Valley International Journal Digital Library

VIJ Digital library
Articles

Holistic Cloud-AI Fusion for Autonomous Conversational Commerce in High-Velocity E-Commerce Channels

PDF
  • Rahul Khurana
Rahul Khurana
Computer Science, Principal Architect, T-Mobile USA INC,
Published in : International Journal of Scientific Research and Management (IJSRM) , Vol. 11 No. 06 (2023)

Submission to VIJ 2023-06-28

Keywords

  • Cloud-AI Fusion, Autonomous Conversational Commerce, High-Velocity E-Commerce, Customer Engagement, Real-Time Data Processing, Machine Learning, NLP (Natural Language Processing), Scalable Infrastructure

Copyright (c) 2023 Rahul Khurana

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Given the pace at which e-commerce is growing, high velocity channels require synchronous, individual, and highly engaging methods of dealing with the customers. In this research, the combination of cloud computing and AI is proposed and an organization conversational commerce framework will be created to enhance digital economies flexibility and autonomy. Taking advantage of cloud-AI synergy, the presented system improves efficiency, computing, and customer interaction through constructive architectural structures and sophisticated algorithms. This research assesses the potential of the proposed framework in improving the overall performance and the level of customer interaction of e-commerce transactions through comparing its efficiency in improving response time and enhancing user satisfaction metrics using a case study approach. The study concludes that moving towards the integration of cloud and AI both facilitates scalability while at the same time improves resource utilization and withstands fluctuating e-commerce conversational systems. Finally, this paper provides suggestions for e-commerce firms interested in implementing such cloud-AI solutions, with a focus on the areas of concern and future research directions.

PDF

References

  1. Munir, A., Kwon, J., Lee, J. H., Kong, J., Blasch, E., Aved, A. J., & Muhammad, K. (2021). FogSurv: A fog-assisted architecture for urban surveillance using artificial intelligence and data fusion. IEEE Access, 9, 111938-111959.
  2. Firouzi, F., Jiang, S., Chakrabarty, K., Farahani, B., Daneshmand, M., Song, J., & Mankodiya, K. (2022). Fusion of IoT, AI, edge–fog–cloud, and blockchain: Challenges, solutions, and a case study in healthcare and medicine. IEEE Internet of Things Journal, 10(5), 3686-3705.
  3. Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022). Distributed artificial intelligence empowered by end-edge-cloud computing: A survey. IEEE Communications Surveys & Tutorials, 25(1), 591-624.
  4. Singh, J. (2022). Deepfakes: The Threat to Data Authenticity and Public Trust in the Age of AI-Driven Manipulation of Visual and Audio Content. Journal of AI-Assisted Scientific Discovery, 2(1), 428-467.
  5. Bharati, V. (2021, August). LiDAR+ camera sensor data fusion on mobiles with ai-based virtual sensors to provide situational awareness for the visually impaired. In 2021 IEEE Sensors Applications Symposium (SAS) (pp. 1-6). IEEE.
  6. Priya, M. M., Makutam, V., Javid, S. M. A. M., & Safwan, M. AN OVERVIEW ON CLINICAL DATA MANAGEMENT AND ROLE OF PHARM. D IN CLINICAL DATA MANAGEMENT.
  7. Singh, J. (2022). The Ethics of Data Ownership in Autonomous Driving: Navigating Legal, Privacy, and Decision-Making Challenges in a Fully Automated Transport System. Australian Journal of Machine Learning Research & Applications, 2(1), 324-366.
  8. Firouzi, F., Daneshmand, M., Song, J., & Mankodiya, K. (2023). Guest Editorial Special Issue on Empowering the Future Generation Systems: Opportunities by the Convergence of Cloud, Edge, AI, and IoT. IEEE Internet of Things Journal, 10(5), 3681-3685.
  9. Tatineni, S. (2022). INTEGRATING AI, BLOCKCHAIN AND CLOUD TECHNOLOGIES FOR DATA MANAGEMENT IN HEALTHCARE. Journal of Computer Engineering and Technology (JCET), 5(01).
  10. Singh, J. (2021). The Rise of Synthetic Data: Enhancing AI and Machine Learning Model Training to Address Data Scarcity and Mitigate Privacy Risks. Journal of Artificial Intelligence Research and Applications, 1(2), 292-332.
  11. Viswakanth, M. (2018). WORLD JOURNAL OF PHARMACY AND PHARMACEUTICAL SCIENCES.
  12. Rossi, D., & Zhang, L. (2022, December). Network artificial intelligence, fast and slow. In Proceedings of the 1st International Workshop on Native Network Intelligence (pp. 14-20).
  13. Singh, J. (2020). Social Data Engineering: Leveraging User-Generated Content for Advanced Decision-Making and Predictive Analytics in Business and Public Policy. Distributed Learning and Broad Applications in Scientific Research, 6, 392-418.
  14. Ikhlasse, H., Benjamin, D., Vincent, C., & Hicham, M. (2020, November). An overall statistical analysis of AI tools deployed in cloud computing and networking systems. In 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) (pp. 1-7). IEEE.
  15. Surianarayanan, C., Raj, P., & Niranjan, S. K. (2023, January). The Significance of Edge AI towards Real-time and Intelligent Enterprises. In 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) (pp. 1-6). IEEE.
  16. Singh, J. (2019). Sensor-Based Personal Data Collection in the Digital Age: Exploring Privacy Implications, AI-Driven Analytics, and Security Challenges in IoT and Wearable Devices. Distributed Learning and Broad Applications in Scientific Research, 5, 785-809.
  17. Chanda, S. K. (2016). Enhancing IT Efficiency: Cloud, AI, and Hyper Automation Strategy-A Left Shift Optimization. Global journal of Business and Integral Security.
  18. Kröger, F. J., & Johansson, F. (2019). Conversational commerce: A quantitative study on preferences towards AI-Fueled c-commerce platforms among digital natives in Sweden and Germany.
  19. Iafrate, F. (2018). Artificial intelligence and big data: The birth of a new intelligence. John Wiley & Sons.
  20. Chatterjee, S., & Byun, J. (2002). Network convergence: Where is the value? Communications of the Association for Information Systems, 9(1), 27.
  21. Mele, C., & Russo-Spena, T. (2022). The architecture of the phygital customer journey: a dynamic interplay between systems of insights and systems of engagement. European Journal of Marketing, 56(1), 72-91.
  22. Sharma, P., & Devgan, M. (2012). Virtual device context-Securing with scalability and cost reduction. IEEE Potentials, 31(6), 35-37.
  23. Malmqvist, L. (2021). Architecting AI Solutions on Salesforce: Design powerful and accurate AI-driven state-of-the-art solutions tailor-made for modern business demands. Packt Publishing Ltd.
  24. Yoon, B. (2022). Proposal for Maximizing E-commerce Sales in the 4th Industrial Revolution.
  25. Sharafuddin, S. (2020). The evolution of business analytics: based on case study research (Master's thesis).
  26. Baughman, A. K., Pan, J. Y., Gao, J., & Petrushin, V. A. (2015). Disruptive innovation: Large scale multimedia data mining. Multimedia Data Mining and Analytics: Disruptive Innovation, 3-28.
  27. Zhang, Y. (2020). Leveraging dynamic capabilities in the creation of virtual servicecape in China (Doctoral dissertation, Queensland University of Technology).
  28. Licina, A. (2020). Big Data and AI in Customer Support: A study of Big Data and AI in customer service with a focus on value-creating factors from the employee perspective.
  29. Pyhämäki, M., & Makkonen, P. D. H. (2012). DIGITAL BUSINESS-TO-BUSINESS MARKETING COMMUNICATIONS IN EMERGING MARKETS.
  30. Hwang, K., & Chen, M. (2017). Big-data analytics for cloud, IoT and cognitive computing. John Wiley & Sons.
  31. Tran-Dang, H., & Kim, D. S. (2021). The physical internet in the era of digital transformation: perspectives and open issues. Ieee Access, 9, 164613-164631.