28 July, 2026 | 5 min read

The sustainability challenge of AI: Data, Cloud and FinOps strategies

Modern data center server room with rows of glass-enclosed racks illuminated by blue LED lights, extending down a long, clean corridor.

Digital sustainability moves up and down the priority scale for both global organisations and governments alike. But with surging electricity demand and consumption from AI usage and associated data centres now consuming 1.5% of global electricity — electricity consumption of data centres is growing at 12% annually — this issue has never been more urgent.

Which leads us to the challenge that all organisations need to consider as their AI usage matures: how to address environmental impact without compromising business performance and the infinite quest for growth?

The answer is not to stop using technology, it lies in treating sustainability in the same way that financial operations teams measure cloud spend. Getting ahead of the curve of the inevitable regulations that will follow.

The moral obligation matters. The business driver is faster.

What digital sustainability actually means

Digital sustainability is the practice of designing, building and operating technology in a way that reduces environmental harm while still delivering business value.

Covering the physical infrastructure that powers digital services, the way systems are architected, and the technology choices organisations make, we need to face the fact that the acceleration of electricity consumption is driven primarily by AI workloads, which are expected to account for 44% of total data centre power usage by 2030 (Gartner).

The impact is created across three connected areas:

Physical infrastructure:

the servers, cooling systems and power supplies operating in data centres.

Architectural design:

the way systems are built, how long they run and whether compute resources actually shut down when not needed.

Technology selection:

the choice between competing platforms may carry vastly different environmental consequences.

Most organisations do not measure any of these dimensions. Teams optimise for speed and cost without visibility into environmental impact. That gap matters, because the same inefficiencies that waste money also waste energy: idle servers, oversized instances, duplicated data, unnecessary storage and workloads running in regions with higher emissions.

Unsurprisingly,  customers are increasingly demanding transparency in this area, and regulators are beginning to force the issue.

Regulatory drivers and increasing pressure

UK public procurement already requires central government buyers to take carbon reduction into account for major contracts, and sustainability expectations are becoming more explicit across digital, cloud and ICT purchasing. The Government Digital Sustainability Alliance, led by Defra, is also shaping guidance and standards for sustainable digital practice across government and its supply chain.

Private-sector organisations face similar pressure from investors scrutinising environmental, social and governance (ESG) commitments, and from end consumers who increasingly expect corporate accountability on sustainability.

This vast energy consumption by companies competing in the AI space is not a fringe concern discussed only at sustainability conferences — it surfaces repeatedly in conversations with customers who care about their environmental footprint, whether from genuine conviction or regulatory necessity.

A futuristic blue-toned digital graphic depicts a cloud computing and FinOps environment. A large cloud icon sits at the center on a technology platform, linked to monitoring dashboards, data storage systems, server racks, security shields, containerized workloads, and DevOps infinity-loop symbols. Bright interconnected pathways illustrate data flow, automation, governance, and cloud financial management across the infrastructure. The image emphasizes cloud operations, cost optimization, security, monitoring, and continuous delivery within a modern enterprise cloud platform.

Measurement, FinOps and better decisions

The effective business argument: cost optimisation and environmental optimisation are largely the same problem.

FinOps for cloud focuses on controlling cloud spend through intelligent resource management. Visibility into consumption often reveals inefficiencies that cost energy as well as money: servers running 24/7 when they could be shut down overnight, redundant resources kept live unnecessarily, or oversized instances purchased to handle peak load despite running well below capacity most of the time.

The overlap between cost and environmental optimisation is substantial. Shutting down compute that is not needed saves both money and the planet. Choosing a data centre region with renewable energy reduces emissions at no additional cost.

The bottom line

Digital sustainability is not about environmental purity. It is about balancing the business need for effective, scalable infrastructure with the legitimate environmental cost of that infrastructure. The goal is to reduce that cost through smarter design, better measurement and deliberate choices – not to eliminate technology, but to use it more thoughtfully.

Organisations that move first on this will find themselves ahead when regulation tightens and customers begin expecting proof of environmental stewardship. Those that wait will face scrambling retrofits and reputational risk.

How we can help

Digital sustainability requires measurement, strategy and deliberate choices about how you build and operate infrastructure. We work across three core areas to embed environmental responsibility into technical delivery:

Ready to act?

If your organisation needs help auditing consumption, designing sustainable systems or implementing emissions tracking, contact us to discuss where digital sustainability fits in your roadmap.

You can also learn more about our Unified Data and AI offering.

 

About the author

Dawn is a strategic data, analytics and AI leader, with over 20 years’ experience helping organisations turn data into business value. Her expertise spans data strategy, governance, analytics, data and AI adoption and enterprise platform delivery, with a strong track record of leading large-scale transformation programmes across global organisations. Combining deep technical knowledge of Microsoft Azure, Microsoft Fabric, Databricks, AWS, Snowflake and Oracle with extensive leadership experience, Dawn is passionate about building high-performing teams and enabling organisations to realise the full potential of their data assets.