Towards Sustainability of AI: Organizational Adaptation for Environmentally Responsible Systems

Artificial Intelligence (AI) has emerged as one of the most transformative technological advancements of the 21st century, revolutionizing industries ranging from healthcare and finance to logistics and education. However, this rapid growth is accompanied by significant environmental challenges, particularly in terms of energy consumption and greenhouse gas emissions. The AI lifecycle encompassing data acquisition, model training, deployment, and inference contributes to substantial ecological footprints, highlighting an urgent need for sustainable development principles to be integrated into AI design and deployment.

This paper explores the dual dimensions of sustainable AI: AI for Sustainability and Sustainability of AI. The former focuses on leveraging AI technologies to address environmental challenges and advance the United Nations' Sustainable Development Goals (SDGs), while the latter addresses the environmental costs associated with AI systems themselves, including high energy consumption, inadequate lifecycle models, and fragmented regulatory frameworks.

Through a qualitative and quantitative analysis of existing literature and case studies, this paper identifies key solutions to mitigate these impacts. These include developing energy-efficient AI hardware such as Neural Processing Units (NPUs), embedding sustainability principles across every stage of the AI lifecycle, and fostering cross-sector collaborations to establish standardized regulatory frameworks. For instance, Kneron Inc. has demonstrated that optimizing AI-specific hardware can reduce energy consumption by approximately 30%, offering a viable model for energy efficiency in AI deployment.

The study concludes that achieving sustainable AI requires simultaneous technological, organizational, and legislative adjustments. Sustainability must not be treated as an afterthought but rather as a foundational principle for AI innovation. By prioritizing environmental considerations, AI systems can evolve into tools that drive progress without compromising the planet's ecological balance.