Zara has overtaken Nike as the world's most valuable fashion brand, according to Kantar's BrandZ 2026 ranking. The Inditex-owned label reached a brand value of $44.1 million, up 18% year-on-year, with Kantar analysts calling it "a clear example" of how brands can build relevance through AI-driven personalised shopping experiences.
The ranking reflects a broader shift in how brands create value. Zara launched a virtual fitting room with generative AI in 2024, allowing app users to create personalised avatars to preview clothes before buying. Kantar's report highlights an evolving market driven by increased AI adoption, with the technology moving beyond operational efficiency into customer experience.
Personalisation and the trust gap
Shoppers increasingly expect brands to understand their preferences. Research from Vercel and the World Retail Congress found 20% of UK consumers now use AI search platforms such as ChatGPT and Google Gemini to start their shopping journey.
But AI is not a guaranteed conversion tool. Although 87% of UK shoppers think AI recommendations are useful, just 55% have completed a purchase based on one. That gap suggests AI's influence on product discovery is significant, but trust remains crucial to turning recommendations into sales.
Targeted offers remain the most valued form of personalisation. As AI scales personalisation, restraint and relevance become more important - automated recommendations must stay human and non-intrusive.
From targeted ads to product discovery
AI's impact extends beyond classic targeted advertising into personalised product discovery. Traditionally, brands served ads based on past behaviour. Now AI helps shoppers identify products that match their needs and intent, making the technology part of the customer experience rather than just another ad delivery channel.
That shift creates a new risk: AI invisibility. Products need to be understood by AI systems so they can be reliably recommended. Brand awareness, advertising, and packaging have long been the main levers for influencing shoppers, but in the AI world, product information must be clear and consistent so machines can interpret it correctly.
Google has published guidelines for merchants to structure product information for AI-driven surfaces, including its AI Mode in Search. Its "product_detail" attribute gives AI systems structured information about products, helping match them to relevant customer queries. In May 2026, Google also announced AI Performance Insights for Merchant Centre, showing brands how their products appear across AI platforms including AI Mode, AI Overviews, and Gemini.
First-party data and the cookiepocalypse
As third-party cookies disappear, retailers' first-party data becomes increasingly valuable. Retailers collect purchase history, browsing behaviour, and product preferences - but much of that data sits disorganised and unused.
Loyalty programmes, personalisation, and retail media can turn that data into value. Loyalty schemes encourage repeat purchases while generating more first-party data, which can then inform personalised offers and targeted ads. The key is for these systems to operate together, connecting marketing, merchandising, and AI to improve retail media targeting and revenue.
For marketers looking to build these skills systematically, an AI Learning Path for Marketing Managers covers campaign optimisation and digital marketing fundamentals. Broader AI for Marketing resources explore how AI reshapes customer engagement.
Why this matters for marketers
Zara's rise shows AI personalisation can drive brand value, but the challenges are clear: trust, visibility, and data quality. Marketers need to connect AI personalisation to a wider customer strategy rather than treating it as a standalone tool. Structured product information makes products discoverable by AI systems, while first-party data helps brands understand preferences and improve recommendations.
The practical takeaway: audit your product data structure and loyalty programme now. If AI systems cannot interpret your product information, your brand risks becoming invisible at the moment of purchase. And if your personalisation efforts outpace customer trust, recommendations will not convert. Both elements - structured data and genuine relevance - need to work together for AI to become a true brand-building channel.
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