The Impact of Artificial Intelligence on Small-Medium Enterprises in Developing Countries

Abstract

This research study investigates the impact of Artificial Intelligence (AI) on small and medium-sized enterprises (SMEs) in developing countries, with a specific focus on three critical dimensions: affordability, accessibility, and skills. Despite AI's potential to improve operational efficiency, decision-making, and innovation, SMEs in developing regions face significant barriers to adoption due to limited financial resources, infrastructural constraints, and human capital deficits. To address these challenges, the study develops and applies a hybrid analytical framework combining the Business Model Canvas (BMC) and PESTEL analysis, offering a multidimensional approach to evaluate both internal and external factors influencing AI integration.
The research is guided by three central questions: (1) What framework can SMEs use to assess the internal and external impact of AI, specifically regarding affordability, accessibility, and skills? (2) What support structures are required for successful AI adoption? (3) How can qualitative value creation through AI be identified in terms of operational growth? Using a qualitative methodology, the study presents ten single-case studies for the first question and employs a multiple-case and cross-case analysis for the second and third questions. Case data were collected from SMEs operating in diverse sectors, including renewable energy, hospitality, fitness, consulting, and telecommunications across countries such as Chile, Namibia, South Africa, India, Vietnam, Ghana, and Malawi.
Findings indicate that skills availability is the most influential factor in determining AI success, shaping the capacity to implement, scale, and sustain AI systems. Affordability and accessibility remain important but can be mitigated through ecosystem engagement and adaptive strategies. The study reveals that AI contributes primarily through qualitative value creation, such as improved service delivery, increased operational efficiency, and enhanced decision-making. It also shows that cultural beliefs, policy support, and regional infrastructure significantly shape AI outcomes.
This research contributes a practical framework for SMEs and policy makers in the Global South (developing countries) to evaluate AI readiness and impact. It offers theoretical insights into AI adoption under conditions of constraint and calls for further research that expands into inclusive, cross-sectoral, and longitudinal analyses. The study concludes that while AI holds transformative potential for SMEs in developing countries, its success depends on strategically aligned internal capacity and supportive external ecosystems.