AI has altered the way users search for products: Target India’s Swapnasarit Sahu

Artificial intelligence (AI) is redefining many aspects of everyday life, and shopping, especially online, is no exception, as retail corporations are delivering newer, personalised experiences for shoppers that are simple and intuitive.

This shift is driven by changing consumer behaviour. Shoppers are no longer relying on just text-based searches as voice and image-based searches are equally becoming important. To meet these evolving expectations, retailers are turning to AI to create an environment where shoppers get what they need.

Target in India (TII), the global capability centre (GCC) of US headquartered retailer Target, is leveraging AI to deliver these experiences by redefining the world of search.

In an e-mail interview with EnterpriseStory, Swapnasarit Sahu, Sr. Director, Data Sciences, TII, explains how AI is enabling them to provide highly specific recommendations based on the products that shoppers are interested in.

Edited excerpts from the interview.

EnterpriseStory [ES]: How is AI redefining the world of search, particularly in the retail industry?

Swapnasarit Sahu (SS): AI has fundamentally altered the pattern of ‘search,’ with guests (shoppers) now using long natural language queries, voice, and visual inputs for exploring their choice of products. In today’s diverse digital ecosystem, keyword matching alone is not enough. The entire concept of search is about understanding the underlying intent and responding in a more human-like, conversational way.

In retail, the expectations are even higher. Guests want search to guide them through exploration, inspire them with relevant recommendations, and personalise results across diverse categories.

Each category is unique and behaves differently—while grocery demands specificity and speed, apparel requires diversity and style relevance, and beauty calls for personal preferences. AI enables us to build category-tuned systems that meet these nuances while maintaining the simplicity and ease guests expect.

ES: As an omnichannel platform, how is Target engaging with AI with respect to search?

SS: At Target, search sits at the forefront of digital interactions. Billions of searches, millions of unique queries, and a large majority of digital purchases start with just a simple query. So, the power of search to convert curiosity into purchase has never been more important.

We’ve woven AI into every part of the experience. At the front end, we’ve introduced Gen-AI-driven autosuggest, which improves discoverability across our huge catalogue. If a guest types a broad query like “table,” the search page shows a visual filter to help them refine their requirement. But if they type a more specific query like “coffee table,” the entire visual filter changes with new attributes and new visuals. And for apparel, where guests care about trends, we show new-items carousels. For grocery, which is deal-sensitive, we show deal carousels. Every UX block is driven by AI tuned to how guests interact with the specific category.

On the back end, AI shapes hybrid search, multi-index retrieval, and multi-channel ranking so that every guest gets relevant, personalised results across channels whether online, app or store.

ES: Given the humongous data that Target handles on a daily basis, how is AI redefining the framework for search?

SS: To operate at Target’s scale, search systems must go far beyond traditional retrieval. AI allows us to build an architecture that balances precision, recall, and personalisation. We use a hybrid approach combining inverted-index search for accuracy, and vector search for semantic understanding. This ensures we can handle both highly specific and more ambiguous guest queries effectively.

Our multi-index framework enables category-level optimisation. Each category, whether fashion, grocery, or home, operates on its own index with independent algorithms, thresholds, and cut-offs. This provides the flexibility and relevance that standard search systems cannot offer.

Multi-channel retrieval incorporates signals such as seasonality, regionality, trends, and availability, making results context-aware at scale. And with both explicit and implicit query understanding, powered by multiple classifiers, we can interpret guest intent, past behaviour, personal preferences, and local cues to deliver a more meaningful and personalised search experience.

ES: What is the role of Target in India in building these AI led search platforms?

SS: Working in close collaboration with our US teams, Target in India plays a pivotal role in developing search algorithms that enhance the guest experience. Our data scientists and engineers are able to address complex business challenges even from thousands of miles away, driven by an innovative and experimentative mindset to deliver what’s best for our guests.

ES: What early benefits or changes has Target seen through the AI powered search platforms?

SS: We are seeing strong early gains in relevance, discoverability, and personalisation. GenAI autosuggest is improving catalogue discoverability, and AI-driven UX components are creating more intuitive journeys tailored to category expectations.

On the retrieval side, hybrid search and multi-channel ranking are producing more context-aligned results. Seasonal relevance, regional preferences, and local availability now play a bigger role in ranking, improving how effectively guests find what they need.

Implicit intent understanding has also significantly enhanced result quality. Even when guests use non-literal or aspirational queries, the system is able to surface relevant options based on behaviour and context. As guests continue to interact more with the system, these models keep improving, making search not just a feature but a strong business driver for Target.