Afternoon BriefAI Search & Discovery

AI Gets Your Product Wrong. 58% of Buyers Blame You, Not the AI.

New research from Rithum shows 58% of shoppers lose trust in the brand — not the AI — when product recommendations contain errors. Here's the three-step audit to find and fix what AI is saying about you before your next buyer asks.

Christian Lehman|Mar 31, 2026

Rithum surveyed 1,046 online shoppers across the U.S. and U.K. and published the results last week. The headline finding: 58% of shoppers say their trust in the brand decreases when an AI recommendation contains incorrect product information. Not trust in the AI. Trust in the brand. Sixteen percent say they would not buy the product at all after a bad AI recommendation (Rithum, "The New Discovery Engine," March 25, 2026).

That reframes the AI visibility problem for every growth team. The question is no longer whether AI mentions you. It is whether the version of your brand that AI delivers to buyers is accurate enough that you would put it in a pitch deck.

The trust transfer problem

The Rithum data reveals something specific about how buyers assign accountability. When ChatGPT, Gemini, or Copilot recommends a product and gets the details wrong, the buyer does not blame the technology. They blame the company whose product was misrepresented.

This is a trust transfer. The AI acts as an intermediary, and when that intermediary delivers bad information, the cost falls on the brand.

It gets worse for B2B operators. Forrester's 2026 State of Business Buying report, surveying nearly 18,000 global buyers, found that 94% of B2B buyers now use AI during their purchase process (Forrester, January 2026). Twenty percent said they felt less confident because the AI output was inaccurate. Procurement professionals were even more affected: 28% said AI-generated information decreased their confidence, and 22% said it wasted their time.

A B2B buyer researching your category through Perplexity or ChatGPT who encounters a wrong description of your platform will not think "the AI was wrong." They will think "this company is not what I expected" and move to the next option. You never get the chance to correct it because you never know the conversation happened.

How bad is the mispositioning problem?

The scale of inaccurate AI brand descriptions is larger than most teams assume.

Research source	Sample	Key finding
Rithum / Retail Dive (March 2026)	1,046 U.S./U.K. shoppers	58% lose trust in brand when AI gets product info wrong
Optimly AI Brand Audit (March 2026)	5,829 brands	60% misrepresented by at least one major AI model
Agentcy AI Visibility Index (Feb 2026)	104 B2B marketing leaders	46% who checked found AI descriptions mixed or inaccurate
HBR / Pernod Ricard case (March 2026)	Portfolio brand audit	AI miscategorized a mass-market product as prestige

The Optimly audit scored brands across ChatGPT, Claude, Gemini, and Perplexity. Their finding: 60% of brands are misrepresented by at least one major AI model (Optimly, March 2026). Not invisible. Described with wrong categories, incorrect competitive positioning, or outdated capabilities. Harvard Business Review reported a direct case: Pernod Ricard discovered that a major AI model had miscategorized Ballantine's Scotch as a prestige product when it is a mass-market brand (HBR, March-April 2026).

The verification gap

Only 5% of shoppers who verify AI recommendations go to the retailer or brand website to check. Twenty-eight percent turn to search engines first. Seventeen percent ask friends or family. Seventeen percent rely on prior experience.

Christian Lehman's read on this data: 95% of the verification that happens after an AI recommendation occurs somewhere your brand does not control. Your product page is not part of the feedback loop.

For B2B, the stakes compound. Forrester found that buying groups now average 13 internal stakeholders and 9 external influences per decision. If an AI engine delivers an inaccurate description of your product, that description can propagate through the buying group before anyone on your side knows about it.

Where the bad data comes from

The mistake most teams make is reaching for their website CMS. If the AI description is wrong, they update the product page, add schema markup, or rewrite the "how it works" section.

That fixes approximately 5-10% of the problem.

The Fullintel-UConn academic study presented at the International Public Relations Research Conference in February 2026 found that 89% of links cited in AI responses come from earned media sources, with 95% from non-paid sources. Muck Rack's Generative Pulse analysis of over one million AI citations reached the same conclusion: 82% earned media, 94% non-paid. Ahrefs found that brand web mentions correlate 0.664 with AI Overview visibility, compared to 0.218 for backlinks. Off-site editorial presence is the dominant input.

The description AI gives your brand is downstream of the editorial record. If your earned media coverage from the past 18 months described you one way and your positioning has shifted, the AI still works off the old signal. If a competitor earned more coverage in the publications AI systems trust, their description crowds yours. If you never had meaningful third-party coverage in authoritative outlets, the AI assembles a description from fragments. That assembled description is what 58% of buyers will blame you for.

The three-step audit

Run this before your next budget review. It takes an afternoon.

Step 1: Run the mispositioning queries. Do not search your company name. Search the category and comparison queries your buyers ask: "best [category] for [company type]," "compare [you] vs [competitor]," "[category] leaders 2026." Run each query across ChatGPT, Perplexity, and Google AI Mode. Record how your brand is described. Note what category it is placed in, which competitors it is grouped with, and whether the product description matches reality.

The Rithum data is specific: 1 in 5 shoppers have purchased from a brand they had never heard of because AI recommended it. If AI recommends your competitor with an accurate description while misrepresenting you, the buyer has already made a choice.

Step 2: Map the source gap. Once you know what AI is saying, identify where it learned it. Search your brand name in the publications AI engines cite most frequently: Forbes, TechCrunch, WSJ, and the trade publications that cover your category. Focus on coverage from the last 18 months. Stacker's research across 87 stories, 30 brands, and 8 AI platforms found that distributing content through earned media channels produces a 239% median lift in AI brand citations compared to brand-owned content alone. The coverage that is recent, in the right publications, is what shapes the current AI description.

If the coverage is old, thin, or absent from the publications that matter, that is the root cause.

Step 3: Correct at the source. This is where Christian Lehman sees most teams make the wrong call. They find a bad AI description and update their website. But AI systems are structurally biased toward third-party editorial sources over brand-owned content. AuthorityTech's earned vs. owned citation research found that earned media distribution produces 325% more AI citations than owned content distribution. Updating your website changes the minority of what AI references. The majority comes from earned media.

Correcting mispositioning means earning new coverage in publications that AI systems index and treat as authoritative. Coverage that accurately describes what you do, who you serve, and what category you belong in. The brief for that coverage needs to be specific: the category phrase, the buyer profile, the use case. Generic coverage from a well-known publication does not fix the problem. Accurate coverage in the right publication does.

The timeline problem

The Rithum data carries an urgency signal most teams will miss. Sixty-four percent of 18-27 year olds buy based on an AI recommendation without verifying it anywhere else. That cohort is entering B2B buying groups now. They are the procurement analysts, the product evaluators, the people building initial vendor shortlists.

The Bain 2025 AI search consumer study found that 80% of search users rely on AI summaries at least 40% of the time, and about 60% of searches end without a click-through. The brand description in that AI summary is what your buyer sees. If that description is wrong, 58% will trust you less. Sixteen percent will walk.

This is the problem that Machine Relations, the discipline coined by Jaxon Parrott, addresses at the infrastructure level. The publications AI engines index and trust are the same publications that built human brand perception for decades. When your brand earns a placement in one of those publications with accurate positioning, AI engines cite it. When buyers verify the AI recommendation, they find that coverage. The mechanism that made PR the original trust engine is the same mechanism that determines whether AI gets your product right.

The brands correcting their AI descriptions now are doing it through the earned authority layer: new coverage in trusted publications that replaces the old or missing signal with current, accurate positioning. The brands debating whether to update their product pages are fixing the wrong input.

Run the audit this week. If your AI description is wrong, your buyers already know. They just blame you for it.

Start with the visibility audit →