AI- and ML-Driven Engineering Excellence for SAP Healthcare and Business Systems in the Cloud

Quentin Gérard Perrin

doi:10.15662/IJRAI.2025.0802802

Authors

Quentin Gérard Perrin Senior IT Manager, France Author

DOI:

https://doi.org/10.15662/IJRAI.2025.0802802

Keywords:

AI-driven enterprise systems, Machine learning (ML), SAP healthcare systems, Cloud computing, Predictive analytics, Intelligent automation, System optimization, Data security and compliance

Abstract

The accelerated adoption of cloud computing technologies, along with advancements in artificial intelligence (AI) and machine learning (ML), has fundamentally reshaped enterprise-level healthcare and business operations. Modern organizations increasingly rely on intelligent, data-driven platforms to manage complex workflows, large-scale data processing, and real-time decision support. This paper examines the integration of AI- and ML-enabled engineering practices within SAP-based healthcare and business systems deployed on cloud infrastructures. By incorporating predictive analytics, intelligent automation, and scalable cloud-native architectures, SAP ecosystems can significantly enhance operational efficiency, optimize resource utilization, and support proactive decision-making. AI-driven insights enable early detection of risks, forecasting of operational demands, and continuous system performance optimization, while ML models facilitate automation across financial, supply chain, patient management, and customer relationship processes. Cloud platforms further provide elasticity, high availability, and seamless integration capabilities, enabling organizations to adapt quickly to evolving business and clinical requirements. The study also explores interoperability strategies that enable effective data exchange between SAP modules and external third-party applications through APIs, microservices, and integration platforms. These approaches improve data consistency, system resilience, and end-to-end visibility across enterprise operations. Additionally, the paper addresses critical challenges associated with deploying AI and ML in regulated environments, including data privacy, cybersecurity, regulatory compliance, model transparency, and governance. Strategies for risk mitigation, secure data management, and responsible AI deployment are discussed to ensure compliance with healthcare and enterprise standards while maximizing system performance and trustworthiness.

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