By Steven Santini, Secure Power Vice President, Schneider Electric Sub-Saharan Africa

The data centre industry has evolved through successive waves of innovation, from virtualisation to cloud computing, and now to AI.  

According to Bloomberg, the market for generative AI is expected to reach USD 1.3 trillion by 2032, while PwC projects that AI could contribute up to USD 15.7 trillion to the global economy by 2030, making it one of the most significant drivers of productivity and innovation this decade.

However by that point, data centres could consume nearly  1,000 TWh per year or about three percent of total global electricity demand.

This relationship between AI and energy is therefore no longer one-directional. Data centres must deliver the power to sustain AI, while AI itself can optimise energy use, accelerate decarbonisation, and ensure digital progress does not compromise sustainability.

It’s a two-way energy conversation - energy for AI, and AI for energy.

Intelligent Power: From Energy-Hungry to Energy-Aware

AI training racks can draw between 100 and 140 kilowatts each, creating unpredictable, high-density loads.

Simply increasing power supply is not a viable solution, doing so without addressing inefficiencies could result in waste and rising emissions. Instead, energy management must become intelligent.

Predictive algorithms now enable operators to forecast energy spikes, adjust dynamically, and smooth out load variability to protect grid stability. Furthermore, smart scheduling allows energy-intensive training tasks to run when renewable supply is abundant, reducing emissions and operational costs.

Flexible power management also lets workloads scale up or down according to computational needs, aligning business performance with sustainability goals.

When guided by AI, data centres can therefore evolve from energy-hungry to energy-aware ecosystems, balancing performance, resilience, and responsibility.

As highlighted in Schneider Electric’s “AI for Energy Transition” Guide, AI’s capacity to optimise energy systems — from microgrids to industrial processes — enables smarter, faster, and more precise decision-making, turning data centres into active contributors to energy efficiency and decarbonisation efforts.

Cooling as an Energy Resource

Cooling has always been one of the most energy-intensive aspects of data centre operations, and AI’s high-density workloads have made it even more critical. As power densities rise, traditional air systems are reaching their limits, driving the shift toward liquid cooling — a method that removes heat directly at the chip level, far more efficiently than air.

This transition represents more than a technical upgrade; it’s a rethinking of how thermal management fits into the wider sustainability agenda. Effective cooling design today must balance efficiency, water stewardship, and circularity, turning a traditional energy cost into an opportunity for recovery.

For instance, waste heat can be repurposed for nearby industrial or agricultural use, while closed-loop systems minimise water consumption and ensure operational continuity in regions facing resource constraints.

AI-Ready by Design: From Grid to Chip and Chip to Chiller

Meeting AI’s power and performance demands requires a unified architecture where power, cooling, and digital management operate in harmony. Reference designs co-engineered by Schneider Electric and NVIDIA illustrate this integrated approach — combining liquid cooling with advanced power management to support racks of up to 142 kilowatts while maintaining efficiency and reliability.

By embedding intelligence across every layer — from grid to chip to chiller — data centres can scale for accelerated computing without letting energy consumption spiral. This holistic view positions sustainability as a core design principle, not an afterthought, ensuring that high-performance computing aligns with global decarbonisation goals.

The findings from Schneider Electric’s White Paper 212: “Bending the Energy Curve” further reinforce this point — showing that even modest improvements in power usage effectiveness (PUE) and compute efficiency can collectively bend the industry’s energy growth curve by up to 17%, decoupling digitalisation from exponential energy demand.

The Future -Data Centres as Energy Ecosystems

The data centre of the future will not be defined merely by its computing capacity, but by how it contributes to broader energy ecosystems.

AI‑ready facilities are set to partner with the grid through flexible operations and load shifting, support communities with waste‑heat reuse and sustainable design, and drive decarbonisation by using AI to optimise energy use and accelerate electrification.

Circular thinking, therefore, prioritising efficiency, reuse, and adaptability, will define the next generation of data centres. The leaders will be those who successfully align energy for AI with AI for energy, achieving competitiveness, sustainability, and resilience together.