Digital Enterprise Platforms Powered by Conversational AI and Serverless Cloud Technologies for Customer Experience
Abstract
The rapid evolution of digital technologies has transformed enterprise operations, creating a growing demand for intelligent, scalable, and customer-centric digital platforms. Organizations across industries are increasingly adopting advanced technologies to improve customer engagement, enhance operational efficiency, and develop innovative service delivery models. Digital enterprise platforms powered by Conversational Artificial Intelligence (AI) and serverless cloud technologies represent a significant advancement in this transformation by combining intelligent human-computer interaction with flexible, scalable, and cost-efficient computing infrastructures. This integration enables enterprises to deliver personalized, responsive, and continuously available customer experiences while optimizing resource utilization and accelerating digital innovation.
Conversational AI has emerged as a critical component of modern enterprise platforms by enabling machines to understand, interpret, and respond to human communication through natural language processing, machine learning, speech recognition, and advanced language models. Unlike traditional automated systems based on predefined rules, contemporary Conversational AI solutions can analyze user intent, maintain contextual awareness, learn from interactions, and provide personalized responses across multiple communication channels. These capabilities allow enterprises to automate customer support, sales assistance, knowledge management, financial services, healthcare interactions, and employee engagement processes. By reducing response times and improving service accessibility, Conversational AI contributes significantly to enhanced customer satisfaction and long-term customer relationship development.
Serverless cloud technologies provide the foundation required for deploying intelligent enterprise platforms at scale. Through serverless computing models, organizations can execute application functions without directly managing physical or virtual server infrastructure. Cloud providers automatically handle resource provisioning, workload scaling, availability management, and infrastructure maintenance, enabling enterprises to focus on application development and service innovation. The event-driven nature of serverless architectures makes them highly suitable for customer experience applications, where service demands can fluctuate significantly due to changing user behavior, market conditions, and business events. This capability allows digital enterprise platforms to maintain high performance, reduce operational costs, and support rapid deployment of new services
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 9 No. 4 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
1288-1298 |
Published |
August 3, 2026 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Vignesh Dhanabal (2026). Digital Enterprise Platforms Powered by Conversational AI and Serverless Cloud Technologies for Customer Experience. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 9 No. 4 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 1288-1298. https://doi.org/10.15662/IJAESIT.2026.0904005 |
References
2. Vas, M. R. (2026). Autonomous Cloud Intelligence Systems Powered by Artificial Intelligence for Secure Data Platforms and Decision Excellence. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 2074-2083.
3. Challa, R. (2025). Architecting GPU-accelerated supercomputing for real-time clinical AI in large hospital systems. Computer Fraud & Security, 2025(2), 2134–2144.
4. Korat, U., & Patel, M. (2026, March). Machine-Learning Driven Performance Modeling and Optimization of Probabilistic and Hardware Accelerators. In 2026 14th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-6). IEEE.
5. Meshram, A. K. (2026). AI-Driven Big Data Processing on Cloud Platforms for Predictive Financial Decision-Making. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 30-40.
6. Mohammed, S. (2021). Hybrid cloud architecture strategy for global infrastructure operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(6), 4078-4081.
7. 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.
8. Koneru, S. P., Bouraima, M. O., Rahman, R., & Hossain, M. (2025, November). Object detection in unstructured driving environments using nasnetmobile and inceptionv3. In 2025 9th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1945-1949). IEEE.
9. Chevva, P. (2025, October). A Survey of Hierarchical Reinforcement Learning for Combinatorial Optimization in Logistics. In 2025 6th International Conference on Data Analytics for Business and Industry (ICDABI) (pp. 1-6). IEEE.
10. Jabed, M. M. I., Khawer, A. S., Ferdous, S., Niton, D. H., Gupta, A. B., & Hossain, M. S. (2023). Integrating Business Intelligence with AI-Driven Machine Learning for Next-Generation Intrusion Detection Systems. International Journal of Research and Applied Innovations, 6(6), 9834-9849.
11. Narapareddy, V. S. R., & Yerramilli, S. K. (2021). Sustainable IT operation system. International Journal of Engineering Technology Research & Management, 5(10), 147–155.
12. Ambalakannu, M. (2026). A data-driven framework for provider income aggregation and 1099 generation. International Journal of Research and Applied Innovations (IJRAI), 9(1), 13672–13675.
13. Gowda, M. K. S. (2026). Driving regulatory innovation: Automated swap data reporting and exception management. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 256–261.
14. Ambati, K. C. (2026). AI-powered procurement architecture: Streamlined UX, scalable ML pipelines, and enterprise efficiency. International Journal of Research and Applied Innovations (IJRAI), 9(1), 13681–13685.
15. Mudusu, S. K. (2025). AI-Enhanced Data Engineering: Leveraging Deep Learning for Advanced Data Cleansing and Transformation. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(1), 1051-1054.
16. Koganti, H., & Anne, S. (2026). Operational Excellence Through AI and ML in Financial Services: A Comprehensive Review of Applications, Challenges, and Future Directions. Int. J. Comput. Intell. Syst., 19(1), 181.
17. Raja, G. V. (2023). AI Driven Secure Intelligent Framework for Fraud Detection Cybersecurity and Cloud Based Enterprise Systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9068-9076.
18. Vollem, S. (2019). Designing a comprehensive observability framework for cloud-native microservices using monitoring platforms to improve system visibility, reliability, and performance analysis. European Journal of Advances in Engineering and Technology, 6(8), 118-129.
19. Wadhwa, R. (2025). Engineering autonomous enterprise systems using event-driven microservices and distributed data intelligence. Frontiers in Computer Science and Information Technology, 6(4), 66–79. https://doi.org/10.34218/FCSIT_06_04_002
20. Meesala, A. (2024). Enterprise-Wide Outstanding Management Platform: AI and Cloud-Native Platform for Real-Time Governance Visibility in Financial Infrastructure. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 357-363.
21. Korat, U. A., & Patel, M. M. (2026, March). Reconfigurable RTL Templates for Fixed Point Neural Network Arithmetic in Edge AI Systems. In 2026 Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6). IEEE.
22. Gunda, S. R. (2025). Optimizing Cloud Computing Resource Utilization Through Intelligent Allocation and Containerization Strategies. Journal of Computer Science and Technology Studies, 7(8), 238-244.
23. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
24. Ayyagari, V. (2026). Context Rot: Why MCP is Breaking in Production and How the Skills Architecture Fixes It. European Journal of Electrical Engineering and Computer Science, 10(4), 1-8.
25. Anand, L. (2025). Modernizing Enterprise Systems through Generative AI Autonomous Operations and Cloud-Native Engineering. International Journal of Humanities and Information Technology, 7(02), 54-69.
26. Koganti, H. (2023). Architecting high-availability Java microservices for financial applications on AWS. International Journal of Science, Research and Technology, 6(3), 9934–9945.
27. Chaba, A. (2023). A scalable real-time customer data platform architecture for cross-channel enterprise personalization. International Journal of Research and Applied Innovations, 6(1), 8392–8396.
28. Konatham, M. R., Guda, D. P., Kaushik, K., Sarma, W., Sharma, R., & Soni, M. (2025, September). Explainable Deep Learning Framework for Real-Time Threat Hunting and Anomaly Attribution in Enterprise Networks. In 2025 2nd International Conference on Integration of Computational Intelligent System (ICICIS) (pp. 1-6). IEEE.
29. Mathew, A. (2023). Sentinel AI: An Investigation into Robust Threat Mitigation Strategies for Artificial Intelligence. Educational Research (IJMCER), 5(5), 108-111.
30. 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.
31. Bhagwat, V. B. (2025). Simplifying Payroll Balance Conversions in Payroll Systems Implementation through the Use of Generative AI.
32. Kesarpu, S. (2025). Zero-Trust Architecture in Java Microservices. International Journal of Networks and Security, 5(01), 202-214.
33. Suddala, V. R. A. K. (2026). Innovation and compliance at global scale: Predictive analytics meets DevOps automation. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 239–245.
34. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
35. Gopinathan, V. R. (2025). Revolutionizing Revenue Cycle Management in the US Healthcare System Using AI-Powered Cloud Solutions. International Journal of Computer Technology and Electronics Communication, 8(4), 11106-11118.
36. Raja, G. V. (2023). Modernizing enterprise systems using AI with machine learning and cloud computing for intelligent systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
37. 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.
38. Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy artificial intelligence. Government Information Quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493