Enhancing SAP Cloud Optimization with Explainable AI: Cost Control, Workload Forecasting, and Performance Enhancement

The increasing complexity of SAP cloud ecosystems, including SAP S/4HANA Cloud, SAP BTP, and SAP Cloud ALM, demands advanced approaches for optimizing resource allocation, workload forecasting, and cost management. Traditional methods often struggle with unpredictable workloads, leading to inefficiencies and overspending. This research explores deep learning integration in SAP cloud environments to enhance predictive capabilities for cost control, workload distribution, and performance optimization. However, the opacity of AI-driven decisions raises concerns about trust, accountability, and regulatory compliance (e.g., GDPR, ISO 27001). To address these challenges, Explainable AI (XAI) techniques such as SHAP and LIME are examined to provide transparency in workload predictions and resource recommendations. Integrating XAI into SAP cloud solutions enhances efficiency, financial performance, and operational resilience.