Year: 2025
Type: article
Abstract: This research explains the extensive impact of artificial intelligence on analytics and reporting in cloud-based business intelligence (BI) systems. The integration of AI-powered automation in cloud BI systems allows companies to automate mundane data processing processes, enhance analytical accuracy, and assist in making data-driven decisions. The abstract explains the possibility of robust machine learning processes and natural language processing automatically producing routine reporting, detecting anomalies, and predicting future trends and patterns, thus reducing human involvement and creating insights. At the center of this conversation is the transition from manual, time-consuming business intelligence methods to a reactive, artificial intelligence-based model that leverages the scalability and accessibility of cloud computing. This synergy not only enhances business efficiency but also brings enormous breakthroughs in the accuracy of data analysis. The paper focuses on case studies that identify successful deployments where AI-driven reporting software offered real-time insights, thereby allowing organizations to react accordingly to market shifts and impending threats. In addition, the research investigates potential pitfalls such as data security threats, integration complexities, and continuous optimization of the algorithm to maintain performance in the dynamic business environment. The research emphasizes the importance of embracing a strategic approach to deploying artificial intelligence to ensure that technology breakthroughs are aligned with organizational goals and data governance processes. Overall, the research gives a complete examination of how AI-based automation of cloud BI reporting can revolutionize the business analytics scene to provide an organizational guide to implementing smart systems as the drivers of sustainable growth and competitive advantage.
Authors: Saurabh Gandhi, Er. Siddharth
Institutions: Sikkim Manipal University, Bennett University
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4414829911
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