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Gartner: 4 Trends Shaping Future of Cloud, Data Center and Edge Infrastructure

Gartner highlighted four trends impacting cloud, data center and edge infrastructure in 2023, as infrastructure and operations (I&O) teams pivot to support new technologies and ways of working during a year of economic uncertainty.

Paul Delory, VP Analyst at Gartner said, "In the current economic climate, the biggest problem companies face in 2023 may not be IT infrastructure. I&O teams, however, will be impacted by economic and geopolitical forces and will have a vital role to play in ameliorating their effects.

"This won't be a year to realize grand ambitions, but it marks a moment to refocus, retool and rethink your infrastructure. In every crisis lies opportunity, and in this case, the chance to make positive changes that may be long overdue."

According to Gartner, the top four cloud, data center and edge infrastructure trends include:

Trend 1: Cloud Teams Will Optimize and Refactor Cloud Infrastructure

Public cloud usage is almost universal, but many deployments are ad hoc and poorly implemented. I&O teams have an opportunity this year to revisit hastily assembled or poorly architected cloud infrastructure to make it more efficient, resilient and cost-effective.

The focus of refactoring cloud infrastructure should be on optimizing costs by eliminating redundant, overbuilt or unused cloud infrastructure; building business resilience rather than service-level redundancy; using cloud infrastructure as a way to mitigate supply chain disruptions; and modernizing infrastructure. According to Gartner, 65% of application workloads will be optimal or ready for cloud delivery by 2027, up from 45% in 2022.

Trend 2: New Application Architectures Will Demand New Kinds of Infrastructure

I&O teams are continually challenged to meet new and growing demands with new types of infrastructure — including edge infrastructure for data-intensive use cases, non-x86 architectures for specialized workloads, serverless edge architectures, and 5G mobile service. Gartner predicts 15% of on-premises production workloads will run in containers by 2026, up from less than 5% in 2022.

I&O professionals must evaluate alternative options with care, focusing on their ability to manage, integrate and transform in the face of constraints on time, talent and resources. "Don't revert to traditional methods or solutions just because they've worked well in the past," said Delory. "Challenging periods are times to innovate and find new solutions to meet business demands."

Trend 3: Data Center Teams Will Adopt Cloud Principles On-Premises

Data centers are shrinking and migrating to platform-based colocation providers. Combined with new as-a-service models for physical infrastructure, this can bring cloud-like service-centricity and economic models to on-premises infrastructure.

According to Gartner, 35% of data center infrastructure will be managed from a cloud-based control plane by 2027, from less than 10% in 2022. I&O professionals should focus this year on building cloud-native infrastructure within the data center; migrating workloads from owned facilities to co-location facilities or the edge; or embracing as-a-service models for physical infrastructure.

Trend 4: Successful Organizations Will Make Skills Growth Their Highest Priority

Lack of skills remains the biggest barrier to infrastructure modernization initiatives, with many organizations finding they cannot hire outside talent to fill these skills gaps. IT organizations will not succeed unless they prioritize organic skills growth.

I&O leaders must make operations skills growth their highest priority this year. Encourage I&O professionals to take on new roles as site reliability engineers or subject matter expert consultants for developer teams and business units. Gartner predicts 60% of data center infrastructure teams will have relevant automation and cloud skills by 2027, up from 30% in 2022.

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Gartner: 4 Trends Shaping Future of Cloud, Data Center and Edge Infrastructure

Gartner highlighted four trends impacting cloud, data center and edge infrastructure in 2023, as infrastructure and operations (I&O) teams pivot to support new technologies and ways of working during a year of economic uncertainty.

Paul Delory, VP Analyst at Gartner said, "In the current economic climate, the biggest problem companies face in 2023 may not be IT infrastructure. I&O teams, however, will be impacted by economic and geopolitical forces and will have a vital role to play in ameliorating their effects.

"This won't be a year to realize grand ambitions, but it marks a moment to refocus, retool and rethink your infrastructure. In every crisis lies opportunity, and in this case, the chance to make positive changes that may be long overdue."

According to Gartner, the top four cloud, data center and edge infrastructure trends include:

Trend 1: Cloud Teams Will Optimize and Refactor Cloud Infrastructure

Public cloud usage is almost universal, but many deployments are ad hoc and poorly implemented. I&O teams have an opportunity this year to revisit hastily assembled or poorly architected cloud infrastructure to make it more efficient, resilient and cost-effective.

The focus of refactoring cloud infrastructure should be on optimizing costs by eliminating redundant, overbuilt or unused cloud infrastructure; building business resilience rather than service-level redundancy; using cloud infrastructure as a way to mitigate supply chain disruptions; and modernizing infrastructure. According to Gartner, 65% of application workloads will be optimal or ready for cloud delivery by 2027, up from 45% in 2022.

Trend 2: New Application Architectures Will Demand New Kinds of Infrastructure

I&O teams are continually challenged to meet new and growing demands with new types of infrastructure — including edge infrastructure for data-intensive use cases, non-x86 architectures for specialized workloads, serverless edge architectures, and 5G mobile service. Gartner predicts 15% of on-premises production workloads will run in containers by 2026, up from less than 5% in 2022.

I&O professionals must evaluate alternative options with care, focusing on their ability to manage, integrate and transform in the face of constraints on time, talent and resources. "Don't revert to traditional methods or solutions just because they've worked well in the past," said Delory. "Challenging periods are times to innovate and find new solutions to meet business demands."

Trend 3: Data Center Teams Will Adopt Cloud Principles On-Premises

Data centers are shrinking and migrating to platform-based colocation providers. Combined with new as-a-service models for physical infrastructure, this can bring cloud-like service-centricity and economic models to on-premises infrastructure.

According to Gartner, 35% of data center infrastructure will be managed from a cloud-based control plane by 2027, from less than 10% in 2022. I&O professionals should focus this year on building cloud-native infrastructure within the data center; migrating workloads from owned facilities to co-location facilities or the edge; or embracing as-a-service models for physical infrastructure.

Trend 4: Successful Organizations Will Make Skills Growth Their Highest Priority

Lack of skills remains the biggest barrier to infrastructure modernization initiatives, with many organizations finding they cannot hire outside talent to fill these skills gaps. IT organizations will not succeed unless they prioritize organic skills growth.

I&O leaders must make operations skills growth their highest priority this year. Encourage I&O professionals to take on new roles as site reliability engineers or subject matter expert consultants for developer teams and business units. Gartner predicts 60% of data center infrastructure teams will have relevant automation and cloud skills by 2027, up from 30% in 2022.

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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 ...