Artificial Intelligence Adoption in Established Women-Led SMEs: A Multiple-Case Study of Competencies, Barriers, and Business Process Applications

Abstract

Artificial intelligence (AI) is increasingly acknowledged as a strategic asset for enhancing innovation and competitiveness among small and medium-sized enterprises (SMEs). Nevertheless, limited research has focused on the experiences of women entrepreneurs regarding AI adoption within established SMEs, particularly in emerging economies. This study investigates the knowledge, attitudes, skills, perceived barriers, and business-process applications associated with AI adoption among women entrepreneurs in Chile. Employing an exploratory multiple-case study design, semi-structured interviews were conducted with fourteen women leading established SMEs across various industries. The cross-case analysis indicates that resilient leadership, social intelligence, and managerial knowledge were frequently mentioned characteristics alongside diverse patterns of AI adoption. The findings reveal that participants identified strategic-planning and management-control knowledge, resilient leadership attitudes, and social intelligence skills as recurring competencies associated with AI adoption into business activities. Participants reported utilising AI primarily to support customer relationship management (CRM), while also describing limited AI readiness as a constraint associated with learning needs and experiences of AI adoption. To distinguish between AI adoption and broader digitalisation and automation, the study defines the following adoption profiles: non-adopter, AI experimenter, substantive AI adopter, and integrated AI adopter. Further cross-case analysis suggests that participants experienced knowledge, attitudes, and skills as mutually reinforcing during AI adoption rather than as isolated competencies. This study contributes to the literature by providing an empirically grounded understanding of how a group of established women entrepreneurs perceive and experience AI adoption in their SMEs. Instead of identifying causal determinants of successful adoption, the findings highlight recurring patterns and relationships that may inform future theory development and comparative research on AI adoption in entrepreneurial contexts. The study also offers practical insights for policymakers, educators, and SME support organisations aiming to enhance AI capabilities among women-led businesses.