For many organisations, conversations about AI have revolved around questions like: ‘How can we use it?’, ‘How do we protect our data?’ and ‘How do we manage the risks?’ Yet as AI adoption increases, another challenge is moving up the agenda: sustainability.

Every AI model and application relies on physical infrastructure behind the scenes, including data centres, networks, cooling systems and power generation. That infrastructure needs electricity, water and computing resources to function, at a scale that is already impossible to ignore.

This isn’t an argument against AI. Rather, it’s a reminder that organisations should consider the environmental impact of their AI adoption alongside the impact on performance. It should be part of their AI strategy from the outset.

AI’s energy demand

AI workflows require far more power than traditional computing. That growth is already feeding through to wider demand:

• The International Energy Agency projects global electricity generation for data centres to increase from around 460TWh in 2024 to over 1000 TWh by 2030.
• Goldman Sachs forecasts global data-centre power demand could increase by as much as 165% by 2030 compared with 2023 levels.

Unfortunately, the infrastructure meant to supply that power may struggle to keep pace. Some grid-connection requests already face waits of up to seven years. Renewables will supply a significant share of this demand further down the line, but until then, we’re relying on gas and coal power.

Bigger facilities. Greater pressure

The AI companies’ solution to the growing demand for their services is to build bigger data centres. A report by Deloitte found that the top three AI hyperscalers’ largest US data centres currently draw a modest 500MW of power. However, this won’t be the case for long. The AI giants are planning data centres covering around 50,000 acres and potentially consuming 5 GW of power. That is enough to supply roughly five million US homes and exceeds the capacity of the country’s largest existing nuclear or gas plants.

AI servers generate significant levels of heat. This requires cooling, which creates another pressure point. Rystad Energy estimates that data centres consumed around 222 billion litres of water directly for cooling in 2025. If expansion turns out as predicted, that could rise to 644 billion litres a year by 2030. It’s hoped that a technological solution to this issue will emerge in the future, but until then, the industry needs water.

How can organisations respond?

There are several practical steps organisations can take right now to reduce the environmental impact of their growing AI adoption. These include:

• Moving AI-heavy workflows to places or times where capacity is easier to access
• Using more efficient chips and hardware that reduce energy consumption
• Choosing suppliers that generate more of their power from renewable sources

However, with power consumption from AI predicted to rise by such a massive amount, the only reliable solution is to future-proof the whole endeavour. Organisations must incorporate sustainability into their AI planning as early as possible, alongside performance, governance and security.

Final thoughts

AI can deliver major benefits, but the infrastructure behind it places more pressure on power systems and water supplies than we can currently cope with. That makes choices around sustainability increasingly important as adoption grows.

There are reasons to be positive, however. Hardware is becoming more efficient, cooling technology is improving and renewable energy is playing a bigger role. Also, the market will have an impact, with customers, investors and regulators focusing more closely on the environmental footprint of AI growth.

One thing is clear: the more consideration organisations give to sustainability in AI, the more likely they are to capture long-term value.

About Sam

Sam Scott is a technology specialist at Rigby Capital. He works across Rigby Capital and Lombard Technology, helping clients better understand technology and how it links to their business ambitions. Sam’s career spans healthcare, medtech and the wider IT sector. Along the way, he’s built experience in infrastructure, software, cybersecurity and AI. As technology evolves at pace, it’s this knowledge that helps Sam show businesses what matters and where to go next.