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Gartner: Top Trends Impacting Infrastructure and Operations for 2025

Gartner, Inc. highlighted the six trends that will have a significant impact on infrastructure and operations (I&O) for 2025.

"These trends give the opportunity for I&O leaders to identify future skills requirements and seek insights to help meet implementation requirements," said Jeffrey Hewitt, Vice President Analyst at Gartner. "They will provide the differentiation needed for enterprises to gain the optimal benefits from their I&O operations in 2025."

Trend No. 1: Revirtualization/devirtualization

The recent license changes for certain vendor-based solutions have forced many I&O teams to re-evaluate their virtualization choices with some moving more to public cloud, some turning to distributed cloud and some moving to private cloud. This involves multiple options beyond just changing hypervisors.

"I&O leaders must inventory all current virtualization implementations and any related interdependencies," said Hewitt. "Evaluate alternative paths including hypervisors, hyperconvergence, distributed cloud, containerization, private cloud and devirtualization. Identify existing I&O skills and how those need to evolve to support top choices."

Trend No. 2: Security Behavior and Culture Programs

As the sophistication and variety of attacks increases, security programs must evolve to address behavior and culture to optimize their effectiveness. Security behavior and culture programs (SBCPs) are enterprisewide approaches to minimize cybersecurity incidents associated with employee behavior.

SBCP programs result in improved employee adoption of security controls and reductions in behavior not considered secure. They enable I&O to help support the more effective use of cybersecurity resources by employees.

Trend No. 3: Cyberstorage

Cyberstorage solutions utilize a data harbor made up of data that is fragmented and distributed across multiple storage locations. The fragmented data can be instantly reassembled for use when needed.

Cyberstorage can be a dedicated solution with comprehensive features, a platform-native service offering with integrated solutions, or a collection of stand-alone products that augment storage vendors with cyberprotection capabilities.

"For cyberstorage to be successful, I&O leaders should identify the risks of costly and disruptive storage threats, combined with increasing regulatory and insurance expenses to build a business case for cyberstorage adoption," said Hewitt.

Trend No. 4: Liquid-cooled Infrastructure

Liquid-cooled infrastructure consists of rear-door heat exchange, immersion and direct-to-chip. It enables I&O to support new chip generations, density and AI requirements, while also providing I&O opportunities to flexibly place infrastructure to support edge use cases.

"Liquid cooling has evolved to move from cooling the broader data center environment to getting closer and even within the infrastructure," said Hewitt. "Liquid-cooled infrastructure remains niche today in terms of use cases but will become more predominant as next generations of GPUs and CPUs increase in power consumption and heat production."

Trend No. 5: Intelligent Applications

Generative AI has revealed applications' potential to operate intelligently, which has created the expectation for intelligent applications. Intelligent applications adapt to their user's context and intent, thereby reducing digital friction. It can interoperate in pursuit of their own, as well as their users' intents, by marshaling the appropriate interfaces to external APIs and connected data.

Ultimately, intelligent applications reduce required intervention and interactions on the part of I&O. It also optimizes processes and utilization while reducing resource overhead.

Trend No. 6: Optimal Infrastructure

Optimal infrastructure is when I&O teams place a highly significant emphasis on the best infrastructure choices for a given use case across a range of deployment styles. This approach utilizes a business-based focus so that executives outside of IT can understand why infrastructure choices are made from their perspectives.

"These choices are ultimately aligned with platform engineering adoption," said Hewitt. "They allow I&O to align infrastructure choices with the business objectives of the overall organization. They also facilitate the support and approval of business unit leaders and C-level executives."

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Gartner: Top Trends Impacting Infrastructure and Operations for 2025

Gartner, Inc. highlighted the six trends that will have a significant impact on infrastructure and operations (I&O) for 2025.

"These trends give the opportunity for I&O leaders to identify future skills requirements and seek insights to help meet implementation requirements," said Jeffrey Hewitt, Vice President Analyst at Gartner. "They will provide the differentiation needed for enterprises to gain the optimal benefits from their I&O operations in 2025."

Trend No. 1: Revirtualization/devirtualization

The recent license changes for certain vendor-based solutions have forced many I&O teams to re-evaluate their virtualization choices with some moving more to public cloud, some turning to distributed cloud and some moving to private cloud. This involves multiple options beyond just changing hypervisors.

"I&O leaders must inventory all current virtualization implementations and any related interdependencies," said Hewitt. "Evaluate alternative paths including hypervisors, hyperconvergence, distributed cloud, containerization, private cloud and devirtualization. Identify existing I&O skills and how those need to evolve to support top choices."

Trend No. 2: Security Behavior and Culture Programs

As the sophistication and variety of attacks increases, security programs must evolve to address behavior and culture to optimize their effectiveness. Security behavior and culture programs (SBCPs) are enterprisewide approaches to minimize cybersecurity incidents associated with employee behavior.

SBCP programs result in improved employee adoption of security controls and reductions in behavior not considered secure. They enable I&O to help support the more effective use of cybersecurity resources by employees.

Trend No. 3: Cyberstorage

Cyberstorage solutions utilize a data harbor made up of data that is fragmented and distributed across multiple storage locations. The fragmented data can be instantly reassembled for use when needed.

Cyberstorage can be a dedicated solution with comprehensive features, a platform-native service offering with integrated solutions, or a collection of stand-alone products that augment storage vendors with cyberprotection capabilities.

"For cyberstorage to be successful, I&O leaders should identify the risks of costly and disruptive storage threats, combined with increasing regulatory and insurance expenses to build a business case for cyberstorage adoption," said Hewitt.

Trend No. 4: Liquid-cooled Infrastructure

Liquid-cooled infrastructure consists of rear-door heat exchange, immersion and direct-to-chip. It enables I&O to support new chip generations, density and AI requirements, while also providing I&O opportunities to flexibly place infrastructure to support edge use cases.

"Liquid cooling has evolved to move from cooling the broader data center environment to getting closer and even within the infrastructure," said Hewitt. "Liquid-cooled infrastructure remains niche today in terms of use cases but will become more predominant as next generations of GPUs and CPUs increase in power consumption and heat production."

Trend No. 5: Intelligent Applications

Generative AI has revealed applications' potential to operate intelligently, which has created the expectation for intelligent applications. Intelligent applications adapt to their user's context and intent, thereby reducing digital friction. It can interoperate in pursuit of their own, as well as their users' intents, by marshaling the appropriate interfaces to external APIs and connected data.

Ultimately, intelligent applications reduce required intervention and interactions on the part of I&O. It also optimizes processes and utilization while reducing resource overhead.

Trend No. 6: Optimal Infrastructure

Optimal infrastructure is when I&O teams place a highly significant emphasis on the best infrastructure choices for a given use case across a range of deployment styles. This approach utilizes a business-based focus so that executives outside of IT can understand why infrastructure choices are made from their perspectives.

"These choices are ultimately aligned with platform engineering adoption," said Hewitt. "They allow I&O to align infrastructure choices with the business objectives of the overall organization. They also facilitate the support and approval of business unit leaders and C-level executives."

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...