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UEM: Closing Costly Visibility Gaps in Application Delivery Management

Julie Craig

Today’s complex computing environments make it difficult to achieve the visibility needed to effectively monitor end-to-end application delivery. For many modern applications, User Experience Monitoring (UEM) solutions are the only real way to measure application quality and responsiveness. As applications become more complex, diverse, and bandwidth intensive, UEM solutions become more essential.

New technologies — such as virtualization, integrations, containers, and microservices — are increasing application complexity and, as EMA’s latest UEM/APM research shows, forcing many IT organizations to rethink their tooling strategies. Organizations still attempting to manage application ecosystems with siloed tools are increasingly falling short. And even those that have invested heavily in enterprise management solutions often still lack the insights they need to adequately support and monitor hybrid cloud, API-centric transactions, carrier service levels, and end-to-end execution.

From the IT perspective, this complexity is driving up the costs associated with developing, operating, monitoring, and maintaining business applications.

From the business perspective, applications built over complex technologies can create production issues which are simply bad business. When performance and availability problems are not proactively addressed, they impact the productivity of internal users as well as the spending habits of external users and customers.

And despite the growing adoption of sophisticated application-focused toolsets, too many IT organizations still first hear about application-related issues primarily from the users themselves.

EMA research revealed that the #1 way IT organizations are most often notified of performance or availability issues is still via user calls, either directly to IT or to the help desk. It also uncovered many of the reasons why these issues not being detected before they begin to impact users.

One reason is that the process of troubleshooting and performing root-cause analysis is simply too time-intensive. The most commonly reported issue with application support is “excessive time spent troubleshooting”. More than 1/3 of IT practitioners say that “troubleshooting takes too long” in their organizations. Often, busy IT practitioners can’t take time out from support and project work to spend the hours necessary to diagnose and fix these problems — so the same problems keep recurring over time.

Another reason is lack of visibility to application ecosystems. More than 80% of respondents said their current tools lack visibility to at least one aspect of application monitoring. They also indicated that their tools did not adequately support collaboration, that they were silo-focused, and that they lacked adequate correlation analytics.

In short, the proliferation of modern applications has created a level of complexity that makes enterprise-grade, application-focused solutions essential to day-to-day application support. And UEM is increasingly key to mitigating the costs and challenges associated with supporting today’s application ecosystems.

As a matter of fact, 80% of respondents to the same survey ranked UEM capabilities as “critical” or “very important” to business and IT outcomes. And when respondents were asked which three application-related products they would purchase if given the chance, UEM solutions topped the “wish list”.

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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 ...

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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 ...

UEM: Closing Costly Visibility Gaps in Application Delivery Management

Julie Craig

Today’s complex computing environments make it difficult to achieve the visibility needed to effectively monitor end-to-end application delivery. For many modern applications, User Experience Monitoring (UEM) solutions are the only real way to measure application quality and responsiveness. As applications become more complex, diverse, and bandwidth intensive, UEM solutions become more essential.

New technologies — such as virtualization, integrations, containers, and microservices — are increasing application complexity and, as EMA’s latest UEM/APM research shows, forcing many IT organizations to rethink their tooling strategies. Organizations still attempting to manage application ecosystems with siloed tools are increasingly falling short. And even those that have invested heavily in enterprise management solutions often still lack the insights they need to adequately support and monitor hybrid cloud, API-centric transactions, carrier service levels, and end-to-end execution.

From the IT perspective, this complexity is driving up the costs associated with developing, operating, monitoring, and maintaining business applications.

From the business perspective, applications built over complex technologies can create production issues which are simply bad business. When performance and availability problems are not proactively addressed, they impact the productivity of internal users as well as the spending habits of external users and customers.

And despite the growing adoption of sophisticated application-focused toolsets, too many IT organizations still first hear about application-related issues primarily from the users themselves.

EMA research revealed that the #1 way IT organizations are most often notified of performance or availability issues is still via user calls, either directly to IT or to the help desk. It also uncovered many of the reasons why these issues not being detected before they begin to impact users.

One reason is that the process of troubleshooting and performing root-cause analysis is simply too time-intensive. The most commonly reported issue with application support is “excessive time spent troubleshooting”. More than 1/3 of IT practitioners say that “troubleshooting takes too long” in their organizations. Often, busy IT practitioners can’t take time out from support and project work to spend the hours necessary to diagnose and fix these problems — so the same problems keep recurring over time.

Another reason is lack of visibility to application ecosystems. More than 80% of respondents said their current tools lack visibility to at least one aspect of application monitoring. They also indicated that their tools did not adequately support collaboration, that they were silo-focused, and that they lacked adequate correlation analytics.

In short, the proliferation of modern applications has created a level of complexity that makes enterprise-grade, application-focused solutions essential to day-to-day application support. And UEM is increasingly key to mitigating the costs and challenges associated with supporting today’s application ecosystems.

As a matter of fact, 80% of respondents to the same survey ranked UEM capabilities as “critical” or “very important” to business and IT outcomes. And when respondents were asked which three application-related products they would purchase if given the chance, UEM solutions topped the “wish list”.

Hot Topics

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 ...