AI Talked 57.5% of Users Out of a Purchase: A GEO Guide for Brands

Users no longer just search for links on Google. They describe their needs to ChatGPT, Gemini, Claude, or Perplexity and ask it to compare options, summarize risks, and explain which product or company is the better fit. AI thus shifts from an interface that delivers information to a pre-decision filter.

The commercial consequence of this shift matters: looking good on your website may not be enough on its own. If the AI answer doesn't mention you at all, you may not make the shortlist; if it describes you incorrectly, you may look like the wrong option; if it highlights weak or negative sources, purchase intent may end before it ever reaches your site.

So the new question isn't just 'Does AI recommend us?' The real question is which needs you're visible for, which words describe you, which sources back you up, and whether the answer moves the user to the next step or to a competitor.

In the survey published by Semrush, 57.5% of participants who use AI said they abandoned a purchase because of information from a chatbot. In the same survey, 57.51% of AI users said they bought a product a chatbot recommended. The two rates being almost equal shows that AI is a two-way decision tool that both creates demand and stops it.

The effect is more pronounced among people who actively use AI in purchase research. 80.93% of participants in this group abandoned at least one purchase because of AI guidance, and 42.81% experienced this more than once. Among those who use AI at least several times a week, the abandonment rate reaches 69.7%, and the rate of purchasing on an organic AI recommendation reaches 73.58%.

Even so, it isn't accurate to generalize 57.5% directly to all consumers, all countries, or every industry. The rate refers to the AI users in the survey and is self-reported. The finding should be read not as a universal conversion rate but as a strong market signal that AI answers are gaining measurable weight in commercial decisions.

A brand being named in an AI answer doesn't mean a positive outcome. The brand may be visible but presented in a frame that weakens purchase intent, such as 'expensive,' 'limited,' 'complex,' 'not suitable for small businesses,' or 'not enough independent sources about it.' That's why measuring only mention counts is like measuring only impressions in classic SEO and ignoring rankings and conversions.

AI visibility needs to be evaluated across four separate dimensions: findability, narrative accuracy, source quality, and decision impact. Findability shows whether the brand appears in the answer; narrative accuracy shows whether the product, price, target audience, and differentiators are conveyed correctly; source quality shows which evidence the answer relies on; and decision impact shows whether the answer positions the brand as recommended, neutral, or eliminated.

The management insight is this: GEO shouldn't be a 'how many answers mentioned us?' project but a system that manages how accurately and persuasively the brand narrative is carried at the moment of decision.

The first mechanism is information compression. Instead of reviewing dozens of pages one by one, users ask AI to combine price, features, reviews, and risks into one answer. A brand that doesn't make it into the compressed answer may fall outside the decision set even if it appears in search results.

The second mechanism is comparison. When a user asks 'A or B?', AI doesn't just put two feature lists side by side; it tries to produce a preference based on the use case. If the brand's target audience, advantage, and limits aren't clear in open sources, a competitor's better-documented narrative can become the default choice.

The third mechanism is risk reduction. In the survey, 74.15% of all participants said their likelihood of buying would decrease at least somewhat if AI flagged mixed or negative reviews. Among AI users, the rate is 85.31%. In other words, reviews and independent evaluations aren't just reputation factors; they're sales signals that enter AI's decision summary.

The fourth mechanism is fit interpretation. Users often ask not for 'the best product' but for the option that best fits their budget, company size, purpose, or technical setup. If brand pages don't explain who they're suited for and under which conditions they should be chosen, AI may fill that gap with third-party reviews or competitor narratives.

59.27% of the AI users in the survey say they discovered a brand or product they didn't know before through a chatbot recommendation. This finding shows that AI visibility doesn't just protect existing brand demand; it can also create new demand in category queries.

On the other hand, 47.54% of all participants occasionally turn to AI for company or seller information before buying. Among AI users, the rate is 65.42%. Most of those doing company research did so recently. This behavior means that even users who come to the brand's website may run a second check through AI before deciding.

Read together, these two findings show that AI doesn't have a single place in the funnel. A user may discover the brand with AI, research it on Google and the website, return to AI to compare alternatives, and abandon the purchase at the final stage after a review or trust check. The measurement model should reflect this multi-touch behavior.

65.01% of AI users say they replaced at least part of their Google searches in product research with chatbots. Even so, the survey also shows that the average user's Google usage didn't drop entirely. The conclusion isn't that SEO is over; it's that query types are being redistributed across channels.

Navigational, current-price, store, and transactional searches may stay on Google, while queries asking for summaries, comparisons, fit, risk, and recommendations may shift to AI. That's why it makes more sense to manage SEO and GEO not as two separate teams or a budget war but as two distribution faces of the same information architecture.

Technical SEO makes sure a page is crawled, indexed, and found in classic search. GEO strengthens the brand meaning, answerable content, entity relationships, and citable evidence built on that foundation. If either one is missing, the visibility chain weakens.

In the survey, 41.63% of participants said they dislike ads in chatbots, while 38.09% are neutral and 20.28% are positive. The share who say their opinion of a product would worsen after seeing an AI ad is 23.12%; the share who say it would improve is 20.36%. The majority say the effect depends on the ad or stays neutral.

This result doesn't mean paid AI ads don't work. Ads can provide access to high-intent conversations; organic visibility affects how the brand is described when the user verifies it after the ad. The healthiest strategy is to see advertising not as an alternative to GEO but as an accelerator.

An ad can say 'consider us'; but when the user immediately asks 'Is this brand trustworthy?', 'What are the alternatives?', or 'Who is it not suitable for?', open-web signals determine the answer. If the paid message and the organic AI narrative contradict each other, an ad click may not turn into purchase intent.

Brand diagnosis queries show current perception: 'What does the brand do?', 'Is the brand trustworthy?', 'What are the brand's reviews like?', and 'What are the brand's strengths and weaknesses?' These questions surface wrong information, outdated data, and reputation risk.

Category discovery queries measure new demand: 'What are the best X solutions?', 'Companies offering service X in Turkey,' or 'X tools suitable for mid-sized companies.' Asked without naming the brand, these questions show real discoverability.

Comparison queries measure competition within the shortlist: 'A or B?', 'What are the alternatives to A?', and 'Which company is the better fit for X?' Here, what matters isn't just being visible but in which use case you're preferred.

Risk and verification queries show the area closest to lost sales: 'What problems does this product have?', 'Are there hidden costs?', 'What do customer complaints say?', or 'What should I know before working with this company?' The sources used in these answers should be monitored regularly.

Purchase and fit queries measure commercial intent: 'Is it right for my company?', 'Is it worth the price?', 'How long does setup take?', and 'Which package should I choose?' Clear product, scope, pricing, and process content is decisive at this stage.

A sound measurement system starts with a fixed set of target queries. The same queries are run at regular intervals in ChatGPT, Gemini, Claude, Perplexity, and other relevant answer engines. The model version, date, location, session context, and the fact that answers can vary are noted; a single screenshot is not accepted as a trend.

The core KPIs are mention rate, recommendation rate, share among the first recommendations, citation rate, positive-neutral-negative narrative distribution, information accuracy, share of voice against competitors, and source diversity. When AI referral traffic, qualified leads, and sales impact are added, visibility can be tied to commercial results.

In particular, 'didn't appear in the answer' must be separated from 'appeared in the answer but was eliminated.' The first is a discoverability problem; the second may be an offer, reputation, product fit, or source problem. The same optimization plan won't solve both.

A management report shouldn't consist only of percentages. Each period, the lost queries, the competitors standing out, the sources used, wrong or risky statements, the pages fixed, and the next content or digital PR priority should be presented together.

The first layer is owned sources: the home page, product and service pages, pricing, FAQ, case studies, author profiles, policy pages, and structured data. In these areas, the brand name, service scope, target audience, and commercial terms should be consistent.

The second layer is verifying sources: industry publications, trusted directories, business partners, customer stories, expert opinions, and independent reviews. Instead of repeating the brand's own claims, these sources verify them from the outside.

The third layer is experience sources: review platforms, communities, forums, and social discussions. These can't be fully controlled; but recurring complaints can be fed back to product, sales, or customer experience teams. GEO isn't about hiding negative content; it's the discipline of strengthening accurate information and fixing real problems.

The fourth layer is technical accessibility. If pages can't be crawled, content disappears on the client side, the canonical structure is confused, or structured data is faulty, even the best narrative may not be processed reliably by systems.

In the first 30 days, a baseline visibility snapshot is taken. Queries are selected from the brand, category, comparison, risk, and purchase clusters; answers, competitors, and sources are recorded. Site crawlability, indexing, schema, brand consistency, and priority information gaps are audited.

Between days 31 and 60, decision pages are strengthened. Product and service scope, target users, price or pricing logic, process, evidence, limitations, and frequently asked questions are made clear. Comparison, guide, and case content is produced based on the sales team's real questions, and the related pages are connected with a strong internal link network.

Between days 61 and 90, external validation and re-measurement begin. Trusted industry sources, corporate profiles, and digital PR opportunities are planned. The same query set is run again to examine the change in mentions, sources, narrative, and competitor share. For queries with no gains, content gaps are separated from authority gaps.

GEO results depend on the platforms' crawling, indexing, and model update cycles. That's why 90 days isn't a promise of final results but the first stage of a measurable learning and improvement cycle.

The marketing team owns the target query set, positioning, content priority, and the visibility report. The SEO and technical team manages crawlability, schema, site architecture, and page performance. The corporate communications and PR team builds consistent brand context in trusted external sources.

Product and sales teams bring the most common objections, wrong expectations, and reasons for lost opportunities into the content system. The customer experience team identifies recurring complaints and makes sure real operational problems are fixed. The legal or compliance team checks the accuracy of claims, especially in healthcare, finance, and other regulated areas.

This distribution matters because AI visibility isn't a problem only the content team can solve. If recurring negativity in answers comes from the real product experience, producing more content won't solve it; the business reality has to change first.

The first mistake is testing a few popular questions once and accepting the result as permanent truth. AI answers can vary by platform, time, and context. Regular, comparable measurement is needed instead of one-off answers.

The second mistake is thinking content that repeats the brand name a lot is GEO. For answer engines, value comes from clarity, evidence, scope, relationships, and source quality. Stuffing keywords or the brand name doesn't close these gaps.

The third mistake is focusing only on your own website. User reviews, independent publications, and consistency across the open web play a strong role in the evaluation stage. On the other hand, producing low-quality bulk backlinks or artificial mentions can create risk instead of building trust.

The fourth mistake is treating SEO and GEO as alternatives to each other. It's hard to strengthen the AI visibility of a site that can't be indexed, is slow, contradictory, or poorly structured with new blog posts alone.

If your brand doesn't appear at all, first examine entity clarity, the category relationship, technical accessibility, and source coverage. If it appears but a competitor is recommended, focus on differentiating value, use cases, evidence, and comparison content.

If wrong information is given, find the source of that information; changing only your own page may not be enough. If negative reviews dominate, fix the root causes in the product and customer experience instead of covering the problem with reputation copy. If you're cited but traffic or leads don't come, evaluate your position within the answer, the call to action, and landing page fit.

The highest priority should go to queries with high commercial value that carry a risk of lost sales. Not every visibility loss is equal; the brand name being missing from an informational query doesn't have the same business impact as being eliminated in a pre-purchase comparison.

To manage technical SEO, content, entity, source visibility, and periodic measurement in one program, you can review our AI search visibility consulting service.

The main source of the consumer figures in this article is the survey titled AI Chatbots Talked 57.5% of AI Users Out of Buying, published by Semrush on September 7, 2026. The study was conducted in July 2026 with 2,338 US adults.

The survey reports a margin of error of about ±2 points at a 95% confidence level for full-sample statistics. For results filtered to subgroups, the error range may be wider because the sample shrinks. 'Consumers,' 'AI users,' 'regular users,' and 'heavy users' have different denominators, so when comparing rates, attention should be paid to who the group consists of.

The study is based on the US market and on participants' self-reports. It doesn't prove the same behavior rate for every industry in Turkey, and it doesn't guarantee that a given GEO investment will increase sales. The measurement and implementation frameworks in this article are strategic interpretations we developed from the survey results.

Still, the main signal is clear: AI answers play a role in brand discovery, vendor research, comparison, and purchase abandonment. That's why measuring a brand's representation in the AI environment is no longer just a communications topic; it's a shared responsibility of marketing, sales, reputation, and data management.