If you don’t define your weakness, AI will

B2B companies hate telling prospects they’re a bad fit. AI may make that habit expensive.

Most product pages are designed to keep as many buyers in play as possible. “It works for enterprises and growing companies. It serves sophisticated teams but is easy enough for beginners. It’s powerful, flexible, scalable, and, conveniently, suitable for just about anyone with a budget.”

That leaves it to buyers — or the AI they are using — to figure out whether the product is right for them. 

Andrew Au, managing partner at B2B agency Intercept, argues that companies need to publish much clearer boundaries around who their products serve, where they fall short, and when another option makes more sense. Otherwise, the AI will fill in the blanks themselves.

“What we all generate is how great we are and how amazing we are and how perfect we are,” Au said. “So nobody talks about where we’re potentially not the right fit.”

That creates an unusual marketing problem. The safest-looking product copy — the kind that avoids exclusions, weaknesses, and anything sales might interpret as leaving money on the table — leaves the company with less control over how AI describes it.

Buyers are using AI to find vendors they don’t know

Au said Intercept and its Watchtower audience-intelligence platform analyzed about 2 million B2B conversations and found technical and nontechnical members of buying committees were more likely to use AI to find new or overlooked vendors. Qualitative interviews pointed in the same direction.

“I remember a guy telling me, ‘I already start with who I know, and then I use AI to find all these other vendors that I don’t know, these niche specialist vendors,’” Au said.

That’s good news for smaller and less familiar vendors. It also gives buyers a much faster way to test where those vendors fall short.

“The thing that the buying committee is using AI for is actually to find vendor weaknesses because everybody says they’re wonderful,” Au said.

That’s where typical B2B content becomes remarkably unhelpful. If a vendor claims to fit every kind of buyer without explaining the trade-offs, someone or something else may end up deciding where the product does and doesn’t belong.

AI will make up the missing context

Au said he sees this happen in audits when there isn’t enough useful content for the model to work from.

“You’ll see … these models are saying our client is not good for these scenarios; that is complete crap,” he said. “That’s just not true. But when you look at it, when there’s no content to actually review or cite, it will make an inference.”

And it’s quite possible that the AI will infer a hallucination. The solution is giving AI better material to work with.

For example, a product built for large, distributed teams can say it doesn’t offer a self-serve plan. Software designed for one industry can state plainly that it doesn’t support adjacent use cases. An enterprise platform can explain that a small business probably doesn’t need the complexity yet.

“If your product is for enterprise, don’t pretend that you’re for small business or for a startup,” Au said.

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The hard part is agreeing who the product isn’t for

The content itself is simple enough. The harder part is getting the company to agree on what it should say.

Product marketing, sales, customer success, and product need to agree on which buyers the product isn’t right for. That can mean admitting a competitor is the better fit in some cases, or telling sales that a prospect shouldn’t stay in the pipeline just because there’s budget attached.

Au said those conversations aren’t easy.

“You want to protect your brand truth, right? And with AI and humans,” he said. “It sounds like an obvious thing, but it is a very uncomfortable conversation that we have with our clients. They’re, ‘No, we’re not publishing that.’”

That discomfort comes from a real trade-off. The company can keep the broadest possible version of its market, or it can be more precise about where the product fits and where it doesn’t.

On the plus side, you don’t have to highlight all that on the landing page. Put it where it fits naturally: product pages, pricing pages, FAQs, buyer guides, and comparisons. The point isn’t to create a confession page. It’s to make the boundaries visible where buyers are already looking.

Better disqualification can improve the funnel

There’s also a straightforward demand-generation reason to spell out who the product isn’t for.

If AI recommends an enterprise accounting platform to a freelancer looking for a cheap self-service tool, that mention is useless. The prospect lands on the site, realizes the product isn’t a fit, and leaves. If they make it further, sales wastes time qualifying a deal that never should have entered the funnel.

Clear exclusions can stop some of that earlier by helping AI systems and buyers rule the product out before the click.

So more AI visibility isn’t necessarily better visibility. The useful version is showing up when the product fits the buyer’s needs and staying out of recommendations where it doesn’t.

Marketing operations teams should judge AI referral traffic the same way they judge other sources: by what happens after the visit. If AI referrals rise but qualification, sales acceptance, or opportunity creation falls, the company is getting more visibility without getting better demand.

Comparison content has to get more honest

This also changes the job of comparison pages.

Most vendor comparisons follow a familiar script: the competitors have some strengths, the vendor acknowledges them politely, and the vendor still turns out to be the best choice. It’s time to retire some of that.

Au said Intercept now creates buyer guides based on user reviews that explain where clients are strong, where they’re weak, and how they compare with two or three competitors.

“We actually have done these very transparent buyer guides based on real user reviews where our client is strong, where it is weak, and we will transparently compare it to the next two or three competitors,” he said. “That has done extremely well with the LLMs and with people because people want transparent, honest.”

The useful part is making the comparison conditional: choose us if these things matter. Choose somebody else if those things matter more.

That gives buyers more useful information. It also gives sales a better way to handle objections without pretending every weakness is imaginary.

If you don’t define the weakness, someone else will

Silence doesn’t keep weaknesses out of AI answers.

Au argues that if a company doesn’t provide useful information about its boundaries, AI systems can pull from other sources, including competitors, when answering questions about fit.

“If you don’t, the model is going to talk about it,” he said. “So, either you’re going to take control of the conversation or at least have a chance of taking control and giving citations that will inform their perspective, or you’re going to let competitors do it that might cite what your weakness is.”

A vendor can’t dictate what an AI system says about it. But it can make sure its own explanation of the trade-offs exists alongside everybody else’s.

If nobody at the company can point to a sentence explaining who shouldn’t buy the product, someone else will eventually write that sentence for them.