Building AI-ready data centres: Why infrastructure design must evolve

By Sanjib Seal, Head of Design and Engineering, Aurionpro Solutions

Every plan to expand AI computing capacity raises a fundamental infrastructure question: can the facility support the equipment, not just accommodate it? Additional rack space does not automatically translate into usable capacity. Without sufficient power distribution, cooling capability, and provision for future expansion, deploying higher-density computing equipment can require costly upgrades and disrupt operations.

This challenge will become more pronounced as AI workloads reshape data centre requirements. The International Energy Agency (IEA) projects that global data centre electricity consumption will rise from approximately 485 terawatt-hours (TWh) in 2025 to 950 TWh by 2030, nearly doubling in five years. Electricity consumption by AI-focused data centres is projected to triple over the same period.
The implication for infrastructure designers is clear: AI readiness cannot be treated as a future upgrade. It must be built into decisions about power architecture, thermal management, space allocation, and phased capacity expansion from the outset.

Aligning Infrastructure Design with AI Workload Demands
“AI-ready” can mean different things to different customers. A facility supporting large model-training workloads will have different requirements from one running smaller AI applications alongside conventional enterprise systems. Before specifying infrastructure, teams need clarity on the equipment being considered, its operating profile and the likely deployment schedule.

An average power density across the building will only tell part of the story. Concentrating demanding equipment in one section can create local constraints even when capacity is available elsewhere.

Planning at the rack and zone level helps identify these issues before equipment arrives. This allows operators to tailor cooling to different heat loads, maintaining reliable performance while avoiding unnecessary energy use.

Rethinking Power and Cooling for High-Density Computing
Power availability needs to be examined all the way from the utility connection to individual racks. Increasing the incoming supply achieves little if downstream distribution equipment cannot deliver it where required. Transformers, switchgear, backup systems, and rack distribution need to be assessed against the same deployment plan.

The scale of this transition is already visible in rack design. Uptime Institute’s 2025 Global Data Center Survey found that rack power densities in the 10–30 kW range are becoming more common, even as very high-density deployments remain relatively uncommon. This reinforces the need to plan electrical distribution and cooling around actual rack-level requirements rather than relying solely on average facility capacity.

Cooling requires equally early attention. Higher-density deployments may call for direct-to-chip liquid cooling, while other parts of the facility continue using air. That combination has implications for pipe routes, coolant distribution, maintenance, and the space needed around equipment. Liquid cooling also brings water-quality management and leak detection into the operating plan. These details influence how reliably the facility can be run.

Building Flexibility into Long-Term Capacity Planning
Forecasting demand is difficult when computing equipment and customer requirements are changing quickly. Installing everything upfront can leave expensive capacity idle. Waiting until demand is confirmed can leave too little time to deliver it.

The useful distinction is between what must be allowed for now and what can be installed later. Space for additional electrical equipment, pipe routes and connection points can be reserved during the initial design. Power and cooling modules can then be added in stages.
Each expansion phase needs a credible construction and commissioning plan around the live facility. Modular systems can help, but only if interfaces and access have been considered from the beginning.

Balancing Capacity Growth with Resource Efficiency
A facility will rarely operate at its final design load from day one. Efficiency therefore needs to be assessed at partial occupancy and through different seasonal conditions.

A facility’s efficiency should be assessed across different operating loads, not only at its design capacity. Power usage effectiveness (PUE) remains an important metric, but it does not capture the full resource footprint of a data centre. Water consumption, computing utilisation, and the energy required to support different cooling configurations also need to be evaluated together.

This is particularly important as AI workloads increase thermal management requirements. A cooling strategy that reduces electricity consumption may increase water demand, while an alternative may require more power to achieve the same thermal performance. The design decision should account for local climate, water availability, operating costs, and equipment requirements rather than optimising for a single metric.

Connecting Design Decisions with Operational Performance
Operations teams should be involved while design choices are still open. They can flag an awkward maintenance route or a component that will be difficult to replace. Resolving those issues on a drawing is considerably easier than doing so inside a working data hall.

Commissioning must test the interaction between electrical, cooling and control systems, including failure and recovery scenarios. Monitoring after handover should then help teams adjust performance as workloads evolve.

The real test of an AI-ready data centre is not how much computing capacity it can support on day one, but how efficiently and reliably it can accommodate the next generation of workloads. If every expansion requires extensive infrastructure rework, the original design has not provided sufficient flexibility.

For operators, the priority should be to design for change without overbuilding upfront: establish clear power and cooling provisions, plan expansion interfaces early, and validate performance under realistic operating conditions. AI-ready infrastructure is ultimately about making future growth predictable, operationally manageable, and resource-efficient.