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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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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

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 MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...