ARON: Adaptive Resource Optimization Network for AI-Driven Business Management

This paper introduces the Adaptive Resource Optimization Network (ARON), a novel AI-driven framework for strategic resource allocation and risk management in enterprise environments. ARON integrates deep reinforcement learning, natural language processing, and adaptive learning mechanisms to dynamically adjust resource allocation strategies in real-time, responding to market fluctuations and internal organizational changes. We present the architecture and methodology of ARON, including its data integration module, deep learning core, and natural language interface. An empirical evaluation across three diverse industry sectors-manufacturing, financial services, and e-commerce-demonstrates ARON's superior performance compared to traditional methods and static AI systems. Results show significant improvements in overall performance (up to 28.5%), resource allocation efficiency (13.4% to 40% increase), and risk management (37.2% reduction in Value at Risk). The study highlights ARON's enhanced adaptability to market volatility and its potential to transform business decision-making processes. We discuss the implications of these findings, acknowledge limitations, and propose directions for future research in AIdriven business management.