NTT DATA Calls for Urgent Action to Make AI Sustainable
NTT DATA, a global leader in AI, digital business, and technology services, has released a new white paper that underscores the critical need to embed sustainability into every stage of AI development and deployment. The report warns that without decisive action, the rapid expansion of artificial intelligence could create an unsustainable demand for energy, water, and raw materials — threatening both the environment and long-term innovation.
The white paper, Sustainable AI for a Greener Tomorrow, explores how AI’s resource-intensive nature is contributing to a growing environmental footprint. Training large language models (LLMs), running inference systems, and maintaining continuous AI operations require enormous amounts of electricity. According to researchers, AI workloads could account for over 50% of data center power consumption by 2028. Beyond electricity, AI infrastructure depends heavily on water for cooling systems, and on rare earth minerals for chip manufacturing—both of which carry significant ecological costs.
“The resource consequences of AI’s rapid growth and adoption are daunting, but the technology can also empower innovative solutions to the environmental problems it creates,” said David Costa, Head of Sustainability Innovation Headquarters at NTT DATA. “AI’s powerful capabilities can manage energy grids more efficiently, reduce emissions, model climate risks, and improve water conservation. It’s vital for organizations to recognize this dual challenge and embed sustainability into AI systems from the very beginning.”
Key Insights from the Report
1. Expand from Performance to Green Priorities
Traditional AI success metrics—accuracy, speed, and performance—are no longer enough. NTT DATA calls for a shift toward holistic sustainability goals, making efficiency and low energy consumption integral parts of AI design, not optional add-ons. Sustainable AI must balance performance excellence with environmental responsibility.
2. Quantify Environmental Impact
To make progress, organizations need standardized, verifiable sustainability metrics for AI systems. Frameworks such as the AI Energy Score and Software Carbon Intensity (SCI) for AI can help quantify the carbon, energy, and water impact of AI applications. Embedding these measures into governance, procurement, and compliance will drive accountability and transparency.
3. Adopt a Lifecycle-Centric Approach
NTT DATA emphasizes lifecycle thinking—assessing environmental impact from raw material sourcing and hardware production through deployment, operation, and end-of-life disposal. Extending hardware lifespan, optimizing cooling systems, and applying circular economy principles are essential steps toward reducing waste and emissions.
4. Foster Shared Accountability Across the Ecosystem
Sustainability is a shared responsibility. From hardware manufacturers and cloud providers to policymakers and consumers, every participant in the AI ecosystem plays a role. NTT DATA calls for cross-sector collaboration to ensure that sustainability becomes a collective, measurable standard, not a competitive afterthought.
Barriers and Best Practices
Despite growing awareness, many organizations struggle to measure and mitigate AI’s environmental impact effectively. Fragmented assessments and inconsistent reporting standards often obscure the true scale of AI’s footprint. Some focus narrowly on energy usage or emissions while overlooking water consumption, e-waste, or material depletion.
To overcome these challenges, the report outlines a set of best practices that organizations can implement immediately:
- Adopt green software engineering patterns to reduce computational load and power consumption.
- Run AI workloads in data centers or at times that align with renewable energy availability.
- Utilize remote GPU services or localized AI infrastructure to minimize energy-intensive data transfer.
- Extend hardware lifespan through modular design, refurbishment, reuse, and responsible recycling.
- Reduce e-waste by prioritizing upgradable components and sustainable procurement strategies.
Building a Greener AI Future
NTT DATA’s white paper concludes that achieving sustainable AI requires a comprehensive redesign of the AI lifecycle—from data collection and model training to deployment and hardware management. By embedding environmental stewardship into the foundation of AI innovation, organizations can unlock efficiency gains, strengthen resilience, and reduce dependence on finite natural resources.
While the path forward is complex, the report argues that sustainability should be viewed not as a cost, but as a strategic advantage. Companies that lead in green AI will not only mitigate regulatory and reputational risks but also shape a more resilient, innovative, and environmentally conscious digital economy.
For organizations seeking to take action, NTT DATA invites readers to download the full white paper and explore its suite of sustainability solutions designed to accelerate the global transition toward a cleaner, smarter AI future.
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