Navigating Uncertainty Through AI Adoption: Dynamic Capabilities, Strategic Innovation Performance, and Competitiveness in Ecuadorian SMEs

Artificial intelligence (AI) is increasingly positioned as an enabler of strategic renewal and competitiveness for small and medium-sized enterprises (SMEs) in emerging economies. However, its adoption remains limited and uneven, constrained by shortages of skilled talent, weak data infrastructures, and financial barriers. This study examines Ecuadorian SMEs as a representative case within this broader context, analyzing survey data from 385 firms to diagnose AI adoption patterns and validate a structural model linking AI adoption, dynamic capabilities, and strategic innovation performance. Results from Partial Least Squares Structural Equation Modeling (PLS-SEM) confirm that AI adoption enhances innovation and competitiveness both directly and indirectly through dynamic capabilities, specifically firms’ abilities to sense opportunities, seize them through innovation, and reconfigure resources. The model explains 41% of the variance in strategic innovation performance, providing robust empirical support for the proposed AI-Driven Dynamic Capabilities Framework for Strategic Innovation and Competitiveness. The study clarifies how perceptual and contextual enablers of adoption (TAM/TOE) interact with capability-building mechanisms (RBV/DCT), offering a more integrated understanding of how SMEs assimilate AI under resource constraints. These findings demonstrate how SMEs translate early adoption into strategic advantage under conditions of uncertainty. The study also offers actionable guidance by showing that the most effective interventions for SMEs focus on strengthening foundational data and organizational capabilities rather than promoting complex AI systems beyond current readiness levels.