{"id":6794,"date":"2026-01-30T13:34:34","date_gmt":"2026-01-30T13:34:34","guid":{"rendered":"https:\/\/www.stanventures.com\/news\/?p=6794"},"modified":"2026-01-30T13:35:46","modified_gmt":"2026-01-30T13:35:46","slug":"ai-brand-recommendations-change-constantly-new-research-reveals","status":"publish","type":"post","link":"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/","title":{"rendered":"AI Brand Recommendations Change Constantly, New Research Reveals"},"content":{"rendered":"<p><b>A new study shows that popular AI tools almost never agree on brand or product recommendations, exposing major flaws in the fast-growing business of AI visibility and ranking analytics.<\/b><\/p>\n<p><a href=\"https:\/\/sparktoro.com\/blog\/new-research-ais-are-highly-inconsistent-when-recommending-brands-or-products-marketers-should-take-care-when-tracking-ai-visibility\/\"><span style=\"font-weight: 400;\">Research<\/span><\/a><span style=\"font-weight: 400;\"> published by marketing strategist Rand Fishkin finds that leading AI systems such as ChatGPT, Claude, and Google\u2019s AI Overviews produce highly inconsistent brand and product recommendations.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Based on thousands of repeated AI responses across consumer and business categories, the study concludes that ranking brands inside AI answers has little meaning, while limited forms of visibility measurement may still offer cautious insight<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_83 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\"><\/p>\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#why-this-question-matters\" >Why This Question Matters<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#how-the-research-was-done\" >How the Research Was Done<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#what-the-data-showed\" >What the Data Showed<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#why-rankings-fall-apart\" >Why Rankings Fall Apart<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#where-there-is-still-signal\" >Where There Is Still Signal<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#the-overlooked-issue-of-prompts\" >The Overlooked Issue of Prompts<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#human-prompts-still-lead-to-familiar-brands\" >Human Prompts Still Lead to Familiar Brands<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#implications-beyond-marketing\" >Implications Beyond Marketing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#what-marketers-and-leaders-should-do\" >What Marketers and Leaders Should Do<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/#key-takeaways\" >Key Takeaways<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"why-this-question-matters\"><\/span><b>Why This Question Matters<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Over the past few years, companies have rushed to understand how their brands appear inside AI answers. That rush has turned into real money. Industry estimates suggest that more than $100 million a year is already being spent on tools that claim to track AI rankings and brand presence.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">What was missing until now was a simple reality check. Do AI systems behave consistently enough to justify those metrics? Fishkin\u2019s research set out to test that assumption directly, after finding no public studies that answered the question.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"how-the-research-was-done\"><\/span><b>How the Research Was Done<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The study relied on more than 600 volunteers who repeatedly ran the same prompts across three major AI platforms.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In total, 2,961 responses were collected from 12 prompts covering a wide range of topics, from chef\u2019s knives and headphones to cloud computing providers, marketing agencies, and cancer care hospitals.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Participants copied AI responses exactly as they appeared. The data was then cleaned and analyzed to identify how often brands appeared, how frequently lists repeated, and whether ordering showed any stability.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The methodology drew inspiration from established academic work on large language model consistency.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"what-the-data-showed\"><\/span><b>What the Data Showed<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The most striking finding was how rarely AI tools repeated themselves.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In fewer than one out of 100 cases did an AI return the same list of brands twice for the same question. Seeing the same list in the same order was even less common, occurring in fewer than one out of 1,000 runs.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-6796\" src=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2026\/01\/Slide9-300x169.webp\" alt=\"AI Brand Recommendations Change Constantly, New Research Reveals\" width=\"300\" height=\"169\" srcset=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2026\/01\/Slide9-300x169.webp 300w, https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2026\/01\/Slide9-1024x576.webp 1024w, https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2026\/01\/Slide9.webp 1280w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">The variation went beyond order. AI systems changed the length of their lists, sometimes offering just a handful of options and other times more than ten. They also differed in which brands appeared at all, occasionally recommending inactive businesses or outdated products.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These patterns held true across all three AI platforms tested. The conclusion was hard to ignore. AI recommendation lists are inherently unstable.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"why-rankings-fall-apart\"><\/span><b>Why Rankings Fall Apart<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Because the order of recommendations changes so dramatically, the research finds that ranking position inside AI answers is effectively meaningless.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A brand might appear first in one response, fourth in the next, and disappear entirely in another, all without any change in the prompt.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This instability creates a risk for misuse. A tool or consultant could rerun prompts repeatedly until a favorable result appears, then present that snapshot as evidence of success.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The findings challenge emerging <\/span><a href=\"https:\/\/www.stanventures.com\/ai-seo-services\/\"><span style=\"font-weight: 400;\">AI SEO<\/span><\/a><span style=\"font-weight: 400;\"> strategies that attempt to optimize brand performance based on ranking position inside AI-generated answers.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"where-there-is-still-signal\"><\/span><b>Where There Is Still Signal<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Despite the disorder, the research uncovered one pattern that held more consistently. While rankings shifted, certain brands appeared again and again across many runs of the same prompt. Measuring how often a brand shows up, expressed as a visibility percentage, proved far more stable than tracking position.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In categories with limited options, such as regional healthcare providers or cloud services, top brands appeared in most responses, sometimes more than 90% of the time. In larger, more crowded spaces, visibility rates dropped into the 30% to 40% range, reflecting the broader pool of possible answers.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The research shows that <\/span><a href=\"https:\/\/www.stanventures.com\/brand-mention-services\/\"><span style=\"font-weight: 400;\">brand mention<\/span><\/a><span style=\"font-weight: 400;\"> frequency across repeated AI responses provides a more reliable indicator than ranking order.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"the-overlooked-issue-of-prompts\"><\/span><b>The Overlooked Issue of Prompts<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">One of the most revealing parts of the study focused on how people actually ask AI questions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When volunteers were asked to write their own prompts for the same task, almost none of them looked alike. Semantic similarity between prompts was extremely low, even when everyone was trying to accomplish the same goal.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This matters because many AI tracking tools rely on a small set of standardized or synthetic prompts.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The research shows that real-world AI usage is far more varied, personal, and unpredictable. Any system that ignores this diversity risks producing misleading results.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"human-prompts-still-lead-to-familiar-brands\"><\/span><b>Human Prompts Still Lead to Familiar Brands<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">To test whether prompt diversity makes tracking impossible, the study compared AI responses from human-written prompts with those generated from synthetic prompt sets.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The outcome was unexpected. Even with wildly different wording, AI systems often returned a familiar group of brands when the underlying intent was the same.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, hundreds of unique prompts about buying headphones still surfaced the same major manufacturers in more than half of all responses.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This indicates that AI models often capture intent beneath the surface language, supporting the idea that visibility percentage can still reflect something real when measured carefully.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"implications-beyond-marketing\"><\/span><b>Implications Beyond Marketing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The findings raise concerns about trust in AI recommendations. Many users assume that AI lists are ordered by quality or expertise. The research shows that this assumption is unfounded.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In sensitive areas such as healthcare, this misunderstanding could have serious consequences.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A hospital or service may appear frequently but rarely at the top of a list, with no clear explanation. The order itself carries no reliable meaning.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Fishkin argues that AI tools should clearly disclose that recommendation lists are randomized outputs rather than endorsements or expert judgments.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"what-marketers-and-leaders-should-do\"><\/span><b>What Marketers and Leaders Should Do<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Organizations considering AI visibility tools should approach them with caution. Ranking position should be disregarded entirely. Any attempt to measure AI presence should rely on large numbers of prompts, repeated runs, and transparent statistical methods.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Vendors should be able to explain how their data is collected, how many runs are required for confidence, and how closely their prompts reflect real user behavior. Without that transparency, AI visibility metrics risk becoming expensive guesswork.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"key-takeaways\"><\/span><b>Key Takeaways<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Research by Rand Fishkin shows AI tools rarely repeat the same brand recommendations.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The study finds the ranking order in AI answers changes too frequently to be reliable.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Repeated brand appearances offer a more stable signal than ranking position.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Highly varied user prompts limit the precision of AI visibility tracking.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Transparent methodology is critical for any AI tracking measurement to hold value.<\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>A new study shows that popular AI tools almost never agree on brand or product recommendations, exposing major flaws in the fast-growing business of AI visibility and ranking analytics. Research published by marketing strategist Rand Fishkin finds that leading AI systems such as ChatGPT, Claude, and Google\u2019s AI Overviews produce highly inconsistent brand and product [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[],"class_list":["post-6794","post","type-post","status-publish","format-standard","hentry","category-ai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI Brand Recommendations Change Constantly, New Research Reveals - Stan Ventures<\/title>\n<meta name=\"description\" content=\"New research shows AI tools give inconsistent brand recommendations, challenging the reliability of AI ranking and visibility metrics.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.stanventures.com\/news\/ai-brand-recommendations-change-constantly-new-research-reveals-6794\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Brand Recommendations Change Constantly, New Research Reveals - 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