Boost your skills with Growth Memo\u2019s weekly expert insights. Subscribe for free!<\/em><\/p> AI Mode is compressing the stage where buyers compare, reject, and discover brands on their own. Our new usability study of 185 documented purchase tasks shows that 74% of AI Mode final shortlists came directly from the AI\u2019s output \u2013 no external check, no triangulation, no second opinion.<\/p> This analysis will cover:<\/p> AI transforms Search from a list of results to a list of recommendations (shortlist). Until now, we have no idea how users treat AI shortlists. Do they take it at face value or thoroughly validate it?<\/p> That\u2019s why I partnered with Citation Labs and Clickstream Solutions to record real users and their interactions when facing high-stakes purchases. This usability study of 48 participants completing 185 major-purchase tasks reveals that AI Mode operates as a recommendation environment, not a comparison one.<\/p> In traditional search, people click through results, comparing across sources to assemble a candidate set. In AI Mode, they accept the AI\u2019s candidates and move on. 74% of AI Mode shortlists came directly from the AI\u2019s output with no external check. In traditional search, more than half of users built their own shortlist from scratch.<\/p> The study covers four categories (televisions, laptops, washer\/dryer sets, and car insurance). Participants completed tasks using both AI Mode and traditional search in a within-subjects A\/B design, producing 149 AI Mode task observations and 36 search observations. The behavioral patterns are consistent enough across categories and participants to carry weight. (Full study design is at the end.)<\/p> From Garret French, founder of Citation Labs:<\/p> \u201cIn AI Mode, buyers often use a shortlist synthesis to shortcut the cognitive effort of Standard Searching and comparing. This raises the value of onsite decision assets and third-party sources that provide AI with clear trade-offs, specific evidence, and sufficient contextual structure to describe a brand\u2019s offering with confidence.\u201d<\/em><\/p> <\/blockquote> From Eric Van Buskirk:<\/p> The absence of narrowness frustration is the most intellectually significant finding. 15% in AI Mode vs 11% in Search, with no meaningful statistical difference. That\u2019s the finding that rules out the obvious alternative explanation: that users accepted the AI\u2019s shortlist because they felt trapped. They didn\u2019t push back. They weren\u2019t frustrated. They were satisfied. That makes the acceptance harder to dismiss.<\/em><\/p> <\/blockquote> Here\u2019s what happened.<\/p> Across the laptop and insurance tasks, where participants used both search surfaces (classic search and AI Mode), the gap in constructing a product shortlist was stark.<\/p> Definitions:<\/p> In classic search, 56% of participants built their own shortlist from multiple sources. In AI Mode, only 8 out of 147 codeable tasks produced a genuinely self-built shortlist. The user\u2019s comparison process didn\u2019t just shrink when using AI Mode. For most participants, it didn\u2019t happen at all.<\/strong><\/p> 64% of AI Mode participants clicked nothing at all during their task. <\/strong>They read the AI\u2019s text, sometimes scrolled through inline product snippets, and declared their finalists. The no-click rate varied by category:<\/p> Insurance participants delegated most heavily. Washer\/dryer participants clicked the most, likely because appliance decisions involve specific physical constraints (capacity, stacking compatibility, dimensions) that the AI summary didn\u2019t always resolve.<\/p> The 36% who did interact with individual results within AI Mode broke into 2 groups:<\/p> A separate 23% of all AI Mode tasks involved at least one visit to an external website<\/strong>, mostly retailers (Best Buy appeared in 10 of 34 tasks with external visits) and manufacturer sites. The destination pattern matters: Users left AI Mode to confirm a candidate they\u2019d already accepted from the AI\u2019s list, not to find new ones.<\/p> Of the 117 participants who adopted the AI\u2019s shortlist directly, roughly 85% showed no internal verification behavior at all. Participants who built their own lists took an average of 89 seconds longer and consulted more than twice as many sources.<\/p> In classic search, the pattern reversed. Nearly 89% of participants clicked on something.<\/strong><\/p> Just like in classic search, the top answer carries outsized weight.<\/strong> 74% of participants chose the item ranked first in the AI\u2019s response as their top pick. The mean rank of the final choice was 1.35. Only 10% chose something ranked third or lower.<\/p> Position one in the AI\u2019s output carries an outsized advantage because of where it sits: inside a curated section that typically contains two to five items, after the AI has already done the filtering. The first item is the AI\u2019s top pick. When people engage with AI mode, we know they read almost all of the output: The first AI Mode study found users spend 50 to 80 seconds reading AI Mode output, more than double the dwell time on AI Overviews. Users are reading carefully. They just read within a set the AI already narrowed.<\/p> However, 26% of participants in this study overrode rank order. The driver: brand recognition.<\/strong> They spotted a brand lower on the list and preferred it regardless of where the AI placed it. TV and laptop categories saw this most, where participants arrived with existing preferences for Samsung, LG, Apple, or Lenovo. But overriding rank did not mean rejecting the AI\u2019s output: 81% of rank-override participants still chose from the AI\u2019s candidate set.<\/p> \u201cTravelers and USAA actually tell me how much, whereas State Farm and GEICO give percentages. Just knowing the exact amount makes me want to pick Travelers or USAA right off the bat.\u201d<\/em><\/p> That quote captures a core pattern in AI Mode trust. The AI\u2019s formatting shaped the decision: Dollar amounts versus percentage discounts determined which brands made the shortlist.<\/p> AI framing (37%), meaning how AI talks about the product, and brand recognition (34%) were the top 2 trust drivers in AI Mode. They run nearly even:<\/p> In classic search, the dominant trust mechanism was multi-source convergence: Participants built confidence by checking whether multiple independent sources agreed about a product.<\/p> Essentially, users triangulated. One checked Progressive, then GEICO, then an Experian article. Another compared aggregated star ratings against reviews on the actual site. They were building a case from separate inputs.<\/p> That behavior was almost absent in AI Mode (5%). Instead, AI framing (how the AI worded its description of a product) and brand recognition were the top 2 trust drivers.<\/p> The split between these two signals tracked closely with product category:<\/p> For televisions and laptops, where most participants arrived with existing brand preferences, brand recognition dominated. For insurance and washer\/dryer, where participants had less prior knowledge, AI framing dominated.<\/p> When you lack a prior view, the AI\u2019s description becomes the trust signal. In AI Mode, the synthesis <\/strong>is<\/strong><\/em> the corroboration. Participants treated the AI\u2019s summary as if the cross-checking had already been done for them.<\/strong><\/p> The first study showed a related pattern from the supply side: AI Mode matches site type to intent, surfacing brands for transactional queries and review sites for comparisons. This study shows the demand side of the same behavior: When the AI surfaces a brand the user already knows, brand recognition drives the decision; when it doesn\u2019t, the AI\u2019s own framing fills that role. The site-type matching and the trust mechanism reinforce each other.<\/p> Purchase outcomes in AI Mode concentrated heavily. For laptops, three brands captured 93% of all AI Mode final choices. In classic search, the distribution was broader: HP EliteBook variants appeared three times, ASUS once, and other brands got consideration they never received in AI Mode.<\/p> Two distinct problems emerged:<\/p> Another participant said when using AI Mode: \u201cI\u2019m already eager to believe these are good recommendations because it mentions LG and Samsung, two brands I consider very reliable.\u201d<\/em> The AI didn\u2019t say those brands were better. The participant inferred it from familiarity.<\/p> Participants didn\u2019t feel constrained by the narrower set. Narrowness frustration appeared in 15% of AI Mode tasks and 11% of classic search tasks, statistically indistinguishable. The option set shrank, but the feeling of having enough options didn\u2019t change. The most skeptical AI Mode participant in the comparison set, who complained the AI kept pointing to \u201cteen drivers, teen drivers, teen drivers,\u201d still chose GEICO and Travelers: the consensus AI result.<\/p> 23% of AI Mode tasks involved an external site visit, but keep in mind these prompts reflect high-stakes situations. In standard search, that figure was 67%.<\/p> The volume difference matters less than the intent<\/em> difference:<\/p> In the first AI Overviews study, we found that high risk leads users to verify AI claims more and reference against answers from other users on UGC platforms (like Reddit).<\/p> In this study, Reddit appeared in 19% of standard search tasks and only twice across all 149 AI Mode sessions. The peer-opinion layer that shapes a large share of traditional Search barely exists in AI Mode behavior.<\/strong><\/p> There\u2019s irony in that pattern. Google leans heavily on Reddit content to train its models. However, the source that users rely on most in standard search is the one they almost never visit when the AI synthesizes those same sources for them.<\/p> The first study found the same pattern at a different scale. Across 250 sessions, clicks were \u201creserved for transactions:\u201d Shopping prompts drove the highest exit share, while comparison prompts drove the lowest. The exit destinations were retailers and brand sites, not editorial or peer-opinion sources. Six months and a different task set later, the pattern holds: When users leave AI Mode, they leave to buy.<\/p> Three things that excite me most about the study:<\/p> First, we can apply the mental model of rankings<\/strong> (higher = better) to AI Mode as well. Most users choose the first product. Now, we can apply this to prompt tracking by focusing more on prompts that lead to shortlists and use our position as a goalpost.<\/p> Second, trust trumps rank<\/strong>. We know this since the first user behavior studies I published, but this study reinforces the importance of building trust with users before they search. It\u2019s the ultimate cheat code.<\/p> Third, we now know buyers trust AI\u2019s recommendations<\/strong>. Obviously, there\u2019s a high risk here if the AI is wrong, but seeing how quickly buyers take the AI\u2019s recommendation also shows us how fast consumers adopt AI. It truly is the future of Search.<\/p> Keep in mind:<\/p> 1. Visibility at the model layer is the new threshold.<\/strong> If AI Mode doesn\u2019t surface your brand, you have a visibility problem at the model layer. Query your own category the way a buyer would (i.e., \u201cbest car insurance for a family with a teen driver,\u201d \u201cbest washer dryer set under $2,000\u201d) and document which brands appear, in what order, and with what framing. Do this across multiple prompt variations. Do it regularly, because AI responses shift over time.<\/p> 2. How the AI describes you matters as much as whether it appears.<\/strong> Brands cited with concrete attributes (specific model, specific price, named use case) held stronger positions than brands described generically. The content on your site that the AI draws from not only affects whether<\/em> you show up, but also how confidently and specifically<\/em> you show up. A brand with structured pricing data, clear product specs, and explicit use cases gives the AI better material to work with.<\/p> 3. For categories with context-dependent pricing, AI Mode creates a false-confidence problem.<\/strong> 63% of insurance participants were rated overconfident about pricing. They accepted AI-quoted rate estimates without checking whether the figures applied to their actual state, driving record, or current insurer. They made elimination decisions based on numbers that may not have applied to them. Where shopping panels showed explicit retailer-confirmed prices (washer\/dryer), 85% of participants understood pricing clearly. Where they didn\u2019t (insurance, laptops), confusion and overconfidence filled the gap. Structured pricing data through Merchant Center feeds and schema markup is the most direct lever for brands selling physical products. For services, the lever is editorial: Make sure your landing pages and FAQ content frame pricing as conditional (\u201cyour rate depends on X, Y, Z\u201d) so the AI has that framing to draw from.<\/p> Citation Labs and Clickstream Solutions ran this as a remote, unmoderated usability study with 48 U.S.-based participants recruited through Prolific. Each participant completed up to four major-purchase shortlisting tasks across televisions, laptops, washer\/dryer sets, and car insurance.<\/p> The comparison between AI Mode and traditional standard search used a within-subjects A\/B design: Participants used both surfaces, not one or the other. Significance calculations were normalized for the exact number of participants in each group (149 AI Mode task observations, 36 standard search task observations). This matters because the groups are unequal in size, and raw percentage comparisons between them would overstate confidence without that correction.<\/p> Sessions were screen-recorded with think-aloud audio. Trained analysts annotated each recording for behavioral markers (click-through, shortlist origin, trust signals, external site visits) and qualitative markers (stated reasoning, brand mentions, frustration signals). The 185 task-level observations provide a larger analytical base than the 48-participant headcount suggests, but confidence intervals remain wider than a large-scale survey. Findings are directional, not population-level estimates.<\/p> Notes on terminology used throughout this report:<\/strong><\/p> Featured Image: Tapati Rinchumrus\/Shutterstock; Paulo Bobita\/Search Engine Journal<\/em><\/p> <\/div> SEO#Consumers #Navigate #HighStakes #Purchases #Mode #sejournal #Kevin_Indig1775570387<\/p> ","protected":false},"excerpt":{"rendered":" Boost your skills with Growth Memo\u2019s weekly expert insights. Subscribe for free! AI Mode is compressing the stage where buyers compare, reject, and discover brands on their own. Our new usability study of 185 documented purchase tasks shows that 74% of AI Mode final shortlists came directly from the AI\u2019s output \u2013 no external check, […]<\/p>\n","protected":false},"author":1,"featured_media":6045,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[220,22596,555,1561,22595,17868,80],"class_list":["post-6044","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-accessibility","tag-consumers","tag-highstakes","tag-kevin_indig","tag-mode","tag-navigate","tag-purchases","tag-sejournal"],"acf":[],"_links":{"self":[{"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/posts\/6044","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6044"}],"version-history":[{"count":0,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/posts\/6044\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=\/wp\/v2\/media\/6045"}],"wp:attachment":[{"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6044"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6044"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/longzhuplatform.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6044"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Why We Conducted The Study<\/strong><\/h2>
1. 88% Of Users Took The AI\u2019s Shortlist Outright<\/strong><\/h2>
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2. The AI\u2019s Top Pick Becomes The User\u2019s Top Pick 74% Of The Time<\/strong><\/h2>
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3. The AI\u2019s Words Become The Trust Signal<\/strong><\/h2>
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4. If You\u2019re Not In The List, You Don\u2019t Exist<\/strong><\/h2>
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5. Users Leave To Buy, Not To Research<\/strong><\/h2>
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6. 3 Levers: Visibility, Framing, And Pricing Data<\/strong><\/h2>
Study Design<\/strong><\/h2>