Tomorrow’s AI Economy Needs Data Center Efficiency Today

By 2030, US data centers could devour enough electricity to power 37 million homes. And the curve isn’t flattening - it's accelerating.  Why? AI. As the race to build bigger, smarter models heats up, a new scramble is underway - not for chips or talent, but for power. Scarcity isn’t a distant threat; it’s the next battleground.

Without sufficient power to drive data centers, the global economy won’t realise the potential benefits from energy-intensive technologies such as AI. To combat this trend, countries worldwide are exploring many options. Some are expanding their renewable energy programmes like solar, nuclear and wind. These investments can take years to show impact – with wait times for securing a grid connection in the European Union ranging from two to 10 years. Others are building more data centers, investing in sites to secure real estate, only for the shortage of power to leave them sitting idle.

To address the energy requirements of AI, we first have to look at our existing data centers. Buildings waste nearly 40% of the energy they use, so there could be a clear fix by operating them more efficiently, representing huge potential to get more out of what we have. And there are other factors too. Data centers are also under pressure from regulators, local communities and investors to operate more efficiently. It’s a win-win.

To do this, operators need to understand how poor energy management affects them, what blockers stand in the way of their transformation and how reducing wastage can support businesses looking to get the most out of AI.

The costs of inefficiency 

Poor energy management isn’t just an environmental problem, but a silent drain on resources. In data centers specifically, this is incredibly complex. Facilities often have multiple power supply systems to ensure uninterruptible service, cooling systems, temperature sensors, lighting, both physical and digital security, just to function. All too often these systems are siloed, making it difficult to get a realistic picture of how the data center is functioning. Without a unified view of all the systems, the chances increase that engineers miss a voltage imbalance that damages equipment.

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 center operators don’t have full visibility over their systems, they could miss the opportunity to use cheaper solar energy instead of grid power, or use several energy intensive systems during peak times due to poor coordination between building and electrical teams. Given a 100 kw data center can face over £200,000 per year in electricity costs minimising times when this happens could save thousands of pounds over the course of a year.  

An unseen talent gap 

Improving data center efficiency also requires enabling the workforce to understand complex data from various systems. For example, translating information on power and energy flowing through a facility into clear insights for engineers can help them more easily take the right actions to reduce wastage. However, we have an aging workforce without enough skilled engineers to replace them. And systems are getting more complex.

This means the need for an intelligent platform like EcoStruxure™ Foresight is urgent, to help engineers translate information into efficiency and empower the next generation with insights to meet stringent compliance targets. At a time when AI is dominating the consumer market too, this generation expects automation to support them at work. To enable the workforce, data centers can be no different.

Efficiency feeds expansion

Reducing energy wastage and enabling data center operators requires simplicity. If unpicking the detail of the systems feeding complex data center infrastructure is not realistic, unifying the data they produce can be.

By bringing together the information generated by energy-draining electrical and mechanical systems, with insights on power flowing from the grid, operators can anticipate failures, prevent downtime and extract more performance from the same footprint. This is about getting fragmented systems to speak the same language, so data center operators can make efficiency gains for the organizations that depend on them.

More data centers might well be necessary to meet AI-centric goals of tomorrow, but we must find a way to get us there by future-readying existing sites and the underlying infrastructure. That primarily means democratising access to disparate systems, so that they don’t run in isolation and stay ahead of issues.

This is the way to ensure scalability and efficiency, while avoiding the high, constant costs of physical expansion. And for data center operators under pressure from regulators, investors and customers, it’s time to realise that information is the source of power they are missing.