THE AI-READY BRAND: PART 2 OF 5
Three Ways AI Gets Your Brand Wrong
You are not one brand to the machines. You are many, and they do not agree with each other.
Ask five different LLMs to describe your company, and you will get five confident answers. Line them up, and a pattern emerges that should worry any CMO: none of them is exactly right or wrong, and none of them is the same. You are, as far as the machines are concerned, a small crowd of near-identical companies, each slightly off, all speaking at once.
That is not a rounding error. It is the first of three specific ways AI gets your brand wrong, and understanding all three is the difference between treating this as a nuisance and treating it as the strategic exposure and risk it actually is.
The three failures are inconsistency, inaccuracy and undifferentiation. They are not ranking problems and they do not respond to optimization, because each one is a question of what is true, coherent and distinctive about the brand at its source. Of the three, undifferentiation is the one that costs the most, and it is the one nobody sees happening.
One. Inconsistency.
Your brand is described differently depending on who asks and which system they ask about. Several versions of your company are in circulation: one emphasizing a business you have de-prioritized, one using language you dropped in your last refresh, one that quietly still lists an executive who departed the company in 2023. None is a lie. Each was true at some point, or true from some angle. But a buyer comparing you to a rival does not see a nuanced, evolving company. They see a brand that cannot hold a single story about itself, which reads, fairly or not, as a company unsure of what it is.
The research shows how wide this spread gets. An INSEAD analysis of the Italian laundry detergent market found the consumer brand Ariel commanding nearly 24% of AI mentions on Meta’s Llama and under 1% on Google’s Gemini. Same brand, same category, two systems, an order-of-magnitude gap in whether it existed at all. Consistency across channels used to be a discipline, but now across models, it is a survival trait.
Two. Inaccuracy.
This one is quieter and more dangerous. Retired product names resurface. Old organizational structures get treated as current. Figures you corrected years ago keep circulating as fact. A system that confidently states a discontinued product, a stale price, or the wrong legal entity is not being untidy; it is creating a liability, and it is likely that nobody in your marketing organization is watching it happen in real time.
Three. Undifferentiation, and what happens to brands AI cannot tell apart.
The most expensive failure of the three, because it is invisible. Your brand gets folded into the same sentence as every competitor, “firms like yours and three others offer,” because nothing in your language was distinctive enough to survive being summarized. The model compresses a category down to its common denominator, and if your positioning lives in tone, art direction, and the feel of your category rather than in explicit, defensible claims, compression erases it. You do not get described badly. You get described generically, which in an AI-mediated market is the same as not being described at all.
Why it gets worse while you wait.
What makes this urgent, rather than merely annoying, is that it hardens over time. And because we are talking about AI, we are not talking about weeks or even months; we are talking about days.
Every page published anywhere, in language nobody signed off on, becomes one more source the systems learn from. The more often a description repeats, the more settled it becomes, and the version that wins is rarely the newest or the best. It is the one repeated most across the most independent sources, which is usually the oldest one you have.
So time compounds against you three ways at once. Where your strategy is unclear, your own teams fill the vacuum with their own words, and you publish several versions of yourself simultaneously. Correction gets harder, not easier: fixing a description that ten sources repeat is a different job from fixing one that fifty repeat, and the fifty arrive on their own. And almost nobody can even size the problem yet, because most companies cannot say how often they come up, which sources speak for them, or how they compare to the firms they are measured against. You cannot manage a liability you cannot see, and this one is growing in the dark.
The two words for what you are trying to win back.
Name the goal and the fix starts to come into focus. Two ideas do most of the work.
The first is Brand Coherence™, the property of a brand whose meaning survives the trip. The same core meaning holds up whether it is read by a person or quoted by an LLM, whether it is used by forty employees or four agencies, whether it appears on your homepage or in a model’s answer three steps removed from anything you own. Coherence is not sameness; it is meaning that does not fracture under pressure. It is the opposite of the small crowd of near-identical companies we started with.
The second is Share of Model: how often, how prominently, and how favorably your brand shows up when the LLMs answer. It is the AI-era counterpart to share of voice, and it is already migrating from theory into the vocabulary of serious marketers. Traditional awareness metrics, recall surveys, search volume, and social mentions do not capture it because a brand can be famous with humans and nearly invisible to models, or vice versa. Share of Model is the number that tells you which one you are.
Those two ideas, Brand Coherence as the standard and Share of Model as the scoreboard, are what the rest of this series is about. Because once you can see how the machine describes you, and once you accept that the goal is a coherent brand rather than a well-optimized page, you are forced into a conclusion the market is currently spending a lot of money to avoid.
The fix for a brand problem is not an SEO or AEO tactic. It is a strategic one. And the former is exactly where a lot of companies are about to waste the next two years.
Related reading: your brand’s second reader and how it got there, and why undifferentiated B2B brands disappear under AI.
Starfish is a Branding and Creative Agency focused on Brand Experience that builds brands with the soul to move people and the coherence to govern AI.
Frequently Asked Questions
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What are the three ways AI gets a brand wrong?
Inconsistency, where different systems describe the same company differently; inaccuracy, where retired products, old structures and corrected figures keep circulating as current; and undifferentiation, where the brand is folded into the same sentence as its competitors and stops being described separately at all.
Why do different AI systems describe the same company differently?
Because each system learns from a different mix of sources and weights them differently. An INSEAD analysis of the Italian laundry detergent market found the brand Ariel taking nearly 24% of AI mentions on Meta’s Llama and under 1% on Google’s Gemini, in the same category at the same time. A brand can be prominent on one model and effectively absent from another.
Why does an inaccurate AI description get harder to fix over time?
Because descriptions harden through repetition. Every uncontrolled mention becomes another source, and the version that wins is the one repeated across the most independent sources rather than the newest or the most accurate. Correcting a description carried by fifty sources is a materially larger job than correcting one carried by ten, and the fifty accumulate on their own.
What is Brand Coherence?
Brand Coherence is the effect when every part of a brand reinforces the same underlying meaning, so the brand stays legible to people and machines alike and its meaning does not fracture as it travels across teams, agencies and systems. Coherence is not sameness. It is meaning that survives being summarized, quoted and repeated by someone who is not you.
What is Share of Model?
Share of Model is the proportion of a fixed panel of realistic category questions in which a brand is named across the major AI platforms, recorded with how the mention was framed, its share against named competitors, and which sources the model cited. It is the AI-era counterpart to share of voice, and traditional awareness metrics do not capture it.
How do you know if AI is describing your brand generically?
Ask the major systems the questions your buyers ask, and read the answers for whether your company is named separately or absorbed into a list. A brand described generically appears inside phrases like “firms in this space typically offer,” with no claim attached that belongs only to it. That is not a bad description. It is the absence of one, and it is the failure most companies never detect.
