The AI Visibility Market Just Split Into Three Camps. Most Businesses Are Talking to the Wrong One.

When a corporate buyer opens ChatGPT and types “laser engraved tumblers, 50 units, ships in 7 days” — they are not searching for a brand. They are not looking for a thought leadership article. They are asking a reasoning engine to find the best specific match for a specific need, right now.

I’ve been running structured experiments on exactly that kind of query since May — querying AI platforms the way buyers actually do, watching which companies surface and which disappear, trying to understand what separates them. The pattern is consistent enough that I think it’s worth writing down.

I walked into the Lead Summit last week already knowing what I’d found in the data. Which made the landscape of tools on the floor unusually legible.

Three camps, not two

Before attending, I assumed the AI visibility market had split into two camps — brand monitoring and content generation. That framing was too simple. What I actually found across 20-plus vendors was three distinct camps, solving three genuinely different problems.

Camp 1 — Monitoring. These tools track how often your brand or products appear in AI-generated responses. Share-of-voice across ChatGPT, Perplexity, Google AI Mode. Citation alerts. Competitive benchmarking. The pitch is essentially: we’ll tell you where you stand. Several credible players have built real products here. The category is growing fast and, from what I observed, beginning to consolidate — when four vendors are offering variations of the same dashboard, differentiation becomes harder to sustain.

Camp 2 — Content and brand authority. The insight driving this camp is correct — AI models favor sources that are authoritative and topic-dense. If you’re not appearing in AI responses, one reason might be that you don’t have enough relevant content for the model to reason from. The solution: generate more of it. Blog posts, FAQ clusters, topical authority pages. Several well-funded tools are building here. The category makes intuitive sense for service businesses, consultancies, and companies whose product is their content.

Camp 3 — Catalog enrichment. A smaller group of tools is working at the data layer — making product records more structured, more complete, and more readable by AI models. Feed optimization. Attribute enrichment. Structured data generation. This camp had fewer vendors than Camps 1 and 2, but the products I saw were more technically mature than I expected. The pitches are harder to demo in fifteen minutes, but the work is substantive.

What Camp 3 is actually building — though few vendors have named it this way yet — is AI-readable commerce infrastructure. Not SEO. Not PIM. Not feed management. Something new, sitting at the intersection of all three, and not yet owned by any incumbent.

Why Camp 3 is harder to sell

Monitoring tools have a clean demo. You type your brand name, you see a number, the value is immediate. Content generation tools produce visible output fast — here’s the blog post, here’s the FAQ page.

Catalog enrichment doesn’t work that way. You have to touch the actual product data. Every business has different field structures, different data standards, different ways their buyers phrase queries. The proof takes weeks, not a fifteen-minute demo. The market has naturally drifted toward the cleaner problems first, which is why Camps 1 and 2 got crowded while Camp 3 is still developing.

But here’s what my research keeps showing: for companies that sell products — not services, not content, but actual SKUs at scale — Camps 1 and 2 are solving adjacent problems. The AI isn’t evaluating your brand authority when a buyer asks for something specific. It’s evaluating whether your product data contains enough structured information to answer the question confidently.

That’s not a monitoring problem. It’s not a content problem. It’s a catalog problem.

What the Camp 2 tools are missing for catalog businesses

This is worth being precise about, because the content generation pitch is compelling and easy to act on.

For a B2B distributor, retailer, or brand with thousands of SKUs, generating more topical content may improve brand-level visibility in AI — your company name might appear more often in general recommendation responses. That’s not nothing.

But it doesn’t help when a buyer asks a specific product query. A blog post about industry trends doesn’t tell ChatGPT how long your production run takes, what decoration methods you support, what quantity tier unlocks a particular price point. The AI needs that information in the product record itself, structured in a way the model can reason from.

Brand authority and web prominence still matter. But for businesses with thousands of SKUs, those signals are largely fixed in the short term. Catalog structure is the variable most companies can actually move.

For catalog-heavy businesses, those are different disciplines — and conflating them leads to effort spent in the wrong place.

AI commerce visibility for catalog businesses is fundamentally a structured data problem, not primarily a content marketing problem.

The open question in Camp 3

Here’s what I’m still working to understand.

The Camp 3 tools I reviewed are solving the enrichment side of the problem thoughtfully. What’s less clear — and what I haven’t seen answered cleanly yet — is the measurement loop: how do you know which specific enrichment decisions actually drove citation lift, for which query types, on which platforms?

Without that connection, enrichment is still partly guesswork. You improve your product data, you wait, you check a monitoring dashboard, you see a number move, you’re not certain why. At least one seed-stage player is explicitly attempting to close this loop — connecting enrichment decisions to monitoring outcomes in a single platform. It’s the right architectural bet. The closed-loop data at scale isn’t there yet, but it’s the most interesting problem framing I saw in the category.

The most counterintuitive finding from my own research so far: the field that drives AI citation lift in one query type can actively hurt you in another. The relationship between data completeness and citation rate is not linear, and it’s not uniform across query types. Which means enriching without measuring — without knowing which fields are being cited for which kinds of buyer queries — is not just inefficient. It can point you in the wrong direction.

I’ll share the data behind that in the next piece, once the experiments have run their full course. But the directional finding is clear enough to state now: for catalog-heavy businesses, the question isn’t just “how do I enrich my product data.” It’s “how do I know which fields to enrich, in which order, for the queries my actual buyers are running.”

That’s the question Camp 3 is beginning to ask. The answers are still early.

Where this leaves us

The AI visibility market is real, growing, and producing genuinely useful tools across all three camps. Camp 1 will consolidate — monitoring is becoming a feature, not a standalone business at scale. Camp 2 will keep growing for service businesses and content-driven brands. Camp 3 is where the most technically interesting work is happening, and it’s the least mature of the three.

If you sell products at scale — B2B distribution, retail, wholesale, deep-catalog ecommerce — the camp you need is the one that’s still figuring out its hardest questions. The tooling is developing faster than most people in the category realize.

Whether it develops fast enough to match how quickly AI search is changing buyer behavior is the open question worth watching.

Disclosure

The author works at the intersection of AI commerce and B2B catalog infrastructure. Some findings referenced are drawn from original research conducted over the past 30 days on AI visibility patterns in B2B ecommerce. No affiliated companies are named or promoted in this article.

If you’re thinking about this from a catalog or product data angle, I’d genuinely like to hear what you’re seeing.