AI-Augmented Enterprise Decision Systems Using Predictive Analytics and Knowledge Graph Integration
Modern enterprises currently operate within industrial ecosystems marked by massive data volumes and complex links. Building timely, accurate decision-making capabilities has become a core measure to establish fundamental competitive barriers. To this end, this paper proposes a self-developed general framework: AI-Augmented Enterprise Decision Systems (abbreviated as AAEDS). Its core technical logic integrates probabilistic predictive analysis and knowledge graphs: predictive machine learning models are used to project future business trends, while knowledge graphs break down connectivity barriers between multi-source structured and unstructured enterprise data such as internal documents and web pages. After embedding these capabilities into full-process decision workflows, the framework can output real-time, interpretable, context-aware personalized decision recommendations for three core scenarios: supply chain management, financial planning, and human resource optimization. This framework overcomes three widespread pain points of traditional decision support systems: data silos, poor interpretability, and overreliance on fixed rules. Leveraging graph neural networks and large language models to enable dynamic orchestration of its inference pipeline, we conducted validation experiments using a selected enterprise dataset, which confirmed that the framework achieves significant improvements across three core metrics: decision accuracy, response latency, and user trust. Additionally, the framework integrates federated learning to adapt to the sensitive data processing needs of distributed enterprise nodes, meeting privacy compliance requirements. This work ultimately establishes a scalable cross-industry implementation path, enabling enterprises to transition from passive report-driven operations to large-scale intelligent decision-making, and laying a solid foundation for the next generation of enterprise cognitive systems.