What Have Authoritative Sites Measured Recently?

On 2026-09-07, Semrush published “AI chatbots talked 57.5% of AI users out of buying.” They surveyed 2,338 U.S. consumers and got two key numbers: 57.5% of AI users had been talked out of a purchase by a chatbot, and 65% of AI users had already replaced part of their product search with a chatbot.

These two numbers should be read together. The first sentence is easily read as “AI is the enemy of sales,” but the second is the real point: more than half of users have handed the search step to AI. In other words, many buyers have already screened you in a chat box before they ever open your website. That they did not come does not mean you were not seen; it only means you did not pass that round.

Semrush also published an article on AI sentiment analysis on 2026-06-15, on how to judge whether AI’s answers about your brand are positive, neutral, or negative. Read the two together and the thread is clear: AI is already doing the first screening for buyers, and the results of that screening can be measured. On brand terms, Ahrefs measured on 2026-10-01 that 83% of branded searches trigger an AI Overview, so even people looking for “you” read a summary first.

What Is This 57.5% Actually Saying?

Let me start with a counterintuitive judgment: when AI talks a buyer out of a purchase, most of the time it is not saying your product is bad, it is thinking things through for the buyer. For example: “a laser cutter at this price cannot reach its claimed accuracy,” “the same model is 30% cheaper on another platform,” “this certification may not be recognized in the EU.” If these statements are true, then AI saved the buyer money and also saved you an order destined for haggling.

The real problem is the other case: AI uses incomplete or outdated information and makes your product look worse. Say your website specs have not been updated in three years, so AI compares prices using the old specs; or your company description is scattered across five platforms, and AI pieces together a wrong picture. That kind of rejection is avoidable, because it is not a judgment problem but a supply problem: you did not give AI enough material.

So my conclusion is: selling in the AI era is not about persuading AI, but about making sure AI holds your latest, most accurate, most specific materials. This does not conflict with advertising or trade shows; it opens the door early at the layer where customers do their homework. You can start with how AI recommendations take shape to build a basic understanding.

The Four Points Where Export Sites Are Most Easily Talked Out of a Sale

Of these four, the first is the cheapest and the most immediately effective. When AI judges an entity, it relies heavily on the consistency of public information. If your website, catalog pages, and social media bios each tell a different story about what you mainly make, where you are, and which markets you serve, the model can only guess, and a wrong guess shows up in its answers. I wrote about the fix in how to fix AI getting your brand information wrong.

The second point is the most underestimated. Export factories generally have ISO, CE, and test reports, but they hide them on the third screen of “About Us,” or offer only a PDF download. If AI cannot read them, they may as well not exist. Writing certificate numbers and scope of application into a readable paragraph costs almost nothing and pays off directly.

The Pain-Point Data We Collected

I ran a round of social media pain-point collection, 252 items from Reddit plus Xiaohongshu. Broken down: 108 AI-related, 38 no traffic, 18 inquiry problems, 14 lessons from mistakes. AI-related items are nearly half, but most of the content is anxiety and speculation; very few people have actually started filling in their information sources. The full record is in field research and methodology notes.

This distribution says one thing: many people in the market know something has changed, but few act on it. For export businesses, that is actually a window: AI’s first-screening capability is already live, while most peers’ materials are still five years old. Just update your specs, spell out your certifications, and state your MOQ clearly, and you gain an edge in a round of AI comparison.

Another reference point is our AIO coverage test on 500 Chinese queries, with an overall trigger rate of 80.9%. In other words, when users search sourcing-related questions, they will most likely see an AI summary first. Whether you have a place in that summary depends on whether your public information contains quotable sentences. You can go through the AI citation readiness checklist item by item.

How Do You Keep AI from Saying Bad Things About You?

First, unify your identity and specs

Company name, main product categories, location, main markets, core certifications: these five items must correspond word for word across your website, catalogs, social media, and B2B platforms. Do this step first, because it is the foundation for all other information. See the service deliverables list to understand what counts as identity material.

Next, write your evidence as readable paragraphs

Do not just post images and PDFs for certifications; add a sentence: which certification, the number, which products it covers, and when it expires. Same for test reports: state the test items and conclusions clearly. What AI needs is sentences, not attachments. Combining this with structured data gives steadier results.

Finally, add notes on what your product is not suitable for

Proactively state which scenarios your product is not suitable for. This looks like talking customers out of buying, but it actually reduces wrong orders and lets AI know your boundaries when comparing, so it is less likely to conclude that you are poor value. This is often how inquiry quality improves; read it together with how to diagnose traffic up, inquiries down.

A Low-Cost Implementation Checklist

Three steps, no money, and you can get through the first pass within a week.

1. Search your company name and record what each platform says: take one screenshot each of your website, catalog, and social media, and mark the inconsistencies.
2. Write certifications and tests as text paragraphs: place one on the product page and one on the homepage, including numbers and scope of application.
3. Add MOQ and lead-time ranges for your three main products: state regular and custom clearly, and state peak-season extension rules clearly.

These three steps need no new tools, but they change the raw material AI gets during first screening. To see how content should work together across a long B2B chain, read content and leads in a long sales cycle.

The Questions Bosses Ask Most Often

Does 57.5% being talked out of buying mean AI is unsuitable for export marketing?

No. The number says that users run a round of elimination with AI before deciding. If your information cannot take part in that elimination, you are absent rather than disliked.

AI says our products are bad. Can that be changed?

Usually it is not about changing its words but about adding information sources. Negative judgments from AI mostly come from information gaps or old information; first unify identity and specs across your website, catalogs, and social media, then watch how the answers change.

We have no third-party reviews. What should we do?

Verifiable facts you write down yourself also count as evidence. Certification numbers, test reports, production line photos, real lead-time records, these can all be cited, and they are not expensive.

Should we build a dedicated page to debunk things?

You can, but do not write it as a complaint. Using one page to positively explain specs, applicable scenarios, and common misunderstandings is more effective than rebutting point by point, and it is easier for AI to excerpt.