B2B product pages are often written to keep every buyer in play: powerful for enterprises and simple for startups, flexible for any team, suitable for almost any budget. That broad positioning is meant to protect the funnel. According to Andrew Au, co-founder and co-CEO of B2B agency Intercept, it is also handing control to the AI systems buyers now use to research vendors.
AI is becoming the first filter for B2B buyers
Intercept and its Watchtower audience-intelligence platform analyzed about 2 million B2B conversations. The finding: buying-committee members—technical and nontechnical—are increasingly using AI to find new or overlooked vendors. One buyer told Au he still starts with vendors he knows, then uses AI to find “all these other vendors that I don’t know.” That is good news for smaller, specialist vendors. It also gives buyers a faster way to test where a vendor falls short, because every vendor already claims to be wonderful.
The safest copy creates the biggest risk
When a vendor claims to fit every buyer without explaining trade-offs, AI fills the gap. Au said he sees this in audits when there is not enough useful content for the model to work from. Models have told clients they are not good for certain scenarios—claims he called “complete crap.” But with no content to cite, the model simply makes an inference. Sometimes that inference becomes a hallucination.
The solution is not to hide weaknesses. It is to give AI better source material. For example:
- A product built for large distributed teams can state that it does not offer a self-serve plan.
- Software built for one industry can say plainly that it does not support adjacent use cases.
- An enterprise platform can explain that a small business probably does not need its complexity yet.
Honest disqualification can strengthen the funnel
The content itself is simple. The harder part is getting the company to agree on what it should say. Product marketing, sales, customer success, and product need to align on which buyers the product is not right for. That can mean admitting a competitor is the better fit in some cases, or telling sales that a prospect should not stay in the pipeline just because budget is attached.
There is a demand-generation payoff. If AI recommends an enterprise accounting platform to a freelancer looking for a cheap self-service tool, that recommendation is useless. The prospect lands, realizes the product is not a fit, and leaves. If the deal moves further, sales wastes time qualifying an opportunity that should never have entered. Clear exclusions can stop some of that by helping AI and buyers rule the product out before the click.
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 better demand.
Comparison pages need to get more conditional
Most vendor comparisons follow a familiar script: competitors have strengths, the vendor acknowledges them politely, and the vendor still wins. Au said Intercept now creates buyer guides based on real user reviews that show where a client is strong, where it is weak, and how it compares with two or three competitors. The useful formula is conditional: choose us if these things matter most; choose someone 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.
Silence does not keep weaknesses out of AI answers. If a vendor does not explain its boundaries, competitors or models will. Either take control of the conversation or let someone else write it.
Source: MarTech
