Clothes Shoppers Show Limited Interest in AI Tools

Only 6% of U.S. clothing shoppers would use general AI tools like ChatGPT or Gemini to discover new apparel, and another 6% would use AI chat tools on retailer sites, according to new YouGov polling. The survey of U.S. adults who shop for clothing found that preferred discovery channels remain in-store browsing (60%), retailer websites or apps (46%), and recommendations from friends or family (40%). The data shows stronger consumer interest in practical AI features, such as stock checks (26%) and size recommendations (25%), over AI-driven discovery or styling suggestions.

Clothes Shoppers Show Limited Interest in AI Tools According to YouGov polling of U.S. adults who shop for clothing, AI-based discovery tools rank low: just 6% said they would use general AI such as ChatGPT or Gemini to discover new clothing, and 6% would use AI chat tools built into shopping sites. YouGov reports that preferred discovery channels remain in-store browsing 60% , retailer websites/apps 46% , and recommendations from friends or family 40% . On specific AI applications, YouGov finds 26% of clothes shoppers interested in AI for product availability or stock checks, 25% for size and fit recommendations, 21% for personalized product discovery, and 16% for styling or outfit suggestions. Editorial analysis: The data indicates consumer appetites favor practical, utility-focused AI features over AI-driven discovery or stylistic recommendations. What happened YouGov published new polling of U.S. adults who shop for clothing that measures receptiveness to AI across the shopping journey. Per YouGov, shoppers place low priority on AI for discovering new clothing: only 6% said they would use general AI tools like ChatGPT or Gemini for discovery, and 6% said they would use AI chat tools embedded in retailer sites or apps. Reported preferred discovery channels are in-store browsing 60% , retailer websites or apps 46% , and recommendations from friends or family 40% . What happened specific use cases YouGov also asked about discrete AI use cases. The most-cited interest areas were product availability or stock checks 26% , size and fit recommendations 25% , and product discovery based on preferences 21% . Fewer clothes shoppers expressed interest in AI-driven styling or outfit suggestions 16% . YouGov notes younger cohorts, specifically Gen Z and Millennials, show slightly higher interest in AI discovery than older cohorts, but even among younger shoppers AI discovery remains behind established channels. Editorial analysis - technical context Industry-pattern observations: Consumer surveys of AI in retail commonly show stronger uptake for pragmatic, transactional features than for creative or discovery uses. Practical features such as stock checks and fit guidance map to measurable friction points availability, sizing uncertainty , which often yield clearer conversion or support-cost benefits compared with broader recommendation systems that rely on trust and personalization. Industry context Industry observers often point out that adoption of AI features in consumer retail depends on perceived accuracy, privacy tradeoffs, and visible benefit. The YouGov numbers align with prior surveys where modest shares adopt new interaction modes until retailers demonstrate reliable outcomes or clear time savings. For practitioners, that pattern implies pilot programs that target verifiable utility metrics tend to produce clearer business cases than broad discovery experiments. What to watch What observers should monitor: A/B tests showing conversion lift from AI size and fit recommendations, privacy disclosures and consent rates for personalization, customer support volumes after introducing AI stock-check tools, and adoption metrics among Gen Z and Millennial cohorts. Also watch whether retailers report measurable ROI from in-store AI concierges versus online chat assistants. Scoring Rationale The YouGov polling provides useful, timely consumer-sentiment data for practitioners evaluating AI pilots in apparel retail. The findings are practical rather than transformational, informing product prioritization more than indicating a broad consumer push to adopt AI discovery. Practice with real Ad Tech data 90 SQL & Python problems · 15 industry datasets Active Search Campaigns by BudgetEasy /problems/sql/active-search-campaigns-by-budget High CPC Clicks & Poor Landing PagesMedium /problems/sql/high-cpc-clicks-poor-landing-page Campaign ROAS by Attribution ModelHard /problems/sql/campaign-roas-by-attribution-model 250 free problems · No credit card See all Ad Tech problems /problems/datasets/adtech