AI-Driven Business Intelligence in Cloud Computing: A Comprehensive Framework for Data Analytics, Decision-Making, and Business Performance Optimization

Abstract

The rapid expansion of digital business operations has produced large volumes of structured and unstructured data from sales, customers, finance, supply chains, and online platforms. Traditional business intelligence systems often struggle to process real-time data, discover hidden patterns, and support fast strategic decision-making. This study proposes an AI-driven business intelligence framework in cloud computing for improving data analytics, decision-making, and business performance optimization. The proposed framework uses cloud-based data storage, data preprocessing, and machine learning models, predictive analytics, and interactive visualization dashboards to support business managers in making accurate and timely decisions. In this study, artificial intelligence techniques are applied to analyze business data, predict future trends, classify customer behavior, and identify performance factors affecting organizational growth. Cloud computing provides scalability, flexibility, and cost-effective infrastructure for handling large business datasets. The expected experimental results will compare traditional business intelligence methods with AI-based models using performance metrics such as accuracy, precision, recall, F1-score, and prediction error. The framework is expected to demonstrate improved decision accuracy, faster data processing, better forecasting performance, and enhanced business efficiency. This research highlights the importance of integrating artificial intelligence with cloud computing to develop intelligent, scalable, and data-driven business intelligence systems.