Retail brands are discovering that securing visibility in AI-generated responses is just the first step in a far more complex challenge. According to new research from Bluefish tracking nearly 200 brands across nine verticals, the brands appearing most frequently in AI answers are often not the ones being described most favorably, a fundamental shift from traditional search marketing.

"Being visible isn't sufficient enough," said Charles Aubert, lead customer AI engineer at Bluefish, during a recent EMARKETER webinar. "While in traditional search, the blue link didn't carry any meaning or any narrative, in the generative world, the answer is the narrative about your brand."

Bluefish's State of Enterprise Brands and AI report evaluated retail brands on five performance dimensions to understand how AI models are representing companies to consumers.

AI rewards topic ownership

The brands achieving long-term success in AI are those that combine high visibility with favorable representation across topics that large language models prioritize, according to Bluefish's analysis.

Target emerged as the only brand in the study to lead both visibility and favorability metrics in its category. The retailer's success stems from producing content that satisfies what AI models seek when making recommendations, Aubert explained.

"The content that they expose actually satisfies what the models are looking for," he said. "Target is actually part of the top 10 sources, meaning that the content they're putting out there resonates with what this brand ambassador is looking for."

Retail brands cannot rely on third-party content alone to shape their AI narrative. Companies mentioned frequently on Reddit or social media may achieve high visibility, but if those mentions are negative, they secure unfavorable sentiment that deters potential customers.

Performance varies dramatically by audience segment

AI's promise of personalization creates wildly different brand experiences depending on who's asking the question. Bluefish's data revealed that some retailers show 84% presence with certain audience segments while dropping below 10% with others.

"Each answer is unique to who's asking the question," Aubert said. "If your narrative is not consistent across your customer segments, your performance will vary greatly."

The telecommunications vertical demonstrated this consistency advantage, with some brands achieving uniform visibility and favorability across all audience segments, a result of maintaining consistent narratives across all marketing channels.

Aggregate performance metrics are insufficient for AI optimization. Brands must measure at the audience level to understand performance drivers for each customer segment. Generic optimizations won't move the needle when AI models personalize every response based on user context.

The source mix determines your narrative control

AI models pull from vastly different source ecosystems depending on the vertical. E-commerce sites dominate in retail, while government sources play larger roles in pharma. In telecommunications, owned content drives significant portions of AI responses.

Bluefish's analysis revealed that the sources AI models cite visibly aren't always the ones shaping the actual narrative. Wikipedia, for example, appears frequently in ChatGPT responses but typically contributes only basic facts like founding dates and employee counts, not the opinionated content that shapes brand perception.

"We're doing a direct comparison of the content on the web pages that AI models are citing with the content inside of the AI answer," Aubert explained. This "AI influence" metric helps brands understand which sources actually drive narrative versus which simply get mentioned.

Marketing teams must optimize across the entire source ecosystem: social media, PR, content marketing, paid media, and affiliate channels. This requires unprecedented cross-functional collaboration, as AI models will surface inconsistent narratives until all teams align on unified messaging.

Accuracy matters more than sentiment

Aubert argued that accuracy represents the most critical performance factor, even more important than favorability.

"When a model hallucinates, it's not a visibility issue, it's not a sentiment issue," he said. "Is the price widely different? Is the performance or the specifications about the device inaccurate? These are the factors that consumers consider before they make a decision."

OpenAI has acknowledged that meaningful double-digit percentages of product-related responses contain inaccurate information. For brands, these hallucinations create revenue risk at the bottom of the funnel, deterring customers who are already considering a purchase.

Brands must ensure AI models access authoritative, accurate information about products and services. Models pulling primarily from social media or unverified third-party sources are more likely to hallucinate, making owned content and trusted publisher relationships essential for accuracy.

For brands with limited resources, Aubert recommended starting with content gap analysis to identify where first-party content can have the most influence, then making strategic, high-quality updates rather than generating content at scale.

Watch the full webinar.

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