“A comprehensive review of AI-enhanced decision making: An empirical analysis for optimizing medication market business”

Abstract

Enterprise Resource Planning (ERP) systems play a critical role in integrating key business functions, including customer relationship management (CRM), inventory control, and financial operations. The integration of Artificial Intelligence (AI) techniques, particularly Machine Learning (ML), has the potential to enhance decision-making and optimize operational efficiency. This study systematically reviews AI-driven enhancements in ERP using the PRISMA methodology to identify trends, applications, and challenges. Additionally, an empirical analysis using a publicly available dataset conducted to demonstrate the impact of ML-driven sentiment analysis on demand forecasting in the pharmaceutical sector. Our findings indicate that AI-enhanced ERP systems improve forecasting accuracy, inventory management, and financial planning, leading to better alignment with market demands. Further, empirical results highlight the transformative role of AI techniques in optimizing ERP functionalities and supporting data-driven decision-making. This research provides actionable insights for enterprises aiming to integrate ML techniques into ERP systems to enhance business performance.

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