Adaptive AI-Driven Enterprise Resource Planning for Scalable and Real-Time Strategic Decision Making

Enterprise Resource Planning (ERP) systems play a critical role in managing organizational assets and operations. However, traditional ERP systems rely on static, rule-based decision-making frameworks that lack the agility and intelligence required for real-time strategic support. To address these limitations, this study proposes an Adaptive AI-Driven Enterprise Resource Planning (A2ERP). This AI-augmented ERP framework integrates adaptive predictive models to enhance decision-making capabilities at scale. The A2ERP architecture features a dynamic data ingestion layer, an adaptive predictive engine utilizing online learning and ensemble methods, and a decision support interface empowered with explainable AI (XAI). It is designed for scalability through a containerized microservices architecture. Experimental results demonstrate that A2ERP achieves a 98% accuracy rate in both training and testing phases, effectively identifying errors such as omission, addition, and overstatement. Comparative evaluations show that A2ERP outperforms traditional ERP methods across key performance metrics, including precision, recall, and F1-score. The framework’s ability to process large-scale, complex data in real-time underscores its effectiveness in delivering timely strategic insights. A2ERP represents a significant advancement toward scalable, adaptive, and intelligent ERP systems, bridging the gap between operational execution and strategic decision-making.