What do buyers actually ask AI about you?

Short answer: comparison questions, drawback questions, and switching questions. Almost never the questions your website was written to answer.

When we rebuilt our prompt generation pipeline, we grounded every benchmark prompt in real Google search data, with measured search volume attached to each one. That gave us an empirical view of what buyers type into AI, not what marketers assume they type. The queries aren’t polite. They’re adversarial, comparative, and specific. Here are the ten patterns that show up most in real buyer search behavior, and why most B2B websites can’t answer them.

1. “Compare [you] vs [competitor] for [specific use case]”

This is the prompt that matters most. It implies a decision, names both vendors, and specifies the use case. Your website has a product page and maybe a comparison page. But does it answer the specific use case? “Compare Acme vs Widgetly for SOC 2 compliance at a Series B startup” is a different question than “Compare Acme vs Widgetly,” and the AI answers it with whatever use-case-specific content exists. Usually that content is the competitor’s.

2. “What are the drawbacks of [your product]?”

Buyers ask about weaknesses explicitly. Your website is designed to highlight strengths. When the AI can’t find a balanced assessment in your content, it builds one from reviews, Reddit threads, and competitor comparison pages. The framing of your drawbacks is written by everyone except you.

3. “Is [your product] worth the price?”

Not “how much does it cost” but “is it worth it.” This asks the AI to make a value judgment. If the AI has your old pricing and doesn’t know about your recent feature launches, the value assessment is built on stale data. The result reads like a review of a product that no longer exists.

4. “Why would I choose [competitor] over [you]?”

The hardest prompt. It asks the AI to make the case for the competitor. If the competitor has published content that answers this question (a comparison page, a migration guide), the AI cites it directly. If you haven’t published your counter-argument, there is no source to balance the narrative.

5. “What’s the best [category] tool for [persona]?”

This is a shortlisting prompt. The AI returns an answer with four to seven named vendors, not ten blue links. If you’re not in the answer, you don’t get evaluated at all. The AI decides category membership from public content: reviews, comparison articles, community mentions. Companies with thin public presence get excluded before the evaluation starts.

6. “[Your product] vs [competitor] pricing comparison”

Pricing-specific comparisons are where AI errors are most damaging, because the AI quotes numbers. If the numbers are wrong, the buyer runs budget math on false data and either disqualifies you as too expensive or arrives at a sales call anchored to a price you don’t offer. Stale pricing pages and third-party pricing roundups are the usual culprits.

7. “Does [your product] integrate with [specific tool]?”

Buyers ask about specific integrations. Your integrations page might list them, but if the page is structured as a logo grid without text, the AI can’t parse it. A dedicated integrations page with plain-text descriptions of each integration is what AI needs to answer this accurately.

8. “What do users say about [your product]?”

This prompt pulls from reviews, Reddit, and community forums. The AI synthesizes user sentiment from sources you don’t control. If the most visible user feedback is a critical Reddit thread from a year ago, that’s what the AI reports, even if you’ve fixed every issue mentioned in it.

9. “[Your product] for enterprise vs [competitor] for enterprise”

Enterprise-specific comparisons require the AI to know about your enterprise features: SSO, SOC 2, SLAs, dedicated support, deployment options. If this information isn’t on a crawlable page in plain text, the AI can’t include it in the comparison, and the vendor whose enterprise page is parseable wins by default.

10. “Should I switch from [competitor] to [you]?”

The migration prompt. The buyer is already using the competitor and considering a switch. Answering requires the AI to assess migration difficulty, feature parity, and switching costs. If you don’t have a migration guide or switching comparison page, the AI has nothing to work with, and “switching is risky” is the default narrative.

What your website was built for vs what AI needs

Your website was built for human visitors who browse, click, and read. AI needs something different: direct answers to specific questions in extractable formats. The gap between those two designs is where evaluation losses happen.

Your website has: a homepage, product pages, a pricing page (maybe with a calculator), a blog, case studies behind a gate.

AI needs: comparison pages for each competitor, FAQ content matching the exact questions above, plain-text pricing, feature pages with explicit capability lists, integration documentation with text descriptions.

Most B2B websites are built to present strengths, not to answer hard questions. That gap is where AI fills in with whatever sources it can find, and those sources are usually not yours.

You can’t answer these questions on your website alone

Here’s the uncomfortable part: publishing better content is necessary but not sufficient, because you can’t see the evaluation from your side of it. The buyer asks, the AI answers, and neither event touches your analytics.

The only workable approach is a measurement loop. First, find out what AI actually answers: run the adversarial questions above, grounded in real search data, across ChatGPT, Claude, Perplexity, and Gemini, and read the responses. Then diagnose which sources produced each answer and which claims are wrong or missing. Then fix the sources: publish the comparison page, the migration guide, the plain-text pricing. Then re-run the same prompts and measure the delta.

That loop is what we benchmark. The ten questions in this post aren’t hypotheticals; they’re the prompt patterns with the highest real search volume in our generation pipeline. Whatever the AI is answering for them today, it’s answering from sources. The question is whether any of those sources are yours.