Leveraging AI Driven Cloud Platforms for Intelligent Enterprise Applications through Autonomous Intelligent Automation
DOI:
https://doi.org/10.15662/IJARCST.2026.0904002Keywords:
Artificial Intelligence, Cloud Computing, AI-Driven Cloud Platforms, Intelligent Enterprise Applications, Autonomous Intelligent Automation, Machine Learning, Digital Transformation, Intelligent Automation, Enterprise Intelligence, Business Process AutomationAbstract
Artificial intelligence (AI)-driven cloud platforms have emerged as a transformative foundation for developing intelligent enterprise applications that enhance organizational efficiency, innovation, and competitiveness. The convergence of cloud computing, AI technologies, and autonomous intelligent automation enables enterprises to process large volumes of data, automate complex workflows, and support real-time decision-making across diverse business functions. AI-driven cloud platforms provide scalable infrastructure, flexible resource allocation, and integrated analytics capabilities that facilitate the deployment of intelligent applications while reducing operational complexity and infrastructure costs. Autonomous intelligent automation extends traditional automation by combining machine learning, natural language processing, robotic process automation, and predictive analytics to execute business processes with minimal human intervention while continuously learning and adapting to changing operational conditions. This study examines the contribution of AI-driven cloud platforms and autonomous intelligent automation to the development of intelligent enterprise applications. The research employs a qualitative methodology based on an extensive review of scholarly publications, industry reports, and technological frameworks. The findings indicate that AI-enabled cloud ecosystems significantly improve business agility, operational resilience, customer experience, resource optimization, and strategic decision-making. However, challenges related to cybersecurity, data governance, interoperability, ethical AI implementation, and workforce readiness remain critical considerations. The study concludes that integrating AI-driven cloud platforms with autonomous intelligent automation represents a strategic pathway toward achieving sustainable enterprise intelligence, digital transformation, and long-term organizational excellence.
References
1. Kotla, M. R. T. (2023). Autonomous enterprise integration: The future of self-healing data and API ecosystems. International Journal of Research and Applied Innovations (IJRAI), 6(3), 5968–5971.
2. Adepu, G. (2025). Generative AI–Powered Epidemiological Modeling Platforms for Autonomous Disease Surveillance. International Journal of Science, Research and Technology, 8(1), 13501-13504.
3. Kaushik, K., Bharti, P., Makkena, B., Narooka, P., & Soni, M. (2025, October). Data-Driven Motion Planning for Autonomous Robots Using Deep Reinforcement Learning in Dynamic Environments. In 2025 1st IEEE Uttar Pradesh Section Women in Engineering International Conference on Electrical Electronics and Computer Engineering (UPWIECON) (pp. 180-186). IEEE.
4. Mohammed, S. (2025). Automation-led digital transformation for banking, insurance, and manufacturing enterprises. International Journal of Data Analytics Research and Development, 3(5), 24–44. https://doi.org/10.34218/IJDARD_03_05_003
5. Rana, M., Srinivas, S., Jamili, L. K., Jaiswal, I. A., Nakka, S., & Kasetti, S. (2025, May). Real-Time Monitoring and Prediction of Blood Sugar Levels in Diabetic Patients with Functional Models. In 2025 International Conference on Engineering, Technology & Management (ICETM) (pp. 1-6). IEEE.
6. Gurram, S. K. (2025). Revolutionizing financial infrastructure: the convergence of blockchain and cloud in next-generation payment networks. Journal of Computer Science and Technology Studies, 7(4), 607-618.
7. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
8. Lakshmi Prasad Rongali. (2025). Integrating AI and Devops Practices to Develop Cybersecurity Frameworks That Enhance Resilience in Utility Infrastructure. Journal of Informatics Education and Research, 5(2). https://doi.org/10.52783/jier.v5i2.2838
9. Singh, A. (2025). Intent-Based Networking in Multi-Cloud Environments. Journal of Engineering and Applied Sciences Technology. SRC/JEAST-411, 288, 2-7.
10. Polamreddy, V. R. (2025). Incremental Change Processing and Financial Data Integrity in Enterprise Cloud Adoption Programs. International Journal of Research and Applied Innovations, 8(1), 11749-11761.
11. Kolhar, A., Soni, H., & Kant, K. (2026). Blockchain and AI Secure Wireless Transactions. In Ensuring Secure Connectivity Through AI-Powered Wireless Systems (pp. 237-268). IGI Global Scientific Publishing.
12. Mudusu, S. K. (2025, December 22). Cognitive data architecture: Designing self-optimizing frameworks for scalable AI systems. CIO (Foundry Expert Contributor Network).
13. Navandar, P. (2024). Governance, risk, and compliance (GRC) in the age of identity and access governance (IAG): A framework for integrated enterprise security and compliance. International Journal of Research and Applied Innovations (IJRAI), 7(2), 10483–10493. https://doi.org/10.15662/IJRAI.2024.0702011
14. Juvvadi, R. R. (2023). Re-architecting intercompany accounting: An event-driven pattern for real-time matching and continuous elimination. International Journal of Applied Engineering & Technology, 5(S4), 414–424.
15. Veershetty, G. (2026). A Secure Reference Architecture for Integrating WhatsApp Business Messaging with SAP ERP and SAP S/4HANA A Practitioner Framework for Conversational Enterprise Service Delivery. Available at SSRN 6891978.
16. Tiwari, S. K. (2025). Automation Driven Digital Transformation Blueprint: Migrating Legacy QA to AI Augmented Pipelines. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(12), 01-20.
17. Sharma, K., Konudula, J., Srinivas, S., & Mamadiyarov, Z. (2025, August). Leveraging AI and ML to Customize Salesforce CRM for Industry-Specific Solutions. In 2025 International Conference on Intelligent and Secure Engineering Solutions (CISES) (pp. 1492-1497). IEEE.
18. Damarched, M. K., & Pandity, S. (2025). Improving Software Reliability Through Automated Testing Frameworks in Enterprise Systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 11183-11190.
19. Ayyagari, V. (2025). Model Context Protocol for Agentic AI: Enabling Contextual Interoperability Across Systems. Int. J. Comput. Exp. Sci. Eng, 11, 6072-6082.
20. Chaganti, S. (2024). A unified MLOps and data architecture blueprint for cross-enterprise decisioning in global financial and tourism ecosystems. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 601–611. https://ijisae.org/index.php/IJISAE/article/view/7960
21. Devineni, A. (2025). Cognitive Load Reduction in On-Call Rotations via Predictive Alert Severity Scoring Using Machine Learning in Financial Cloud Operations. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(1), 268-273.
22. Sivakumer, D. (2026). AI capability maturity assessment model for ServiceNow enabled digital enterprise transformation. International Journal of Science, Research and Technology (IJSRAT), 9(1), 100–110.
23. Anumula, S. K., Bhwsar, R., & Patil, M. (2026). The digital backbone for architecting smart operations with a unified namespace. Discover Computing, 29(1), 300.
24. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
25. Kandula, S. T. R., & Boyapati, P. K. (2026, February). Advancing Cybersecurity in Critical Infrastructure Systems via Machine Learning-Based Threat Detection and Mitigation. In 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC) (pp. 1-7). IEEE.
26. Kodela, S., Kurada, S. B., Mogili, V. B., & Duggirala, J. (2026, March). Deep Learning-Enhanced Cloud Accounting Model for Real-Time Fraud and Financial Risk Prediction. In 2026 Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6). IEEE.
27. Soni, H., Perla, S., Maddela, S., & Kumar, U. (2025, November). Generative AI in Cloud CRM: Securing Intelligent Workflows in Multi-Cloud Environments. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1-9). IEEE.
28. Mahajan, A. S., Yamsani, N., & Uddandarao, D. P. (2025, November). Actuarial Science Driven Personalization: Optimizing Offers Through Compliance. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-6). IEEE.
29. Shewale, V. (2026). Cybersecurity Integration Framework for Mergers and Acquisitions. International Journal of Science, Research and Technology, 9(3), 816-824.
30. Hossain, M. S., Ali, M., Rahman, M. W., & Hossain, M. S. (2026). Using Predictive Analytics to Enhance Productivity and Innovation in the Advanced US Manufacturing Sectors. Journal of Business and Management Studies, 8(5), 01-23.
31. Gopakumar, S. (2026, February). SentiForesight: An AI Framework for Prescriptive Analytics on Social Streams. In 2026 Contemporary Computing Innovations Conference (CCIC) (pp. 1-6). IEEE.
32. gollapudi, R. (2026, April). AI-Driven Stability Classification of Database Workloads in Regulated Systems. In 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 1005-1010). IEEE.
33. Upadhyay, H. (2026). Agentic AI orchestration frameworks for composable commerce ecosystems: A case study of enterprise transformation. American Journal of Technology, 5(1), 40-54.
34. Rao, G. R. R. (2026). Efficient incremental data modeling in Apache Iceberg-based analytical pipelines: Partitioning and snapshot optimization strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(2), 655–659.
