Organizational strategies for ethical and sustainable AI implementation

The rapid diffusion of artificial intelligence (AI) has intensified debate about its dual role as a driver of efficiency and a source of environmental, social, and ethical challenges. These challenges include the growing energy and water demands of data centers, hardware-related emissions and e-waste, data governance risks, workforce concerns, and increasing regulatory pressure. While prior research highlights AI’s sustainability potential, less is known about how organizations translate ethical and sustainability principles into concrete implementation strategies. This study examines responsible AI adoption under sustainability constraints, using the Technology-Organization-Environment framework extended with ESG and ethics considerations, and the Triple Bottom Line perspective to interpret outcomes. Empirical insights are based on five semi-structured expert interviews with senior executives, AI specialists, and sustainability professionals across industries and national contexts. The findings show that AI supports efficiency, predictive analytics, and sustainability monitoring, but responsible deployment depends on readiness, data quality, workforce capabilities, and governance structures.