within the enterprise and with customers. A clear linkage has emerged with how improvements in application performance and customer experiences are driving financial benefits. These include reduced costs, higher productivity and new revenue streams. But in order to realize the benefits of satisfied customers, application performance must been stellar – consistently.

CIOs can more closely align with end user objectives and corporate strategy by recognizing their role in employee engagement and customer satisfaction. The right tools can identify root cause of application issues and perform real-time triage to optimize user experience. A proactive approach improves their company’s employee responsiveness to build customer loyalty, increase revenues, and drive operational efficiency.

A more efficient IT team enables businesses to act on operational intelligence gained from a unified APM platform. Improvements have a domino effect across all functional areas of the organization – from sales and marketing to product development, manufacturing and supply chain management. They also help companies strengthen financial management, reduce risk and ensure adherence with governance, regulatory and compliance requirements.

To meet end user expectations, IT teams need to adapt a more holistic approach to performance management and decision analytics. Through best practices, they can help their companies leverage IT investments to discover, interpret and respond to the myriad events that impact their operations, security, compliance and competitiveness.

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 ...