Intelligent Rule Extraction and Decision Management: An AI-Driven Architecture for Enterprise Decision Optimization

Abstract

The article describes an Intelligent Rule Extraction and Decision Management (IREDM) architecture that tackles the key problem of Decision Debt in businesses where rich business logic is languishing in unchangeable policy documents. The AI-based framework automatically derives actionable rules out of unstructured documentation and translates them into computational decision models in a fully traceable manner. A three-plane architecture separates functionality into knowledge, decision, and governance planes, resulting in a modular system with an appropriate balance between automation and suitable controls. The solution itself provides the transformative gains of consistent application of rules, verifiable compliance, expedited execution of policies, scalability to an enterprise-wide level, and continuous improvement in the form of feedback loops. The document provides comprehensive examples, such as the process of automated vendor onboarding, to depict how organizations can instantiate their institutional knowledge, transforming their static policies into dynamic, governed decision capabilities that change in response to changing business and regulatory conditions.