How SAP Frames AI Sustainability in Data Centre Efficiency
As artificial intelligence adoption accelerates, the energy and environmental impact of digital infrastructure has moved to the forefront of sustainability discussions.
The World Economic Forum estimates that AI could reduce annual emissions by three to six gigatonnes of carbon dioxide equivalent by 2035, but only if the technology itself is developed and deployed responsibly.
AI and Sustainability at SAP
Against this backdrop, SAP has launched its whitepaper AI and Sustainability at SAP, outlining how the company applies AI across its products while reducing the environmental footprint of the infrastructure that supports it.
For data centre operators and digital infrastructure leaders, the document highlights the growing importance of energy efficiency governance and ethical design as AI workloads scale.
Designing AI with Data Centre Efficiency in Mind
A central theme of the whitepaper is the need to curb the energy intensity of AI systems, particularly as training and inference workloads drive higher-density computing environments.
SAP outlines how it optimizes all AI assets and processes under its direct operational control for energy consumption, linking efficiency gains directly to emissions reduction and cost control.
The company emphasizes that monitoring and proactive management of AI-related emissions must continue as infrastructure scales. This has direct implications for data centre design and operations, where power usage effectiveness and workload optimization increasingly shape sustainability outcomes.
SAP also positions responsible sourcing as part of this equation, aiming to create responsible AI data supply chains by engaging partners and external networks to ensure that models implemented by SAP are sourced and developed responsibly.
Governance and Ethics Alongside Infrastructure Growth
Beyond energy efficiency, SAP highlights governance as a critical pillar of sustainable AI deployment.
The company has established a Global AI Ethics Policy that sets rules for the development, deployment, use, and sale of AI systems, reflecting a broader trend toward aligning infrastructure growth with ethical and regulatory expectations.
Ensuring transparency, security, and accountability is becoming as important as meeting capacity and latency targets for data centre operators supporting enterprise AI platforms.
Business Value Driven by AI Workloads
The whitepaper outlines how SAP Business AI can help organizations translate large volumes of internal and external data into actionable sustainability strategies.
From a data centre perspective, this reinforces the link between compute-intensive analytics and enterprise reporting requirements.
SAP states that AI can help CFOs and CSOs generate sustainability reports in 80% less time compared with traditional approaches. For COOs, AI optimization algorithms can improve demand forecasting and supply chain efficiency across production plants and warehouses, driving more predictable workloads and infrastructure utilization.
Data Centres as Enablers of Sustainable AI
The whitepaper sits within a wider industry conversation about how large-scale digital infrastructure can grow responsibly.
Hyperscale data centres are increasingly expected to balance capacity expansion with commitments on energy sourcing, water use, and community impact.
Microsoft has outlined a similar stance through its Community-First AI Infrastructure programme, which sets expectations for how the company builds, owns, and operates its data centres.
This initiative includes commitments to cover electricity costs, replenish water used by facilities, and invest in local jobs and AI skills training.
In conclusion, SAP’s whitepaper positions data centres not just as passive enablers of AI but as active participants in sustainability outcomes through efficiency governance and ethical design.
