Articles Open Access
AI Driven Enterprise Integration with Microservices API Governance Salesforce Automation and Cloud Native Digital Transformation
Full Stack Developer, Optionality, Quebec, Canada
Abstract
Artificial Intelligence (AI) driven enterprise integration has become a critical approach for organizations seeking scalable, intelligent, and adaptive digital transformation. The integration of microservices architecture, API governance, Salesforce automation, and cloud-native technologies enables enterprises to modernize legacy systems, improve operational efficiency, and create agile business ecosystems. This research explores an AI-powered framework that combines intelligent automation, secure API management, distributed microservices, and cloud-native platforms to support real-time decision-making and seamless enterprise connectivity. AI capabilities such as machine learning, predictive analytics, natural language processing, and intelligent agents enhance integration workflows by automating business processes, optimizing resource utilization, and identifying operational risks. Microservices-based architectures provide modularity and scalability, while API governance ensures secure, reliable, and standardized communication among enterprise applications. Salesforce automation further enhances customer relationship management through intelligent workflows, predictive insights, and personalized engagement strategies. Cloud-native digital transformation enables enterprises to leverage containerization, serverless computing, Kubernetes orchestration, and DevOps practices for continuous innovation. The proposed approach addresses challenges related to system complexity, data fragmentation, security, and integration scalability. By adopting AI-driven integration strategies, organizations can achieve improved agility, enhanced customer experiences, and sustainable digital growth in rapidly evolving technology environments.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 5 |
| Number | 4 |
| Pages | 8560-8569 |
| Published | 2021-08-06 |
| Issue | Vol. 5 No. 4 (2021): International Research Journal of Innovative Engineering (IRJIE) |
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2. Beyer, B., Jones, C., Petoff, J., & Murphy, N. R. (2016). Site Reliability Engineering: How Google Runs Production Systems. O’Reilly Media.
3. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
4. 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.
5. Adepu, G. (2021). Zero-Trust Digital Government Platforms: Secure Identity, API Governance, and Cloud-Native Service Architecture. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), 3089-3093.
6. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
7. Vankayala, S. C. (2021). Engineering Quality into Cloud-Native Financial Platforms on Microsoft Azure. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(1), 4361-4367.
8. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication (IJCTEC), 3(6), 2900–2903.
9. Veershetty, G. (2019). From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization. American International Journal of Computer Science and Technology, 1(1), 23-27.
10. Kotla, Mutha Ravi Tej (2021). Machine learning-based predictive observability for enterprise integration platforms. Journal of Computer Engineering and Technology, 4(3), 1–24. https://doi.org/10.34218/JCET_04_03_001
11. Pushparathi, V. G., Sudha, M., David, D. J., Anbazhagan, K., & Vethamani, S. E. (2020). A Continuous Decision Based Multi Kernel Median Filter for Noise Removal on Brain MRI Images. Advanced imaging, 1(3), 5.
12. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105-5111.
13. Gummadi, V. P. K. (2019). Microservices architecture with APIs: Design, implementation, and MuleSoft integration. Journal of Electrical Systems, 15(4), 130-134.
14. Mohammed, S. (2021). Hybrid cloud architecture strategy for global infrastructure operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(6), 4078–4081.
15. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.
16. Parasa, M. (2020). Control-mapped AI governance for high-risk HR decisions in SAP SuccessFactors: Audit-ready metrics for recruiting, performance calibration, and internal mobility. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(2), 153–168. https://doi.org/10.18090/samriddhi.v12i02.15
17. Vayyasi, N. K. (2019). Reimagining financial compliance automation: Using Java microservices and generative AI on AWS Bedrock for regulatory intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 2(3), 1992–1210.
18. 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.
19. 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
20. Anbazhagan, K., SUGUMAR, D., Mahendran, M., & Natarajan, R. (2012). An efficient approach for statistical anonymization techniques for privacy preserving data mining. International Journal of Advanced Research in Computer and Communication Engineering, 1(7), 482-485.
21. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
22. Rajasekar, M., Celine Kavida, A., & Anto Bennet, M. (2020). A pattern analysis based underwater video segmentation system for target object detection. Multidimensional Systems and Signal Processing, 31(4), 1579-1602.
23. Kotla, Mutha Ravi Tej (2021). Machine learning-based predictive observability for enterprise integration platforms. Journal of Computer Engineering and Technology, 4(3), 1–24. https://doi.org/10.34218/JCET_04_03_001
24. Juvvadi, R. R. (2019). Smart contracts in supply chain finance: Automating accounts payable and the three-way match. Journal of Information Systems Engineering and Management, 4(1), 1–12.
25. Konakalla, K. (2020). Automated commission calculation and sales quota management in Salesforce: A code-driven approach for sales efficiency. International Journal, 7, 125-127.
26. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
27. Polamreddy, V. R. (2021). Engineering Reversible Enterprise Data Migrations: A Phased Rollout Framework for Financially Critical Retail Platforms. International Journal of Computer Technology and Electronics Communication, 4(2), 3414-3427.
28. Veershetty, G. (2019). From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization. American International Journal of Computer Science and Technology, 1(1), 23-27.
29. G. Vimal Raja, K. K. Sharma (2014). Analysis and Processing of Climatic data using data mining techniques. Envirogeochimica Acta 1 (8):460-467
30. 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.
31. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
32. Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19, 171–209.
33. Bughin, J., Hazan, E., Ramaswamy, S., et al. (2017). Artificial Intelligence: The Next Digital Frontier. McKinsey Global Institute.
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