Abstract

Artificial intelligence (AI) is increasingly influencing how small and medium enterprises (SMEs) organize operations, identify opportunities, respond to customers, allocate resources, and compete in changing markets. This conceptual paper examines the role of AI in promoting entrepreneurial success among SMEs by focusing on AI adoption, predictive analytics, and AI-driven decision support. The paper adopts a conceptual research approach through a structured review and synthesis of the empirical and theoretical literature contained in the study. The reviewed literature indicates that AI adoption can strengthen operational efficiency, innovation, competitiveness, and organizational responsiveness; predictive analytics can improve forecasting, customer insight, opportunity identification, and strategic planning; while AI-driven decision support can enhance the timeliness, quality, and evidence base of managerial decisions. The paper argues that these capabilities are not isolated technologies but complementary dimensions of an AI capability system. AI adoption provides the foundation for organizational use of intelligent technologies, predictive analytics converts available data into forward-looking intelligence, and AI-driven decision support transforms intelligence into managerial action. The paper further identifies high implementation costs, inadequate digital infrastructure, limited AI skills, data privacy concerns, and resistance to organizational change as important constraints that may weaken the ability of SMEs to obtain value from AI. Drawing on the conceptual logic contained in the reviewed literature, the paper proposes that each AI capability is positively associated with entrepreneurial success and that their integration can generate complementary benefits. The paper contributes by presenting an integrated conceptual framework that links distinct AI capabilities with entrepreneurial success and provides a basis for future empirical investigation among SMEs, particularly in developing-economy contexts