International Journal of Scientific Research and Engineering Development

Financial enterprises increasingly rely on SAP-centric systems for accounting, auditing, payroll, ERP analytics, and compliance reporting. However, the rapid expansion of transaction volumes, real-time reporting demands, and regulatory workloads has caused significant rises in compute usage, energy consumption, and operational costs within financial data centers. This study presents an AI-Enhanced CloudOps Framework designed to optimize SAP workloads deployed in virtualized data centers, improving performance stability while achieving measurable reductions in energy and resource overhead. Building on the methodology of “Energy-Efficient Data Center Virtualization: Leveraging AI and CloudOps for Sustainable Infrastructure”, this research adapts virtualization, workload prediction, and auto-scaling strategies specifically for SAP financial systems. The results demonstrate that integrating AIbased forecasting, dynamic VM orchestration, and automated CloudOps governance can reduce energy consumption by 18–27%, lower SAP batch-processing