Industry 5.0 and AI-powered competitiveness: Redefining business models for the future (#111)

Authors

Silva Atencio, Gabriel

Abstract

This research investigates the transformational impact of Artificial Intelligence (AI) on corporate competitiveness within the framework of Industry 5.0, using a mixed-methods approach that combines the Resource-Based View (RBV) and Dynamic Capabilities Theory to tackle implementation issues particular to the industry. The study aims to concentrate on (1) measuring AI-induced productivity enhancements across various sectors, (2) analyzing human-AI cooperation frameworks, and (3) scrutinizing ethical governance structures, with a specific emphasis on Latin American settings. Methodologically, the research integrates qualitative case studies with quantitative surveys, using theme and regression analysis to corroborate results. The most important findings show that there are big differences across sectors: AI diagnoses can cut healthcare costs by 35%, whereas retail needs hybrid human-AI models to work best (15% benefits), and better governance frameworks can cut bias incidences by 58%. The conclusions stress that AI may be both a strategic resource and an adaptable capacity, depending on the culture of the business and the needs of the industry. Recommendations stress the need for tiered AI deployment roadmaps for Small and Medium-sized Enterprises (SMEs), ethical oversight committees, and initiatives to retrain workers. The subsequent study needs to investigate the long-term effects of AI implementation in circular economic transitions and culturally tailored governance frameworks, therefore filling the voids in studies concerning developing economies. This study connects global theoretical frameworks with regional empirical evidence, giving policymakers and practitioners useful information that fits with relevant research that balances technological innovation with socioeconomic equity.