Sadiq Syed, SVP Digital Buildings, Schneider Electric, argues that with power availability tightening and grid upgrades moving slowly, the fastest route to sustaining AI progress is squeezing more performance from the sites we already run.

By 2030, US data centres could consume enough electricity to power 37 million homes. And the demand is only accelerating, driven largely by AI. As the race to build ever-larger, more advanced models accelerates, so too does the competition for the power needed to run them.

Yet the scarcity of available energy is becoming a critical and immediate constraint. Closing the gap between AI’s growing demands and the capacity of the data centres that support it will be essential to sustaining future progress.

Without reliable access to sufficient power, AI-driven progress will stall. This reality is pushing countries around the world to boost investment in new energy infrastructure, from solar and wind to nuclear. However, these projects take years to make a tangible impact.

In the EU, securing a grid connection alone can take anywhere from two to 10 years. In the meantime, many operators are racing to build additional data centre sites and pouring huge sums into expansion to stay ahead of the growing power shortfall.

To successfully address the energy requirements of AI, we first have to look at our existing data centres. Buildings waste nearly 40% of the energy they use, so being more efficient with what we already have could be a faster fix.

As data centres are also under pressure from regulators, local communities and investors to operate more efficiently, it’s a win-win for operators.

Turning the corner on efficiency

Poor energy management is a silent drain on organisations. It not only harms the environment but also steadily depletes a company’s resources.

This is particularly damaging in data centres. To maintain uninterrupted operations, facilities rely on multiple power supplies, cooling infrastructure, temperature monitoring, lighting, and both physical and digital security systems. Too often, these systems operate in silos, making it difficult to gain a true picture of overall performance.

Without a unified view across all systems, engineers are more likely to miss issues such as voltage imbalances, which can lead to costly equipment damage.

Overly complex and fragmented systems can also expose organisations to higher prices. Many utility providers calculate bills based on energy charges (the total electricity used over a month) and demand charges, based on the highest rate of power consumed during any short interval.

If data centre operators don’t have full visibility over their systems, they could miss the opportunity to use cheaper solar energy instead of grid power. They may also run several energy-intensive systems during peak times due to poor coordination between building and electrical teams.

Given a 100 kW data centre can face over £200,000 per year in electricity costs, minimising the times when this happens could save thousands of pounds over the course of a year.

Empower the workforce

Interpreting data from power and energy systems effectively often takes decades to master, which is why engineers in critical facilities require deep technical expertise to do their jobs effectively. However, the workforce is aging, with too few skilled engineers coming through to replace those retiring.

Compounding the challenge, data centre systems are becoming increasingly complex, increasing the pressure on remaining workers.

By combining data from disparate systems into one unified platform, AI can help engineers identify efficiency savings through advanced analytics. These insights can help workers meet compliance targets and flag issues before they develop into significant problems.

This enables facility managers to become more proactive and reduce unnecessary damage or downtime to equipment. At a time when AI is also dominating the consumer market, this generation expects automation to support them at work. Data centres should be no different.

Efficiency is the bedrock for growth

Keeping operations simple is essential to fighting energy waste. By centralising data from energy-intensive electrical and mechanical systems and combining it with AI-powered insights, operators can anticipate failures, prevent downtime, and achieve greater performance from the same physical footprint.

There is no doubt that additional data centres will be needed to meet AI’s potential, but we must find a way to get there by future-proofing existing sites and the underlying infrastructure. That primarily means democratising access to disparate systems, so they don’t run in isolation and operators can stay ahead of issues.

There is only finite space, so the next race for data centre operators will be towards simplicity and efficiency – whether by simplifying infrastructure or supporting engineers.