How Do Companies Manage the Environmental Sustainability of AI? An Interview Study About Green AI Efforts and Regulations

2025-05-12

AI Summary

This study reveals a critical gap in industry’s adoption of green AI practices: nine of eleven interviewed practitioners overlook AI’s environmental impact, only one actively monitors energy consumption, and six companies implement no mitigation measures. Through semi-structured interviews with 11 organizations, thematic coding, and comparative analysis against the EU AI Act and the Corporate Sustainability Reporting Directive (CSRD), we systematically identify enterprises’ cognitive blind spots, implementation gaps, and weak regulatory responsiveness regarding AI sustainability. Findings indicate that an efficiency-first paradigm marginalizes environmental considerations; enforcement of existing regulations remains weak; CSRD-driven accountability is limited; and the sector lacks actionable tools and standardized assessment frameworks. The study empirically substantiates a pronounced policy–practice divide and provides foundational evidence and key leverage points for developing context-sensitive, industry-adapted governance pathways for green AI.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Machine Learning: Efficient ML / Green AI
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Sustainability and climate impact of web technologies
• Security and Privacy: Security and privacy of machine learning and AI applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

With the ever-growing adoption of artificial intelligence (AI), AI-based software and its negative impact on the environment are no longer negligible, and studying and mitigating this impact has become a critical area of research. However, it is currently unclear which role environmental sustainability plays during AI adoption in industry and how AI regulations influence Green AI practices and decision-making in industry. We therefore aim to investigate the Green AI perception and management of industry practitioners. To this end, we conducted a total of 11 interviews with participants from 10 different organizations that adopted AI-based software. The interviews explored three main themes: AI adoption, current efforts in mitigating the negative environmental impact of AI, and the influence of the EU AI Act and the Corporate Sustainability Reporting Directive (CSRD). Our findings indicate that 9 of 11 participants prioritized business efficiency during AI adoption, with minimal consideration of environmental sustainability. Monitoring and mitigation of AI's environmental impact were very limited. Only one participant monitored negative environmental effects. Regarding applied mitigation practices, six participants reported no actions, with the others sporadically mentioning techniques like prompt engineering, relying on smaller models, or not overusing AI. Awareness and compliance with the EU AI Act are low, with only one participant reporting on its influence, while the CSRD drove sustainability reporting efforts primarily in larger companies. All in all, our findings reflect a lack of urgency and priority for sustainable AI among these companies. We suggest that current regulations are not very effective, which has implications for policymakers. Additionally, there is a need to raise industry awareness, but also to provide user-friendly techniques and tools for Green AI practices.

Problem

• Investigates how companies manage AI's environmental sustainability impact
• Examines industry practices and regulations influencing Green AI adoption
• Assesses awareness and effectiveness of current Green AI mitigation efforts

Innovation

• Conducted 11 interviews on Green AI practices
• Explored AI adoption and environmental impact mitigation
• Assessed influence of EU AI Act and CSRD