Perceived sustainability outcomes of generative artificial intelligence in small and medium sized enterprises

While generative AI is widely recognized for improving efficiency, limited research explains how it may relate to sustainability, particularly through the mechanisms that generate societal value. Existing studies often focus on either perceptions or high-level outcomes, with less attention to the processes linking technology adoption to sustainability-related effects. This study addresses this gap by examining the perceived sustainability impact of generative AI usage among Caribbean small and medium-sized enterprises (SMEs), drawing on the Triple Bottom Line framework. Using a sequential mixed-methods design, we surveyed 366 SME owners and conducted in-depth interviews with 26 owners across six small island developing states. The findings indicate that generative AI usage is positively associated with perceived sustainability impact. Respondents reported that AI supported inclusive hiring, enhanced employee well-being, and enabled environmentally responsible practices through mechanisms such as cost substitution, knowledge access, and efficiency gains. These findings suggest that generative AI usage is associated with broader perceived societal spillovers beyond productivity improvements. Building on these insights, the study proposes a Contextualized Impact Verification Framework (CIVF) as a conceptual pathway for future validation of sustainability impacts in resource-constrained SME contexts.