How Fashion Brands Get AI Visibility Right

Fashion brands are increasingly investing in AI visibility, but the results of these investments are uncertain. According to Adobe Analytics, 41 percent of US consumers utilized generative AI shopping in June 2026, and AI-referred visitors generated 40 percent more revenue per visit compared to traditional sources. Unlike search engines, AI assistants rely on a mix of learned representations and, in some instances, live web searches to recommend and recall brands.

This can result in brands being well-known to AI models yet seldom recommended, appearing frequently but for the wrong products or occasions, or consistently featured in AI responses yet placed behind competitors. To dissect these discrepancies, BoF Insights analyzed 120 luxury, high street, contemporary, and sportswear/outdoor brands using ChatGPT, Claude, and Gemini between January and July 2026.

The study, based on BoF Insights Brand Pulse, a proprietary fashion and luxury brand-tracking tool, evaluates AI's performance by measuring three aspects: Explore Brand Pulse. While more brands are being recommended, the same names consistently dominate AI visibility. Across the brands assessed, 78 percent increased their recommendation frequency between January and July 2026; however, the most frequently recommended brands remained relatively unchanged.

This suggests that increased recommendation frequency does not guarantee competitive advantage. Performance must be assessed against relevant peers and within the prompts that matter most to the business. Not a single brand led in all aspects of AI performance. For instance, Zegna excelled in understanding, Burberry had the highest recommendation frequency, and Chanel achieved the best average placement.

The same pattern persisted across other segments, with distinct brands leading in understanding, recommendation, and placement. Being chosen does not equate to being understood. Brands that AI recommends more frequently also tend to rank higher when recommended. Among the segments analyzed, this relationship is strongest in luxury, where AI assistants repeatedly return to a consistent order of brands.

Understanding scores, however, operate independently. Across all 120 brands assessed, there's little to no correlation between recommendation frequency and how well AI understands a brand. This highlights an essential point: a narrowly defined brand may only cater to a limited set of needs, restricting its reach. A frequently recommended brand with generic or inconsistent associations may appear widely but could be recommended for inappropriate products, audiences, or occasions.

Neither strength is sufficient in isolation. When comparing fashion brands' understanding scores with recommendation frequency in AI, four competitive positions emerge: Each position requires a distinct response. Established brands must protect and expand their lead, while brands chosen without clarity need to enhance the associations shaping their representation.

Clear but overlooked brands must broaden their relevance across more prompts. Unplaced brands must first establish a clearer foundation before aiming for greater reach. The full BoF Insights report, "How Fashion Brands Get AI Visibility Right," details the findings and recommended actions for each competitive position in the AI visibility matrix, and BoF Insights Brand Pulse offers the opportunity to benchmark how AI represents and recommends your brand against its competitors.