This article will explore the impact of artificial intelligence (AI) and Generative AI (GenAI) on e-commerce search and product discovery. As the shortcomings of keyword-based search systems in delivering personalized and context-aware results become increasingly clear, AI-driven technologies such as Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and vector-based search are being used to enhance e-commerce experiences. The article explores the practical applications of these technologies, such as improving product data quality and completeness, enhancing search relevance, and delivering personalized recommendations at scale. Approaches such as Modular RAG, which integrates structured and unstructured data to provide richer search outcomes, are discussed alongside a case example in industrial manufacturing.
Despite challenges such as latency, cost, and AI hallucinations, businesses can overcome these hurdles. The article discusses approaches that include ensuring clean data, developing a well thought out knowledge architecture, and using phased adoption of AI. It will also explore the potential of AI to significantly improve e-commerce search, creating a seamless, personalized customer experience that drives better customer engagement and increased revenue.
Read more on VKTR: The Impact of Generative AI on E-Commerce Search. This is the second in a series of articles summarizing key takeaways from Earley's seven-part webinar series AI and Search: Navigating the Future of Business Transformation. Patrick Hoeffel — managing partner at Patrick Hoeffel Partners — and Sanjay Mehta — an advisor at Earley Information Science — contributed to this article.