A Multi-Layer AI-Driven Decision Intelligence Framework for Enterprise and Healthcare System

Abstract

This paper proposes a comprehensive multi-layer AI-driven decision intelligence framework designed for enterprise and healthcare systems, addressing critical limitations in existing AI implementations where data, models, and decision-making processes often operate in isolation, thereby restricting scalability, explainability, and real-world applicability. The proposed framework introduces five interconnected layers data, intelligence, decision, application, and analytics each performing a distinct role in transforming raw data into actionable insights. By integrating machine learning models, rule-based reasoning, and advanced analytics, the framework ensures adaptive, accurate, and transparent decision-making. A healthcare case scenario is utilized to validate the framework, demonstrating improvements in clinical decision support, resource allocation, and response time. The evaluation results indicate enhanced decision accuracy, reduced processing time, and improved operational efficiency compared to traditional approaches. This study contributes a scalable and domain-independent architecture that bridges the gap between AI models and actionable decisions, supporting future intelligent systems with better interpretability and performance.

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