Leveraging Generative AI for Actionable Insights in Cloud Computing: Innovations and Applications

Abstract

Generative AI (GenAI) has emerged as a transformative tool in cloud computing, enabling advanced predictive analytics, explainable decision-making, and context-aware recommendations. This paper synthesizes academic research and industry advancements to explore four critical applications of GenAI: (1) time series classification for customer growth and churn prediction, (2) explainability in machine learning propensity models, (3) retrieval-augmented generation (RAG) systems for augmented insights, and (4) domain-specific fine-tuning for action recommendations. Drawing on peer-reviewed studies, we demonstrate how transformer-based architectures achieve 89% accuracy in churn prediction, counterfactual explanations improve stakeholder trust by 41%, and RAG systems reduce hallucinations in cost-optimization tools by 16%. Challenges such as data quality, ethical governance, and real-time scalability are analyzed alongside solutions like semi-supervised learning and hybrid indexing. The paper concludes with future directions, including multimodal RAG and federated explainability frameworks, positioning GenAI as a cornerstone of next-generation cloud analytics