AI-Driven MIS Architectures: Deep Learning–Enabled Decision Support for Business Operations
Md Amran Hossen Pabel *
Department of Marketing, Wright State University, Ohio, USA.
Deawn Md Alimozzaman
Department of Computer Science and Engineering, Brac University, Dhaka, Bangladesh.
Sharmin Sultana
Department of Economics, University of Massachusetts Boston, USA.
Tahsina Akhter
Department of Management Information Systems, Lamar University, Beaumont, Texas, USA.
Shamsun Nahar
Department of Management Information Systems, Lamar University, Beaumont, Texas, USA.
Sazid Al Mehdi
Department of Business Analytics, Trine University, Allen Park, MI, USA.
Marzia Tabassum
Department of Management Information Systems, Lamar University, Beaumont, Texas, USA.
*Author to whom correspondence should be addressed.
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.
Keywords: Artificial Intelligence (AI), Machine Learning (ML), Management Information Systems (MIS), Decision Support Systems (DSS) and business intelligence