Exploring Artificial Intelligence in Economics: Methods, Applications and Limitations

The rapid expansion of data in the digital economy has necessitated the adoption of advanced analytical tools in economic research. This chapter examines the growing integration of Artificial Intelligence (AI) in economic analysis, emphasising its methodological foundations, diverse applications, and inherent limitations. It explores key AI techniques including machine learning, deep learning, and natural language processing, and their effectiveness in enhancing predictive accuracy, pattern recognition, and decision-making across economic domains. The study highlights the application of AI in both macroeconomic and microeconomic contexts, such as economic forecasting, demand estimation, dynamic pricing, labour market analysis, financial risk management, and policy formulation. By leveraging large and complex datasets, AI-driven models offer significant improvements over traditional econometric approaches in terms of efficiency and scalability. However, the chapter also critically evaluates the challenges associated with AI adoption, including issues of data privacy, algorithmic bias, and lack of model transparency. These concerns underscore the need for ethical frameworks and explainable AI systems in economic practice. The findings suggest that while AI has transformative potential in reshaping economic analysis and policymaking, a balanced approach that integrates technological innovation with ethical considerations is essential for sustainable and inclusive economic development.