A new white paper from NTT Data highlights the urgent need to embed sustainability into every layer of AI development and deployment to counteract the technology’s environmental impact.
Deploying innovative solutions for sustainable AI is a corporate responsibility and a strategic opportunity to create lasting value, build organisational strength and consume fewer essential resources.
The new paper, Sustainable AI for a Greener Tomorrow, illustrates the growing environmental impact of AI and outlines a path to sustainable innovation.
The technology requires enormous volumes of electricity to support surging computational demands to train large language models, run inference pipelines, and maintain always-on services. Researchers predict AI workloads will drive more than 50% of data centre power consumption by 2028. Other primary environmental impacts include water consumption for data centre cooling systems, e-waste, and rare earth mineral extraction for hardware production.
“The resource consequences of AI’s rapid growth and adoption are daunting, but the technology also can empower innovative solutions to the environmental problems it creates,” says David Costa, head of Sustainability Innovation Headquarters at NTT Data. “AI’s amazing capabilities can help manage energy grids more efficiently, reduce overall emissions, model environmental risks, and improve water conservation. It’s vital for organisations to recognise the challenge and build sustainability into AI systems from the start.”
Key insights from the white paper include:
- Expand from performance to green priorities: NTT Data’s AI experts and sustainability consultants urge the use of holistic sustainability goals, not just conventional AI performance metrics such as accuracy and speed. Efficiency must be prioritized – not as a trade-off, but as a core design principle.
- Quantify environmental impact: AI’s energy consumption, carbon emissions, and water footprint need standard and verifiable metrics. Industry benchmarks such as the “AI Energy Score” and “Software Carbon Intensity (SCI) for AI” offer ways to embed sustainability into governance, procurement, and compliance protocols.
- Lifecycle-centric approach: Sustainable AI requires lifecycle thinking – from raw material extraction and hardware production to system deployment and ultimate disposal. Important steps include lengthening hardware lifespans, optimising cooling systems, and applying circular-economy principles.
- Shared accountability across the ecosystem: Responsibility is widely distributed encompassing hardware manufacturers, data centre operators, software developers, cloud providers, policymakers, investors, and consumers. Cross-sector cooperation is essential for systemic change.
Barriers and best practices
Today, fragmented assessments and inconsistent metrics frequently prevent meaningful benchmarking. Many organisations focus narrowly on energy or emissions without considering water usage, rare material depletion, and e-waste. These and other factors must be addressed comprehensively. Even when environmental goals are set, organisations often lack actionable methods to apply sustainability at every stage of the AI lifecycle.
To address these and other concerns, the report outlines numerous best practices including:
- Applying green software engineering patterns to reduce resource consumption.
- Running AI workloads in locations and at times that align with renewable energy availability.
- Leveraging remote GPU Services and on-premises AI.
- Reducing e-waste by prioritising modular and upgradable components, and extending hardware lifespans through refurbishment, reuse, and responsible recycling.