Skip to main content

Gartner: Top Trends Impacting Infrastructure and Operations for 2021

Gartner, Inc. highlighted the six trends that infrastructure and operations (I&O) leaders must start preparing for in the next 12-18 months.

"The coronavirus pandemic has forced IT executives to adapt their operations to address increased work-from-home scenarios and unpredictable changes to IT requirements," said Jeffrey Hewitt, Research VP at Gartner. "Yet, COVID-19 isn't the only impetus for the majority of I&O staff to work from home moving forward. The nature of infrastructure is evolving to the point where remote I&O teams make sense to support new scenarios, use cases and technologies."

Hewitt identified the top emerging trends that are impacting I&O and provided recommendations to best respond to them to achieve optimal results in a post-pandemic environment:

Trend No. 1: Anywhere Operations

Gartner expects that 48% of employees will work from home, even after the pandemic, compared with 30% pre-pandemic. This shift will force IT executives to develop flexible and resilient organizations that enable staff to work from anywhere, allow customers everywhere to access services, and manage the deployment of business services across distributed infrastructures.

"The traditional, structured processes within I&O made organizations fragile when it comes to the flexibility of location," said Hewitt. "Anywhere operations enable organizations to decentralize staff and activate operations where it makes business sense. It even makes way for broader talent choices as organizations do not need to necessarily recruit staff in a specific geography."

Trend No. 2: Optimal Infrastructure

"The key for anywhere operations is developing programmable infrastructure that enables the right work in the right place at the right time – this is the crux of optimal infrastructure," said Hewitt. "As infrastructure and operations evolves into integration and operations, various solutions such as hyperconverged infrastructure or computational storage must be matched with the optimal use case."

Optimal infrastructure will also involve data center and edge infrastructure, which can be difficult to measure and lead to complex deployments. Hewitt recommended organizations take a business viewpoint and look at both optimizing costs and tools to build their case for a given infrastructure deployment.

Trend No. 3: Operational Continuity

Increasingly, workloads will need to support geographically dispersed customers and employees. As a result, IT services must be continuous, regardless of external factors, providing automated deployments and minimal-touch maintenance. By 2025, 60% of organizations will use automation tools to deploy new compute resources, reduce deployment time and deliver greater agility.

"When done correctly, this trend increases efficiencies and allows for faster workload deployment. The main downside is the learning curve that comes with using new and sometimes complex tools or processes that support continuity," said Hewitt.

Trend No. 4: Core Modernization

In order to ensure enterprise infrastructure evolves in lockstep, maintaining core operations should be viewed as an ongoing process, not a one-time project. Enterprises will need to coordinate infrastructures on- and off-premises that minimize legacy drag.

"The upside of modernizing infrastructure is that it lowers technical debt and paves the way for agile infrastructure to respond to the growing list of digital business requirements," said Hewitt. "Enterprises must implement a modernization plan with a realistic timeline, one which accounts for shifting skill requirements."

Trend No. 5: Distributed Cloud

Another major trend is distributing cloud resources so that the cloud becomes decentralized and the burden of support shifts to cloud service providers. This approach will enable flexible location and result in latency reduction.

"Since the distributed cloud market is currently immature, costs can be high and deployment models complex. Organizations should still have it on their horizon as a part of the future of cloud computing, since most cloud service platforms will provide at least some distributed cloud services that execute at the point of need over the next four years," said Hewitt.

Trend No. 6: Critical Skills Versus Critical Roles

"I&O skills requirements will continue to evolve as organizations adapt to new business environments," said Hewitt. "Specifically, there is a shift in focus from infrastructure roles toward collective, critical skills. This challenges the traditional ‘territorial' thinking of belonging to a specific infrastructure team and instead encourages collaboration."

By 2022, I&O leaders can expect to plan for at least 12 high-priority skills in their organizations. While hiring for these skills now while the IT talent market remains a buyer's market is recommended, Gartner said I&O leaders should consider the fundamental culture changes this trend will bring and to plan accordingly.

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 ...

Gartner: Top Trends Impacting Infrastructure and Operations for 2021

Gartner, Inc. highlighted the six trends that infrastructure and operations (I&O) leaders must start preparing for in the next 12-18 months.

"The coronavirus pandemic has forced IT executives to adapt their operations to address increased work-from-home scenarios and unpredictable changes to IT requirements," said Jeffrey Hewitt, Research VP at Gartner. "Yet, COVID-19 isn't the only impetus for the majority of I&O staff to work from home moving forward. The nature of infrastructure is evolving to the point where remote I&O teams make sense to support new scenarios, use cases and technologies."

Hewitt identified the top emerging trends that are impacting I&O and provided recommendations to best respond to them to achieve optimal results in a post-pandemic environment:

Trend No. 1: Anywhere Operations

Gartner expects that 48% of employees will work from home, even after the pandemic, compared with 30% pre-pandemic. This shift will force IT executives to develop flexible and resilient organizations that enable staff to work from anywhere, allow customers everywhere to access services, and manage the deployment of business services across distributed infrastructures.

"The traditional, structured processes within I&O made organizations fragile when it comes to the flexibility of location," said Hewitt. "Anywhere operations enable organizations to decentralize staff and activate operations where it makes business sense. It even makes way for broader talent choices as organizations do not need to necessarily recruit staff in a specific geography."

Trend No. 2: Optimal Infrastructure

"The key for anywhere operations is developing programmable infrastructure that enables the right work in the right place at the right time – this is the crux of optimal infrastructure," said Hewitt. "As infrastructure and operations evolves into integration and operations, various solutions such as hyperconverged infrastructure or computational storage must be matched with the optimal use case."

Optimal infrastructure will also involve data center and edge infrastructure, which can be difficult to measure and lead to complex deployments. Hewitt recommended organizations take a business viewpoint and look at both optimizing costs and tools to build their case for a given infrastructure deployment.

Trend No. 3: Operational Continuity

Increasingly, workloads will need to support geographically dispersed customers and employees. As a result, IT services must be continuous, regardless of external factors, providing automated deployments and minimal-touch maintenance. By 2025, 60% of organizations will use automation tools to deploy new compute resources, reduce deployment time and deliver greater agility.

"When done correctly, this trend increases efficiencies and allows for faster workload deployment. The main downside is the learning curve that comes with using new and sometimes complex tools or processes that support continuity," said Hewitt.

Trend No. 4: Core Modernization

In order to ensure enterprise infrastructure evolves in lockstep, maintaining core operations should be viewed as an ongoing process, not a one-time project. Enterprises will need to coordinate infrastructures on- and off-premises that minimize legacy drag.

"The upside of modernizing infrastructure is that it lowers technical debt and paves the way for agile infrastructure to respond to the growing list of digital business requirements," said Hewitt. "Enterprises must implement a modernization plan with a realistic timeline, one which accounts for shifting skill requirements."

Trend No. 5: Distributed Cloud

Another major trend is distributing cloud resources so that the cloud becomes decentralized and the burden of support shifts to cloud service providers. This approach will enable flexible location and result in latency reduction.

"Since the distributed cloud market is currently immature, costs can be high and deployment models complex. Organizations should still have it on their horizon as a part of the future of cloud computing, since most cloud service platforms will provide at least some distributed cloud services that execute at the point of need over the next four years," said Hewitt.

Trend No. 6: Critical Skills Versus Critical Roles

"I&O skills requirements will continue to evolve as organizations adapt to new business environments," said Hewitt. "Specifically, there is a shift in focus from infrastructure roles toward collective, critical skills. This challenges the traditional ‘territorial' thinking of belonging to a specific infrastructure team and instead encourages collaboration."

By 2022, I&O leaders can expect to plan for at least 12 high-priority skills in their organizations. While hiring for these skills now while the IT talent market remains a buyer's market is recommended, Gartner said I&O leaders should consider the fundamental culture changes this trend will bring and to plan accordingly.

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 ...