Research on the problems of artificial intelligence empowering comprehensive budget management

Abstract

Traditional comprehensive budget management suffers from multiple shortcomings such as the absence of strategic orientation, budget slack, and insufficient organizational coordination, making it difficult to adapt to complex and dynamic business environments. Digital technologies such as artificial intelligence and big data provide a technological path for the transformation of budget management. This paper systematically reviews the mechanisms and application scenarios of AI technologies-including time-series forecasting, anomaly detection, causal inference, reinforcement learning, and large language models-throughout the entire process of budget preparation, execution monitoring, analysis and adjustment, and assessment and review. The research shows that AI, relying on data-driven approaches, can achieve accurate forecasting, real-time risk identification, quantitative attribution, and dynamic resource optimization, effectively resolving the inherent pain points of traditional budget management. From the four dimensions of data, technology, organizational talent, and institutional risk control, this paper constructs a guarantee system for the implementation of AI budgeting, while also pointing out the practical obstacles currently faced by intelligent budget transformation, such as insufficient model interpretability, data silos, a shortage of compound financial talent, and imperfect supporting institutions. This paper enriches the theoretical system of intelligent budget management and provides implementation insights for enterprises to advance the digital-intelligent transformation of budgeting.