Online shopping has evolved from simple keyword searches to a conversational experience powered by AI. Instead of searching for specific products, shoppers describe their needs and constraints, and AI tools like ChatGPT and Gemini ask clarifying questions to recommend relevant products. This shift is changing consumer behavior and the commerce landscape.
AI recommendations happen in two key stages. First, the model selects products that fit the shopper’s constraints and category — for instance, a shampoo suited for sensitive scalps within a price range — filtering out products that lack clear positioning or data. Then, AI ranks the filtered set, prioritizing items with trustworthy signals like certifications, consistent product data, and reliable reviews. Products with well-structured, AI-friendly content gain much higher visibility.
Brands must adapt by providing precise, verifiable product information that AI can parse and validate across multiple platforms including retail sites and forums. The aim isn’t just SEO keywords but building a robust digital footprint that enables AI to confidently recommend their products. Clear attribute data, use cases, and trust signals are critical for rising in AI-driven recommendations. As AI increasingly shapes what consumers see, having accurate and comprehensive product data will be a competitive advantage.
Kimberly Shenk, cofounder and CEO of Novi, emphasizes that brands need to focus on why they might be excluded from AI recommendations and what it takes to be included, rather than just appearing for specific search prompts.
