From GenAI functionality to social impact in small agricultural businesses

Purpose This study aims to investigate how small agricultural enterprises translate generative artificial intelligence (GenAI) functionality into social impact. It examines the iterative improvement loop (IIL) as the relational mechanism that activates the required dynamic capabilities (DC). Design/methodology/approach The research uses a qualitative, longitudinal multicase study design, analyzing four small farms in Western Europe. Data collection spanned eight months in 2025, using interviews, follow-ups and on-site observations. Findings Findings reveal that GenAI social impact creation depends on the depth and continuity of the IIL. The study identifies three depths of improvement, demonstrating that sustained feedback cycles between farmers and providers are essential to mature DC from sensing to reconfiguring. Originality/value This paper introduces iterative improvement as a necessary underlying process of DC for social impact. It extends DC theory by showing that capability evolution unfolds relationally.