“Just Do It” is shorthand for one of the most recognizable names in sports.
Nike's annual revenue is roughly 30 times Brooks'. Yet for searches tied to specific runner needs, Brooks beats the global sports giant across LLM recommendations, AI Overviews, and Google.
Researchers from Harvard Business Review asked ChatGPT, Claude, and Gemini for running-shoe recommendations. They found that Brooks appeared consistently across each model, while Nike appeared far less often. Out of 716 brands identified in the study, only 8.4% received consistent recommendations, and 55% of multi-model brands received different positioning depending on the platform.
Brooks offers a rare generative engine optimization (GEO) case study to explain this gap. For decades, the company has aligned its products with common runner problems, technical requirements, proprietary technologies, and supporting data. Harvard Business Review calls this expert documentation an “AI interpretable brand.”
This structured messaging strategy gives Brooks a unique advantage when users ask AI models a question like, “What are the best running shoes for flat feet?" These types of user queries present problems for the models to solve. Brooks’ extensive content connects those problems to its product requirements and technologies, making it easy for models to find relevant user information and make a recommendation.
Brooks Built Their Products Around Runners' Problems
Under longtime CEO Jim Weber, Brooks invested in product engineering and biomechanical research. The company went on to build its product language around runner needs like overpronation, gait mechanics, cushioning, stability, and support.
Brooks’s content strategy gave them a competitive advantage long before ChatGPT appeared, because AI recommendations often start with relevant users’ needs rather than overall brand claims and marketing speak.
Compare these two searches:
“What are the best running shoes?”
and
“What running shoes provide support for overpronation?”
The first question is subjective and puts the model in a position to “guess” what the user means by “best.”
The second question gives the model a request that it can translate into requirements.
The HBR research found a similar pattern across categories. Exploratory prompts generated 95% more brand mentions than goal-oriented prompts, while only about 11% of brands appeared in both prompt types. The prompt research found that specific consumer needs produce a different competitive set than broad category searches.
Brooks Makes Product-to-Model Connections Easy to Follow
The Brooks Adrenaline GTS 25 shows how the structure works.
Brooks describes the shoe as designed for structured support, road running, walking, and comfort. The product page lists a 10 mm midsole drop, a weight of 10.6 ounces for the referenced men’s model, GuideRails support, and nitrogen-infused cushioning.
Brooks defines what GuideRails technology does. The system uses two foam components designed to limit excess heel movement and reduce movement away from a runner’s habitual motion path. Brooks connects the technology with ankle movement, knee movement, overpronation, and gait mechanics.
The resulting chain of evidence gives the model a path from the consumer’s question to the product:
Overpronation → stability requirement → GuideRails → Adrenaline GTS
AI models tend to reject broad claims like “better support” and instead retrieve information tied to specific runner problems, defined product features, and specific brand lines.
How Third-Party Sources Amplify The Brooks Story
Brooks doesn’t have to rely on its product pages to connect common problems with its product solutions.
Runner’s World named the Adrenaline GTS 25 its “Best Overall” stability running shoe in the 2026 stability-shoe guide. The publication discussed the shoe using many of the same concepts found in Brooks materials, including GuideRails, a 10 mm drop, stability, pronation, and support during longer runs.
Doctors of Running described the Adrenaline GTS 25 as a moderate-stability daily training shoe, reported a 10 mm drop, discussed medial support and GuideRails, and gave the shoe an “A” for stability.
RunRepeat reached a similar conclusion through lab testing, identifying stability as a defining strength and connecting the GuideRails system with runners who experience pronation.
In AI search, earned media value increases through repetition across independent sources.
Why Nike’s Strong Brand Awareness Doesn’t Guarantee Broad LLM Recommendations
The traditional marketing funnel builds awareness, consideration, and decisions. AI search adds another decision process before a recommendation reaches the consumer.
LLMs must interpret the consumer’s need, translate it into product requirements, identify brands associated with those requirements, compare available information, and choose products that fit the user's answer.
Nike enters the AI model selection process with enormous brand recognition. Brooks enters and wins with a tighter connection between a specific problem and a specific product.
Broad awareness helps a model identify a brand. Interpretability helps the model connect the company with the information relevant to its answer.
Brooks built that interpretability long before brands began talking about generative engine optimization (GEO). The company organized information for runners, retailers, coaches, and clinicians. Today, AI models use a similar structure to retrieve information.
What Brands Can Learn From Brooks
While a comprehensive, expert-led content strategy doesn’t develop overnight, most brands don’t need decades of ongoing research to replicate Brooks’s strategy.
Brands should start their AI visibility work by identifying common customer problems. Once defined, find the product attributes connected with each need, and use consistent terminology across product pages, FAQs, comparison pages, retailer descriptions, and owned and earned content.
Public Relations (PR) comes in here. Independent coverage validates the relationship between the consumer need, the product attribute, and the brand. Reviews, testing, expert commentary, and credible media coverage strengthen connections that a brand can’t establish through its website.
Measurement follows a similar structure. Brands can test category and problem-specific prompts across major models, record which competitors appear, identify the sources behind each recommendation, and measure whether their inclusion changes as information and third-party support improve.
The ultimate goal of this strategy is to make the brand easier to connect with consumer needs it can legitimately claim to meet.
How Brands Can Build a “Brooks Advantage”
5WPR works with brands to identify and build the information behind AI recommendations, map product attributes to consumer queries, strengthen third-party evidence, organize brand information, test visibility across models, and measure competitive inclusion over time.
Brands that want a stronger position in AI search start with a structured review of the questions, attributes, evidence, and sources shaping their category. Explore 5WPR’s Generative Engine Optimization services to build a brand that models can connect with the right consumer need.
