AI Brand Representation

Disclaimer 1: I spent a few weekends playing around with these AI systems out of pure curiosity. It is not peer-reviewed research but just me noticing stuff that seemed interesting and thought was worth sharing. Take it as food for thought, not gospel.

Disclaimer 2: The AI landscape is changing so fast that parts of this article might be outdated by the time I publish it versus the first test I did, and the time you read it.

OK, dear marketer, let's talk about one of the elephants in the marketing room that nobody's addressing: AI systems might be influencing how consumers encounter your brand, and probably your meticulously crafted marketing strategy has approximately zero answers for this. Someone had to say it.

Remember the SEO panic of 2010? The Google Mobilegeddon and the social media scramble of 2015? This feels eerily similar, except the stakes are arguably higher and, bizarrely, fewer marketers seem to be paying attention. Just try to search or prompt about it. Maybe we're all just exhausted from digital pivots at this point?

I decided to test this myself

After attending several AI conferences where no one could give me a clear answer about how AI represents brands, I decided to run my own tests over a few weekends. What I found was quite interesting.

I took Interbrand's top 10 2024 Best Global Brands rankings and compared them to how four AI models, ChatGPT, DeepSeek, Claude, and Gemini, rank and represent these brands.

To analyze sentiment encoding, I prompted each model to generate adjective associations for brands and identify historical events shaping brand perception. I tested responses for stability, context drift, and model-specific bias, repeating queries to detect inconsistencies.

Fired up each platform, created similar prompts about brand rankings, products, and sentiment, then documented everything. Not identical prompts. Identical prompts fired on different models generate biases.

I looked at several key areas to compare how AI systems view brands versus how we humans rank them.

Industry Recognition: How frequently does the brand appear in AI-generated knowledge panels?
Product Awareness: How well-defined are its key offerings in AI responses?
Competitive Positioning: How uniquely does AI distinguish it from competitors?
Bias Indicator: How polarized is the brand's perception across different AI models?
Market Leadership Perception: Does AI describe the brand as a leading entity in its industry?

What happened?

The four AI models considered those top brands, listed by Interbrand, in very different ways.

Big names going missing

Nike completely disappeared from ChatGPT's list of top 10 global brands. The brand showed up prominently in Claude, DeepSeek, and Gemini's responses to the same prompts. I ran this test three times to be sure. The results were consistent.

Luxury brands get overlooked

Mercedes-Benz and BMW, ranked number 8 and number 10 on Interbrand's list, were severely underrepresented across all AI platforms. DeepSeek, Claude, and Gemini did not even include them in their rankings, despite their massive global presence and brand investments.

Even more surprising: not a single luxury brand, Louis Vuitton, Rolex, or Gucci, appeared in any AI system's top 10 rankings. These systems seem to have a blind spot for high-end brands, possibly because they have less overall digital content compared to mass-market brands.

Inconsistent brand perception

Google and Amazon showed dramatic variations in sentiment across platforms. Google's Bias Indicator ratings ranged from neutral, 3 out of 10 on ChatGPT and Gemini, to highly polarized, 7 out of 10 on DeepSeek and 6 out of 10 on Claude.

Apple, Interbrand's number one brand, was seen as significantly more polarizing by Claude, 7 out of 10, than by ChatGPT, 2 out of 10.

Google and Microsoft showed striking differences in competitive positioning scores. Claude and DeepSeek rated them significantly lower, 8 out of 10, than ChatGPT and Gemini, 9 out of 10.

McDonald's consistently received lower scores for competitive differentiation, suggesting AI systems struggle to distinguish it from other fast-food chains despite its iconic status.

Different AI models are thinking about your brand in different ways. It is like they have opinions.

While a Google Search lets you pick and choose what attracts you, yeah, not really, I know, AI systems give you an opinionated view of the world.

AI systems do not rank and retrieve content like search engines. They synthesize probability-based outputs from their training data, which means they are essentially building their own internal representations of what your brand is based on what they have seen during training. And if they use live online data, they might change opinion slightly, but in a weird way.

Those representations might bear little resemblance to the brand you have invested millions building.

While consumers still engage with brands through many channels beyond AI, these inconsistencies and opinions are concerning as AI increasingly influences how people discover and evaluate products.

The visibility gap: how brand representation varies in AI systems

These differences in how AI represents brands reflect deep structural biases in AI training data and digital information ecosystems. I spotted at least four key biases: platform-specific, frequency-dependent, source-generated, and English-language dominance.

Platform-Specific Processing Differences

Google's Gemini: Gemini incorporates some Knowledge Graph information and its training combines various data sources with reinforcement learning techniques, balancing between real-time information and its training data. Sometimes it favors results that would rank highly in search, but other times it surfaces completely different information. It is less like a search engine and more like a statistical model of what Google thinks is true about the world.

ChatGPT: OpenAI's models use a sophisticated mix of supervised fine-tuning and reinforcement learning from human feedback. A researcher explained to me that different training objectives between model versions can lead to surprising shifts in how brands are portrayed, even when the underlying data has not changed significantly. It is like having different editors with different priorities at different publications.

Claude: Anthropic's approach emphasizes constitutional AI alongside human feedback, creating different information priorities. This means Claude sometimes makes completely different judgments about which brand attributes matter most. I found this particularly evident when looking at pharmaceutical brands: Claude consistently emphasized ethical considerations that other models barely mentioned.

Perplexity AI: Its hybrid approach is a bit more complex than a search plus AI setup, because it involves reasoning about information quality in real time. For example, not surprisingly, it cites extremely recent press releases that other systems had no knowledge of. This recency advantage creates different dynamics for brands with active PR machines. But it also creates biases given the questionable quality of some sources.

Training data frequency bias: the digital Matthew Effect

Large, established brands dominate AI training datasets due to their extensive media coverage, creating a self-reinforcing cycle of visibility. When an AI model processes a query, it naturally gravitates toward the brands it has seen most frequently during training.

This creates what researchers might call a digital version of the Matthew Effect, where the rich get richer in terms of representation. Brands with established digital footprints receive disproportionate attention in AI outputs, while emerging brands or those with less digitized history struggle for recognition regardless of their actual market significance or consumer relevance.

The problem could compound over time. As consumers increasingly rely on AI systems for information, the brands these systems highlight might gain additional visibility, creating a feedback loop that further entrenches established players while making it harder for newcomers to break through the digital ceiling.

For marketers, this could present both a challenge and an opportunity. Understanding how our brand's digital presence translates into AI visibility will become a critical component of brand strategy when the AI-mediated marketplace grows.

Platform Source Biases

When AI models like Perplexity, ChatGPT with browsing enabled, or Gemini with live search are using online sources in a semi-live mode, utilizing search results as Retrieval-Augmented Generation, what I saw is even more brand-dystopic. Digital platforms that supply training data significantly shape brand representation. That is known. But the way they do it in mixed dataset plus search AI results is even more weird.

Product Aggregators and Retailers: Ecommerce sites position brands to drive sales. These platforms present brand information based on commercial relationships, not brand integrity. When an aggregator lists five competing luggage brands side-by-side, it is focused on selling any of them, not accurately representing each brand's unique positioning. AI models then ingest this commercially motivated content as factual information, completely missing the sales context.

Content Websites: News outlets, blogs, and forums are not impartial information sources. They are increasingly influenced by affiliate programs, native advertising, and sophisticated PR campaigns. Content creators often emphasize friendly brands that drive affiliate revenue or feature products from companies with aggressive PR teams. The brand attributes highlighted in these pieces time to time do not align with official brand positioning. They emphasize what sells clicks and commissions. AI systems cannot distinguish between authentic editorial coverage and commercially motivated content, absorbing these monetization-driven representations as definitive brand descriptions.

Media Visibility Distortion: The New York Times mentioned Apple 22 times more frequently than equally valuable B2B brands in my analysis. This visibility gap is both an editorial bias and a cultural footprint exceeding market share. I bet that when AI models encounter certain brands repeatedly across training data, they develop a distorted perception of market importance. Apple's cultural ubiquity means it receives disproportionate attention in AI outputs compared to equally valuable companies with less media presence, creating a representation that reflects public awareness rather than actual market significance.

As AI assistants become more integrated into product discovery, there is reason to believe this could impact market dynamics. Just imagine being the founder of a DTC skincare brand that fights giants with an effective positioning on social media that generates differentiation and word of mouth, but despite that, when someone asks an AI assistant about the best products for sensitive skin, you do not even make the list. That is bad, is it not?

The English-Language Dominance Effect

There is another critical blind spot in how AI systems represent brands that nobody seems to be talking about: the overwhelming dominance of English-language sources in training data.

Linguistic Imbalance in AI Training: Major AI models are disproportionately trained on English-language content, creating a fundamental bias toward brands with strong English-language presence. When I compared how systems represented Mercado Libre, Latin America's e-commerce giant, versus Shopify, the difference was striking. Despite Mercado Libre's massive regional dominance, it received significantly less nuanced treatment.

Do you want to make a test too? Replicate this: I asked ChatGPT o3 to rank top global motorbike and scooter brands and it appropriately included Hero MotoCorp, one of the world's largest motorcycle manufacturers by volume, as third globally. But when I simply changed the prompt to ask for the best manufacturers, Hero completely disappeared, replaced by premium Western brands like BMW and Ducati. One word changed, and the global volume leader vanished from the results. The system apparently associates best with Western quality perceptions rather than actual market leadership.

Cultural Context Loss: Beyond simple representation, the English-language bias means cultural nuances get flattened. Brand attributes that resonate deeply in specific markets are often missing from AI representations if they are primarily discussed in non-English content.

Market Implications: This creates a double disadvantage for brands from non-English-speaking regions. Not only are they less visible in AI outputs, but their unique positioning is often misrepresented or oversimplified. De facto, AI systems are actively encoding Western cultural biases about what constitutes quality or best.

It is potentially a competitive disadvantage baked into the emerging AI-mediated marketplace. But look, I am not suggesting you panic. I am suggesting you start paying attention.

So, anything you can do? Awareness is power. Let's start from there.

AI Brand Visibility: my 2 cents for a measurement framework

I have sketched out a possible approach that might help you navigate these representation challenges. Nothing groundbreaking, actually it is quite simple, just a practical framework combining basic measurement, optimization, and some defensive tactics. I gift it to you hoping that someone will take it from here.

1. Measuring your AI brand position

First, you need to know where you stand. Establish your current AI representation baseline using these key metrics.

Brand mention frequency: Measure how often your brand appears in AI-generated responses to industry-relevant queries using standardized prompts.

What are the top brands in [your industry]?
Describe [your brand] and its key offerings.
Compare [your brand] with [competitor A] and [competitor B].

Now think about your desired positioning, what you know about the market, and compare and contrast. The disparities might surprise you. In my experimentation, just testing these three prompts revealed very relevant inconsistencies even for the world's most valuable brands.

Sentiment analysis: Evaluate the tone and emotional valence of AI-generated content about your brand.

Ask models: What is your overall sentiment about [your brand]? Provide a score from -1 to +1.
Try: List five adjectives that best describe [your brand] and explain why you chose each one.
Ask: Would you characterize [your brand] as innovative, controversial, or traditional? Explain your reasoning.
Track consistency of sentiment across different AI platforms.
Monitor changes in sentiment following product launches or events.

Sentiment might vary dramatically across platforms, with some positioning you as innovative and others focusing almost exclusively on other dimensions.

Citation tracking: Identify which sources AI systems reference when discussing your brand.

Ask: What are your primary sources of information about [your brand]? Be specific.
Try: List the top 3 most authoritative sources you would reference when discussing [your brand].
For recent developments, ask: What industry reports or analysts do you associate most strongly with [your brand]?
Document which platforms and publications appear most frequently.
Assess the accuracy and currency of these citations.
Identify gaps where authoritative sources about your brand are missing.

Competitive positioning: Compare your AI representation directly against key competitors.

Ask: Compare [your brand] to [Competitor A] and [Competitor B] in terms of key differentiators.
Try: What unique value proposition does [Your Brand] offer compared to others in the [industry] space?
Ask: Rank the following brands in order of innovation: [your brand], [Competitor A], [Competitor B].
Use: Create a positioning map for the [industry] market showing where [your brand], [Competitor A], and [Competitor B] sit on axes of [attribute 1] and [attribute 2].
Analyze where your brand leads or lags in key attribute associations.
Identify competitor strategies that may be influencing AI representations.

Content structure and consistency: Here is a strange truth: in my view, the biggest determinant of accurate AI representation was not market size or marketing budget. It was information consistency.

The low-hanging fruit is often just ensuring you are telling the same story everywhere, because AI systems struggle with contradictory information. Focus on:

Creating clear, factual content about your brand that AI models can easily process.
Maintaining consistent messaging across all digital touchpoints.
Developing content that directly addresses common industry questions consumers ask AI assistants.
Implement Structured Data: Use schema markup where appropriate. It is not just for search engines anymore. AI systems increasingly reference this structured information.
Comprehensive Style Guide: Develop an internal style guide that ensures messaging consistency regardless of which team member is creating content.
Content Audits: Schedule periodic content reviews to catch outdated claims or positioning that might confuse AI systems. Contradictory information on different pages of your own site can create representation problems.

2. Optimizing your digital presence for AI recognition

With your baseline measurements established, implement these strategies to enhance your AI visibility.

Authoritative Source Management

Build a strong Wikipedia, et similia, presence: Create a factually accurate, comprehensive Wikipedia page with reliable citations. Despite seeming obvious, many major brands maintain outdated or sparse Wiki entries.
Update your Knowledge Graph: Ensure your Google Knowledge Panel contains current, accurate information that aligns with your desired brand positioning.
Maintain industry database profiles: Keep information updated on industry associations and professional networks where AI systems often source factual data.
Verify your digital identity: Establish and maintain verified profiles on LinkedIn, Google Business Profile, and other platforms that AI systems trust as authoritative sources.
Collaborate with thought leaders: Partner with industry experts on content creation. AI systems frequently cite these collaborations when discussing brand expertise.
Conduct regular information audits: Schedule quarterly reviews of your digital properties.

Strategic platform diversification

Identify high-impact platforms: Focus on content platforms and aggregators that most strongly influence AI perceptions in your sector.
Expand your digital footprint: Establish presence on specialized review sites and niche platforms where your audience engages.
Diversify content formats: Supplement traditional content with podcasts, webinars, and interactive Q&As, which AI systems cite more frequently in responses.
Form strategic partnerships: Collaborate with complementary brands on joint content initiatives.

Implementing even half of these strategies can dramatically improve how AI systems represent your brand. Success comes not from marketing budget size but from consistency and strategic approach to your digital presence.

3. Advanced measurement techniques

For deeper insights into how AI systems perceive your brand, these measurement approaches might be worth exploring.

Embedding similarity analysis: Use vector representations to measure how closely your brand aligns with target attributes: Convert [your brand] into a vector representation and compare similarity to luxury, innovative, and trustworthy. Provide cosine similarity scores. Of course, you will have to choose the target attributes that you want to monitor.

OK, this one is admittedly a bit technical, but it provides mathematical precision to brand positioning analysis. If you want to test this technique directly, without complex development, use Claude Pro. To do it with ChatGPT you have to use APIs, while Gemini plays dumb.

Here is an example using direct prompting on Claude.

It is the vector representations of Apple and Samsung and compared their similarities to each other and to the concepts of luxury, innovative, and trustworthy.

Apple versus Samsung similarity using luxury, innovative, and trustworthiness attributes: 0.974, or 97.4 percent similar.

They have very similar semantic positioning in the Claude vector space, suggesting they occupy similar market segments and brand categories. On the other hand, they embed some subtle differences that tell a fascinating story: Apple scores 12.7 percent higher on luxury and 7.3 percent higher on innovative, while Samsung edges ahead by 4.2 percent on trustworthiness. Mathematical proof of how AI systems encode brand perception differences that shape consumer discovery.

Summing up

As AI systems proliferate and LLMs get commoditized, protecting your brand from misrepresentation becomes critical.

Systematic monitoring:

Deploy regular prompting patterns across multiple AI platforms to catch emerging inaccuracies.
Set up alerts for significant sentiment shifts or factual errors.
Track citation patterns to identify problematic information sources.

Correction protocols:

Document discrepancies with supporting evidence.
Submit formal correction requests to AI platform providers.
Engage directly with editors of authoritative sources, for example Wikipedia, when necessary.
Follow up systematically to ensure corrections persist.

Proactive defense strategy:

Publish authoritative content that addresses common misconceptions.
Create a robust digital footprint that overwhelms potential misinformation.
Develop rapid response capabilities for emerging AI representation issues.

What's next

If AI systems represent brands inconsistently, this raises interesting questions about the future of brand building in an AI-mediated world.

IMHO, this represents a notable shift in how consumers may encounter and form impressions of a brand in the coming years. And right now, there is a good chance those impressions are being shaped without our input.

Will every brand need a comprehensive AI visibility strategy eventually? Probably. Do you need to panic about it today? Definitely not.

But I bet that the brands that start measuring, optimizing, and defending their AI representations now will likely enjoy significant advantages as these systems increasingly mediate consumer perception and decision-making.

The AI landscape is changing so fast that parts of this article might be outdated by the time you read it. I am just trying to start a conversation about something that seems important but nobody is really talking about. If this sparked some ideas, my weekend experiments were worth it.