AI-Enhanced Disaster Recovery Strategies for SAP S/4HANA in the Cloud
Harikrishna Madathala · 2025
This study presents a detailed investigation into the application of Artificial Intelligence (AI) technologies for disaster recovery in SAP S/4HANA systems deployed in cloud environments. The research emphasizes three primary aspects: proactive failure prediction, automated response mechanisms, and the optimization of Recovery Time Objectives (RTO). By employing advanced machine learning models, predictive analytics, and intelligent automation, the study highlights the potential for transforming disaster recovery strategies in dynamic and complex SAP landscapes. Key findings indicate that AI driven approaches significantly enhance system resilience and reduce downtime by enabling early anomaly detection, adaptive workload distribution, and efficient data replication techniques. Predictive models demonstrated accuracy rates exceeding 90% in identifying potential failures, while AI-optimized strategies achieved up to 62% improvement in recovery times. Furthermore, the integration of AI with cloud-native solutions and emerging technologies, such as edge computing and federated learning, offers promising directions for addressing the challenges of scalability, data security, and system complexity. The study underscores the need for continuous refinement of AI models to adapt to evolving SAP environments and calls attention to ethical considerations in autonomous decision-making. These advancements, coupled with quantum computing and other innovations, are poised to redefine disaster recovery practices, enabling businesses to maintain robust, flexible, and resilient IT infrastructures.