Stanley 1913 is adapting its marketing strategy to perform well in AI search and chatbot recommendations, recognizing that traditional visual-first content built for human audiences doesn't translate effectively to language models. The brand is restructuring product information by building clearer links between features and benefits, adding product-level FAQs, care instructions, and usage guides around common questions—such as gifting, hydration, fitness, and travel—that people ask AI engines (Modern Retail). Chief brand officer Kate Ridley noted that occasion-based marketing campaigns had been heavy on imagery but light on the descriptive copy that LLMs need to surface products in natural-language answers.
The urgency is real: more than four in 10 U.S. adults (42%) now use AI chatbots to search for information (Modern Retail), and Stanley 1913's website traffic has surged 35.5% year-over-year, with 6.6 million global visits in July 2026 (Modern Retail). The brand is treating this as a cross-functional effort spanning content, SEO, e-commerce, technology, PR, and marketing, testing Shopify and Google's Universal Commerce Protocol to integrate its product catalog directly into chat conversations, and using structured data and measurement frameworks to track how often it appears in LLM outputs (Modern Retail).
For commerce practitioners, Stanley 1913's approach underscores a broader shift: AI platforms are becoming discovery channels as influential as human creators. The brand is not abandoning social and influencer marketing but recognizing that LLMs pull from product data, brand storytelling, and third-party sources—meaning visibility in AI conversations now requires the same strategic attention that earned media and affiliate coverage have long received. This signals that brands must invest in detailed, structured product information and cross-platform content infrastructure to compete in an AI-driven discovery landscape.