AI-Powered Modernization of SAP-Centric Core Enterprise Systems for Healthcare and Business in Hybrid and Multi-Cloud Environments
Main Article Content
Abstract
By leveraging AI for capacity planning, anomaly detection, performance tuning, and data-driven decision support, the proposed approach enhances system agility while reducing downtime, migration risk, and operational costs. The framework supports seamless interoperability between SAP S/4HANA, SAP Business Technology Platform, and cloud-native services across public and private cloud environments. Experimental evaluation and industry case analysis demonstrate measurable improvements in system performance, resource utilization, compliance readiness, and business continuity. The results indicate that AI-powered SAP modernization provides a scalable foundation for intelligent healthcare and business platforms operating in complex hybrid and multi-cloud ecosystems.
Article Details
Section
How to Cite
References
1. Chen, Y., Wang, X., & Li, J. (2022). AI-driven optimization for cloud-native healthcare systems: Performance and risk management. Journal of Healthcare Informatics Research, 6(3), 450–472. https://doi.org/10.1007/s41666-022-00123-4
2. Sugumar, R. (2025). An Intelligent Cloud-Native GenAI Architecture for Project Risk Prediction and Secure Healthcare Fraud Analytics. International Journal of Research and Applied Innovations, 8(Special Issue 2), 1-7.
3. Tamizharasi, S., Rubini, P., Saravana Kumar, S., & Arockiam, D. Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments.
4. Adari, V. K. (2024). The Path to Seamless Healthcare Data Exchange: Analysis of Two Leading Interoperability Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11472-11480.
5. Sakinala, K. (2025). Monitoring and observability for cloud-native applications. Journal of Computer Science and Technology Studies, 7(8), 101-115.
6. García, F., & Pérez, R. (2021). Machine learning approaches for fraud detection in healthcare business processes. Computers in Industry, 129, 103452. https://doi.org/10.1016/j.compind.2021.103452
7. Chandramohan, A. (2017). Exploring and overcoming major challenges faced by IT organizations in business process improvement of IT infrastructure in Chennai, Tamil Nadu. International Journal of Mechanical Engineering and Technology, 8(12), 254.
8. Huang, T., Zhao, L., & Chen, S. (2020). Intelligent database auto-tuning in cloud-native environments. IEEE Transactions on Cloud Computing, 8(4), 1023–1035. https://doi.org/10.1109/TCC.2019.2902105
9. Bussu, V. R. R. (2023). Governed Lakehouse Architecture: Leveraging Databricks Unity Catalog for Scalable, Secure Data Mesh Implementation. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6298-6306.
10. Li, H., & Sun, Q. (2021). Agile and cloud-native architectures for scalable healthcare applications. Journal of Systems and Software, 177, 110956. https://doi.org/10.1016/j.jss.2021.110956
11. Kavuru, L. T. (2025). Sustainable Project Scheduling: Balancing Human Well-being, AI Automation, and Productivity. International Journal of Research and Applied Innovations, 8(3), 13035-13042.
12. Nguyen, P., Tran, T., & Pham, D. (2022). Self-supervised deep learning for anomaly detection in healthcare systems. Artificial Intelligence in Medicine, 126, 102187. https://doi.org/10.1016/j.artmed.2022.102187
13. Al Rafi, M. (2022). Intelligent Customer Segmentation A Data-Driven Framework for Targeted Advertising and Digital Marketing Analytics. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7417-7428.
14. Kasaram, C. R. (2023). Structuring Reusable API Testing Frameworks with Cucumber-BDD and REST Assured. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(1), 7626-7632.
15. Kumar, R. K. (2024). Real-time GenAI neural LDDR optimization on secure Apache–SAP HANA cloud for clinical and risk intelligence. IJEETR, 8737–8743. https://doi.org/10.15662/IJEETR.2024.0605006
16. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
17. Parameshwarappa, N. (2025). Building Bridges: The Architecture of Digital Inclusion in Public Services. Journal Of Multidisciplinary, 5(8), 96-103.
18. Joyce, S., Pasumarthi, A., & Anbalagan, B. (2025). SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURENATIVE TOOLS AND PRACTICES.||.
19. Kagalkar, A., Sharma, A., Chaudhri, B., & Kabade, S. (2024). AI-Powered Pension Ecosystems: Transforming Claims, Payments, and Member Services. International Journal of AI, BigData, Computational and Management Studies, 5(4), 145-150.
20. Meka, S. (2025). Fortifying Core Services: Implementing ABA Scopes to Secure Revenue Attribution Pipelines. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11794-11801.
21. Ponnoju, S. C., & Paul, D. (2023, April 3). Hybridizing Apache Camel and Spring Boot for Next-Generation microservices in financial data integration. https://lajispr.org/index.php/publication/article/view/37
22. Zerine, I., Islam, M. M., Rahman, T., Akter, M., & Pranto, M. R. H. (2024). Optimizing Capital Allocation and Investment Decisions in the US Economy Through Data Analytics. Available at SSRN 5606870.
23. Kumar, S. N. P. (2022). Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 67–79. https://ijhit.info/index.php/ijhit/article/view/140/136
24. Nagarajan, G. (2024). A Cybersecurity-First Deep Learning Architecture for Healthcare Cost Optimization and Real-Time Predictive Analytics in SAP-Based Digital Banking Systems. International Journal of Humanities and Information Technology, 6(01), 36-43.
25. Patel, K., & Sharma, R. (2020). Risk-aware AI frameworks for enterprise business processes. Information Systems Frontiers, 22(5), 1173–1188. https://doi.org/10.1007/s10796-019-09933-x
26. Kumar, S. S. (2024). SAP-Based Digital Banking Architecture Using Azure AI and Deep Learning for Real-Time Healthcare Predictive Analytics. International Journal of Technology, Management and Humanities, 10(02), 77-88.
27. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
28. Vasugi, T. (2022). AI-Enabled Cloud Architecture for Banking ERP Systems with Intelligent Data Storage and Automation using SAP. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4319-4325.
29. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
30. Jeetha Lakshmi, P. S., Saravan Kumar, S., & Suresh, A. (2014). Intelligent Medical Diagnosis System Using Weighted Genetic and New Weighted Fuzzy C-Means Clustering Algorithm. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 213-220). New Delhi: Springer India.
31. Thambireddy, S. (2022). SAP PO Cloud Migration: Architecture, Business Value, and Impact on Connected Systems. International Journal of Humanities and Information Technology, 4(01-03), 53-66.
32. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325-330). IEEE.
33. Zhang, Y., Liu, X., & Wang, H. (2019). Intelligent UI performance optimization using machine learning in cloud-native applications. ACM Transactions on Internet Technology, 19(3), 1–20. https://doi.org/10.1145/3310134