Why AI is changing infrastructure capital allocation

In summer 2026, PwC projected that annual data center capital expenditure will rise from around US$800 billion per year to $1.8 trillion by 2050. The consultancy firm also forecasts a total of $31.6 trillion in CAPEX between 2026 and 2050 to build the compute capacity needed to meet demand, with much of this AI capital allocation flowing into increasingly powerful semi-conductors, networking equipment, data center facilities, and power.

But artificial intelligence isn’t just increasing businesses’ data center capital expenditure. It’s changing the infrastructure investment equation itself, transforming how companies decide where, when, and how to invest in infrastructure. Let’s have a look at this evolution and how businesses are keeping their infrastructure portfolios productive, resilient and adaptable as technology and constraints shift.

Why is AI making infrastructure investment more complex?

Traditionally, businesses planned their IT CAPEX around predictable hardware refresh cycles, capacity requirements (such as when scaling), and whether their existing assets met performance requirements. While these remain important, capital allocation decisions in the AI era increasingly depend on a broader set of factors:

Accelerating technology advancements

As demand for more powerful AI and faster connectivity grows, servers and networking equipment are evolving to keep up. That means what meets performance requirements today may be obsolete in a few years.

Indeed, PwC predicts that “recurring chip upgrades—not [data center] construction—will drive the majority of long-term capital investment”, with IT assets, chips, servers and networking equipment increasing from 70% of total investment in 2026 to 93% by 2050. This is supported by Jonathan Mauck’s assertion, cited by Capacity Global, that “data centers are built as 20-year industrial infrastructure, while the GPUs inside them have a useful life of five to seven years”.

What does this mean for businesses? Capital allocation has to account for the possibility that today’s high-performance architecture may become outdated before the physical asset reaches the end of its useful life.

Power as a constraint

AI workloads consume vast amounts of power. This means reliable energy is essential for AI data center deployment, while the affordability of that energy is key to keeping budgets under control. Further, data centers prioritizing sustainability should also consider low-carbon or renewable energy options.

As a result of power availability and costs affecting where and when AI infrastructures can be deployed, energy has shifted from an operational expense (OPEX) to a significant part of capital investment decisions.

With AI driving up energy consumption, a business must consider power availability, long-term costs and its ability to absorb them, when deciding on infrastructure capital allocation.

Access, supply chains and geography

Demand for high-performance GPUs and CPUs is accelerating, putting increasing pressure on supply. Combined with supply chain disruptions and geopolitical tensions affecting export controls and tariffs, this is limiting and delaying access to critical AI infrastructure components for businesses. This can heavily impact investment plans, as businesses may need to:

• adjust deployment timelines according to when they will receive assets
• reconsider their technology choices
• secure alternative supply sources, such as refurbished solutions or GPU as a service.

To learn more about AI’s impact on the IT supply chain, read our blog Why AI is driving a global RAM price increase and how to manage the shortage.

Meanwhile, PwC noted that “sovereign AI strategies are accelerating investment in Europe and the Middle East”. AI sovereignty strategies are affecting where businesses across the world are building their data center infrastructures. This has a direct influence on CAPEX, since land, energy and licensing costs vary between regions and countries.

Operational requirements and support

The infrastructure required to run AI workloads does not stop at high-performance servers. To extract the most value from their investments, businesses must consider how to deploy, connect, operate and support that hardware across their IT environment.

This should take into account elements such as:

Infrastructure factor	Why it matters
Connectivity and networking	To support high-performance workloads and enable fast, reliable communication.
Cabling, cooling and other data center infrastructure	To accommodate the power, thermal and physical requirements of high-density AI deployments.
Onsite presence, specialized expertise and resources	To install, integrate, and maintain new technologies effectively.
Multi-vendor support	To provide consistent support where AI infrastructure operates alongside existing systems from multiple brands.

Maintaining and optimizing existing infrastructure to support AI technologies and workloads—and maximize the value of new investments—also involves long-term costs that should be factored into the decision-making process.

New AI capacity does not replace existing infrastructure

When shifting from more traditional data center setups to AI-ready infrastructure, businesses do not build these new systems in isolation. Unless operating completely in the cloud, most companies with data centers already possess:

• Servers
• Storage devices 
• Networking equipment
• Power and cooling capacity

Any AI assets added will depend on, or at least work in conjunction with, this existing infrastructure. This means companies must make strategic decisions, including:

• Which assets are missing and need to be purchased?
• What existing infrastructure should be kept?
• Of the devices retained, which require optimization?
• What needs replacing or upgrading?
• What hardware can be consolidated or retired?

By neglecting these decisions, businesses risk heavy expenditure without benefitting fully from their investments. This is demonstrated clearly in DDN’s 2026 State of AI Infrastructure report. The report cites that:

• Many businesses are experiencing slowed day-to-day progress following AI implementation due to pressures including rising infrastructure complexity, unexpected power and cooling demands, and operational skill gaps.
• Only 41% of businesses interviewed reported that their recent investments had led to efficiency gains.
• 65% of infrastructure sits idle while still consuming power.

The conclusion, then, is that the key to a strong AI infrastructure strategy is not how much you spend on new capacity. Instead, it requires a comprehensive approach that maximizes the productive value of the entire infrastructure portfolio.

Blockquote: Investing in new AI capacity does not eliminate the need to optimize existing infrastructure.

What are the risks of building AI islands?

AI islands are high-performance environments that are technically capable but poorly integrated into the wider infrastructure, whether physically or operationally.

An AI cluster may have exceptional theoretical performance. However, its real value to a business can be seriously hindered if:

• The asset is difficult to operate and maintain.
• It is disconnected from existing infrastructure.
• The hardware lacks the necessary technical support and spare-parts availability in case of issues.
• It cannot be scaled or evolved easily.

At worst, isolated AI environments can create operational and financial drains on a business. This is why integrating it properly into the complete infrastructure is critical to overall success.

Why system availability and hardware utilization are key success drivers

In enterprise AI data centers, theoretical compute capacity is only one part of infrastructure value. Factors that make the real difference between an infrastructure’s potential and its productivity include availability and utilization; that is, the proportion of time the system stays online and whether the capacity paid for is actually used.

High system availability ensures that expensive GPU clusters (and the entire system) remain online and productive, while any downtime can lead to severe revenue drains.

At the same time, effective asset utilization is critical to a system’s overall efficiency.

Cast AI’s 2026 Kubernetes study finds average enterprise GPU utilization at around 5%, with widespread overprovisioning of CPU, memory and GPUs leaving nodes underutilized. This matters because idle capacity is essentially capital a business has already spent but isn’t getting any benefit from.

Low utilization can stem from several factors beyond the GPUs themselves, including bottlenecks elsewhere in the infrastructure. If a cluster is underutilized because data isn’t reaching it fast enough, optimizing or upgrading storage or networking may deliver more value than purchasing additional GPUs.

Together, availability and utilization determine – more than performance alone – whether AI infrastructure delivers reliable, cost‑effective performance at scale. To safeguard them, businesses must invest in optimizing the entire infrastructure, not just adding more nodes.

The AI infrastructure lifecycle and capital allocation

Like traditional IT infrastructure, AI tech procurement is only the beginning of a much longer process. To avoid unexpected costs later, capital allocation should consider the full lifecycle of AI infrastructure.

Lifecycle stage	Cost factors
Procurement	Initial purchase costs
Deployment	Specialist installation and configuration Cabling Integration with existing systems
Operations and maintenance	Expert, onsite engineering presence for support, maintenance and troubleshooting Training for teams Power and cooling costs Spare parts
Scaling	Adding capacity Upgrading or adapting infrastructure Increased overheads as the environment expands
Refresh or retirement	Component or system replacements Decommissioning

At the end of an asset’s life, businesses must consider the balance between maximizing ROI through lifecycle extension and the risk of aging hardware holding back operational efficiency.

The AI infrastructure lifecycle isn’t an afterthought when allocating CAPEX. It determines whether or not businesses will see a return on their investment.

How businesses are building adaptable, productive IT infrastructures

How businesses plan their capital allocation is shifting dramatically. The core priority is not simply how much to invest in AI, but how to create and maintain a resilient infrastructure portfolio that meets and adapts to performance demands over time.

Here’s what smart businesses are doing to achieve that:

1) Take into account both new and existing infrastructure. No one system functions in isolation.

2) Consider the availability, utilization and integration you actually need, not just peak performance capabilities.

3) Include the entire lifecycle when estimating costs. This includes installation, cabling, deployment, onsite maintenance and support, spare parts, and scaling or decommissioning needs.

How can third-party maintenance and professional IT services help businesses get more value from their AI infrastructure investments?

As AI infrastructure becomes more complex and distributed, businesses need to evolve their operational capabilities to match the infrastructure. These means specialized skills to support infrastructure throughout its lifecycle, rapid onsite engineer deployment, and fast availability of spare parts to minimize downtime.

AI-ready, on-demand professional services and third-party maintenance should include:

• AI infrastructure deployment
• Cabling and connectivity
• Onsite technical support and troubleshooting
• Spare-parts logistics
• Multi-vendor maintenance, covering OEMs and ODMs
• Comprehensive lifecycle support

With a global network of trained engineers, Evernex provides all these services. We cover both traditional and AI technology, integrating and supporting enterprise systems as a whole to help businesses maximize resilience, productivity, and cost-efficiency.

The bottom line

Companies that treat AI infrastructure investments as part of a long-term portfolio rather than a series of isolated acquisitions will be better positioned to control performance, resilience, costs and agility.

Contact Evernex