Enterprises to prioritize infrastructure modernization in 2026

Readying enterprise infrastructure for AI and other resource-heavy applications is high on the to-do list for businesses looking to stay competitive 2026.

The rise of AI has heightened the importance of IT modernization, as many organizations are still reliant on outdated, legacy infrastructure that is ill-equipped to handle modern workload requirements, says tech solutions provider World Wide Technology (WWT).

“A key aspect of any refresh initiative is gaining better visibility and control over the existing asset base. Too often, organizations don’t have a clear understanding of what hardware and software they have deployed, which maintenance contracts are in place, or how all the pieces fit together. This lack of visibility makes it extremely difficult to plan meaningful modernization beyond reactive ‘rip-and-replace’ cycles,” WWT stated in its report, IT infrastructure Modernization Priorities for 2026.

“When you look at the traditional data center or infrastructure modernization that’s going on, I don’t know that there are wildly new trends, but there are some things that are accelerating, like addressing of technical debt to keep the enterprise agile, efficient and capable of supporting cutting-edge innovations — particularly AI-powered applications,” Neil Anderson, vice president and CTO of cloud, infrastructure, and AI solutions for WWT, told Network World. 

Application modernization is one area Anderson sees accelerating. “If you kind of intersect what’s going on with AI software coding assistance and the problem of that modernization, it starts to become much more feasible at to do app modernization at scale,” Anderson said. “You can translate languages, you can re-platform and re-architect, all with the assistance of these AI tools. Some apps are still written in COBOL. This is a once-in-a-generation opportunity to kind of catch up on some of those problems.”

A move to modernize data center infrastructure has many organizations are looking at private cloud models, according to the WWT report: “The drive toward private cloud is fueled by several needs, with one primary driver being greater data security and privacy. Industries like finance and government, which handle sensitive information, often find private cloud architectures better suited for meeting strict compliance requirements. Additionally, private clouds offer more customization, allowing organizations to tailor their environment to specific workloads and performance needs, which is difficult to achieve in one-size-fits-all public clouds.”

WWT reports the rise of specialized private clouds for AI and high-performance computing—for example, neocloud providers that offer GPU-as-a-service. “These on-premises environments can be optimized for performance characteristics and cost management, whereas public cloud offerings, while often a quick entry point to start AI/ML experimentation, can become prohibitively expensive at scale for certain workloads,” WWT stated.

There is also a move to build up network and compute abilities at the edge, Anderson noted. “Customers are not going to be able to home run all that AI data to their data center and in real time get the answers they need. They will have to have edge compute, and to make that happen, it’s going to be agents sitting out there that are talking to other agents in your central cluster. It’s going to be a very, distributed hybrid architecture, and that will require a very high speed network,” Anderson said. 

Real-time AI traffic going from agent to agent is also going to require a high level of access control and security, Anderson said. “You need policy control in the middle of that AI agent environment to say ‘is that agent authorized to be talking to that other agent? And are they entitled to access these applications?’”

That’s a big problem on the horizon, Anderson said. “If a company has 100,000 employees, they have 100,000 identities and 100,000 policies about what those people can do and not do. There’s going to be 10x or 100x AI agents out there, each one is going to have to have an identity. Each one is going to have an entitlement in a policy about what data they are allowed to access. That’s going to take upgrades that don’t exist today. The AI agent issue is growing rapidly,” Anderson said.

In addition, the imperative to run AI workloads on-premises, often dubbed “private AI,” continues to grow, fueled by the need for greater control over data, enhanced performance, predictable costs and compliance with increasingly strict regulatory requirements, WWT stated. It cited IDC data projecting that by 2028, 75% of enterprise AI workloads are expected to run on fit-for-purpose hybrid infrastructure, which includes on-premises components.

“This reflects a shift toward balancing performance, cost and compliance, especially for private AI deployments,” WWW wrote, noting that Grand View Research is predicting the global AI infrastructure market will reach $223.45 billion by 2030, growing at a 30.4% CAGR, “with on-premises deployments expected to remain a significant portion of this growth, particularly in regulated industries like healthcare, finance, and defense.”

“Implementing private AI is not simply a matter of deploying new software or adding a few servers. The complexity and scale of modern AI workloads, ranging from machine learning model training and inferencing to real-time analytics, require a comprehensive modernization of the underlying infrastructure,” WWT wrote.

Such modernization needs to take into consideration power and cooling needs much more than ever, Anderson said. “Most of our customers are not sitting there with a lot of excess data center power; rather, most people are out of power or need to be doing more power projects to prepare for the near future,” he said.

Steps for building AI-ready infrastructure should include implementing efficient cooling technologies, WWT recommends: “Given the significant heat output of dense AI clusters, traditional air cooling may be insufficient. [Customers should] investigate advanced technologies such as direct-to-chip liquid cooling, immersion cooling tanks or rear-door heat exchangers. These methods can enhance thermal efficiency, lower energy consumption and help control data center operating costs. Partner with vendors who provide integrated solutions and ongoing support, and consider deploying environmental sensors throughout your facility to monitor temperature gradients and airflow in real time,” WWT wrote.

“What we’ve found by working with some of the leading manufacturers, like Nvidia on liquid cooling, is that if you cool the GPUs properly, you actually require less power,” Anderson said.