A Systematic Review of Artificial Intelligence Integration in Management Information Systems
Main Article Content
Abstract
Introduction: Introduction: The integration of artificial intelligence (AI) within management information systems (MIS) has become a significant focus in modern organizational research and practice. This systematic review consolidates existing knowledge on this topic, highlighting current trends, challenges, and research gaps. Problem Statement: As MIS play a crucial role in organizational decision-making and operational efficiency, understanding the implications, opportunities, and challenges of AI integration is increasingly important. However, the existing literature lacks a comprehensive overview of findings in this field. Objective: This study aims to systematically examine current literature on AI integration in MIS to (1) identify central themes and trends, (2) review methodologies applied, (3) evaluate key findings and impacts, and (4) suggest future research directions and practical applications. Methodology: The study adopts a systematic literature review approach, including the systematic selection, screening, and analysis of relevant academic articles from established databases. Defined inclusion criteria will ensure relevance and rigor among selected studies, while data extraction and synthesis will provide a comprehensive analysis of identified research. Results: This review will offer insights into the current state of AI integration in MIS, outlining key themes, common methodologies, technological advances, organizational impacts, and literature gaps. Conclusion: By synthesizing existing research, this systematic review will enhance understanding of AI integration in MIS, identify research gaps, and suggest future directions for both academic and practical applications. Findings will offer valuable insights for researchers, practitioners, and policymakers on the potential and challenges of using AI within MIS.