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ServiceOps: ITSM and ITOps Move from Cooperation to Collaboration

Valerie O'Connell
EMA

Nothing drives IT change like … change. There has been plenty of that to go around in the past few years. Planned digital transformation initiatives turbocharged into accelerated implementation as employees working from anywhere raised the stakes of day-to-day IT operations to business-critical levels.

Complexity, criticality, and the velocity/volume of change transformed AI/ML and automation from pilots into survival essentials. In response, enterprises increasingly turned to platforms for AI-enabled end-end visibility, workflows, and action.

It stands to reason that all of these changes would drive advances in how the core functions of IT service and IT operations work together. EMA undertook a deep dive research project with 400+ global IT leaders to understand the practical realities of IT ServiceOps today and in the near future.

Spoiler alert: Part technology and organizational approach, ServiceOps by any name will become the prevailing IT operational model because it is practical and makes good business sense.

Staffed by very different talent profiles aimed at distinct spheres of responsibility, the two groups traditionally interacted only when absolutely required by circumstances such as outages and changes required by DevOps. Today, the notion of ServiceOps represents the growing fact that in a healthy enterprise, it is increasingly difficult to say where one function ends and another begins. It's all about IT service to the business, and there is no service without effective IT operations.

Execution and service are inextricable.

It turns out that organizational siloes can be just as deadening as siloed toolsets and systems. The combination of AI and automation can mitigate both. Automation, AI/ML/analytics, and platforms that welcome cross-functional workflows make cooperation a practical reality. The research panel covered a lot of ground when asked.

How Do IT Operations and ITSM Collaborate Using AI/ML and Automation?

In this converged reality, both ITOps and ITSM take advantage of mutually beneficial solutions that are aimed at and measured by business goals. The long-heralded IT/business alignment is a natural byproduct of cross-functional capabilities, as well as a prerequisite to effective IT automation.

ITSM and ITOps remain distinct functions with specific charters. However, shared technology softens the boundaries and moves them closer organizationally. The research showed very strong correlation between the degree to which IT service and operations collaborate using AI-enabled automation and self-reported quality of IT service, end-user experience, business innovation, and increased IT budget.

ServiceOps, by whatever name, will soon be the prevailing IT operational model. It is the logical product of common sense and technology combined for practical purposes. Both IT service and IT operations have to be at the top of their respective games. Hitting that mark calls for platform-enabled, AI-assisted automation that flexibly connects people and machines across the enterprise.

Digital transformation, business innovation, and a world filled with surprises guarantee a constant state of change in IT. With a heavy assist from technology, the ServiceOps model positions IT to be organizationally as responsive and agile as the business demands.

Valerie O'Connell is EMA Research Director of Digital Service Execution

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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 ...

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.

ServiceOps: ITSM and ITOps Move from Cooperation to Collaboration

Valerie O'Connell
EMA

Nothing drives IT change like … change. There has been plenty of that to go around in the past few years. Planned digital transformation initiatives turbocharged into accelerated implementation as employees working from anywhere raised the stakes of day-to-day IT operations to business-critical levels.

Complexity, criticality, and the velocity/volume of change transformed AI/ML and automation from pilots into survival essentials. In response, enterprises increasingly turned to platforms for AI-enabled end-end visibility, workflows, and action.

It stands to reason that all of these changes would drive advances in how the core functions of IT service and IT operations work together. EMA undertook a deep dive research project with 400+ global IT leaders to understand the practical realities of IT ServiceOps today and in the near future.

Spoiler alert: Part technology and organizational approach, ServiceOps by any name will become the prevailing IT operational model because it is practical and makes good business sense.

Staffed by very different talent profiles aimed at distinct spheres of responsibility, the two groups traditionally interacted only when absolutely required by circumstances such as outages and changes required by DevOps. Today, the notion of ServiceOps represents the growing fact that in a healthy enterprise, it is increasingly difficult to say where one function ends and another begins. It's all about IT service to the business, and there is no service without effective IT operations.

Execution and service are inextricable.

It turns out that organizational siloes can be just as deadening as siloed toolsets and systems. The combination of AI and automation can mitigate both. Automation, AI/ML/analytics, and platforms that welcome cross-functional workflows make cooperation a practical reality. The research panel covered a lot of ground when asked.

How Do IT Operations and ITSM Collaborate Using AI/ML and Automation?

In this converged reality, both ITOps and ITSM take advantage of mutually beneficial solutions that are aimed at and measured by business goals. The long-heralded IT/business alignment is a natural byproduct of cross-functional capabilities, as well as a prerequisite to effective IT automation.

ITSM and ITOps remain distinct functions with specific charters. However, shared technology softens the boundaries and moves them closer organizationally. The research showed very strong correlation between the degree to which IT service and operations collaborate using AI-enabled automation and self-reported quality of IT service, end-user experience, business innovation, and increased IT budget.

ServiceOps, by whatever name, will soon be the prevailing IT operational model. It is the logical product of common sense and technology combined for practical purposes. Both IT service and IT operations have to be at the top of their respective games. Hitting that mark calls for platform-enabled, AI-assisted automation that flexibly connects people and machines across the enterprise.

Digital transformation, business innovation, and a world filled with surprises guarantee a constant state of change in IT. With a heavy assist from technology, the ServiceOps model positions IT to be organizationally as responsive and agile as the business demands.

Valerie O'Connell is EMA Research Director of Digital Service Execution

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.