Stanley 1913 is rethinking its marketing strategy for the AI search era - not by gaming algorithms, but by restructuring how it translates its brand story into machine-readable detail. Already, 42% of U.S. adults use AI chatbots to search for information, per Pew Research Center, which makes the 6.6 million monthly visits to Stanley 1913's website increasingly dependent on content that large language models can cite and summarize.
The brand's visual-heavy marketing, built for human audiences on social platforms, didn't work for AI, the company found. Imagery around Mother's Day, Teacher Appreciation, and Nursing Appreciation campaigns lacked the written detail about "the use, the occasion and why it was a great gift option," said chief brand officer Kate Ridley. That absence meant the brand wasn't surfacing when people asked LLMs about those occasions. "We weren't necessarily writing copy that was really detailing the use, the occasion and why it was a great gift option," Ridley said. "Now we recognize that's actually really important for LLMs."
Building for natural-language queries
Stanley 1913 is building direct links between product features and customer benefits. The brand is adding product-level FAQs, care instructions, and usage guides organized around the kinds of questions people actually ask AI engines - on topics such as gifting, hydration, fitness, and travel. The goal is to make its content appear when LLMs search the open web for product information.
"It's about ensuring our authentic brand experiences translate into natural-language answers without losing the human touch," Ridley said.
To do that, the brand is treating the organizational shift as a cross-functional effort spanning content, SEO, e-commerce, technology, PR and marketing - not as a narrow SEO project. "We're actively building out dedicated LLM guidance and measurement frameworks so everyone stays aligned," Ridley said.
Testing new format standards and infrastructure
On the technical side, Stanley 1913 is testing Shopify and Google's Universal Commerce Protocol to bring product catalogues directly into cloud conversations. It uses structured data to keep product information consistent across both traditional search and AI agents. Working with partners including Yotpo's Discovery product, the brand measures how often its products appear in LLM outputs and get cited.
The work extends beyond the brand's own site, as LLMs rely heavily on third-party sources: earned media, affiliate coverage, and user reviews. Stanley 1913's team tracks which sources agentic search tools treat as authorities and directs efforts accordingly. Cultural partnerships like the recent Kacey Musgraves campaign generate press coverage beyond the brand's own channels.
"Stanley did an incredible job of reintroducing its brand to consumers via human creators and influencers," said Debra Aho Williamson, founder and chief analyst at Sonata Insights. "Now, brands like Stanley are recognizing that they need to think of AI platforms as influencers of a different type." They are becoming important places where consumers discover new brands, learn about them, and then decide purchase decisions, she said. "Stanley is treating this as a training problem, not a hacking problem."
Why this matters for marketing professionals
For marketers, Stanley 1913's approach confirms that AI search adaptation requires reworking content structures and cross-team collaboration, not just SEO tactics. The brand's gap analysis - where it found strong visual content with weak textual context for gifting occasions - will likely mirror what other product-driven brands will find as they face the same shift. The playbook: make product detail easy for LLMs to parse while preserving the human-level brand voice, and treat it as a multi-team effort to cover every channel where product info lives.
For marketers wanting a deeper understanding of AI for Marketing, this case shows the show process of aligning brand messaging with how LLMs consume information. If you're starting to prepare your own content for AI search, the first step is to audit where existing copy falls short in answering the questions LLMs are trained to process.
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