Emerging Use of AI in E-commerce: A Technical Review of Transformative Technologies

Abstract

The contemporary e-commerce ecosystem is undergoing a profound transformation driven by the integration of advanced Artificial Intelligence (AI) technologies that are redefining the architecture and capabilities of e-commerce platforms. This technical review examines how AI implementations spanning Machine Learning (ML) algorithms, Large Language Models (LLM), and cloud-native systems are creating intelligent, adaptive infrastructures capable of understanding and responding to complex customer behaviors. Core advancements include personalized recommendation systems based on neural collaborative filtering and autoencoder architectures that analyze multimodal data such as text, images, and behavioral signals to deliver highly individualized shopping experiences. Conversational AI systems powered by LLMs enable natural, context-aware customer interactions, sustaining dialogue continuity across extended service sessions. Meanwhile, cloud-native infrastructures support scalable deployment through containerized AI services, GPU-accelerated computation, and edge integration for real-time responsiveness. Applications such as AI-driven fraud detection, anomaly recognition, behavioral biometrics, and dynamic pricing optimization illustrate the breadth of operational impact. Finally, emerging paradigms such as multimodal AI and federated learning introduce new frontiers for privacy-preserving intelligence and distributed optimization. Together, these developments open significant opportunities for innovation while raising critical challenges in data quality, model interpretability, ethics, and regulatory compliance that organizations must address strategically.