Optimizing Business Analytics and Decision-Making through the Application of Generative Artificial Intelligence in Forecasting and Simulation

Abstract

This thesis investigates how generative artificial intelligence (AI) can optimize business analytics and decision-making in forecasting and simulation. Employing a qualitative synthesis of academic literature and industry reports, the study examines how AI models enhance forecasting accuracy and generate realistic simulations. Results indicate that AI-driven forecasting improves decision quality by reducing uncertainty, lowering costs, and mitigating risks, while also offering a competitive advantage. However, significant challenges remain, including algorithmic bias, data privacy concerns, high computational requirements, and regulatory hurdles. The findings underscore the need for robust governance and further research on broader applications of generative AI in strategic decision-making today. CZU: 005.311.6:004.89; JEL: M10, M20, C53, O33