A Unified Enterprise Transformation Framework Based on Artificial Intelligence, Cloud Resilience, and Data Excellence

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Published: 2023-10-10
DOI: https://doi.org/0.15662/IJRPETM.2023.0605020
Keywords:
Artificial Intelligence, Cloud Resilience, Data Excellence, Digital Transformation, Enterprise Architecture, Data Governance, Business Intelligence, Cloud Computing, Organizational Agility

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Solomon Hykes
Software Engineer, Dagger, France

Abstract

The rapid evolution of digital technologies has transformed the way organizations operate, compete, and innovate. Enterprises increasingly rely on Artificial Intelligence (AI), cloud computing, and data-driven decision-making to achieve sustainable growth and operational excellence. However, many organizations struggle to integrate these capabilities into a unified transformation strategy. This study proposes a Unified Enterprise Transformation Framework (UETF) that combines Artificial Intelligence, Cloud Resilience, and Data Excellence as interconnected pillars of organizational modernization. The framework emphasizes intelligent automation, scalable and secure cloud infrastructure, and robust data governance to enhance agility, innovation, and business continuity. Through an extensive review of existing literature and contemporary digital transformation models, the study identifies critical success factors, implementation mechanisms, and organizational requirements necessary for successful adoption. The proposed framework enables enterprises to align technology investments with strategic objectives while ensuring resilience against operational disruptions and cyber threats. Furthermore, the framework promotes data quality, accessibility, and analytics-driven decision-making across business functions. The research contributes to both academic and practical domains by providing a holistic approach to enterprise transformation that integrates technological advancement with organizational capabilities. The findings suggest that organizations adopting the proposed framework can achieve improved efficiency, competitive advantage, innovation capacity, and long-term digital sustainability

Article Details

Issue

Vol. 6 No. 5 (2023): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

A Unified Enterprise Transformation Framework Based on Artificial Intelligence, Cloud Resilience, and Data Excellence. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9780-9787. https://doi.org/0.15662/IJRPETM.2023.0605020

References

1. Yamsani, N. (2018). Operationalizing regulatory governance through enterprise master data design: A practical examination of OFAC, KYC, and GDPR controls at Elavon. International Journal of Scientific Research & Engineering Trends, 4(6). https://doi.org/10.5281/zenodo.18196005

2. Socrates, S., Shanmugapriya, M., Murugeshwari, B., & Angalaeswari, S. (2024). Efficient Design for Implantable Device Constant Current Induction Doubly Fed Generating Incorporating Grid Connectivity. In Intelligent Solutions for Sustainable Power Grids (pp. 382-392). IGI Global Scientific Publishing.

3. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.

4. Rajendran, S., Sundarapandi, A. M. S., Krishnamurthy, A., & Thanarajan, T. (2022). An intelligent face recognition technology for iot-based smart city application using condition-cnn with foraging learning pso model. International Journal of Pattern Recognition and Artificial Intelligence, 36(14), 2256018.

5. Anand, L., & Syed Ibrahim, S. P. (2018). HANN: a hybrid model for liver syndrome classification by feature assortment optimization. Journal of medical systems, 42(11), 211.

6. Adepu, G. (2022). Machine learning-driven environmental monitoring systems for real-time regulatory compliance and risk detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 22–37.

7. Watham, S. D., & Vimal, V. R. (2013). Design and Implementation of Data Sanitization Technique For Effective Filtering With Enhanced Medical Support System in Cloud Architecture Diagram. International Journal of Emerging Technology and Advanced Engineering, 3(12), 471-473.Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.

8. Shewale, V. (2022). IT/OT Convergence: A Zero Trust Reference Architecture for the Energy Sector. International Journal of Science, Research and Technology, 5(5), 8494-8502.

9. Parasa, M. (2022). Addressing the underutilization of exit interview data: A structured AI-assisted framework for actionable workforce insights in SAP SuccessFactors. Global Scientific and Academic Research Journal of Multidisciplinary Studies, 1(6), 42–52. https://gsarpublishers.com/abstract-2326/

10. Raja, G. V. (2022). Integrating network forensics with data mining for advanced cybercrime investigation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5321–5326.

11. Padwal, R. A., & Mulajkar, R. M. (2016). A COMPARATIVE STUDY OF IMAGE SEGMENTATION METHOD. International Journal of Advance Research in Engineering, Science & Technology, 3(7), 151-163.

12. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.

13. Rajasekar, M., Aruldoss, A. C., & Bennet, M. A. (2018). A novel method to detect corrosion in underwater infrastructure using an image processing. ARPN Journal of Engineering and Applied Science, 13(7), 2556-2561.

14. Subramanyam, S. P. (2022). CyberArk integrated privileged access security for Azure DevOps environments. International Journal of Research and Applied Innovations (IJRAI), 5(1), 9478–9485. https://doi.org/10.15662/IJRAI.2022.0501008

15. Namdeo, A. (2022). Graph neural networks for real-time supply chain risk. International Journal of Humanities and Information Technology, 4(1–3), 175–192.

16. Fung, J., & Panyala, V. R. (2020). Automating multi-region scalable CI/CD framework for managing AWS CloudWatch alerts. International Journal of Engineering & Extended Technologies Research, 2(5), 1854–1858.

17. Kasireddy, J. R. (2022). From Raw Trades to Audit-Ready Insights Designing Regulator-Grade Market Surveillance Pipelines. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4609-4616.

18. Adepu, R. (2022). Building secure multi-cloud infrastructure for mission-critical enterprise workloads. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 14–32.

19. Narayanan, S. (2022). Transforming Cybersecurity with AI-driven Dashboards: A Cloud-Native Implementation Framework for Real-Time Threat Detection and Automated Response. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9217.

20. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.

21. V. B. Sarabu. (2018). Building foundational data integrity in enterprise retail systems: A structured approach to early-stage data governance. International Journal of Research Publications in Engineering, Technology and Management, 1(1), 2457–2465

22. Bhende, M., Thakare, A., Saravanan, V., Anbazhagan, K., Patel, H. N., & Kumar, A. (2022). [Retracted] Attention Layer‐Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer. BioMed Research International, 2022(1), 3947434.

23. Sengupta, J., & Alzbutas, R. (2022). Intracranial hemorrhages segmentation and features selection applying cuckoo search algorithm with gated recurrent unit. Applied Sciences, 12(21), 10851.

24. Kunadi, S. K. (2022). Designing high-performance data pipelines using Snowflake and cloud-native architectures. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8220–8230.

25. Prasad, P. K. (2021). Kubernetes everywhere: Operating hybrid and multi-cloud infrastructure at scale. International Journal of Engineering & Extended Technologies Research, 3(4), 3393–3401.

26. Dama, H. B. (2023). Designing highly available multi-cloud database architectures for global financial services. International Journal of Research and Applied Innovations, 6(1), 8329-8336.

27. Boddupally, H. L. (2022). Architectural-driven intelligent refactoring for resilient cloud-native. NET systems. Available at SSRN 6270479.

28. Raj, A. A., & Sugumar, R. (2022, October). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp. 1-6). IEEE.

29. Dhinakaran, D., Prathap, P. J., Selvaraj, D., Kumar, D. A., & Murugeshwari, B. (2022). Mining privacy-preserving association rules based on parallel processing in cloud computing. International Journal of Engineering Trends and Technology, 70(3), 284-294.

30. Vimal, V. R., Anandan, P., & Kumaratharan, N. (2022). Heart Disease Diagnosis Using Electrocardiography (ECG) Signals. Intelligent Automation & Soft Computing, 32(1).

31. Vanitha, C., Sanmugam, A., Yogananth, A., Rajasekar, M., Kuppusamy, P. G., & Devasagayam, G. (2022). A facile synthesis of polyaniline-WO3 hybrid nanocomposite for enhanced dopamine detection. Materials Letters, 328, 133149.

32. Mathew, A. (2022). Leveraging Big Data Analytics to Power AI and ML (Machine Learning) Automation. Educational Research (IJMCER), 4(5), 131-134.

33. Bharti, N. S., & Mulajkar, R. M. (2015). Detection and classification of plant diseases. International Research Journal of Engineering and Technology, 2(2), 2267-2272.