The Future of E-Commerce and Distributed Systems Through the Lens of Gen AI

Srikanth Vadlamani; (Dr) Punit Goel

doi:10.36676/urr.v12.i1.1502

Authors

Srikanth Vadlamani Duke University NC USA Maharaja Agrasen Himalayan Garhwal University Uttarakhand, India
(Dr) Punit Goel NC USA Maharaja Agrasen Himalayan Garhwal University Uttarakhand, India https://orcid.org/0000-0002-3757-3123

DOI:

https://doi.org/10.36676/urr.v12.i1.1502

Keywords:

Generative AI, optimization for e-commerce, distributed systems, scalability, dynamic pricing, product recommendation, customer experience, automation based on AI, system design, synchronization of data, fault tolerance, decentralized networks

Abstract

The convergence of Generative Artificial Intelligence (Gen AI) into the fields of e-commerce and distributed systems depicts an emerging landscape with immense potential. Although immense progress has been achieved within both fields, little is known about the long-term implications of AI-driven systems on e-commerce processes, scalability, and customer engagement. The aim of this research is to investigate how Gen AI can enhance and transform key aspects of e-commerce platforms, particularly in product recommendation systems, customer support, and dynamic pricing models. Although e-commerce has already witnessed the advantages of AI in the form of automation and personalization, the use of generative models for forecasting and creating customer needs, content, and interactions is in its nascent stage. In addition, distributed systems, the backbone of modern e-commerce sites, are faced with certain challenges related to scalability, fault tolerance, and data consistency. The integration of generative artificial intelligence and distributed systems offers the potential to tackle these challenges in creative and new ways. This study seeks to bridge the gap between current AI capabilities and their implementation in real-world e-commerce settings, focusing on how generative models can make system architecture less complicated, reduce latency, and facilitate data synchronization in decentralized networks.

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