Skip to main content

An APM Approach to Application Loyalty

Steven Long
AppDynamics

Today's always-on, constantly connected consumers run on applications — from the moment they wake up in the morning (asking Alexa for the day's weather report) until they go to sleep at night (tapping their Smartwatch to track their nocturnal cycle). People expect the host of applications they use on a daily basis to deliver exceptional digital experiences, but matching performance with consumers' expansive and increasingly complex demands is near impossible without a robust application performance monitoring (APM) solution.

The new App Attention Index Report from AppDynamics finds that consumers are using an average 32 digital services every day — more than four times as many as they realize. What's more, their use of digital services has evolved from a conscious decision to carry around a device and use it for a specific task, to an unconscious and automated behavior — a digital reflex.

Additionally, the number of consumers claiming to be more loyal to an app than a brand has doubled over the last two years (46% in 2019 compared with 23% in 2017). Imagine, then, their genuine disappointment and anger — e.g. cursing, throwing phones and/or becoming irritable with those around them — when performance lags. In fact, the vast majority (84%) of consumers have experienced problems with digital services in the past year, leading half (49%) to switch to competing brands or suppliers.

So what does all this mean for the IT teams driving application performance on the backend? Bottom line: delivering seamless and world-class digital experiences is critical if businesses want to stay relevant and ensure long-term customer loyalty.

Here are some key considerations for IT leaders and developers to consider:

Make Better Decisions Faster with Real-Time Monitoring

It is essential for IT teams to deliver top-notch digital experiences across the full technology stack, from application development all the way to end users' devices. Enterprises should consider taking an AIOps approach to their real-time data monitoring — which leverages the power of artificial intelligence, automation and APM to help IT teams tackle performance issues before they become customer-impacting problems.

By switching to an AIOps model, APM vendors can quickly identify business-impacting problems from vast amounts of data — yielding insights that businesses can easily understand and can help them prevent problems from happening in the future. For digital enterprises, automating root cause analysis through machine learning is one of the best and fastest ways to increase agility in the face of potential downtime.

Converge Business Outcomes and Application Performance

In today's digital-first world, aligning application performance to business outcomes is no longer a question — it's a requirement for survival. By correlating data across monitoring silos into a single source of truth, APM is able to track and aggregate information across the enterprise, on almost anything that could impact app performance (CPU usage, error rates, response times, customer satisfaction and more).

This correlation saves IT teams time that would be spent manually searching through individual event logs or building synthetic monitors. More than that, and the most critical piece of information for digital enterprises — using technology that connects application performance, user experience and business outcomes allows organizations to improve decision-making and take action based on real-time data and insights.

The good news for organizations is that APM technology is evolving at the same pace as consumer expectations. Digital enterprises need to be aware, however, that the technology is most effective when adopted across the entire organization. Doing so not only maps application performance back to business objectives like customer experience and revenue, but helps to ensure digital services — from production, to monitoring to deployment stages — are the top priority for decision makers across your company.

These are just a few considerations for business and IT leaders to consider when it comes to rethinking their APM approach amid shifts in loyalty from brands to apps. Ignoring the impact that AI and machine learning could have on your digital service performance, customer experiences and, inherently, your bottom line - could plague or irrevocably damage your business for years to come.

Steven Long is Regional CTO at AppDynamics

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 ...

An APM Approach to Application Loyalty

Steven Long
AppDynamics

Today's always-on, constantly connected consumers run on applications — from the moment they wake up in the morning (asking Alexa for the day's weather report) until they go to sleep at night (tapping their Smartwatch to track their nocturnal cycle). People expect the host of applications they use on a daily basis to deliver exceptional digital experiences, but matching performance with consumers' expansive and increasingly complex demands is near impossible without a robust application performance monitoring (APM) solution.

The new App Attention Index Report from AppDynamics finds that consumers are using an average 32 digital services every day — more than four times as many as they realize. What's more, their use of digital services has evolved from a conscious decision to carry around a device and use it for a specific task, to an unconscious and automated behavior — a digital reflex.

Additionally, the number of consumers claiming to be more loyal to an app than a brand has doubled over the last two years (46% in 2019 compared with 23% in 2017). Imagine, then, their genuine disappointment and anger — e.g. cursing, throwing phones and/or becoming irritable with those around them — when performance lags. In fact, the vast majority (84%) of consumers have experienced problems with digital services in the past year, leading half (49%) to switch to competing brands or suppliers.

So what does all this mean for the IT teams driving application performance on the backend? Bottom line: delivering seamless and world-class digital experiences is critical if businesses want to stay relevant and ensure long-term customer loyalty.

Here are some key considerations for IT leaders and developers to consider:

Make Better Decisions Faster with Real-Time Monitoring

It is essential for IT teams to deliver top-notch digital experiences across the full technology stack, from application development all the way to end users' devices. Enterprises should consider taking an AIOps approach to their real-time data monitoring — which leverages the power of artificial intelligence, automation and APM to help IT teams tackle performance issues before they become customer-impacting problems.

By switching to an AIOps model, APM vendors can quickly identify business-impacting problems from vast amounts of data — yielding insights that businesses can easily understand and can help them prevent problems from happening in the future. For digital enterprises, automating root cause analysis through machine learning is one of the best and fastest ways to increase agility in the face of potential downtime.

Converge Business Outcomes and Application Performance

In today's digital-first world, aligning application performance to business outcomes is no longer a question — it's a requirement for survival. By correlating data across monitoring silos into a single source of truth, APM is able to track and aggregate information across the enterprise, on almost anything that could impact app performance (CPU usage, error rates, response times, customer satisfaction and more).

This correlation saves IT teams time that would be spent manually searching through individual event logs or building synthetic monitors. More than that, and the most critical piece of information for digital enterprises — using technology that connects application performance, user experience and business outcomes allows organizations to improve decision-making and take action based on real-time data and insights.

The good news for organizations is that APM technology is evolving at the same pace as consumer expectations. Digital enterprises need to be aware, however, that the technology is most effective when adopted across the entire organization. Doing so not only maps application performance back to business objectives like customer experience and revenue, but helps to ensure digital services — from production, to monitoring to deployment stages — are the top priority for decision makers across your company.

These are just a few considerations for business and IT leaders to consider when it comes to rethinking their APM approach amid shifts in loyalty from brands to apps. Ignoring the impact that AI and machine learning could have on your digital service performance, customer experiences and, inherently, your bottom line - could plague or irrevocably damage your business for years to come.

Steven Long is Regional CTO at AppDynamics

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 ...