Integrating Cloud Computing and Generative AI for Scalable Predictive Analytics in Business Intelligence

The intersection of Cloud computing and generative artificial intelligence (AI) can be a game changer for business intelligence (BI), particularly when it comes to enhancing predictive analytics capabilities at scale. In this paper, we propose an integrated framework that exploits the elasticity of cloud infrastructure along with the creative problem-solving and data synthesis capabilities of generative AI models. Using generative AI in the cloud empowers organizations to access and apply dynamic data modeling, automated pattern discovery, and real-time forecasting across large, distributed datasets. This study initiation of a scalable architecture for predictive analytics which is cost-effective, provides accuracy, and designed to abstract the complexities of the underlying models from business stakeholders enabling smooth business decisions. The model's adaptability across industries with variable volume of data and analytical needs is demonstrated with case studies and simulations. These results reinforce that this integrated point of view greatly enhances performance, agility, and cost-savings in leading-edge BI environments.