International Data Centre Day: How AI is reshaping the industry

However, many industry experts have expressed their concerns around the sustainability and infrastructure challenges that this will bring. This International Data Centre Day, we spoke to three experts to find out their views on the importance of balancing AI and sustainability efforts, the barriers enterprises are facing at the edge, and how synthetic data is shaping the future.  

Balancing AI and sustainability

In the current era of AI development, data centres are now seeing an unprecedented rise in demand from the technology, which in turn is bringing significant issues on how to balance it with sustainability. Terry Storrar, Managing Director at Leaseweb UK, recognises this, arguing that the rapid growth of AI technologies “is driving more focus on GPUs, and data centres must now deliver high volumes of controlled power in a denser environment to fulfil customer compute demand. In fact, around 70 percent of overall demand for data centre capacity is predicted to be for sites set up to host advanced AI workloads by 2030.”

He adds: “Aside from supplying the power for AI technologies, these are becoming more widely used in data centre operations, most notably for reporting tools and systems that help identify and improve efficiencies to drive down energy usage. The world’s data centres are ably employing AI for reporting processes to capture data and analyse how things can be done better. It goes without saying that demonstrating investment into sustainability is an absolute priority for the data centre industry.  Achieving and keeping compliance with international industry regulations, including ISO standards, is a solid way of showing accountability for environmental targets. And the industry must plan for long term investment as sustainability measures will only become more stringent in future years”.

Relieving the pressure

With this increasing demand, enterprises that have sites at the edge are finding that they are struggling to meet local needs while their main IT infrastructure is based at alternative locations further away. 

Bruce Kornfeld, Chief Product Officer at StorMagic, says: “Unreliable internet performance and latency have become the norm. But now, the massive need for digital transformation and the rapid development of AI analytics is forcing change on an unprecedented scale. We are seeing many more workloads being fully run locally at the edge.

“IoT devices, from smart shelves in retail stores to patient monitoring devices in hospitals, are creating vast amounts of data daily which all needs to be managed,” he adds. “This is putting the pressure on existing data centres and cloud computing services. Crucially, the data generated at the edge is often so time-sensitive that the AI engines behind the technology must be deployed locally. There simply isn’t time to send the data to a data centre or cloud for AI processing – decision-making must be instantaneous.”

Kornfeld notes how, because of this, “enterprises are increasingly looking for more suitable solutions. 

“Hyperconverged infrastructure (HCI) at the edge as part of a wider cloud strategy is one such alternative. HCI combines computing, networking, and storage resources into a single, streamlined data centre architecture using virtualisation to reduce server requirements. Designed specifically for smaller sites, modern HCI solutions run applications and store data securely at the edge yet can connect to the cloud and data centre as often as needed. Implementing solutions like this both eases the load on the data centres and improves the performance of local devices that, thanks to advances in AI, are now delivering more valuable insights than we ever imagined.”

Utilising synthetic data

The future of data centres is also being shaped by the increasing use of synthetic data. As explained by Geoff Barlow, Product and Strategy Director at Node4: “Artificially generated data that mimics real-world datasets is increasingly being used to train AI models at scale. Synthetic data offers clear benefits - it enables AI models to train on diverse, bias-free datasets while reducing ethical, privacy, and compliance risks. However, as AI adoption accelerates, so does the sheer volume of data created and stored. This exponential growth is driving huge demand for scalable, efficient storage and compute capacity.”

One method to ensure these developments are as sustainable as possible is “intelligent data tiering - using AI-driven tools to automatically move infrequently accessed (“cold”) data to low-energy storage, such as tape. With up to 80% of enterprise data rarely accessed, smart tiering can significantly reduce costs and energy consumption. Analysts estimate such approaches could cut storage costs by up to 40% while improving environmental performance. 

“At Node4, we see synthetic data and AI-driven workloads as catalysts for both innovation and change. They amplify the need for modern, sustainable infrastructure - combining scale, security, and energy efficiency. The future of data centres isn’t just about capacity but about meeting growing demand responsibly, with AI helping organisations manage data more intelligently while supporting their sustainability goals.”

Clearly, the data centre industry is facing significant challenges off the back of developments in AI. However, whether it be implementing hyperconverged infrastructure or utilising intelligent data tiering, the sector is continuing to make advancements in data centre technology to overcome the barriers it faces, while still striving to hit sustainability goals.