October 2026 Summaries

Kearney research based on more than 21,500 consumers across 20 countries suggests that expanding product choice can create information overload, making effective product discovery a growing competitive priority for retailers. The piece argues that AI-powered search, recommendations, and real-time personalization can help shoppers find relevant products by combining current intent, behavior, preferences, product attributes, inventory, and delivery context rather than relying only on past purchases or customer segments. Examples from Kleinanzeigen and Vinted illustrate how Vespa.ai is used to manage large, rapidly changing inventories, with Vinted reporting improved transactions and gross merchandise value after upgrading its discovery capabilities. As consumers increasingly value functional outcomes over brand familiarity and show more willingness to accept AI suggestions than autonomous purchasing, the article contends that reliable retrieval, ranking, and current product data are essential foundations for AI shopping assistants. It concludes that retailers can differentiate themselves by applying proprietary product, customer, inventory, and behavioral data through specialized AI models and discovery systems that reduce effort and improve relevance.