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What is BSM Anyway?

The Unique Relationship Between APM, Infrastructure and the End-User Experience

Defining BSM has always been a challenge. Many tool vendors have their own definitions of BSM, or use other terms such as ITSM, BTM or APM instead of BSM. At BSMdigest, we are not too strict about the semantics – we like to focus on the BSM concept, which includes all of these technologies.

In 2006 I wrote an article for BSMdigest – which I have included in this issue – stating that end-user monitoring was a key to BSM. It was easy to make that pronouncement, but the BSM technology did not live up to that expectation at the time. Today, I think there may be an answer: The new generation of Application Performance Monitoring (APM) solutions.

In the past, BSM has depended mostly on infrastructure monitoring. The new generation of APM tools not only look at performance from the inside, such as resource utilization, but also from the outside, from the end-user perspective.

“One of the biggest complaints the market has about BSM is that it does not reflect the end-user experience,” explains Berkay Mollamustafaoglu, Product Strategy Manager, Netuitive, Inc. “Everything may look fine according to your infrastructure monitoring tools, but the application could still be slow, the user could still be experiencing a performance problem.”

Agents on the user's desktop and transaction management are two effective components of APM that enable you to gain new visibility into the user's perspective and manage response times. Desktop agents are the most clear cut way to visualize the end-user experience but obviously it is not always possible to deploy an agent on every user's desktop. Business Transaction Management (BTM) provides reliable insight into the end-user experience, however, by monitoring the performance of all the components involved in completing a transaction.

“But once you have identified an issue with the user experience, you still have to find a way to determine the root cause and resolve the problem via infrastructure monitoring tools,” Berkay continues. “We are starting to see large enterprises using advanced self-learning analytics as a way to show context between dynamic virtual infrastructure performance and end-user experience data being generated by APM tools to deliver on the vision of Business Service Management.”

“In cloud and virtual environments it becomes even more important to have both infrastructure monitoring and end-user monitoring, to be able to do true capacity management,” he adds. “You have to optimize your resource allocation so that you can maintain fast response times. APM allows you to see the response times, and see where you can optimize the infrastructure to maintain acceptable response times, by adding servers, memory or CPU. You need these new tools to support new paradigms in the virtual world. Seeing the state of the business service is not the end goal.”

With the emergence of a new breed of APM tools and the migration to virtual and cloud environments, BSM is at a crossroads. This is probably a good time to officially evolve our definition of BSM to include APM, BTM, self-learning analytics and, most importantly, the end-user experience.

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 ...

What is BSM Anyway?

The Unique Relationship Between APM, Infrastructure and the End-User Experience

Defining BSM has always been a challenge. Many tool vendors have their own definitions of BSM, or use other terms such as ITSM, BTM or APM instead of BSM. At BSMdigest, we are not too strict about the semantics – we like to focus on the BSM concept, which includes all of these technologies.

In 2006 I wrote an article for BSMdigest – which I have included in this issue – stating that end-user monitoring was a key to BSM. It was easy to make that pronouncement, but the BSM technology did not live up to that expectation at the time. Today, I think there may be an answer: The new generation of Application Performance Monitoring (APM) solutions.

In the past, BSM has depended mostly on infrastructure monitoring. The new generation of APM tools not only look at performance from the inside, such as resource utilization, but also from the outside, from the end-user perspective.

“One of the biggest complaints the market has about BSM is that it does not reflect the end-user experience,” explains Berkay Mollamustafaoglu, Product Strategy Manager, Netuitive, Inc. “Everything may look fine according to your infrastructure monitoring tools, but the application could still be slow, the user could still be experiencing a performance problem.”

Agents on the user's desktop and transaction management are two effective components of APM that enable you to gain new visibility into the user's perspective and manage response times. Desktop agents are the most clear cut way to visualize the end-user experience but obviously it is not always possible to deploy an agent on every user's desktop. Business Transaction Management (BTM) provides reliable insight into the end-user experience, however, by monitoring the performance of all the components involved in completing a transaction.

“But once you have identified an issue with the user experience, you still have to find a way to determine the root cause and resolve the problem via infrastructure monitoring tools,” Berkay continues. “We are starting to see large enterprises using advanced self-learning analytics as a way to show context between dynamic virtual infrastructure performance and end-user experience data being generated by APM tools to deliver on the vision of Business Service Management.”

“In cloud and virtual environments it becomes even more important to have both infrastructure monitoring and end-user monitoring, to be able to do true capacity management,” he adds. “You have to optimize your resource allocation so that you can maintain fast response times. APM allows you to see the response times, and see where you can optimize the infrastructure to maintain acceptable response times, by adding servers, memory or CPU. You need these new tools to support new paradigms in the virtual world. Seeing the state of the business service is not the end goal.”

With the emergence of a new breed of APM tools and the migration to virtual and cloud environments, BSM is at a crossroads. This is probably a good time to officially evolve our definition of BSM to include APM, BTM, self-learning analytics and, most importantly, the end-user experience.

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 ...