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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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One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

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The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

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APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

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

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...