Artificial Intelligence-Driven Economic Decision Making: A Conceptual Framework and Policy Implications
Modern economies are undergoing a profound digital transformation in which economic decision-making processes are being reshaped under conditions of increasing data intensity, algorithmic complexity, and uncertainty. Although artificial intelligence (AI) has significantly enhanced the accuracy, speed, and analytical capacity of economic decision-making, the existing literature largely addresses decision support systems, predictive models, human-AI interaction, and AI governance as separate research streams. Studies that integrate these components into a comprehensive conceptual architecture for economic decision-making remain limited. This study aims to develop an integrated conceptual framework that explains AI-driven economic decision-making from technical, managerial, and ethical perspectives. Adopting a conceptual synthesis approach, the study systematically reviews and integrates the interdisciplinary literature on economics, decision sciences, artificial intelligence, and public policy. Based on this synthesis, the AI-EDG Framework (Artificial Intelligence-Driven Economic Decision and Governance Framework) is developed. The proposed framework conceptualizes economic decision-making as an integrated system comprising a data ecosystem, AI analytics, human-AI collaborative reasoning, ethical governance, decision implementation, and continuous learning through feedback loops. Building upon this framework, policy implications are systematically examined for the public sector, private enterprises, and the labor market. The findings highlight trustworthy AI governance, explainability, human oversight, and institutional compliance as essential determinants of sustainable AI-driven economic decision-making. By integrating fragmented streams of literature into a unified theoretical framework, the study introduces a novel conceptual model that bridges AI-based decision support systems and ethical governance approaches, providing a testable theoretical foundation for future empirical research and evidence-based policy design.