When a buyer asks ChatGPT to point them toward a brand in your category, the model will hand back three or four names, a sentence about each, and a recommendation. Do not be mistaken: that answer is the new front door to your market, and if your company’s name isn't on it, you’ve been cut from the consideration set before anyone on your team knew there was one.

Most marketing leaders who check this for themselves and dislike the result go looking for a technical fix: schema markup, cleaner metadata, an llms.txt file, a content refresh. That work has its place for sure, and we do plenty of it, but the reason most brands get left off the shortlist sits further upstream, in the story itself.

Kendra Rainey, Edgar Allan’s VP of Brand Strategy and Performance, described the shift in a recent episode of Building the Next Web: "now we're engineering differentiation simply in order to be visible." 

The point: differentiation used to help brands win a comparison a human was already making, and now it decides whether a brand makes it into the comparison at all.

The AI shortlist is now your consideration set

Buyers are doing their vendor comparison inside AI tools, before they reach anyone's website. Semrush's July 2026 survey of US B2B professionals found that 66% of those who use AI at work regularly use it to research vendors and solutions, and that agencies and service providers top the list of what they research.

That AI answer also decides whose website gets a visit. When an AI tool mentions a vendor, 71% of those buyers go to that vendor's site and 63% search for the company on Google, so whatever the final search method, the traffic tends to follow the names the model chose.

That’s a huge shift in buyer behavior that changes what you're competing for. Take portable sanitation, a category we work in. A buyer asks an AI tool which provider is best for a three-day outdoor festival, and the model folds in their location and whatever it already knows about them from a logged-in session. The race then is for one of three slots in the paragraph it writes back, not pride of placement in the top blue links on the page.

Generic messaging is absorbed into the category answer

A model building a shortlist reads everything it can find about a category and looks for reasons to name a winner that answers the inquiry best. Claims that every competitor makes give it no such reason, so it compresses them into a single category-level sentence ("most providers offer reliable, high-quality service") and moves on without citing anybody. 

Kendra's image for this is the one I keep coming back to: "Generic information about your company that is the same as everybody else's is a haystack." Every piece of hay in the massive pile looks the same to an AI search bot. Until it hits a specific detail. That’s the needle; the one thing in the pile the model can pick out and attach to your name.

Buyers notice the hay too. In the same Semrush survey, the most common complaint about AI vendor recommendations was that they're too generic for the buyer's use case, and a close match to that use case mattered far more to buyers than name recognition.

The hay is easy to recognize once you look for it. "Reliable," "fast," "trusted," and "best-in-class" describe satisfaction, not differentiation, and customers are happy to say them about you – and your three nearest competitors. B2B brands could get away with it when a sales team did the differentiating in person, but an AI model has no sales team to fall back on.

Here's what the difference looks like in content. A post titled "Top five tips for event planning" could come from any portable sanitation company in the country, which is why a model passes over it in favor of the story of the time your crew serviced 40 units through a hurricane evacuation without missing a pickup, and what you changed about your process afterward. A buyer remembers that story, and a model can retrieve it, because nothing else in its sources looks like it. It’s a great deal for marketers: the narrative does double duty.

Publishing more of the first kind of content makes the problem worse. More posts on the same generic positioning pile up more hay in front of the model, faster.

Answer-ready branding is the work of making a company's story specific enough that an AI system can describe it in one sentence no competitor could copy. It starts with positioning, shows up in the details a brand publishes, and ends with a model that names you because nothing else it has read sounds like you.

Schema and structure can't rescue messaging that sounds like the category

Technical AEO matters, and our Webflow AEO services include plenty of it. Structured data, clear headings, FAQ sections, and clean architecture all make your brand easier for a model to read. A perfectly readable page that repeats what dozens of competitors say gives the model a clear view of the same stack of indistinguishable yellow stuff.

Our own citation data shows how this plays out. In the Webflow agency category, AI models cite edgarallan.com at roughly a 5.7% share of voice. Nearly all of that comes from one page: our data-backed rankings of the best Webflow agencies, built on four years of Webflow Awards results. Models cite it about 40 times as often as our AEO service page, because it contains data nobody else has.

Edgar Allan is structured around and approaches projects with a story-first and performance-driven lens: brand and AEO function as one discipline here, and specificity does a whole lot of the lifting. 

An AI visibility program that never touches messaging is a waste of budget, because every technical improvement makes generic content easier to parse and just as easy to pass over. 

That conviction is why we built our Visibility Engineering and Optimization service, which runs brand, SEO, AEO, and CRO as a single loop: the story gives the model something specific to say, the technical signal makes sure it can find it, and measurement tells you whether it's saying it.

Do this prompt test before you touch your site

Run your category and your brand through ChatGPT, Gemini, Claude, and Perplexity the way a buyer would, using real questions with real constraints ("best enterprise Webflow agency for a regulated healthcare company" beats "Webflow agency"). Don’t just do one, you’ll want to see the spread. For each answer, record three things: 

• Whether you're named
• How you're described
• Who appears beside you

Then read the description of your company and ask whether it could describe three of your competitors with only light editing. If it could, the model has nothing distinct to say about you, and that's the finding. The gap between what the model says and what you'd want your best salesperson to say is the brief for everything that follows.

So, how do you fix it? Start with an inventory of the specific claims, stories, and outcomes only your company can tell about. You’ll find that most of that material already exists outside marketing’s orbit: in sales calls, in support tickets, with your crew in the field. A dedicated Slack channel where anyone can drop a story, followed by a short recorded conversation with whoever posted it, pulls that detail into the open (and gives your marketing team or agency a repository of juicy details to work from).

Once you have it, rewrite your highest-traffic pages so those details lead. The structural pieces from our AEO playbook and our checklist of technical and content fixes for AEO in Webflow can ship within a week once the messaging is specific.

Fund positioning first, implementation second, and measurement on a cadence

The order of spend matters as much as the amount. If your positioning isn't specific, pause the technical AEO budget until it is, because you'll redo any execution built on generic messaging once the messaging changes.

Recommendation patterns move as models update and competitors publish, so measurement runs on a recurring schedule. We use Profound to track how models answer category prompts and where the gaps are, and ag-nts, our AI agent traffic measurement tool for Webflow, to see which pages AI systems are visiting.

The prompt test is the cheapest evidence you'll bring to a budget meeting

It takes maybe an afternoon, and it gives you the evidence you need before the next AEO-focused line item gets approved. If the models can't tell you apart from your competitors, your leadership team will see it too.

Or, if you want a second set of eyes on what you find, book a call with us, and we'll tell you where the hay is.

_{This article grew out of a conversation between Edgar Allan founder Mason Poe and Kendra Rainey, VP of Brand Strategy and Performance at Edgar Allan, for the ongoing series, Building the Next Web. It's the same series that's brought us conversations with ourCMO's Josh Webb, Code & Wander's Alessia Sannazzaro, Edgar Allan's own Witt Langstaff, Webflow's Nathan Huening, and more.}