Application of Artificial Intelligence in Cross-Departmental Budget Execution Monitoring and Deviation Correction for Enterprise Management

Abstract

This research investigates the application of artificial intelligence technologies in enhancing cross-departmental budget execution monitoring and deviation correction within enterprise management frameworks. Traditional budget management systems face significant challenges in real-time variance detection and coordinated response across multiple organizational departments. This study proposes an integrated AI-enhanced framework that leverages machine learning algorithms for predictive variance detection, natural language processing for automated financial narrative analysis, and intelligent resource reallocation mechanisms. The framework incorporates real-time data integration protocols, automated early warning systems, and collaborative decision-making support tools. Experimental validation demonstrates substantial improvements in budget variance detection accuracy, reduction in correction response times, and enhanced cross-departmental coordination effectiveness. The proposed system achieves 94.2% accuracy in deviation prediction with 73% reduction in manual intervention requirements. Results indicate significant potential for AI-driven budget management systems to transform enterprise financial control processes while maintaining organizational agility and resource optimization efficiency.