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Putting the "User" into User Experience Management

Dennis Drogseth

In the course of researching, documenting and advising on user experience management needs and directions for more than a decade, I've found myself waging a quiet (and sometimes not so quiet) war with several industry assumptions. Chief among these is the notion that user experience management (UEM) is purely a subset of application performance management (APM). This APM-centricity misses some of UEM's most critical value points, and in a basic sense fails to recognize what UEM is truly about.

What research over the course of ten years has consistently shown is that UEM is at core a two-way mirror. One side of the mirror indeed looks back at the application and its performance in terms of transactional latencies as the end user experiences them. But the other side of the mirror looks out at the end user/consumer with an eye to productivity, application usability, business impact, usage, value and relevance.

The 6 Dimensions of UEM

The next step in looking at UEM from an end-user perspective is to consider the use-case values documented in our research. EMA has validated six UEM use-case values, of which application performance is only one. These are listed based on prevalence — what people are actually doing. (When ranked by importance, the order stayed the same except that business impact outranked application performance.)

Application performance: This is the most tilted toward pureplay APM, and yet it, too, will gain from insights into user/consumer interactions relevant to completing critical business processes or other transactions.

Business impact: Here understanding the user is front and center — as most business outcomes are ultimately generated (and measurable) via end-user interactions, whether in terms of business process efficiencies, or customer/consumer outcomes.

Change management: Changes made to applications are often viewed in purely technical terms by IT. But in reality, any new application release may cause issues that transcend server or network performance. Validating change without understanding end-user impact is a story only partly told.

Design: Here end-user interaction is key, and far too often neglected. What looks good to developers in the back office may not work in real-world situations. UEM can document behaviors that not only suggest latency issues, but problems with application look and feel.

User productivity: This is all about the end-user interacting with the application. Some relevant productivity metrics ranked by their prevalence in real-world deployments are as follows:
- User productivity (number of processes executed)
- User effectiveness (success versus failure ratio)
- User efficiency (number of steps per process)
- User identification (time zone, location, computer name, IP)
- User attributes (geography, department, role)
- User proficiency (number of errors)

With this information, an entire portrait of user/consumer behavior can be mapped, understood and optimized, whether through changes in the application, improved app delivery, or actual training.

Service usage: Once again this is all about how, where, and potentially even why the end-user interacts with applications.

In the Last 3 Years …

Speaking of service usage, it isn't so surprising that in EMA's most recent research, the two areas that have become most important to UEM over the past three years are application performance and portfolio planning and optimization, which were tied for first place.

Putting the user into user experience management can provide insights into:

■ What's being used and what's not?

■ And to what effect?

■ What are the business outcomes?

■ What business processes are enhanced? Which are slowed?

■ And at what cost to business performance?

When it comes to truly optimizing application investments, all this data is critical both to IT and business stakeholders.

Business and IT Alignment Anyone?

More than anything else, applications are the "products" of IT — the core services through which businesses evolve and perform. Digital transformation wouldn't be "digital" without applications of some kind. And any smart product creator/deliverer will seriously invest in understanding how the consumers behave when engaging with their products, whether it's about toys, cars, cameras or — to take a modest leap in context — SAP.

When we asked if user experience management was an IT concern, a business concern, or a joint IT-to-business concern, the responses were consistent with years past:

■ 20% felt that UEM is primarily a business concern

■ 21% felt that UEM is primarily an IT concern

59% recognized the truth: UEM is (or should be) equally an IT and a business concern

There's Always More

Putting the user into UEM has many additional values. For instance, in creating a true common ground between development and operations for DevOps, where not only performance, but usability and design can be understood in both pre- and post-production environments. With the right linkages, understanding end-user behaviors will also bring value in industry compliance, business process assessments, and, needless to say, to training needs for complex business-critical ERP and other applications. It can also help to unify operations and service desk insights into a truly integrated strategy for UEM across all of IT.

The list continues, but hopefully I've made my point. And I welcome yours. Feel free to reach out to me at drogseth@emausa.com

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 ...

Putting the "User" into User Experience Management

Dennis Drogseth

In the course of researching, documenting and advising on user experience management needs and directions for more than a decade, I've found myself waging a quiet (and sometimes not so quiet) war with several industry assumptions. Chief among these is the notion that user experience management (UEM) is purely a subset of application performance management (APM). This APM-centricity misses some of UEM's most critical value points, and in a basic sense fails to recognize what UEM is truly about.

What research over the course of ten years has consistently shown is that UEM is at core a two-way mirror. One side of the mirror indeed looks back at the application and its performance in terms of transactional latencies as the end user experiences them. But the other side of the mirror looks out at the end user/consumer with an eye to productivity, application usability, business impact, usage, value and relevance.

The 6 Dimensions of UEM

The next step in looking at UEM from an end-user perspective is to consider the use-case values documented in our research. EMA has validated six UEM use-case values, of which application performance is only one. These are listed based on prevalence — what people are actually doing. (When ranked by importance, the order stayed the same except that business impact outranked application performance.)

Application performance: This is the most tilted toward pureplay APM, and yet it, too, will gain from insights into user/consumer interactions relevant to completing critical business processes or other transactions.

Business impact: Here understanding the user is front and center — as most business outcomes are ultimately generated (and measurable) via end-user interactions, whether in terms of business process efficiencies, or customer/consumer outcomes.

Change management: Changes made to applications are often viewed in purely technical terms by IT. But in reality, any new application release may cause issues that transcend server or network performance. Validating change without understanding end-user impact is a story only partly told.

Design: Here end-user interaction is key, and far too often neglected. What looks good to developers in the back office may not work in real-world situations. UEM can document behaviors that not only suggest latency issues, but problems with application look and feel.

User productivity: This is all about the end-user interacting with the application. Some relevant productivity metrics ranked by their prevalence in real-world deployments are as follows:
- User productivity (number of processes executed)
- User effectiveness (success versus failure ratio)
- User efficiency (number of steps per process)
- User identification (time zone, location, computer name, IP)
- User attributes (geography, department, role)
- User proficiency (number of errors)

With this information, an entire portrait of user/consumer behavior can be mapped, understood and optimized, whether through changes in the application, improved app delivery, or actual training.

Service usage: Once again this is all about how, where, and potentially even why the end-user interacts with applications.

In the Last 3 Years …

Speaking of service usage, it isn't so surprising that in EMA's most recent research, the two areas that have become most important to UEM over the past three years are application performance and portfolio planning and optimization, which were tied for first place.

Putting the user into user experience management can provide insights into:

■ What's being used and what's not?

■ And to what effect?

■ What are the business outcomes?

■ What business processes are enhanced? Which are slowed?

■ And at what cost to business performance?

When it comes to truly optimizing application investments, all this data is critical both to IT and business stakeholders.

Business and IT Alignment Anyone?

More than anything else, applications are the "products" of IT — the core services through which businesses evolve and perform. Digital transformation wouldn't be "digital" without applications of some kind. And any smart product creator/deliverer will seriously invest in understanding how the consumers behave when engaging with their products, whether it's about toys, cars, cameras or — to take a modest leap in context — SAP.

When we asked if user experience management was an IT concern, a business concern, or a joint IT-to-business concern, the responses were consistent with years past:

■ 20% felt that UEM is primarily a business concern

■ 21% felt that UEM is primarily an IT concern

59% recognized the truth: UEM is (or should be) equally an IT and a business concern

There's Always More

Putting the user into UEM has many additional values. For instance, in creating a true common ground between development and operations for DevOps, where not only performance, but usability and design can be understood in both pre- and post-production environments. With the right linkages, understanding end-user behaviors will also bring value in industry compliance, business process assessments, and, needless to say, to training needs for complex business-critical ERP and other applications. It can also help to unify operations and service desk insights into a truly integrated strategy for UEM across all of IT.

The list continues, but hopefully I've made my point. And I welcome yours. Feel free to reach out to me at drogseth@emausa.com

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 ...