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Gap Inc.

2024

Revolutionizing Fashion Discovery with Conversational AI

Athleta AI Stylist

Prototyping

Cross-Functional Collaboration

Usability and Accessibility

As the Brand Strategy Product Designer focused on Gap Inc.’s Athleta brand, I led the design of the Athleta AI Stylist—a conversational shopping assistant integrated into the iOS app. This AI-powered tool uses natural language processing to provide personalized style recommendations, helping users discover products effortlessly while testing cutting-edge AI capabilities in search and conversational interfaces.


Designing for Conversational Discovery

The Athleta AI Stylist project aimed to explore how AI-powered conversational tools could enhance product discovery and improve the shopping experience. The initial phase focused on integrating natural language search into the iOS app, allowing users to type phrases like “sweaters for cold weather workouts” or “present for my niece.” The backend extracted keywords to generate relevant search results, testing the system’s ability to interpret intent accurately. This MVP was internally tested (dogfooded) to validate functionality before considering public release.


Beyond natural language search, we envisioned a next step: conversational refinement. If a user’s search returned no results or a broad query like “pants,” the AI Stylist would prompt clarifying questions through a chat interface. For instance, it might ask about style preferences, category, or intended use, refining the search query in a conversational, human-like manner. This approach laid the foundation for a feature designed to handle complex user needs in a seamless, scalable way.


Building an Intuitive AI Chat Interface

The design evolved to introduce the AI Stylist at key user interaction points, such as when users scrolled through search results or hit the “no results” page. We integrated contextual prompts like “Not finding what you need? Try our AI Stylist!” to encourage users to engage. The chatbot included guided filtering questions and displayed product cards with recommendations, offering personalization and contextual reasoning for each suggestion.


Working closely with engineering, we designed and tested prototypes in TestFlight with internal teams. This included refining user flows, designing loading indicators, and incorporating feedback on interaction design, such as resizing text fields and clarifying bot responses. Our goal was to make the experience both intuitive and efficient, while also testing key hypotheses about customer needs.


Personalized, Scalable Shopping Experiences

The MVP launched as an internal beta, allowing employees to test its effectiveness in understanding and responding to user intent. We collected feedback through A/B testing on the chatbot’s design, flow, and entry points, continuously iterating based on insights. While the initial focus was on natural language search, the long-term vision includes scaling the chat experience to offer product personalization, handle more complex inquiries, and integrate across Gap Inc.’s other brands.


This project emphasized balancing experimental AI technologies with practical, user-friendly designs, showcasing the potential for conversational AI to transform online shopping. By aligning AI’s capabilities with real-world use cases, we set the stage for a scalable feature that enhances trust, engagement, and satisfaction for Athleta customers.

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