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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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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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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 ...