A Cloud-Native AI-Driven Enterprise Architecture Supporting Intelligent Operations Organizational Resilience and Data-Driven Decision Making with SAP Integration

Leonardo Samuel Moura

doi:10.15662/IJRAI.2022.0502004

Authors

Leonardo Samuel Moura Independent Researcher, Brazil Author

DOI:

https://doi.org/10.15662/IJRAI.2022.0502004

Keywords:

Cloud-native architecture, Artificial intelligence, Enterprise systems, SAP integration, Organizational resilience, Data-driven decision making, Predictive analytics, MLOps, Digital transformation

Abstract

Enterprises are increasingly adopting cloud-native architectures and artificial intelligence to modernize legacy systems, improve operational efficiency, and enhance organizational resilience. At the same time, SAP-based enterprise landscapes continue to play a critical role in core business operations such as finance, supply chain, logistics, and human resources. This paper proposes a cloud-native, AI-driven enterprise architecture that integrates SAP platforms with modern technologies including MLOps, predictive analytics, software-defined infrastructure, and real-time data processing. The proposed architecture enables intelligent operations, supports organizational resilience, and facilitates data-driven decision making across enterprise functions. By leveraging SAP integration, cloud-native services, and machine learning-driven insights, organizations can improve business process agility, system reliability, and analytical capabilities. The study highlights architectural components, integration patterns, and operational benefits while discussing performance, scalability, and resilience considerations in large-scale enterprise environments.

References

1. Gartner. (2022). Building resilient enterprise architectures in the cloud. Gartner Research.

2. Soundarapandiyan, R., Krishnamoorthy, G., & Paul, D. (2021, May 4). The role of Infrastructure as code (IAC) in platform engineering for enterprise cloud deployments. Journal of Science & Technology. https://thesciencebrigade.com/jst/article/view/385

3. Jaikrishna, G., & Rajendran, S. (2020). Cost-effective privacy preserving of intermediate data using group search optimisation algorithm. International Journal of Business Information Systems, 35(2), 132-151.

4. Kavuru, L. T. (2021). Project Immunity Building Organizational Resilience through Pandemic Driven Lessons. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5266-5273.

5. Sudha, N., Kumar, S. S., Rengarajan, A., & Rao, K. B. (2021). Scrum Based Scaling Using Agile Method to Test Software Projects Using Artificial Neural Networks for Block Chain. Annals of the Romanian Society for Cell Biology, 25(4), 3711-3727.

6. Humble, J., & Farley, D. (2010). Continuous delivery: Reliable software releases through build, test, and deployment automation. Addison-Wesley.

7. 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

8. Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard Business Review, 93(10), 96–114.

9. Rajurkar, P. (2021). Deep Learning Models for Predicting Effluent Quality Under Variable Industrial Load Conditions. International Journal of Research and Applied Innovations, 4(5), 5826-5832.

10. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.

11. Gopalan, R., & Chandramohan, A. (2018). A study on Challenges Faced by It organizations in Business Process Improvement in Chennai. Indian Journal of Public Health Research & Development, 9(1), 337-341.

12. Ross, J. W., Weill, P., & Robertson, D. (2006). Enterprise architecture as strategy: Creating a foundation for business execution. Harvard Business School Press.

13. Karnam, A. (2021). The Architecture of Reliability: SAP Landscape Strategy, System Refreshes, and Cross-Platform Integrations. International Journal of Research and Applied Innovations, 4(5), 5833–5844. https://doi.org/10.15662/IJRAI.2021.0405005

14. Hollis, M., Omisola, J. O., Patterson, J., Vengathattil, S., & Papadopoulos, G. A. (2020). Dynamic Resilience Scoring in Supply Chain Management using Predictive Analytics. The Artificial Intelligence Journal, 1(3).

15. Villamizar, M., Garcés, O., Ochoa, L., Casallas, R., Gil, S., Valencia, C., Zambrano, A., & Lang, M. (2016). Infrastructure evolution with microservices: A cloud-native approach. IEEE Software, 33(3), 44–51. https://doi.org/10.1109/MS.2016.64

16. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.

17. Singh, A. (2021). Unlocking Mesh Networks: Tackling Scalability in Dynamic Environments. IJSAT-International Journal on Science and Technology, 12(1).

18. Sivaraju, P. S. (2021). 10x Faster Real-World Results from Flash Storage Implementation (Or) Accelerating IO Performance A Comprehensive Guide to Migrating From HDD to Flash Storage. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(5), 5575-5587.

19. S. M. Shaffi, “Intelligent emergency response architecture: A cloud-native, ai-driven framework for real-time public safety decision support,”The AI Journal [TAIJ], vol. 1, no. 1, 2020.

20. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.

21. Thambireddy, S. (2021). Enhancing Warehouse Productivity through SAP Integration with Multi-Model RF Guns. International Journal of Computer Technology and Electronics Communication, 4(6), 4297-4303.

22. Zaharia, M., Chen, A., Davidson, A., Ghodsi, A., Hong, S. A., Konwinski, A., Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., Xie, F., & Zheng, M. (2018). Accelerating the machine learning lifecycle with MLflow. IEEE Data Engineering Bulletin, 41(4), 39–45.