Navigating the AI Energy Challenge: A Sociotechnical Framework and Strategic Solutions for Sustainable Artificial Intelligence

Abstract

Artificial intelligence is at the intersection of innovation and escalating energy demands. This paper addresses the AI energy paradox through an integrated sociotechnical framework that combines technological architectures, organizational practices, and adaptive governance. Comprehensive case analyses reveal critical leverage points where targeted interventions boost performance while significantly reducing energy consumption. Our findings challenge conventional views of inherent efficiency–performance trade-offs, showing that these limitations largely stem from outdated design choices. We propose a balanced strategy: deploy mid-scale models for routine, high-efficiency tasks (e.g., dataset processing and rapid document summarization) and reserve high-capacity models with advanced reasoning for complex challenges. By aligning optimized hardware architectures with strategic policy measures, our approach offers considerable economic, operational, and environmental benefits. Furthermore, our analysis highlights an urgent need for innovative, energy-conscious AI development strategies. This roadmap empowers researchers, practitioners, and policymakers to harness AI’s transformative potential while ensuring ethical and sustainable development for current and future generations.