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IT Professionals Experiencing Substantial Shift in Responsibilities

The roles and activities performed by IT professionals have evolved dramatically over the past ten years due to the convergence of modern development technologies, cloud platforms, and as-a-service offerings that can significantly improve overall productivity.

Today, many of these professionals find themselves in hybrid roles that combine traditional development activities with activities that formerly were associated with operations professionals who historically had few or no development-oriented responsibilities. A new International Data Corporation (IDC) report provides an extended census and forecast with detail for both traditional IT operations roles and these new hybrid roles.

"The census data shows that a dramatic, once-in-a-generation shift in the composition of the IT workforce is underway. This shift is akin to what took place during the years from 1997 to 2002 when the emergence of the commercial internet and the .com era turned priorities upside down for much of corporate IT and led to the hiring of vast numbers of web developers and networking experts," said Al Gillen, Group VP, Software Development and Open Source, IDC. "The increased adoption of cloud computing is driving similar transitions today in IT teams supporting this modern deployment model."

In developing this data set, IDC used the following definitions to describe the roles broken out in the study:

DataOps uses a combination of technologies and methods with a focus on quality for consistent and continuous delivery of data value, combining integrated and process-oriented perspectives on data with automation and methods analogous to agile software engineering.

DevOps uses collaborative, agile approaches paired with extensive automation development pipelines, testing, infrastructure configuration, provisioning, security controls, and life-cycle continuous integration (CI) for continuous development and continuous delivery (CD).

DevSecOps uses a methodology that asserts that security needs to be prioritized at the beginning of the DevOps delivery pipeline. It enables DevOps teams, collaborating with security, to act as key stakeholders in defining and implementing security policies.

ITOps uses technology and methods to provide routine, scheduled tasks and unscheduled support activities related to IT systems. ITOps professionals may spend as much as 50% of their time engaged with business users in support, the elicitation of requirements, and performing contingent or secondary business tasks.

MLOps uses technology and processes to streamline and automate the entire machine learning (ML) life cycle. The key capabilities include managing and automating ML data and pipelines, ML code, and ML models from data ingestion to model deployment, tracking, and monitoring. MLOps uses similar principles to DevOps practices, applied to machine learning processes.

Platform engineering is a discipline of designing and building toolchains and workflows that enable self-service capabilities focused on managing and optimizing the software delivery process to deploy applications and services to cloud platforms.

Site reliability engineering (SRE) includes software engineers who build scripts to automate IT operations tasks such as maintenance and support. To enable efficiency and reliability, SRE teams fix operational bugs and remove manual work in rote tasks.

Systems administrators configure, maintain, and support computer systems and systems of systems using a variety of tools and methods appropriate to the system or systems of systems in use. They may spend as much as 50% of their time engaged with business users in defining key requirements, business goals, and adaptations needed to maintain fit for use and fit for purpose.

At a macro level, the study shows that a substantial shift in the responsibilities of IT professionals will occur over the next five years. The data indicates that IT professionals in the most purely operational roles are facing a transition to a more technical or focused role that very often may involve some level of software development work. Accordingly, the roles of IT operations and system administrators, respectively, are projected to decline at compound annual growth rates (CAGR) of -8.2% and -7.8% over the 2022–2027 forecast period. By comparison, the recently emerging roles of DataOps and MLOps are projected to have CAGRs of 17.9% and 20.1% respectively, although the growth is starting from comparatively small numbers.

DevOps and DevSecOps roles are also forecast to continue growing with DevSecOps roles showing a double-digit CAGR over the forecast period. DevSecOps roles will benefit from the growing application threat landscape and the dependence that organizations have on their software capabilities to be competitive, combined with the recognition that incorporating security as early as possible in the software development life cycle reduces costs and increases quality. DevOps growth will be muted somewhat by the growth in platform engineering roles, which will absorb some of these same functions.

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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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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 ...

IT Professionals Experiencing Substantial Shift in Responsibilities

The roles and activities performed by IT professionals have evolved dramatically over the past ten years due to the convergence of modern development technologies, cloud platforms, and as-a-service offerings that can significantly improve overall productivity.

Today, many of these professionals find themselves in hybrid roles that combine traditional development activities with activities that formerly were associated with operations professionals who historically had few or no development-oriented responsibilities. A new International Data Corporation (IDC) report provides an extended census and forecast with detail for both traditional IT operations roles and these new hybrid roles.

"The census data shows that a dramatic, once-in-a-generation shift in the composition of the IT workforce is underway. This shift is akin to what took place during the years from 1997 to 2002 when the emergence of the commercial internet and the .com era turned priorities upside down for much of corporate IT and led to the hiring of vast numbers of web developers and networking experts," said Al Gillen, Group VP, Software Development and Open Source, IDC. "The increased adoption of cloud computing is driving similar transitions today in IT teams supporting this modern deployment model."

In developing this data set, IDC used the following definitions to describe the roles broken out in the study:

DataOps uses a combination of technologies and methods with a focus on quality for consistent and continuous delivery of data value, combining integrated and process-oriented perspectives on data with automation and methods analogous to agile software engineering.

DevOps uses collaborative, agile approaches paired with extensive automation development pipelines, testing, infrastructure configuration, provisioning, security controls, and life-cycle continuous integration (CI) for continuous development and continuous delivery (CD).

DevSecOps uses a methodology that asserts that security needs to be prioritized at the beginning of the DevOps delivery pipeline. It enables DevOps teams, collaborating with security, to act as key stakeholders in defining and implementing security policies.

ITOps uses technology and methods to provide routine, scheduled tasks and unscheduled support activities related to IT systems. ITOps professionals may spend as much as 50% of their time engaged with business users in support, the elicitation of requirements, and performing contingent or secondary business tasks.

MLOps uses technology and processes to streamline and automate the entire machine learning (ML) life cycle. The key capabilities include managing and automating ML data and pipelines, ML code, and ML models from data ingestion to model deployment, tracking, and monitoring. MLOps uses similar principles to DevOps practices, applied to machine learning processes.

Platform engineering is a discipline of designing and building toolchains and workflows that enable self-service capabilities focused on managing and optimizing the software delivery process to deploy applications and services to cloud platforms.

Site reliability engineering (SRE) includes software engineers who build scripts to automate IT operations tasks such as maintenance and support. To enable efficiency and reliability, SRE teams fix operational bugs and remove manual work in rote tasks.

Systems administrators configure, maintain, and support computer systems and systems of systems using a variety of tools and methods appropriate to the system or systems of systems in use. They may spend as much as 50% of their time engaged with business users in defining key requirements, business goals, and adaptations needed to maintain fit for use and fit for purpose.

At a macro level, the study shows that a substantial shift in the responsibilities of IT professionals will occur over the next five years. The data indicates that IT professionals in the most purely operational roles are facing a transition to a more technical or focused role that very often may involve some level of software development work. Accordingly, the roles of IT operations and system administrators, respectively, are projected to decline at compound annual growth rates (CAGR) of -8.2% and -7.8% over the 2022–2027 forecast period. By comparison, the recently emerging roles of DataOps and MLOps are projected to have CAGRs of 17.9% and 20.1% respectively, although the growth is starting from comparatively small numbers.

DevOps and DevSecOps roles are also forecast to continue growing with DevSecOps roles showing a double-digit CAGR over the forecast period. DevSecOps roles will benefit from the growing application threat landscape and the dependence that organizations have on their software capabilities to be competitive, combined with the recognition that incorporating security as early as possible in the software development life cycle reduces costs and increases quality. DevOps growth will be muted somewhat by the growth in platform engineering roles, which will absorb some of these same functions.

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...