Integration of cloud computing and artificial intelligence to optimize economic management processes: a systematic review

Abstract

This systematic review examines existing literature on the role of AI-driven cloud computing in optimizing economic management processes, identifying key trends, benefits, challenges, and future research directions. The study adheres to the PRISMA framework to systematically collect and analyze research from academic databases, including Scopus, Web of Science, IEEE Xplore, and Google Scholar. Findings reveal that AI-powered cloud solutions offer scalability, real-time data analytics, cost reduction, and automation of business processes. However, challenges such as data security risks, ethical concerns, and regulatory constraints hinder full-scale adoption. The study also highlights emerging trends, including AI-driven financial forecasting, intelligent automation, and Explainable AI (XAI) models, which facilitate transparent decision-making. Additionally, the research identifies gaps in the literature, particularly in the adoption of AI within public sector economic management and regulatory frameworks. The discussion compares these findings with existing studies, exploring theoretical and practical implications for businesses, policymakers, and researchers. Key recommendations include the need for robust cyber-security frameworks, ethical AI governance, and industry-specific AI applications. Future research should focus on longitudinal studies, cross-sectoral analyses, and the role of AI in sustainable economic growth. This review contributes to the growing body of knowledge on AI-cloud integration, offering insights to drive effective and responsible adoption in economic management.