Interview with Purva Gupta, Co-Founder and CEO of Lily AI: 5 Inquisitive Insights
In the ever-evolving world of retail, a technological breakthrough by Lily AI is making waves, promising to drive eight- to nine-figure revenue uplift for retailers and brands.
Founded in 2015 by Purva Gupta and Sowmiya Choka Narayanan, Lily AI was created with a mission to improve the shopping experience by understanding the emotional context of the shopper. Gupta, the CEO of Lily AI, identified a key issue that led to the founding of the company – personal frustration with how fashion retail was failing to understand the details that shoppers use in real life to describe the items they're looking to buy.
Lily AI's demand forecasting solutions use AI to help retailers make decisions on what merchandise to order earlier, ensuring the right size, color, and style mix of items will still be ordered ahead of longer lead times. This technology is designed to improve retailer decision-making and reduce bottlenecks in the product supply chain.
The AI-powered product attributes platform by Lily AI is currently being used by renowned retailers like The Gap, Bloomingdale's, Macy's, and thredUP to improve on-site search conversion, personalized product discovery, and demand forecasting. With AI-powered demand forecasting, retailers can gain valuable signals about what to stock based on past purchases, such as the preference for floral print black dresses with lace.
Moreover, Lily AI's technology helps retailers build the right product taxonomy to capture both common and long-tail searches, reducing the chances of irrelevant or no results in product searches. This improved product forecasting can lead to a reduction in forecasting timelines, such as from three months to one month, as demonstrated by a multi-brand retailer.
The future of AI in retail is promising, with applications set to optimise both in-store and online shopping experiences. AI enables hyper-personalization and localization, adapting content, product recommendations, and promotions to local languages, cultures, and preferences in real time. In-store AI, such as computer vision, generates heat maps of foot traffic to optimise product placement and layouts, monitors shelf stock levels for timely replenishment, and drives personalised smart screen offers.
Augmented reality combined with AI can deliver interactive virtual product demos both online and in-store. Voice commerce will expand, with AI-powered voice assistants enabling smoother, context-aware, and personalized shopping via voice commands. AI systems that read customers' emotions via facial expressions or biometrics will adjust store environments, creating mood-based shopping experiences.
Retail automation will grow with AI and robotics managing inventory, warehousing, customer assistance, and fully autonomous stores becoming common within 5–10 years. AI agents will perform more autonomous tasks such as shopping on behalf of customers through natural conversations, managing stock orders, filling staffing gaps, and making marketing decisions aligned with inventory.
Lily AI's technology, with its focus on understanding the language of the customer, can significantly impact a retailer's topline revenue. The implementation of Lily AI's technology can positively impact a retailer's revenue, with projected impacts of up to $48 million for one retailer. However, a core layer of customer language and expanded taxonomy of product attributes is still needed to accurately connect shoppers with relevant products, a key aspect for the future of AI in retail.
In conclusion, the applications of AI in retail are set to make both in-store and online retail experiences smarter, faster, more efficient, and highly personalised, driving retail growth and competitive advantage.
- The implementation of AI, such as Lily AI's technology, can significantly enhance the shopping experience by understanding the emotional context of the shopper, thereby driving retail growth and competitive advantage.
- Retail automation with AI and robotics is poised to manage inventory, warehousing, customer assistance, and even operate fully autonomous stores within the next 5–10 years, streamlining retail operations and improving efficiency.
- AI-powered systems are set to optimize retail decision-making across multiple dimensions, including demand forecasting, product placement, personalized product recommendations, and automated marketing decisions, ultimately driving eight- to nine-figure revenue uplift for retailers and brands.
