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APM and APM: When Two Acronyms Collide

Dennis Drogseth

According to most industry perceptions, application performance management (APM) and application portfolio management (APM) might seem to be worlds apart — or at best connected by a very thin thread. Much of this, admittedly, comes from application portfolio planning's roots in project and portfolio management, which lived in another realm and in my view in another era — when a cloistered development team got most of its go-ahead information from often equally cloistered business analysts. In other words, when the fertile dialog that's emerging between development, operations and ITSM teams was still in its infancy.

In this blog, I'd like to highlight three areas that are bridging the APM-to-APM divide: digital experience management, application discovery and dependency mapping (ADDM), and agile/DevOps lifecycle planning.

Digital Experience Management

In my view, probably the single most important lane in our 3-lane bridge connecting the two APMs is digitalor user experience management. Coincidentally, this is a technology area where I've witnessed another set of colliding acronyms — user experience management (UEM) and unified endpoint management(UEM), which also have at least a plank to unite them.

EMA's recent research revealed a striking connection between digital experience management and application portfolio planning right out of the gate. When asked, "Over the past three years, what has become more important for digital experience management?" application portfolio planningtied with application performance managementfor first place! If you're curious, agile, business development and customer management and cloudcame next.

Why was this just waiting to happen? Our data suggests that the answer lies in the fact that digital experience management embraces not only application performance, but also application outcomes and relevance. For instance, when we asked, "When you talk about digital experience management, what do you see bringing you the most value?" the answers in ranked order were:

1. Business impact

2. Performance

3. Change management

4. Design

5. Productivity

6. Usage

Of these, business impact, design, productivityand usageall directly inform business RELEVANCE and VALUE. In other words, if you wanted to plan your application portfolio meaningfully, wouldn't you want to capitalize on these insights which are, by the way, dynamic, real-time, and can be trended to correlate with business performance overall?

But COST was also a factor. In fact, given the pressures on IT for transparency in the "age of cloud" cost has become increasingly central to IT executive planning. When we asked about business metrics applied to digital experience management, the top five were:

1. Cost-related external SLAs with cloud and other service providers and partners

2. Business activity management impacts

3. Revenue-related impacts

4. Business process impacts

5. Service desk operational efficiencies

What you see is a sandwich — with two pieces of bread focused on cost (one and five) and the middle section (lettuce, cheese and ham?) squarely focused on value. All of these are relevant sources for meaningful application portfolio planning and management.

Application Discovery and Dependency Mapping

ADDM is really a bridge to many things. As you know, it can be central in understanding, prioritizing and resolving performance issues associated with application services by capturing application-to-infrastructure, as well as application-to-application, interdependencies. It is also an area of vast innovation in the industry, tied to multiple use cases with multiple product architectures and designs.

Two of the more prominent use cases for ADDM are change management and asset management. The latter is particularly relevant here because it connects business services with actual costs. Costs in terms of public cloud investments, on-premise hardware and software, and potentially even operational costs associated with everything from infrastructure management to software audits. In other words, ADDM can provide inestimable value in mapping the end products of IT (its application/business services) to all the associated costs surrounding the creation, delivery and support of those products.

Of course to do this, more than ADDM is required. More advanced investments in IT service management (ITSM), IT governance analytics, and more fluid approaches to IT asset management (ITAM) and software asset management (SAM) are needed to color in the picture. Best of all, though, once again, all this data is real (not just surmised), dynamic and current, and can be trended over time to capture historical insights into the real costs of managing an application business service.

Agile/DevOps Planning

On the one hand, linking application portfolio management to agile and DevOps should be a no-brainer. Pretty easy to figure that associated planning needs to be done before speedy execution. But I'm highlighting the connection here because the current focus on agile is all about speed, not about relevance. The truth is, as I like to say, you can "automate train wrecks." You can also, frankly, be "agile and dumb" –speedily doing enhancements that don't bring the most value at the cost to others that are far more relevant to business outcomes. So, I'd like to suggest a new brand for "agile" called "Informed Agile" — where APM truly meets APM.

In wrapping up, I'd like to add that I didn't mean these three lanes in the bridge between the two APMs to be complete or the last word. I'm sure there are other areas where APM meets APM, beyond these three. The very nature digital transformation, and the closely associated role of IT transformation, could add any number of layers, from SecOps requirements to advance IT analytics.

It seems to me that the time has already arrived for IT to look beyond traditional ways of working. The idea notion that business experts sit on one side of a wall, and IT professionals sit on the other now seems to belong to the past. That wall is crumbling, and the opportunity to have common conversation with common data points is finally emerging.

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 ...

APM and APM: When Two Acronyms Collide

Dennis Drogseth

According to most industry perceptions, application performance management (APM) and application portfolio management (APM) might seem to be worlds apart — or at best connected by a very thin thread. Much of this, admittedly, comes from application portfolio planning's roots in project and portfolio management, which lived in another realm and in my view in another era — when a cloistered development team got most of its go-ahead information from often equally cloistered business analysts. In other words, when the fertile dialog that's emerging between development, operations and ITSM teams was still in its infancy.

In this blog, I'd like to highlight three areas that are bridging the APM-to-APM divide: digital experience management, application discovery and dependency mapping (ADDM), and agile/DevOps lifecycle planning.

Digital Experience Management

In my view, probably the single most important lane in our 3-lane bridge connecting the two APMs is digitalor user experience management. Coincidentally, this is a technology area where I've witnessed another set of colliding acronyms — user experience management (UEM) and unified endpoint management(UEM), which also have at least a plank to unite them.

EMA's recent research revealed a striking connection between digital experience management and application portfolio planning right out of the gate. When asked, "Over the past three years, what has become more important for digital experience management?" application portfolio planningtied with application performance managementfor first place! If you're curious, agile, business development and customer management and cloudcame next.

Why was this just waiting to happen? Our data suggests that the answer lies in the fact that digital experience management embraces not only application performance, but also application outcomes and relevance. For instance, when we asked, "When you talk about digital experience management, what do you see bringing you the most value?" the answers in ranked order were:

1. Business impact

2. Performance

3. Change management

4. Design

5. Productivity

6. Usage

Of these, business impact, design, productivityand usageall directly inform business RELEVANCE and VALUE. In other words, if you wanted to plan your application portfolio meaningfully, wouldn't you want to capitalize on these insights which are, by the way, dynamic, real-time, and can be trended to correlate with business performance overall?

But COST was also a factor. In fact, given the pressures on IT for transparency in the "age of cloud" cost has become increasingly central to IT executive planning. When we asked about business metrics applied to digital experience management, the top five were:

1. Cost-related external SLAs with cloud and other service providers and partners

2. Business activity management impacts

3. Revenue-related impacts

4. Business process impacts

5. Service desk operational efficiencies

What you see is a sandwich — with two pieces of bread focused on cost (one and five) and the middle section (lettuce, cheese and ham?) squarely focused on value. All of these are relevant sources for meaningful application portfolio planning and management.

Application Discovery and Dependency Mapping

ADDM is really a bridge to many things. As you know, it can be central in understanding, prioritizing and resolving performance issues associated with application services by capturing application-to-infrastructure, as well as application-to-application, interdependencies. It is also an area of vast innovation in the industry, tied to multiple use cases with multiple product architectures and designs.

Two of the more prominent use cases for ADDM are change management and asset management. The latter is particularly relevant here because it connects business services with actual costs. Costs in terms of public cloud investments, on-premise hardware and software, and potentially even operational costs associated with everything from infrastructure management to software audits. In other words, ADDM can provide inestimable value in mapping the end products of IT (its application/business services) to all the associated costs surrounding the creation, delivery and support of those products.

Of course to do this, more than ADDM is required. More advanced investments in IT service management (ITSM), IT governance analytics, and more fluid approaches to IT asset management (ITAM) and software asset management (SAM) are needed to color in the picture. Best of all, though, once again, all this data is real (not just surmised), dynamic and current, and can be trended over time to capture historical insights into the real costs of managing an application business service.

Agile/DevOps Planning

On the one hand, linking application portfolio management to agile and DevOps should be a no-brainer. Pretty easy to figure that associated planning needs to be done before speedy execution. But I'm highlighting the connection here because the current focus on agile is all about speed, not about relevance. The truth is, as I like to say, you can "automate train wrecks." You can also, frankly, be "agile and dumb" –speedily doing enhancements that don't bring the most value at the cost to others that are far more relevant to business outcomes. So, I'd like to suggest a new brand for "agile" called "Informed Agile" — where APM truly meets APM.

In wrapping up, I'd like to add that I didn't mean these three lanes in the bridge between the two APMs to be complete or the last word. I'm sure there are other areas where APM meets APM, beyond these three. The very nature digital transformation, and the closely associated role of IT transformation, could add any number of layers, from SecOps requirements to advance IT analytics.

It seems to me that the time has already arrived for IT to look beyond traditional ways of working. The idea notion that business experts sit on one side of a wall, and IT professionals sit on the other now seems to belong to the past. That wall is crumbling, and the opportunity to have common conversation with common data points is finally emerging.

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 ...