AI-Driven MIS Architectures: Deep Learning–Enabled Decision Support for Business Operations

Abstract

The rapid evolution of decision support systems (DSS) and management information systems (MIS) has transformed how organizations process data and make strategic and operational decisions. While traditional MIS relied on structured reporting and descriptive analytics, recent advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) enable more predictive and prescriptive decision-making capabilities. This paper presents a conceptual MIS architecture that integrates AI-driven components across data, model, decision, execution, and oversight layers. The framework, developed through an extensive review of existing MIS models, AI governance literature, and contemporary applications, offers a structured approach for designing intelligent and explainable decision-support environments. The study also outlines key challenges related to data quality, legacy system integration, model interpretability, and organizational readiness. Emerging technologies such as federated learning, edge computing, and quantum optimization are discussed as potential future enhancements. The paper concludes with recommendations for organizations seeking to adopt AI-enabled MIS, emphasizing transparency, ethical governance, and continuous learning to sustain competitive advantage.