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Gartner: Infrastructure & Operations Must Shift Focus from Efficiency to Adaptive Resilience

In a world where constant change is becoming routine, Gartner said that infrastructure and operations (I&O) leaders must shift their traditional focus from efficiency to one of adaptive resilience.

"I&O leaders must re-imagine how they manage their talent, their platforms and operations, if they want to dynamically and quickly exploit new opportunities," said Dennis Smith, Research VP at Gartner.

I&O Leaders Must Retain, Attract and Evolve Talent

In a world where being adaptable is paramount, talent management plays a more critical role than ever for I&O organizations. A Gartner survey showed that talent availability is the most significant adoption barrier to 64% of emerging technologies. This dynamic — combined with a fiercely competitive labor market — are putting more pressure than ever on I&O leaders.

I&O leaders must learn how to retain the talent they have, attract new talent, and evolve everyone's skills along the way. By embracing diversity, equity, and inclusion's best practices, organizations can build the right resources over time and use agile learning methods to adapt and evolve skillsets as needed.

Gartner analysts predict that by 2026, 50% of large organizations will use agile learning as the upskilling/reskilling method.

I&O Leaders Must Build Adaptive Platforms

"With over 40% of organizations' staff now acting as business technologists, we have a wider variety of users depending on IT departments today than ever before," said Douglas Toombs, Research VP at Gartner. "The rapid growth of "citizen IT" business technologists, paired with the vast array of public and private infrastructure choices available in the market, has placed more pressure on I&O to perform than ever before."

By building adaptive platforms that are loosely coupled, but tightly integrated, I&O can empower creators of all types of systems throughout the organization.

"As hyperautomation is a critical path to achieve growth and operational excellence, I&O leaders must make automation a first-class discipline in everything they do," said Toombs.

By embracing hyperautomation strategies, I&O can pave the way for intelligence systems, such as AIOps and incident response automation, that play a key role in the day-to-day operations of IT.

Gartner estimates that by 2025, 60% of I&O teams will use AI-augmented automation across their organizations, up from 1% in 2020.

I&O Leaders Must Co-Create with the Business

"No matter how good your technologies or solutions are, no matter how talented your staff is, I&O leaders must align I&O with the way the business works to continuously adapt in a world of constant change," said Julia Palmer, Research VP at Gartner. "I&O leaders will have to learn to work towards adaptive operations to focus on multiple business models, and rethink how I&O engages and partners with the business."

I&O leaders must help their organization grow adaptively by sensing and responding to business changes. Embedding I&O closer to the business with the use of fusion teaming will enable leaders to quickly adjust plans, forecasts, budgets, and resources as business environments change.

"This is not about growing I&O. It is about I&O enabling the growth of the business," Palmer concluded.

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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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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 ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Gartner: Infrastructure & Operations Must Shift Focus from Efficiency to Adaptive Resilience

In a world where constant change is becoming routine, Gartner said that infrastructure and operations (I&O) leaders must shift their traditional focus from efficiency to one of adaptive resilience.

"I&O leaders must re-imagine how they manage their talent, their platforms and operations, if they want to dynamically and quickly exploit new opportunities," said Dennis Smith, Research VP at Gartner.

I&O Leaders Must Retain, Attract and Evolve Talent

In a world where being adaptable is paramount, talent management plays a more critical role than ever for I&O organizations. A Gartner survey showed that talent availability is the most significant adoption barrier to 64% of emerging technologies. This dynamic — combined with a fiercely competitive labor market — are putting more pressure than ever on I&O leaders.

I&O leaders must learn how to retain the talent they have, attract new talent, and evolve everyone's skills along the way. By embracing diversity, equity, and inclusion's best practices, organizations can build the right resources over time and use agile learning methods to adapt and evolve skillsets as needed.

Gartner analysts predict that by 2026, 50% of large organizations will use agile learning as the upskilling/reskilling method.

I&O Leaders Must Build Adaptive Platforms

"With over 40% of organizations' staff now acting as business technologists, we have a wider variety of users depending on IT departments today than ever before," said Douglas Toombs, Research VP at Gartner. "The rapid growth of "citizen IT" business technologists, paired with the vast array of public and private infrastructure choices available in the market, has placed more pressure on I&O to perform than ever before."

By building adaptive platforms that are loosely coupled, but tightly integrated, I&O can empower creators of all types of systems throughout the organization.

"As hyperautomation is a critical path to achieve growth and operational excellence, I&O leaders must make automation a first-class discipline in everything they do," said Toombs.

By embracing hyperautomation strategies, I&O can pave the way for intelligence systems, such as AIOps and incident response automation, that play a key role in the day-to-day operations of IT.

Gartner estimates that by 2025, 60% of I&O teams will use AI-augmented automation across their organizations, up from 1% in 2020.

I&O Leaders Must Co-Create with the Business

"No matter how good your technologies or solutions are, no matter how talented your staff is, I&O leaders must align I&O with the way the business works to continuously adapt in a world of constant change," said Julia Palmer, Research VP at Gartner. "I&O leaders will have to learn to work towards adaptive operations to focus on multiple business models, and rethink how I&O engages and partners with the business."

I&O leaders must help their organization grow adaptively by sensing and responding to business changes. Embedding I&O closer to the business with the use of fusion teaming will enable leaders to quickly adjust plans, forecasts, budgets, and resources as business environments change.

"This is not about growing I&O. It is about I&O enabling the growth of the business," Palmer concluded.

Hot Topics

The Latest

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.