There are some actors who are unforgettable in a role, and from then on, it's the only type of part you'll ever see them in. It's a shame, really - these actors have range. But casting simply stopped seeing it. They've been typecast.
Companies get typecast too. Your positioning, everything you say about what you are and who you're for, is your audition. Every time a buyer asks AI about your market, AI reads that audition and decides whether you make it into the answer, and if you do, what the buyer hears about you.
Positioning complex B2B products is the part of marketing I've loved most, and I've spent my whole career doing it. So when I realized that AI is now shaping how buyers understand whole markets, I wanted to know what it was doing to the positioning work underneath. I saw that AI was telling buyers something recognizable about companies, but it wasn't necessarily what the company would have chosen to say. The analysis that follows is me pulling at that thread.
AI doesn't relay what you say about your company, even in branded queries when you are quite literally the subject of the buyer's question. It's explaining the category to the buyer and using you as one piece of that explanation. It keeps what helps it tell that story. The rest, about 70 percent, gets cut.
Then it builds what's left into a role: one line for what you are and who you're for. That line is the company the buyer meets. It's narrow by design. AI doesn't need all of you. It needs you to play one part in the story it's telling.
Take Freshservice. It started as an IT help desk tool, and it has been building outward: enterprise service management for HR, facilities, finance, not just IT. Freshservice positions itself as the modern alternative to ServiceNow for companies that want enterprise-grade service management without the enterprise complexity. A buyer asks AI when to switch from a legacy help desk to a modern service management platform. Here is part of the answer:
The buyer didn’t ask AI to compare these companies. AI volunteered the sorting because that is how it explains a category.
Look at the role Freshservice was assigned: mid-market value. The label is fair. But it is not the company Freshservice is building. Freshservice is pushing into enterprise service management, displacing incumbent platforms, landing its first million-dollar deals. None of that is in the label. What is in the label is a price ceiling, and a frame that tells the buyer which competitors to weigh Freshservice against. The buyer’s mind is already shaped before a sales rep ever gets in the room. Every company whose AI label is narrower than the company it is building toward pays a version of this cost.
Being seen and being understood are different problems. Most of the work companies do on AI today is about being seen: making sure AI mentions you at all.2 What buyers actually hear about you once you do get mentioned is what I set out to explore.
Key findings
AI filters your positioning.
Take Snyk. It is an application security company. Here is how it describes itself. The blue text is what reaches the buyer. Everything else is cut.
There is a pattern here, true not only for Snyk but for every company in this study. Only claims that explain what the product does, how it is built, where it fits, have a chance of making it through. Claims that signal trust, analyst rankings, customer counts, aspirational claims, are consistently cut. Every single one.
It took me a while to see what these claims have in common, and then it was obvious: they all exist to help a human decide. They build confidence, create aspiration, give a person something to feel. But AI doesn’t need to feel confident. AI isn’t scared of getting fired for a bad recommendation. It has no gut to validate. So it drops everything designed to move a human and keeps only what helps it explain the category.
But not all claims that explain the category reach the buyer. Some of it depends on the query: where the buyer is in the buying cycle, whether they ask about the category in general or name you directly. A more specific question lets more of your positioning through. You would expect that to be the main factor. It is not. The strongest predictor I found was how clear the company’s own positioning is: how focused and single-minded.3 That is good news. It is something you control.
A machine reads your positioning differently than a human. Companies have been describing themselves for human buyers, and doing it well. Now a machine stands between that work and the buyer, and it keeps only what it needs.
AI does not list your claims back to the buyer. It distills them into a label, a short description that captures what it thinks differentiates you from competitors in your category. That label draws on your positioning, but it is not a copy of it. It is AI’s interpretation of what makes you different.
Each company gets the trait that most clearly differentiates it from competitors. Sometimes that is where you started. Sometimes it is your market tier. Sometimes it is one product capability out of dozens you offer.
For some companies, AI cannot find a trait that separates them from competitors. Those companies get the generic label: just the category name. “Fleet management platform.” “Customer data platform.” “Contract lifecycle management software.” No trait, no angle, no reason to choose them over anyone else in the list.
Within any single company, the pattern is more textured. AI tends to land on one label and repeat it, but not uniformly. The typical company gets its most common label about two-thirds of the time.4 The remaining third is a mix of other descriptions, some distinctive, some generic.
Consider fleet management.
Motive started in trucking compliance, selling electronic logging devices (ELDs) that truckers are required by law to carry, then expanded into a full fleet platform with dashcams, GPS tracking, and spend management.
About 40% of the time, AI calls Motive the ELD compliance company, a distinctive label drawn from where it started. Another 30%, the AI safety and dashcam platform, a different distinctive label drawn from a different capability. The rest, mostly the generic label: a fleet management provider. Two distinctive labels and a category name, none dominant.
Compare that to Gong, which gets “conversation intelligence” consistently, or Freshservice, which gets “mid-market alternative” consistently.5 The label is stickier for some companies than others.
Three things determine how AI represents your company. They are independent of each other, driven by different inputs, and fixed by different kinds of work.
| The lever | What sets it | Where your effort goes |
|---|---|---|
| Whether AI mentions you at all | How much gets written about you across the web | Visibility and coverage, which you are probably already doing |
| How much of your story comes through | How clear your positioning is, and how your claims are framed | Sharpening what you say so more of it reaches the buyer |
| What label AI gives you | Your market’s structure and your positioning choices within it | Claiming or reframing a label you can own |
This is driven by third-party coverage. You are almost certainly already working on this, and there are good solutions to help you do it.
Two independent things determine how much of your positioning survives the filter.
The first is positioning clarity: how focused, coherent, and single-minded your positioning is as a whole. AI cannot synthesize a scattered positioning into a coherent story. In treasury management, FIS and Kyriba get generic labels at almost identical rates, but Kyriba gets nearly four times as much of its actual story through. Kyriba positions around one thing: cloud treasury and liquidity management for CFOs. FIS positions around several at once and lets AI decide which one matters. Across industries, clarity was a stronger predictor of how much positioning came through than company size, content volume, or third-party coverage.
The second is what your claims are built to do. A company with perfectly clear positioning can still lose half its story if the claims are framed as trust signals rather than structural descriptions. Effective positioning claims for AI focus on explaining what the product is and how it works.
Clarity is not the same as differentiation. You can be perfectly clear about a position that does not set you apart. But clarity gives AI something coherent to work with, so more of what you say reaches the buyer.
Two things determine whether AI gives you a distinctive label or a generic one, and they carry about equal weight: competitive structure of your market, and your positioning choices within it.
Some markets are structurally differentiated, with natural lanes AI drops each vendor into. Others are commoditized, where every vendor gets a version of the same generic description. And some are split: companies in the same category get dramatically different outcomes, some earning a distinctive label, others marked as generic players.
In every market type, the companies that get a distinctive label are the ones whose positioning names something no other vendor claims. Snyk gets “developer-first security”. That is a strength: a label that matches the position they built, repeated consistently across every AI model. But a distinctive label is only a strength if it is the one you want.
Essentially, two things can go wrong: the label is distinctive but smaller than you are, or there is no distinctive label at all. Different problems, different fixes:
| Distinctive but wrong | Generic | |
|---|---|---|
| Work within your category | Claim a different label. You are in the right category, but the label AI gave you is smaller than you are. You make the case for a different one. | Sharpen the category you claim. A more specific claim pulls you into a tighter frame. HubSpot’s positioning stayed specific enough to earn “CRM for scaling companies” rather than just “CRM.” |
| Change your category | Play in a different category. Choose your competitive frame instead of inheriting it. Snowflake positioned as a “data cloud” rather than competing as a data warehouse, which changed which companies AI compares it to. | Redefine the category itself. Change how the category is understood, so the frame is one you set. Gong did this with “conversation intelligence” and “revenue intelligence”: the terms it coined became its distinctive labels. |
Where to focus depends on your market. If it is structurally differentiated, you probably already have a distinctive label, so spend your effort on coverage and how your story comes through. If it is split, your positioning choices decide the outcome, and that is where effort pays off most. If it is commoditized, changing your label is much harder — it’s a strategic bet, not an incremental one.
The implicit safety that not every buyer reads the same version of you is becoming a thing of the past. You can still address an objection on the fly, reframe a capability during a demo, respond to a concern the buyer hasn’t articulated yet. But the buyer is increasingly walking in with a view already formed by AI, and your live effort goes toward undoing that view instead of shaping a new one.
Positioning has always been hard to measure. Brand sentiment, win/loss analysis, focus groups — none of them tell you whether your intended differentiation is actually landing with the buyer. Now AI hands you that measurement directly. Structured, repeatable, comparable. You can literally read how your buyer understands you. The typecast actor never finds out what they were reduced to. You can.
And that measurement is not just a diagnostic. The labels AI assigns when a buyer asks about your market are becoming the context in which purchasing decisions get made, by humans today, and by buying agents acting on their behalf tomorrow. I tested a subset of the same companies with prompts ranging from simple category questions to structured procurement simulations with weighted scoring criteria. The labels did not change.6 The window of opportunity for influencing it is open right now.
When AI describes a category the same way every time, giving every vendor the same generic label, buyer perception of differentiation compresses. What does that mean for how you compete, price, and evolve?
The pattern this study lays out, how AI assigns you a label and what that label does to your positioning, opens onto questions I'm still working through: how AI draws its own map of your market, one no vendor authored. What happens to positioning after an acquisition. Why repositioning in AI channels takes longer than anyone wants to hear. And more.
If you've seen similar patterns in your category, or the opposite, I'd like to hear about it. I'm interested in examples that corroborate these findings, examples that contradict them, and especially in real buying decisions: what a buyer asked AI, what it answered, and whether it changed the shortlist.
I also run this analysis for individual companies. If AI’s version of your company doesn’t match the company you’re building, that is what this work is for: maya.poleg@gmail.com
1 The percentages in this chart come from checking each positioning claim against each AI model’s answer to a relevant buyer query. Every query was run three times per model, and I took the majority answer. To keep industries with more companies from skewing the averages, each of the 15 industries is weighted equally. Scoring was done by AI (Claude Opus), with a second model (Gemini 2.5 Pro) used to cross-check consistency. The exact percentages are approximate: a second scorer would land within about 10 to 15 points on any single rate, but the patterns, which types of claims get through and which do not, hold regardless of who scores. ↑
2 Two things decide whether AI names you. You need independent coverage, other people writing about you, so you exist in what AI reads. And you need to be filed in the category the buyer asked about. How much you publish showed no relationship to how much of your story gets through. Once you are named, whether your positioning comes through is a separate question, and it turns on what you say: how you frame yourself, whether you fit the category, whether your claims are clear. Not your content volume. Not your citation count. Coverage gets you into the room. It does not get your story told. ↑
3 Clarity is a company-level score, not a per-claim measure. For each company, I scored positioning clarity on a 1-to-5 scale from their website and marketing materials, blind to the AI output. A 5 means the company’s positioning is focused, coherent, and single-minded. A 1 means the positioning is scattered or vague enough that a reader would struggle to say what the company does. I then tested clarity against company size, claim architecture, content volume, and third-party coverage to see which best predicted how much of a company’s positioning survived in AI answers. Clarity outpredicted all four in 13 of 14 industries (one industry had too few companies to test). Separately, I tested whether a company’s industry alone could predict how distinctive its AI label would be. It explains about half the difference (one-way ANOVA, eta-squared = 0.517, p < 0.001). The other half varies by company within the industry. This is consistent with independent research finding that AI’s treatment of brands is largely determined by their structural position in the market, not by recent content or messaging changes (Peres, Schreiber, and Roth, arXiv:2605.30207, 2026). ↑
4 The role-concentration figure is based on 70 of the 75 companies. Five were excluded due to insufficient data. For each remaining company, I read every AI description and classified it into a role. Concentration is the share of descriptions that land on the company’s single most common role. The average concentration across the 70 companies is 64%, reported here as “roughly two-thirds.” ↑
5 I tested the two-thirds figure across several dimensions to make sure it holds broadly, not just on average.
| Dimension | % of companies where the label holds |
|---|---|
| By buyer persona (CFO, engineer, functional leader) | 72% |
| By buying stage (awareness, evaluation, decision) | 75% |
| Branded vs. organic query | 75% |
| Across AI models | 72% |
6 Which vendors appear in any given AI answer varies from query to query, as others have documented. What each vendor is called when it does appear is what stays fixed. The labels in this study held across models, buyer personas, buying stages, and prompt complexity. ↑