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IT Service Management: Hub or Spoke in the Anxious World of IT?

Dennis Drogseth

If you're a CIO, VP, director, manager or even a hands-on support professional who cares about the future, you can hear the seemingly contradictory voices in the news.

On the one hand: "You're empowered … You're transformative … It's the digital age and your products are our future."

On the other hand: "You've got to cut costs … How can you justify your operational overhead? … IT is too slow … We have other options, you know."

Based on ongoing data and research, IT leadership is more driven to be innovative than ever, but also more in need of justifying costs and showing value than ever. Combining the two is no mean feat, especially when individual technologies are put forward as the single tantalizing answer.

"It's all about microservices." Or at least, "It's all about cloud." Or maybe "It's all about mobile." Or else speed is held up as sacrosanct: "It's all about agile."

The truth is it's all about all of these things and a great deal more. The "anxious world of IT" is as much about cultural change as it is about new technologies. And that, of course, in a techno-centric mindset, doesn't tend to make things any better.

So where should IT leaders turn to bring the two together?

Culture + technology? One option is to spend a great deal of money on outside consultants, who often focus in a specialized area — e.g. process, or organization, but not both. And this can lead to a lot of circular spinning without real progress, especially since neither group is centered in technology adoption.

But maybe the answer doesn't lie altogether outside the IT organization.

Within IT, there may be a few surprising answers.

And believe it or not, based on many dialogs, hard data, and, admittedly some level of intuition, the "hub" within IT may sometimes be the IT service management (ITSM) team — either in itself, or as a key part of an overlay team brought together to combine operations, ITSM and development.

The irony here, is that the role of ITSM teams, is often seen as "mature" at best, "passé" at worst. And yet progressive ITSM is far more likely to be at the hub of IT transformation than relegated to being a delinquent spoke in going forward.

Here are a few reasons why:

Progressive ITSM

First of all, note the phrase "progressive ITSM." I have written about progressive ITSM, or "Next-Generation ITSM" extensively for APMdigest, but the core attributes:

■ Integrated operations, including ideally integrated analytics and automation.

■ Support for DevOps with insight into service value, project management, costs and usage, as well as integrated support for change management.

■ A more dynamic approach to incident, problem and availability management.

■ Support for IT governance — including documenting operational efficiencies throughout IT.

■ Support for integrated IT asset management, including optimizing IT asset for cost and value. In the best of cases, this extends to public and private cloud resources.

■ Embracing mobile as both a resource to be managed and a resource to further ITSM and operations efficiencies.

Becoming Service-aware

Effective ITSM teams have a history of helping IT become more service-aware. That may sound old-fashioned to some of you, so let's parse it out. ITSM teams can help operations cut through siloed walls with common processes, practices, metrics and objectives. My favorite quote from 2016 was an interview with an ITSM deployment where the CIO described his organization's progress of going from "goat rodeo" to the equivalent of good and efficient. While this can't happen if it's done only within ITSM — it needs executive support, ITSM is well positioned to be the "hub" of best practices within and across IT.

Operational efficiencies

In parallel, I'd like to highlight the focus on operational efficiencies, a requirement that screams out at me in virtually all my research — whether it's Optimizing IT for Financial Performance or User, Customer and Digital Experience: Where Service and Business Performance Come Together, (the two most recent projects from late 2016) just as examples. Based on data from both research projects, operational efficiency is among the most sought-after benefits, while at the same time being one of the most onerous challenges for IT executives and their organizations. And "next-generation ITSM" is well positioned to become a hub in coordinating and optimizing operational efficiencies, not only for the service desk, but for IT as a whole.

A system of integrations

Another attribute of the next-generation ITSM hub is that it is becoming a system of integrations. While ITSM in itself usually isn't a manager of managers, it can provide a core foundation for data exchange, process workflows and process automation, and communication (including social IT). As such ITSM can be an integral part of uniting the IT mosaic for consistency and efficiency, and a key contributor to IT-to-IT and IT-to-business communication on service status, costs, and relevance.

This is what I've seen based on the data and wide-ranging conversations I've had so far. But, like many analysts, I continue to seek validation with fresh data and fresh inquiry, with deeper dives into what's currently happening in all of these areas, as well as others, like the broader impacts of cloud, digital transformation, and growing pressures for integrated support for security and fraud detection. With this in mind, EMA is planning some new research for Q2: Next-Generation IT Service Management: How Real Is It Today, and Where Is It Going in the Future? When the data's in later in April, we should have current fresh perspectives on all of these trends and more.

In the meantime, I welcome your thoughts and comments. Click here to email me.

Dennis Drogseth is VP at Enterprise Management Associates (EMA).

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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.

IT Service Management: Hub or Spoke in the Anxious World of IT?

Dennis Drogseth

If you're a CIO, VP, director, manager or even a hands-on support professional who cares about the future, you can hear the seemingly contradictory voices in the news.

On the one hand: "You're empowered … You're transformative … It's the digital age and your products are our future."

On the other hand: "You've got to cut costs … How can you justify your operational overhead? … IT is too slow … We have other options, you know."

Based on ongoing data and research, IT leadership is more driven to be innovative than ever, but also more in need of justifying costs and showing value than ever. Combining the two is no mean feat, especially when individual technologies are put forward as the single tantalizing answer.

"It's all about microservices." Or at least, "It's all about cloud." Or maybe "It's all about mobile." Or else speed is held up as sacrosanct: "It's all about agile."

The truth is it's all about all of these things and a great deal more. The "anxious world of IT" is as much about cultural change as it is about new technologies. And that, of course, in a techno-centric mindset, doesn't tend to make things any better.

So where should IT leaders turn to bring the two together?

Culture + technology? One option is to spend a great deal of money on outside consultants, who often focus in a specialized area — e.g. process, or organization, but not both. And this can lead to a lot of circular spinning without real progress, especially since neither group is centered in technology adoption.

But maybe the answer doesn't lie altogether outside the IT organization.

Within IT, there may be a few surprising answers.

And believe it or not, based on many dialogs, hard data, and, admittedly some level of intuition, the "hub" within IT may sometimes be the IT service management (ITSM) team — either in itself, or as a key part of an overlay team brought together to combine operations, ITSM and development.

The irony here, is that the role of ITSM teams, is often seen as "mature" at best, "passé" at worst. And yet progressive ITSM is far more likely to be at the hub of IT transformation than relegated to being a delinquent spoke in going forward.

Here are a few reasons why:

Progressive ITSM

First of all, note the phrase "progressive ITSM." I have written about progressive ITSM, or "Next-Generation ITSM" extensively for APMdigest, but the core attributes:

■ Integrated operations, including ideally integrated analytics and automation.

■ Support for DevOps with insight into service value, project management, costs and usage, as well as integrated support for change management.

■ A more dynamic approach to incident, problem and availability management.

■ Support for IT governance — including documenting operational efficiencies throughout IT.

■ Support for integrated IT asset management, including optimizing IT asset for cost and value. In the best of cases, this extends to public and private cloud resources.

■ Embracing mobile as both a resource to be managed and a resource to further ITSM and operations efficiencies.

Becoming Service-aware

Effective ITSM teams have a history of helping IT become more service-aware. That may sound old-fashioned to some of you, so let's parse it out. ITSM teams can help operations cut through siloed walls with common processes, practices, metrics and objectives. My favorite quote from 2016 was an interview with an ITSM deployment where the CIO described his organization's progress of going from "goat rodeo" to the equivalent of good and efficient. While this can't happen if it's done only within ITSM — it needs executive support, ITSM is well positioned to be the "hub" of best practices within and across IT.

Operational efficiencies

In parallel, I'd like to highlight the focus on operational efficiencies, a requirement that screams out at me in virtually all my research — whether it's Optimizing IT for Financial Performance or User, Customer and Digital Experience: Where Service and Business Performance Come Together, (the two most recent projects from late 2016) just as examples. Based on data from both research projects, operational efficiency is among the most sought-after benefits, while at the same time being one of the most onerous challenges for IT executives and their organizations. And "next-generation ITSM" is well positioned to become a hub in coordinating and optimizing operational efficiencies, not only for the service desk, but for IT as a whole.

A system of integrations

Another attribute of the next-generation ITSM hub is that it is becoming a system of integrations. While ITSM in itself usually isn't a manager of managers, it can provide a core foundation for data exchange, process workflows and process automation, and communication (including social IT). As such ITSM can be an integral part of uniting the IT mosaic for consistency and efficiency, and a key contributor to IT-to-IT and IT-to-business communication on service status, costs, and relevance.

This is what I've seen based on the data and wide-ranging conversations I've had so far. But, like many analysts, I continue to seek validation with fresh data and fresh inquiry, with deeper dives into what's currently happening in all of these areas, as well as others, like the broader impacts of cloud, digital transformation, and growing pressures for integrated support for security and fraud detection. With this in mind, EMA is planning some new research for Q2: Next-Generation IT Service Management: How Real Is It Today, and Where Is It Going in the Future? When the data's in later in April, we should have current fresh perspectives on all of these trends and more.

In the meantime, I welcome your thoughts and comments. Click here to email me.

Dennis Drogseth is VP at Enterprise Management Associates (EMA).

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.