LJ Janse van Rensburg
Abstract
Artificial Intelligence (AI) presents significant opportunities for economic growth and innovation in third-world countries, yet its effective implementation faces unique challenges. This study provides a comprehensive analysis of AI applications in developing economies, addressing the lack of synthesised knowledge in this rapidly evolving field. Through a systematic literature review of 121 papers using the TCCM (Theory, Context, Characteristics, and Methodology) framework, complemented by a bibliometric analysis of 3,770 articles, we investigate global AI trends, local opportunities, sector-specific innovations, and strategies to overcome barriers. Our findings reveal increasing AI applications in agriculture and healthcare, with a trend towards lightweight models suitable for resource-constrained environments. The integration of AI with IoT, blockchain, and 5G emerges as significant. While local resources present opportunities for context-specific solutions, limited infrastructure, skill gaps, and regulatory challenges hinder adoption. The bibliometric analysis uncovers potential language and geographic biases in current research. This study contributes theoretically by synthesising frameworks for understanding AI adoption in developing contexts and practically by offering insights for policymakers and managers. It provides a roadmap for strategic investments and capacity building, laying the groundwork for future research in this critical area of development economics and technology management.