This study examines patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), with an emphasis on functional diversity across value chain activities. Based on a cross-sectional dataset of 781 enterprises and an entropy-based model, it assesses internal variability in AI use and explores its relationship with strategic perception and dynamic capabilities. The findings reveal predominant partial adoption, alongside high functional entropy in sectors such as mining and services, suggesting an ongoing phase of technological experimentation. However, a significant gap emerges between perceived strategic use and actual functional configurations—especially among microenterprises—indicating a misalignment between intent and organizational capacity. Barriers to adoption include limited technical skills, high costs, infrastructure constraints, and cultural resistance, yet over 70% of non-adopters express future adoption intentions. Regional analysis identifies both the Andean Highlands and Coastal regions as “innovative,” although with distinct profiles of digital maturity. While microenterprises focus on accessible tools (e.g., chatbots), small enterprises engage in data analytics and automation. Correlation analyses reveal no significant relationship between functional diversity and strategic value or capability development, underscoring the importance of qualitative organizational factors. While primarily descriptive, the entropy-based approach provides a robust diagnostic baseline that can be complemented by multivariate or qualitative methods to uncover causal mechanisms and strengthen policy implications. The proposed framework offers a replicable and adaptable tool for characterizing AI integration and informing differentiated support policies, with relevance for Ecuador and other emerging economies facing fragmented digital transformation.
García-Vidal et al. (Fri,) studied this question.