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In a May 2023 test, AI shopping assistants could understand conversational requests, but reviewers often disliked the products they recommended. The experience showed why a chatbot’s ability to talk about an occasion is not the same as finding something a shopper would actually buy.
What the test found
Business of Fashion’s May 10, 2023, article, “Putting AI Shopping Assistants to the Test,” compared assistants from Shopify, Kering/KNXT, Mercari and ShopWith AI. Staffers gave different bots the same prompts and asked varied questions to get an overall sense of their abilities. Their requests included outfits for a boat party in Europe, a graduation ceremony in Chicago and a cricket match near London.
The assistants generally understood the prompts and replied coherently. The sticking point was product relevance: reviewers were often underwhelmed by what the bots surfaced. The article’s evidence is qualitative; it reports reviewer reactions and examples, not a success rate or formal performance score. Read the Business of Fashion report.
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Occasion and style did not always line up
For a summer boat-party request, one tester found Shopify’s suggestions somewhat more curated and sent a white button-front maxi dress to a friend. KNXT’s Madeline, by contrast, suggested Balenciaga looks that one reviewer considered unsuitable for the occasion. In another search for Bottega Veneta menswear, the reviewer still received Balenciaga suggestions.
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Graduation-dress results also exposed a trade-off between style and usability. One reviewer preferred Madeline’s style suggestions but found its interface clunky. Shopify was quick and easy to use, but the reviewer disliked several of its options.
Location and weather could help
For a cricket match near London, Shopify suggested a raincoat. Two testers said the assistant appeared to account for weather. That was a useful detail in a specific prompt, not proof that its recommendations consistently handled local context.
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More questions were not always better
Mercari’s Merchat drew criticism for recommendations that reviewers found insufficiently curated. One tester appreciated its attempt to gather context about a shopper who was 6 feet 3 inches tall; another felt it asked too many questions. Personalization can make suggestions more relevant, but repeated follow-ups add friction if they do not lead to better choices.
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A persona helped in one case
One reviewer was unimpressed with ShopWith AI’s graduation-dress options, while its Lenny Kravitz persona performed better for a concert-outfit prompt. A result that worked for one persona and occasion did not establish that the assistant could recommend well across categories.
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Why fluent conversation was not enough
A chat interface lets shoppers describe a situation in ordinary language and ask for suggestions rather than relying only on search terms. But a coherent reply can still point to the wrong products. Reviewers also found that some responses sounded like marketing copy, which did not solve the central problem: whether the items matched the request.
Catalogue fit matters, too. If a retailer does not carry products suited to a shopper’s constraints, an assistant may have little useful to offer. That leaves the shopper unsure whether the prompt needs refining or the available selection is the limitation. ShopWith AI co-founder Sarah Stark described its brand selection as curated: “It’s still a very curated approach. It’s not the mainstream brands you’re getting on the first three pages of your Google search results.” Bain reported that statement in the 2023 article.
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What to look for when judging a shopping assistant
The test suggests practical questions to ask when trying any shopping assistant. These are useful evaluation dimensions, not a scoring system used by Business of Fashion.
- Product relevance: Do the items fit the stated occasion, style and constraints, rather than merely sharing a broad category?
- Fit and personalization: Does the assistant use relevant details, such as height, without asking for information that does not improve its suggestions?
- Catalogue suitability: Does the retailer offer a plausible range of items for the request? A weak selection can limit results regardless of how well the assistant understands the prompt.
- Local context: Does it take account of the location or conditions that matter to the request?
- Useful follow-up: Do questions clarify a real ambiguity, or do they make shopping feel like work?
- Path to purchase: Does the interface make it easy to inspect and act on a suitable recommendation?
How to read the result today
The article is a historical snapshot of a qualitative test published on May 10, 2023—not a current review of these services. It does not establish whether Shop.ai, Madeline, Merchat or ShopWith AI remain available, or what their present capabilities are. Its lasting lesson is narrower: conversational fluency is not evidence of strong recommendations. The reviewer response captured in the article was: “Can’t say I’m convinced.”
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