From Paradox to Progress A Net Positive AI Energy Framework 2025

AI-driven battery grading for scalable, sustainable manufacturing BOX 10 Business case Cross-sector collaboration drives cost- and energy-efficient AI. Governments, industry and researchers align investment, infrastructure and governance to create shared standards, build trust and advance responsible, sustainable growth. Challenge Traditional battery grading tests each cell through full charge-discharge cycles – a slow, energy- intensive process that limits scale, raises costs and hinders sustainable, efficient production. Solution –AI-modelled battery capacity prediction using real-world production data –AI-driven performance learning improving prediction accuracy and reducing manual intervention –AI-enabled manufacturing optimization increasing throughput and lowering environmental impact Impact Near-term impacts realized or anticipated (less than one year) –750,000kWh saved per gigawatt-hour (GWh) of production –32% faster production cycles –Less than 1% manual oversight requiredFurther impacts realized or anticipated (more than one year) –$7.5 million in annual savings –200–300 internal users across multiple sites Reviewing the levers in action Multistakeholder task force: Collaborates with stakeholders to advance AI-enabled battery systems Interoperability frameworks: Joint initiatives with regional governments and energy agencies to scale infrastructure Joint infrastructure planning: Active participant in technology clusters linking AI, mobility and renewable integration Policy harmonization: Contributes to battery safety and data-sharing standards* Incentive alignment: Co-funded demonstration projects; broader financing collaboration remains limited* *See Table 6 for relevant “ecosystem collaboration” use case examples. Sources: Amazon Web Services (AWS). (2025). Unlocking Europe’s AI Potential in the Digital Decade 2025. https://www.unlockingeuropesaipotential.com/; MINDS 2025. 28 From Paradox to Progress: A Net-Positive AI Energy Framework