Key points:
- Businesses are focusing on IT modernization to stay competitive, with a key aspect being gaining better visibility and control over existing infrastructure to support AI and resource-heavy applications.
- Organizations are looking at private cloud models to meet strict compliance requirements and achieve greater data security and privacy, with a growing trend towards specialized private clouds for AI and high-performance computing.
- Building AI-ready infrastructure requires a comprehensive modernization of underlying infrastructure, including efficient cooling technologies and high-speed networks, to support real-time AI traffic and ensure access control and security.
As we move into 2026, businesses are prioritizing the modernization of their enterprise infrastructure to stay competitive. According to World Wide Technologies (WWT), a tech solutions provider, the rise of AI has highlighted the need for IT modernization. Many organizations are still using outdated, legacy infrastructure that is not equipped to handle modern workload requirements. WWT notes that a key aspect of any refresh initiative is gaining better visibility and control over the existing asset base, including hardware, software, and maintenance contracts.
Neil Anderson, vice president and CTO of cloud, infrastructure, and AI solutions for WWT, emphasizes the importance of addressing technical debt to keep the enterprise agile, efficient, and capable of supporting cutting-edge innovations, particularly AI-powered applications. He also highlights the acceleration of application modernization, which is being driven by the use of AI software coding assistance. This allows for the translation of languages, re-platforming, and re-architecting of applications, making it more feasible to modernize apps at scale.
The move to modernize data center infrastructure has many organizations looking at private cloud models, which offer greater data security and privacy. WWT reports that the drive towards private cloud is fueled by the need for greater control over data, enhanced performance, and predictable costs. Additionally, private clouds offer more customization, allowing organizations to tailor their environment to specific workloads and performance needs. Microsoft Azure, for example, offers a range of private cloud solutions, including Azure Stack and Azure Private Cloud, which provide organizations with a secure and flexible way to deploy and manage their applications.
WWT also notes that there is a growing trend towards specialized private clouds for AI and high-performance computing, such as 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 can become prohibitively expensive at scale for certain workloads. Nvidia, a leading manufacturer of GPUs, is working with WWT to provide integrated solutions for AI and high-performance computing.
As businesses build out their AI-ready infrastructure, they will need to consider the need for high-speed networks and edge computing. Anderson notes that customers will need to have edge compute to support real-time AI traffic, which will require a very distributed hybrid architecture and high-speed networks. This will also require a high level of access control and security, including policy control to ensure that AI agents are authorized to access certain applications and data. Microsoft, for example, offers a range of security solutions, including Azure Security Center and Microsoft Intune, which provide organizations with a comprehensive way to manage and secure their AI and IoT devices.
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 cites 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. Microsoft, for example, offers a range of AI solutions, including Microsoft Azure Machine Learning and Microsoft Cognitive Services, which provide organizations with a secure and flexible way to deploy and manage their AI workloads.
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 require a comprehensive modernization of the underlying infrastructure, including power and cooling needs. Anderson notes that most customers are not sitting with a lot of excess data center power, and most people are out of power or need to be doing more power projects to prepare for the near future. WWT recommends implementing efficient cooling technologies, such as direct-to-chip liquid cooling, immersion cooling tanks, or rear-door heat exchangers, to enhance thermal efficiency, lower energy consumption, and help control data center operating costs. Microsoft, for example, offers a range of sustainability solutions, including Microsoft Sustainability Manager, which provides organizations with a comprehensive way to manage and reduce their environmental impact.
As the AI infrastructure market continues to grow, with Grand View Research predicting it will reach $223.45 billion by 2030, growing at a 30.4% CAGR, organizations will need to prioritize building AI-ready infrastructure that is secure, efficient, and scalable. WWT and other industry leaders, including Microsoft, are working together to provide organizations with the solutions and support they need to succeed in this rapidly evolving landscape. With the right infrastructure in place, organizations can unlock the full potential of AI and drive business success.
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