Brand Visibility in AI Search: How to Turn Brand Search into AI Visibility

Brand visibility in AI search depends on a variety of factors, including answering user queries to solve specific problems, brand content authority, attributes, third party evidence, and using customer vocabularies to turn brand search into AI visibility.

AI search is providing small and medium sized companies with outsized opportunities to compete. Those that learn to adapt can improve brand visibility in AI search against much larger rivals.

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AI assisted search is taking off and it’s providing small and medium sized companies with the chance to out-compete industry leaders. Brand visibility no longer depends on market share or advertising budgets. Yet only 16% of companies, large or small, track how their products and services appear in AI search results, reports McKinsey. That gap can leave opportunities on the table.

1.   When Planet Fitness – one of the largest fitness brands – analyzed searches across several AI platforms, it found that a much smaller brand was being recommended more consistently, according to the MIT Sloan Management Review. “We were shocked when a small, local company in Houston was landing better in AI searches.”

2.   A financial services company with the biggest market share and biggest digital marketing budget failed to appear at all in AI search results, write the MIT authors. A much smaller competitor was the top choice.

3.   When testing running shoe recommendations on ChatGPT, Claude, and Gemini, researchers found, “the relatively small brand Brooks appeared reliably.” By contrast, “Nike, the world’s largest athletic brand, appeared far less consistently.” (I wear Brooks, and I am not being paid by them to say this.)

Unlike traditional search results, which depend on exposure and attention, AI search results work to solve specific user problems. Once a brand is included in AI search results, nearly 80% of brand mentions are positive on the AI platform, according to the Harvard Business Review. But to be included in AI is the challenge for most brands. 

This article looks at how to improve brand visibility in the changing landscape of AI search.

By the way, 10 Plus Brand is a small brand building, brand marketing agency, yet we have elevated one of our clients to be #1 on ChatGPT for his industry – very proud to mention that.

How is AI changing brand search?

Roughly 50% of consumers were using some form of AI assisted search at the end of 2025, according to a McKinsey report. By the middle of 2026, that figure had grown to nearly 60% overall and to 75% among Gen Z and Millennial consumers, according to NielsenIQ.

The McKinsey authors project that $750 billion in consumer spending will go through AI search by 2028. Brands that are unprepared “may experience a decline in traffic from traditional search channels” of between 20% and 50%, the authors write.

To win the battle for search relevance, brands need to think beyond keywords and SEO. The McKinsey authors state, “While SEO focuses on [your] own-site content, in many cases, a brand’s own sites only comprise 5 to 10 percent of the sources that AI-search references.” To influence AI search outcomes, “brands need to understand what questions consumers are asking and which sources are shaping answers.” 

In this new landscape, successful brands realize they can shape but not control the brand journey. Consumers, influencers, reviewers, and others can take brands to unexpected places.

Examples include Nike becoming a lifestyle brand through the influence of hip-hop culture. Or Apple’s iPhone transforming into an icon of “creator culture” used in video and music production. 

To capture the moment, brands must be willing to “co-create” the brand journey with their users, fans, and influencers while staying in touch with the core values that build brand trust. It’s how brands like Nike and Apple engage with their audiences without compromising their brand identity.

How do brands compete in the age of AI search?

Share of Model in AI search.

In an earlier article, we examined how to improve brand visibility with Share of Model, a measure of how frequently brands appear in AI search results. One big challenge is that each AI model (or platform) returns different results. Few brands appear consistently and the way AI models arrive at their decisions has been called a “black box.” Even the developers of AI models don’t fully understand the process, as of now. 

But there are concrete steps brands can take to adapt to AI search, no matter which model (or platform) consumers use.

1.  Solve specific problems. Traditional search results favor attention, or the number of links and mentions a brand or product receives. AI search favors resolution, or how closely a brand or product answers a customer query or solves a customer problem. Offering “the best running shoes” isn’t effective. Offering running shoes that solve specific problems (like providing arch support, relieving knee pain, and so on) wins the race.

2. Establish authority. AI platforms won’t “take your word for it.” Brands need to establish authority in order to appear in recommendations. Some of the ways to show authority are:

–  Product / service details. Provide in depth information about your products or services, including technical specifications and use cases.

– Independent sources. Link to third party studies, primary research, and expert reviews.

– User content. Leverage user reviews and recommendations on social media and other user generated media such as video sharing sites and podcasts.

– Brand content. The brand’s own thought leading content, in the form of topical articles and blogs, is an excellent way to establish expert authority.

3. Make content AI friendly. AI platforms look for answers. Making it easy to find answers increases brand visibility. Headings and captions can help, as can a question and answer format. According to MIT Sloan “FAQs, for example, are great for providing information that AI platforms can use to create more conversational responses.”  (Here are some examples of effective, conversation-style FAQs.)

In traditional search, less is more since “search crawlers don’t like repetition,” say the MIT authors. “For AI-driven search, more is more.” 

For example, AI platforms use what a 2024 HBR article calls “retrieval-augmented generation” (RAG). For practical purposes, RAG means that AI platforms search a wide range of information for background and context, not just product descriptions and reviews. The more in-depth information brands offer, the more they will attract AI attention.

4. Coding for AI. Brand websites need to be optimized so that AI models can easily find the information they need to make recommendations. The MIT authors advise keeping the “robots.txt” file “clean and intentional.” List your site map and allow crawling of all valuable pages, only blocking “low-value or private content.” A 2026 HBR article recommends adopting the “llms.txt” format, which provides a roadmap of your site’s most important content for large language models.

AI Recall Share.

In a June 2026 Harvard Business Review article, the authors extend the Share of Model framework by emphasizing what they call “interpretability.” They write: “AI systems favor brands that can be translated into attributes and evidence, brands whose value can be articulated clearly in response to a user’s query.” 

• Attributes are product or service features described in a way that answers questions for an AI assisted search. 
• Evidence is information that describes or supports brand attributes. The authors stress the importance of third party reviews but, as we’ve discussed, AI favors more information from all sources.

Why brands lose ground in AI search?

 The authors describe three reasons brands fail to perform up to expectations in AI search results.

1.   Fragmentation. After testing 716 unique brands, the authors found only 8.4% appeared consistently on ChatGPT, Claude, and Gemini. Most brands only appeared on one platform. They write, “What matters is whether a[n AI] model can arrive at your brand as a credible answer to a specific problem.”

2.   Framing. Among the brands that appeared on more than one AI platform, “55% are framed differently across systems.” ChatGPT might describe a brand as a “premium innovator” while Claude describes the same brand as a “budget alternative.” In addition, brands often appear as “interpretable sub-units” – that is, as individual products or services that resolve a specific consumer problem or query. An example may be that a Toyota model (say RAV4) may appear as an answer to someone’s query, but not the entire Toyota brand. 

3.   User queries control results. “AI assistants build recommendations based on how consumers articulate their problem,” the authors write. They urge brands to “shape the vocabulary” consumers use through messaging, media, and other channels. Describe your products or services using the words that your customers are using now, instead of what you THINK they use, as described earlier, co-create the brand journey by responding to the ways consumers already describe their wants and needs. 

How to improve brand visibility in AI search?

These are four steps to improve AI search results for your brand:

1.   Survey major AI platforms. Create queries related to your industry, product or service and test them on major AI platforms. Check which brands appear in each platform’s responses and where your brand appears in relation to competitors. See how your brand is framed (premium, budget, etc.) and whether the framing is both accurate and consistent across AI platforms. 

If your brand is not being accurately portrayed, make efforts to educate the AI platform by updating brand content until results improve. Brands including Pernod-Ricard and Danone regularly monitor AI responses and make “targeted interventions” to improve brand visibility.

2.   Audit your “attributes.” Can consumers, and AI models, name three attributes (or features) of your product or service and connect them to specific user needs? If not, make sure the attributes that differentiate your brand are “clearly named and consistently used wherever your brand appears,” the HBR authors write. (You need to first conduct a brand audit to zoom in on your most prominent brand attributes or “unique value propositions“.) 

3.   Map third party evidence. Determine which independent sources (reviewers, experts, media outlets, etc.) mention your brand using its key attributes. Brands that break through in AI search are those with “the most consistent and credible external validation.” Third party evidence takes time to accumulate and is more credible than “media spend.”

4.   Learn consumer vocabulary. The final step is to use social listening, surveys, and interviews to find out how consumers actually describe their wants and needs, and how they describe your brand. Brands can then try to influence the discussion or learn to adopt the vocabulary consumers are already using. Creating a favorable “query landscape” increases the odds your brand will be recommended.

The age of AI-assisted search is changing the game. Brands that monitor the landscape and learn to adapt can compete with much larger competitors. If you would like to learn more about staying ahead of the age of AI search, please contact us.  

About the Author, Joanne Z. Tan, 10 Plus Brand, and AIXD

Joanne Z. Tan is a global brand strategist, thought leadership coach, and founder of 10 Plus Brand, Inc. and its subsidiary AIXD.world. She advises executives, founders, and organizations on building influential, differentiated brands that drive visibility, credibility, and revenue growth. Her work integrates strategic positioning, AI-era brand architecture, and executive thought leadership to help leaders become recognized authorities in their industries.

AIXD (AI Experience Design) is a global platform and community focused on advancing responsible, human-centered artificial intelligence. It brings together leaders, technologists, strategists, and innovators to explore how AI can be designed, implemented, and governed to enhance human experience and long-term business value. Through thought leadership, collaboration, and strategic dialogue, AIXD aims to shape the future of AI–human symbiosis with impact, innovation, and integrity.

© Joanne Z. Tan, 2026. All rights reserved.