At a glance: 

• Shoppers who get a clear explanation of why an AI recommended a product are nearly 2x more likely to buy without verifying anywhere else. 
• Only 32% of shoppers named accuracy as the top trust-builder in AI recommendations. 49% chose a clear explanation of why a product was selected. 
• 47% of 28-to-43-year-olds say AI makes them faster decision-makers. Shoppers “very familiar” with AI tools are 3x more likely to purchase without verification. 
• When product data is wrong or incomplete, 80% of shoppers stay in the AI channel and ask again. 

A shopper asks ChatGPT for noise-canceling headphones for an open office, under $300. One result explains why it fits: isolates low-frequency hum, weighs less than comparable models, includes a transparency mode for conversations. Another lists a name, a rating, and a price. 

In a Rithum and Retail Dive survey of 1,046 U.S. and U.K. online shoppers, 49% named a clear explanation of why a product was chosen as the top trust-builder in an AI recommendation. Always-accurate information came in at 32%. And shoppers who get that explanation are nearly twice as likely to buy without checking anywhere else. 

Why shoppers value AI explanation over accuracy in product recommendations 

Shoppers expect AI to get the basics right. 67% named price as the top detail AI needs to be accurate on, followed by reviews and availability. But when asked what would most increase their trust, they reached past accuracy. 49% chose a clear explanation of why a product was selected. Always-accurate information came in at 32%. 

Any ecommerce team has seen this on a product detail page. Accurate price and clean specs keep a listing live. Rich attributes are what make it sell. The same applies to AI. An LLM builds its explanation from whatever product data it can find. If your listing includes driver size, noise cancellation type, and a note about comfort for all-day wear, the AI has something specific to say. If it doesn’t, the AI defaults to price. 

A jacket listed with fabric composition, weight, care instructions, and a note that it runs slim through the shoulders gives AI something to work with. A jacket listed as “men’s jacket, blue, available in S-XL” gives AI a price to compare. 

Newer brands with complete, attribute-rich product data already use this to their advantage, earning more persuasive recommendations than established names running on thin listings. When product data is wrong or incomplete, 80% of shoppers stay in the AI channel and ask again. The next answer is built on whatever data is available at that point. 

AI-powered shoppers buy faster and verify less 

47% of 28-to-43-year-olds say AI makes them faster decision-makers, compared to 21% of shoppers 60 and older. Shoppers who are “very familiar” with AI tools are 3x more likely to purchase without verification.   

For these shoppers, the explanation in the recommendation has to do the work that a product page, a review site, or a friend’s opinion used to handle. When the explanation falls short, the shopper moves to the next option in the response. There is no second visit, no follow-up search. The sale goes to whichever product explained itself best.   

How to optimize product content for AI recommendations 

Product content built for explanation earns stronger AI recommendations than content built only for visibility.   

• Enrich product attributes beyond the minimum required fields. Include use cases, compatibility notes, and sizing context. 

• Keep pricing and availability current across every channel where AI pulls data. 

• Monitor where shoppers verify AI recommendations and ensure your data holds up in those places too. Shoppers turn to search engines first at 28%, followed by online reviews at 19% and friends and family at 17%. 

• Test your own visibility: ask an LLM about your product category and evaluate whether your products appear with a clear, specific reason attached. 

• Prioritize data hygiene: validate and standardize titles, attributes, categories, and inventory/pricing sync so AI doesn’t amplify broken inputs across channels. 

Prioritize data hygiene: validate and standardize titles, attributes, categories, and inventory/pricing sync. AI can’t fix bad data. It can only move faster with whatever you give it, and when the inputs are off, that speed works against you. 

For a full breakdown of the data, download The New Discovery Engine report. 

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At a glance:

• More than a third of surveyed commerce leaders say key tasks are partially automated but still require manual checks. Many workflows still depend on a human proof step before go-live, according to the 2026 commerce readiness index.  
• Another 29% said data is scattered and stockouts show up after orders, turning routine pricing and inventory updates into avoidable cleanup work.  
• 34% of commerce leaders said dashboards are available, but teams are still forced into manual exports to confirm what they’re seeing.  

Retailers and brands have spent years layering ecommerce automation into pricing, inventory, listings, and media. But many still stop for a manual proof step before go-live.  

The double-check has not gone away. It often comes just before a team is ready to act. A price looks right in one system but off in another. Inventory looks stable until the channel view suggests otherwise. The team stops to verify the basics before moving ahead. That pause turns automated work back into manual work. 

Ecommerce automation still needs a proof step  

In the 2026 commerce readiness index, 34% of commerce leaders said key tasks are partially automated but still require manual checks. Nearly half of executives said 26%-50% of workflows still depend on spreadsheets, and repetitive tasks like manual data entry, and approvals.  

Promotions punish messy handoffs  

Marketing campaign promotions don’t leave much room for hesitation. Price, inventory, product content, and media all move at once, yet teams are often working against slower decision cycles: more than half of retailers said they act on meaningful performance signals within 48 hours, while brands are more likely to take three to five business days.  

The readiness assessment helps explain why: 32% said signals are spread across tools and reports, while 29% said they have dashboards and alerts but unclear workflows. When product feeds, pricing, availability, and catalog updates refresh on different schedules, a promotion can look ready until something slips and the team has to stop to confirm what is actually true. Rithum’s retail media guide describes the same problem from the campaign side: by the time the dashboard reflects it, time and budget may already be gone. 

Scattered data turns ordinary work into manual work  

The trouble is often small at first. A price changes here but not there. Inventory moves, but not everywhere at once. Reporting can show that something changed without showing where it began. In the index, 29% of respondents said stockouts appear only after orders come in. Routine work turns into cleanup.  

Dashboards lose their authority when something shifts  

A dashboard can feel reliable until something starts moving faster than your reporting can explain. When a product moves faster than expected, a promotion performs differently across channels, or a price update appears in one system but not another, the dashboard can flag the issue without showing its cause. The team has to look elsewhere to confirm what changed.  

The readiness assessment points to the same problem: 34% said core dashboards are standardized, but edge cases and new channels still depend on manual exports they know are unreliable. Another 26% said the data they work from is incomplete, late, or manually tweaked, but they still use it because it is all they have. The gap is not only in dashboard coverage, but in confidence in the data underneath it. 

Rithum’s retail media guide points to what’s missing: product context alongside campaign performance. When teams can see which products absorbed the budget, what changed in price or availability, and which issues need attention first, reporting stays useful while teams are still deciding what to do.  

The window to act is getting tighter  

The gap becomes more expensive during peak shopping events. In the report, more than half of retailers said they act on meaningful performance signals within 48 hours, while brands are more likely to take three to five business days.  

Rithum’s Prime Days 2025 data shows how timing can be problematic. One brand held spend until conversion and AOV recovered, then pushed harder. Another brand pivoted mid-event toward back-to-school assortments, bundles, and sharper titles and keywords, finishing 15% above the prior year’s Prime Day. The advantage was a timely read on what had changed, while there was still room to respond.  

What teams should standardize now to achieve the benefits of ecommerce automation  

Instead of adding more ecommerce automation, retailers and brands should look at where to cut back on verification work. Start with the handoffs that break trust most often. Set clear source-of-truth rules for product data, pricing, and inventory so a routine channel change does not trigger a manual review. Exceptions should surface early, with enough context for teams to understand what changed without going back into spreadsheets.  

Where Rithum helps by providing automation tools for ecommerce  

Rithum helps cut down the verification work that piles up between systems by offering automation tools for ecommerce. Error tracking flags mismatches earlier. Automated tools and workflows reduce the same reconciliation loop playing out over and over. Connected commerce and media insights bring pricing, listings, inventory, fulfillment, and performance into a view teams can actually use.  

For retail media teams, product changes and campaign performance sit closer together, so it’s easier to spot what shifted, what needs attention, and where to act first. As a result there are fewer exports, less back-and-forth, and less time spent confirming what should already be clear.  

Download the full 2026 commerce readiness index to see where retailers and brands are still losing time, and what it takes to move faster without adding risk. Then take our readiness assessment to see where you stand in comparison.  

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TL;DR 

• Product journeys, from discovery to decision, are shifting to AI. Among AI-active shoppers, 90%+ use LLMs to research products and compare options, and 53% use them to decide where to buy. 
• AI is diminishing buyer loyalty. 19% say they now buy from brands or products they had not heard about before, and 13% say they are more likely to switch retailers or products after using an LLM. 
• Brand and retailer sites have less time to influence the decision. 32% of shoppers spend less time browsing other sites after using LLM tools, and only 5% verify AI shopping information on a retailer or brand site. 
• Product information now plays a bigger role in trust. 49% say a clear explanation would do the most to increase trust in an AI recommendation, 67% say price is the most important detail to get right, and 58% say trust in the brand drops when an LLM provides incorrect product information. 

Rithum’s new report, The new discovery engine: How consumers are using AI to find, trust, and choose brands, and what’s at risk for those they never see, has a clear message for retailers and brands: the shopping journey is no longer confined to shelves, search results, category pages, or product detail pages. 

Based on a survey of 1,046 online shoppers in the U.S. and U.K., the report shows how LLMs have become the entire shopping journey and a one-stop shop where products get researched, compared, narrowed down, and chosen. Among AI-active shoppers, more than 90% use LLMs to research product information and compare options, while 53% use them to decide where to buy. By the time a shopper lands on a product page, the filtering may already be over. 

That shift to AI is creating room for brands that were not previously in the mix. In the survey, 19% of shoppers say they now buy from brands or products they had not heard about before. Established brands are facing a tougher version of the same market. Recognition still helps, but it has less force when AI is doing more of the sorting, ranking, and explaining before the click. 

The shortlist is forming earlier  

AI shopping adoption is the starting point for 80% of shoppers ages 18-to-27 and 80% among shoppers ages 28-to-43. Among households earning $100,000 to $150,000, it reached 84% adoption. These are commercially important shoppers, and they are already weaving LLMs into their purchase journey. 

The filtering that used to happen across tabs, retailer sites, and review pages is now happening inside LLMs. A product that appears high in the response moves ahead. One that doesn’t can disappear before the shopper has seriously considered it. 

ChatGPT’s product comparison feature adds to that shift. Shoppers can compare products side by side inside the chat, with price, features, reviews, and other details presented in one place instead of scattered across multiple retailer tabs. 

More than half of shoppers already trust AI tools as much as brand websites, and among high-income households, confidence in AI accuracy climbs as high as 80%. That trust gives LLM recommendations real weight early in the decision process, according to the report. 

Retailers now have more riding on how products are represented on their page, even before a shopper lands on the site. Brands face the same pressure, but with fewer natural intervention points. A retailer may still appear as the place to buy, even if a brand is filtered out earlier. If a brand’s product information is incomplete, inconsistent, or hard for AI to explain, it can be dropped from consideration before its own product page or brand story has a chance to influence the decision. 

New brands are finding room where familiar brands once had an easier ride 

You can see the LLM effect far beyond just initial research, with ripples into what shoppers buy. Nineteen percent of shoppers say they are more likely to buy from brands or products they had not heard about before if an LLM suggests it. Another 13% say they are more likely to switch retailers or products after using an LLM. Together, those numbers create a shopping environment where familiar brands have less room to rely on recognition alone. 

That creates an opening for challenger brands. A newer brand does not need years of broad recognition to get in front of a shopper. It needs usable, consistentproduct information and enough context for AI to present it clearly and convincingly. 

Established brands have less room to lean on familiarity alone. Customer loyalty still helps, but it no longer ensures that they go to your site first. Nearly half of shoppers say a clear explanation of why a product or brand was chosen would do the most to increase trust in an LLM recommendation. What carries weight here in an LLM recommendation is not name recognition but whether the recommendation feels specific, informed, and ready to act on. 

Brand-owned sites get fewer chances to influence the outcome 

The shopping journey used to leave more room for second thoughts. A shopper could open a few tabs, compare prices, read reviews, leave, come back, then change course. LLMs have shortened that process.  

In the survey, 32% of consumers say they spend less time browsing other sites when using LLM tools to shop. Another 36% say they make faster decisions, while 34% say they feel more confident about their purchases.  

These three stats don’t live in a vacuum. They indicate a continual trust-building experience for the shopper: they’re saving time, they’re finding what they need faster, and they feel better about their purchases. Why would they leave that experience to go back to a retailers website? 

The same pattern appears in how people verify what they see. Shoppers who double-check an LLM recommendation rarely begin with looking for confirmation on a brand or retailer site. Twenty-eight percent turn to search engines (which is likely also relying on AI tools), 19% specifically look for online reviews, 17% ask friends and family, and only 5% go to a retailer or brand website. A beautiful, brand-forward website won’t convince them to buy your product. They won’t even see it. But a PDP with in-depth specifications, GEO-optimized keywords, and highly relevant descriptions will impact consumers’ decisions, even if they don’t see the page. 

The recommendation is only as strong as the product story behind it 

Ask an LLM why it chooses one product over another, and it has to build that answer from the product facts it can find: materials, dimensions, compatibility, intended use, etc. The recommendation that an LLM givesis assembled from those pieces in real time. 

For brands, that raises the standard for product content. Copy, attributes, use cases, and supporting details are no longer sitting off to the side as content maintenance. They are becoming part of the recommendation itself. When the product story is thin, generic, or inconsistent, the answer reads that way too. 

Retailers feel the same pressure across the assortment. Pricing, inventory, attribute completeness, and feed quality all shape how products are represented before a shopper ever reaches the site. Anyone who has spent time inside a catalog has seen how quickly that can start to fray. A bad price, a missing dimension, or stale availability can make a solid product look less reliable than it is. 

The harder question is whether the product story still holds together everywhere that LLMs are pulling from. This includes product content, syndication, pricing, availability, and the systems that keep those details aligned. It also includes sources brands and retailers cannot fully control, such as reviews, forums, and social discussion. When those external signals surface alongside structured product data, inconsistencies become more visible. That makes it even more important for the information you do control to be accurate, complete, and easy for AI to explain. LLMs only give recommendations they can trust, based on the information that holds it together. 

Trust is moving closer to the data itself 

The survey leaves little ambiguity on price. In an AI shopping recommendation, 67% say it is the most important detail to get right. Reviews, availability, where to buy, and technical specifications all come after it in the list of prioritization 

That order will feel familiar to anyone who has watched shoppers abandon a cart over a mismatch or lose confidence over a number that does not look right. A wrong price or stale detail does not stay in the background. It becomes part of the recommendation, which means it becomes part of the shopper’s impression of your brand. 

The report puts numbers behind that. When an LLM provides incorrect product information, 58% say trust in the product or brand decreases, and 16% say they leave the purchase altogether. 

At that point, the issue is no longer confined to data quality. The recommendation may come from the model, but shoppers are not spending time sorting out where the error began. They decide whether the information feels reliable, and the brand lives with the result. 

The next phase is close enough to shape decisions now 

The report also looks ahead to a shopping flow where the model takes on more of the decision itself. More than 25% of AI-active shoppers say they are already very likely to hand purchasing decisions to AI, and another 39% say they are somewhat likely to consider doing this, if and when it’s available. Among the most AI-active shoppers, 65% say they are very or somewhat likely to use an AI agent that would buy for them. 

What AI sees already shapes what shoppers buy. Thin product content, stale pricing, patchy attributes, and inconsistent availability all weaken the recommendation before the shopper has done anything beyond type in a prompt. 

The priorities are clear. Keep your product story consistent. Keep pricing accurate. Keep availability current. Make products easier to compare, easier to explain, and less likely to be misread. New brands already have more room to enter the conversation. Established brands have less room for weak information, stale details, or missing context. 

For more details on the survey and a full breakdown of the results, download the report here.  

Methodology 

Rithum’s 2026 report is based on a survey of 1,046 online shoppers in the U.S. and U.K. Some questions look at behavior in the last 3 months, some category questions use the last 6 months, and some trust and behavior questions are broader and are not tied to a single recall window. 

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Returns are no longer a background cost or occasional loss. They are influencing how consumers shop, what they expect from retailers and brands, and whether they come back. Our newly published 2025 Global Returns & Profit Impact Report offers a detailed look at where returns are happening, why they happen, and what retailers and brands can do to reduce the impact.  

Based on responses from more than 6,000 consumers worldwide, our report highlights the behaviors and expectations that are shaping the post-purchase experience—and what separates companies that manage returns well proactively compared with those still taking a reactive approach. 

Shoppers plan to return before they even check out 

The data from that survey suggests that returns are not just a post-purchase inconvenience—they are a core part of online shopping today. According to the consumer survey, 36% of shoppers admit to overbuying with the intention of returning part of the order. This behavior, also known as “bracketing,” is especially prevalent in apparel categories, such as shoes and clothes. It’s also an even stronger trend among shoppers under the age of 35, where 50% say they commonly buy more than they need (for example, multiple sizes and colors), knowing they will return part of the order. 

Bracketing is likely a strong factor behind why apparel and footwear dominate return volumes, with 68% of consumers saying they returned clothing or shoes in the past 12 months. Electronics also rank high among returned items, particularly in Europe, where more than half of German and UK consumers returned an electronic product last year.  

Poor product content is still driving returns 

One of the top reasons cited for returns is poor fit, mentioned by 61% of consumers surveyed. But returns are not only about sizing. A third of respondents said they returned products because the item didn’t match its online description or images. This mismatch highlights the need for retailers to go beyond generic content and ensure that each listing reflects the reality of the product. While consumers many not always say it directly, this lack of confidence often leads to bracketing. When shoppers aren’t sure how any item will look, feel or fit as expected, they buy extras to cover their bases. 

Customer reviews are also playing a larger role in the purchase decision, especially in apparel. Half of consumers say they rely heavily on reviews when buying clothing or shoes. This makes transparency not just a bonus, but a competitive requirement. Inaccurate listings, missing details, or outdated visuals aren’t just conversion risks, they lead directly to a cycle of poor reviews and costly returns. 

Return policies are becoming make-or-break 

Return policies are shaping not just immediate buyer behavior, but long-term loyalty. According to the survey, 88% of consumers now expect free returns to be a standard feature, and 41% say they consider a retailer’s return policy before making a purchase. Nearly half (47%) said they’ve stopped shopping with a retailer because the return policy didn’t meet their expectations. 

This shift makes return policies a delicate balance. Offering free returns may cut into margins in the short term, while unclear or overly restrictive policies can damage trust and reduce repeat business. Retailers and brands are finding success by combining accessibility with just enough friction. One effective lever is time: 51% of global consumers consider a return window of 14 days or less to be reasonable. When positioned well, these types of limits can feel fair—especially when paired with fast, convenient return options and clear communication.

Localization matters as one size does not fit all 

Return behavior varies widely by region. In Europe, shorter return windows are more accepted: 57% of German consumers and 64% of French consumers believe 14 days or less is reasonable. In contrast, North American shoppers tend to expect longer windows and often treat the return period as an extension of the shopping process. 

Product category also influences return behavior across regions. In parts of Europe, it’s common for over 60% of apparel purchases to be refunded. Meanwhile, beauty and personal care products are more likely to be returned in North America, reflecting regional norms around trial and satisfaction guarantees. 

These patterns make clear that brands and retailers need region-specific policies and messaging. A policy that feels fair in one market may feel restrictive in another, and a uniform global return process risks alienating loyal shoppers. 

Turn returns into an advantage 

While return rates rise, margins are under pressure. Free shipping, free returns, restocking, and reverse logistics all eat into profit. But many of these costs are avoidable with the right tools and strategy. Our new report identifies key ways that returns are often a costly, but avoidable, downstream effect of poor upstream processes. With the right data and a proactive approach, retailers and brands can reduce return volume, recapture margin, and retain more loyal customers. 

The 2025 Global Returns & Profit Impact Report is a guide for doing exactly that. Download the full report to explore category-specific trends, country-level return insights, and steps you can take today to reduce costs and protect profit. 

Read the full report here. 

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