CLOUD-POWERED GENAI FOR OMNICHANNEL OPTIMIZATION: ELEVATING WEB AND APP PERFORMANCE IN THE ERA OF DIGITAL TRANSFORMATION
Keywords:
Generative AI, Cloud Computing, Omnichannel Optimization, Web Performance, App Performance, Digital Transformation, Personalization, Real-Time AdaptationAbstract
In the era of digital transformation, optimizing omnichannel experiences has become critical for businesses seeking to engage users consistently across web, mobile, and other digital platforms. This paper examines the role of cloud-powered Generative AI (GenAI) in enhancing web and app performance within omnichannel ecosystems. By leveraging GenAI for real-time adaptability and personalized user experiences, organizations can address key performance challenges, such as latency, resource allocation, and user engagement. Comparative analysis between traditional methods and GenAI-powered approaches reveals significant improvements in response time, load balancing, and engagement metrics, highlighting GenAI's capacity to meet dynamic user demands. This study also discusses the challenges and limitations of implementing GenAI within cloud environments, emphasizing the need for robust data privacy measures and scalable solutions. Future research directions are proposed to explore accessible GenAI frameworks, user-controlled personalization, and hybrid models that combine cloud and edge AI. The findings underscore GenAI's transformative potential to revolutionize omnichannel optimization and elevate digital customer experiences.
References
Kavis, M. J. (2014). Architecting the cloud: Design decisions for cloud computing service models (SaaS, PaaS, and IaaS). Wiley.
Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176–189.
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., & Khan, S.U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115.
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS'14) (pp. 2672–2680). MIT Press, Cambridge, MA, USA.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.
Benaich, N., & Hogarth, I. (2019). State of AI Report 2019. London: State of AI.
Chen, J., & Liu, L. (2020). Adaptive optimization using AI techniques: Trends and applications. IEEE Access, 8, 43721-43736.
Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing. MIT Sloan Management Review, 54(4), 23.
Verhoef, Peter C. & Kannan, P.K. & Inman, J. Jeffrey, 2015. "From Multi-Channel Retailing to Omni-Channel Retailing," Journal of Retailing, Elsevier, vol. 91(2), pages 174-181.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420
Beck, N., & Rygl, D. (2015). Categorization of multiple channel retailing in Multi-, Cross-, and Omni-Channel Retailing for retailers and retailing. Journal of Retailing and Consumer Services, 27, 170–178.
Downloads
Published
Issue
Section
License
Copyright (c) -1 Rahul Khurana (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
