The Impact of Generative AI on E-Commerce Search

Editor's note: This is the second in a series of articles summarizing key takeaways from Earley Information Science’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.

GenAI in E-Commerce Search

AI and E-Commerce
The Current Landscape of E-Commerce Search
Search as a Customer Experience Pillar
How GenAI Improves Product Data
Transforming E-Commerce Search With Vector Models
Modular RAG: Taking Personalization to New Heights
E-Commerce Search in Industrial Manufacturing
Overcoming Challenges and Looking Ahead
Conclusion

AI and E-Commerce

The convergence of artificial intelligence (AI) and e-commerce is transforming how businesses manage and enhance their product discovery, search capabilities and overall customer experience (CX). As e-commerce continues to evolve, the implementation of AI, particularly generative AI (GenAI), in product and content search is poised to significantly improve how businesses operate and engage with customers. AI-powered tools are shaping the future of e-commerce search. We cover the key factors to consider in moving to AI-powered search, practical use cases in various industries and the road ahead for companies looking to integrate AI-driven solutions.

The Current Landscape of E-Commerce Search

E-commerce search has traditionally revolved around keyword-based systems that help users navigate product catalogs. While these systems work, they lack the sophistication needed to deliver deeply contextualized and personalized search experiences. This is where AI steps in, specifically through techniques such as large language models (LLMs), retrieval-augmented generation (RAG) and vector-based search. These AI tools allow businesses to move beyond simple keyword searches toward delivering the kind of meaningful, predictive and contextualized results that contribute to customer acquisition and retention.

For example, when a user searches for a specific tool or component in an industrial e-commerce setting, traditional search engines generally only provide relevant products based on keyword matches. If the search term is in the product description, it will be found. However, an AI-powered search system — by leveraging the user's past behavior, preferences and additional contextual signals — can predict the user’s intent and provide tailored product recommendations, even if the query is vague.

Search as a Customer Experience Pillar

In the e-commerce domain, search is not just about finding products. It’s about creating a seamless user experience. As noted during our recent webinar, “search is really about surfacing information in these different contexts,” which means it’s less about keyword entry and more about intelligently understanding and interpreting user intent. This understanding can be applied to various stages of the customer journey, enabling businesses to not only provide the right products, but also to produce the right content, support information and services to enhance customer engagement and satisfaction. Search can certainly be behind the scenes and frequently appears as a navigational element. The familiar example of faceted navigation is a search application. In many cases, links on a site can execute search queries and therefore dynamically present information behind what looks like a static link.

An AI-driven search experience allows companies to provide contextualized and personalized product recommendations by leveraging data, such as customer behavior, transaction history, preferences and even inferred characteristics, like budget sensitivity or brand loyalty. This approach is crucial because, as Phil Ryan, SVP of strategy and innovation at Lucidworks, pointed out in the webinar, customer signals drive relevance. AI tools, such as GenAI, can interpret and act on this data, helping businesses refine how they display information and offer products to customers.

How GenAI Improves Product Data

At the heart of any successful e-commerce AI strategy lies a recognition of the importance of accurate product data. As the adage goes, "garbage in, garbage out,” and no AI model can perform well without clean and well-organized data. The role of AI in e-commerce is increasingly about ensuring that product data is not only accurate, but also enriched with contextual information that makes search more intuitive and accurate for customers. Knowing that a customer is in a rural area of Montana versus downtown Philadelphia can affect the product that is recommended, even if the search term is the same.

RAG is a particularly powerful approach in this regard. RAG can process customer queries by accessing internal knowledge databases enriched with metadata from various sources — product catalogs, digital assets, industry standards and user feedback. This enables companies to deliver richer, more relevant responses that go beyond traditional search results.

For instance, a product query for "home automation systems" could be enhanced not only with product details, but also with related content, such as setup guides, product comparisons and user-generated content, creating a more holistic experience for the customer. As Sanjay Mehta, principal architect of AI and e-commerce at Kin + Carta, noted in the webinar, AI can "augment the search experience itself" by integrating product and content enrichment features that bring value beyond the initial search query.

Transforming E-Commerce Search With Vector Models

The application of vector models is impacting how search engines interpret and respond to queries in e-commerce environments. In vector-based search, concepts and terms are embedded into high-dimensional conceptual spaces (creating a mathematical model of the content for interpretation by the LLM), enabling AI to understand relationships between products, concepts and user intent more effectively.

According to Ryan, vector models help e-commerce systems recognize product similarities based not just on product names or categories, but on deeper conceptual connections. "By using additional signals, like metadata, customer behaviors, preferences and configurations, the model gets closer to that location in multi-dimensional vector space," Ryan said. This means AI-powered systems can now provide search results that are more relevant, personalized and contextualized to the specific user, enhancing the overall e-commerce experience.

Modular RAG: Taking Personalization to New Heights

An exciting development in AI-driven search is the concept of modular RAG, which adds a new layer of sophistication to how search engines operate. Just like traditional search pipelines, which rely on multiple methods for processing different types of data, modular RAG treats content and user interactions as dynamic workflows. Each module can be optimized for a particular task, whether it's processing structured data from product catalogs or retrieving unstructured content, such as user reviews and social media posts.

This modular approach to AI search allows for greater flexibility and precision. As discussed in my recent article on personalized knowledge architectures, modular RAG enables systems to dynamically retrieve the most appropriate information for each query, integrating structured and unstructured data from a variety of sources. It empowers companies to provide more accurate, relevant results that meet the customer’s needs, regardless of how complex or esoteric the query may be.

E-Commerce Search in Industrial Manufacturing

A compelling example of AI-enhanced e-commerce search comes from the industrial manufacturing sector, where businesses deal with complex, technical products that require highly accurate data representation. A typical industrial manufacturer might have thousands of parts, each with its own set of attributes that need to be captured and understood by the search engine.

In such cases, the challenge is not just about helping customers find the right product, but ensuring the product data itself is accurate, complete and enriched with the necessary attributes for quick and efficient retrieval. AI systems can assist by automatically enriching product descriptions with industry-standard terminology and ensuring all data complies with internal quality guidelines.

Overcoming Challenges and Looking Ahead

While AI promises to revolutionize e-commerce search, it also comes with its challenges. Latency, cost and the risk of generating inaccurate or irrelevant results, commonly known as hallucinations, are all barriers to successful implementation. However, as Mehta explained, organizations can mitigate these challenges by fine-tuning their models, leveraging domain-specific knowledge architectures and integrating high-quality data sources.

Moreover, as AI technologies continue to evolve, businesses will benefit from smaller, more efficient models tailored to specific e-commerce tasks. The key is adopting a phased approach, starting with controlled experiments and gradually expanding the use of AI as the technology and internal processes mature.

Conclusion

E-commerce search is undergoing a profound transformation thanks to AI and GenAI technologies. From product discovery to personalized recommendations, AI is helping businesses create seamless, context-aware experiences that are more relevant to customers. As these technologies continue to evolve, companies that invest in building the right knowledge architectures and clean data pipelines will be well-positioned to thrive in the new era of AI-powered e-commerce.

By understanding customer needs, leveraging powerful AI tools and developing rich and accurate product data and knowledge models, businesses can dramatically improve their search functionalities, which will deliver superior customer experiences and drive improved business results.

Learning Opportunities

Webinar