Artificial Intelligence in Business Scenario Analysis: A Framework for Enhanced Decision-Making Through What-If Simulations

Abstract

This article examines the transformative impact of artificial intelligence on scenario analysis and what-if simulations in business analytics, addressing a critical gap in current literature regarding the integration of AI-driven predictive modeling with traditional business planning methodologies. Through a systematic analysis of implementation cases across financial services and e-commerce sectors, the article demonstrates how AI-enhanced simulation models significantly improve the speed, accuracy, and adaptability of scenario planning processes. The findings indicate substantial improvements in analysis time and prediction accuracy compared to traditional methods, particularly in areas of pricing optimization, risk assessment, and stress testing. The article synthesizes data from multiple enterprise implementations to develop a comprehensive framework for AI integration in scenario analysis, addressing key challenges in data quality, model reliability, and real-time processing capabilities. Results suggest that organizations leveraging AI-driven scenario analysis demonstrate enhanced capability in anticipating market fluctuations, optimizing resource allocation, and responding to environmental changes, though implementation success is heavily dependent on data infrastructure maturity and organizational readiness. This article contributes to both theoretical understanding and practical application of AI in business analytics, providing actionable insights for practitioners while identifying critical areas for future research in the evolving landscape of intelligent business simulation technologies.