Fashion has always been about seduction, longing, belonging and differentiation. Aura, as people who are significantly younger than me say. We desire a look, a feeling, a style, a way of life. This desire is triggered by inspiring people – on social media, catwalks, in pop culture, advertising and among friends.
The senders of the desire have always been diverse. From glossy pictures to the cool guy in the bar last night wearing that crazy velor jacket. While the senders of the desire are different, the recipient was always the same: a human being.
It is precisely this matter-of-factness that is now disappearing. AI agents are increasingly becoming an additional authority between the brand and the purchase decision. Today, half of users already make a purchasing decision with the help of generative AI (Accenture study, 2025).
Thomas Knüwer is Chief Creative Officer (CCO) at Accenture Song, based in Hamburg. He has been developing brand communication at the interface of culture, technology and emotion for over 20 years – with the aim of creating relevant ideas that really reach people. Knüwer has worked for brands such as Netflix, Google, Aldi, Booking.com and Zalando. His projects have won over 140 national and international awards, including at festivals such as Cannes Lions and D&AD Awards.
Desire for algorithms
But that’s just the beginning. Today the artificial intelligence (AI) recommends, tomorrow it buys for us. Personal agents, trained to a person’s life, budget and tastes, become elementary gatekeepers of comprehensive purchasing decisions. This leads to a radical shift in communication: from AI as a channel to AI as a target group. But how do you create desire in someone who doesn’t feel desire?
For a long time it was crucial whether a look was desirable. But can a machine actually read, classify and recommend desire? Fashion thrives on hints, context, codes, references. Assistants, on the other hand, reduce ambiguity to structured comparability: color, price, material, occasion, silhouette, delivery time, reviews, return security.
Fashion brands learned SEO, then thought social first, then optimized performance. Now they have to learn how to become recommendable not only rationally but also emotionally in intelligent recommendation systems.
Agents don’t reward aura, they reward clarity. Platforms like Google’s Shopping Graph now process more than 50 billion product listings and structure fashion primarily through objective characteristics such as price, reviews, color options and availability. Those who remain semantically unclear may not become invisible, but they may become interchangeable. And therefore quickly irrelevant in the recommendation chain.
This shifts the focus: not only products, but the brands behind them must be machine-readable. Translated aesthetics so clear and structured that an assistant can not only find it, but also distinguish it from ten similar offerings.
Machine-readable marketing
This requires a rethink that goes far beyond marketing: brand management is becoming a data and structure discipline. The question is no longer just what a brand looks or sounds like, but how consistently it exists in machine-readable levels. Product data, image contexts, description logic and categorizations become part of the same brand architecture. What was once considered backstage becomes the brand’s visible stage.
This also shifts the organizational logic. Brand, content and e-commerce no longer work in separate worlds of inspiration and conversion, but in a common system of meaning and decision data. Inconsistency not only endangers brand perception, but also its recommendability.
And that is exactly where the new competition lies: no longer just for human attention, but for machine-readable consistency.
Fashion remains a deeply visual medium. But in the future, images will increasingly be evaluated by non-human eyes: as a search signal, match criterion, style reference.
Two target groups: humans and machines
Pinterest is continuing to expand visual search in women’s fashion because shopping often starts with a “vibe,” not with the right search term. And this is exactly where the break occurs: campaign images that only convey mood are too blurry for AI systems. On the other hand, images that make it machine-readable why a look is special are effective. Proportion, styling, movement, body proximity, occasion.
Anyone who does not proactively manage the AI perception of their own brand will become a carrot among the carrots. Maximum comparability, minimum distinctiveness. Rationally efficient but creatively destructive. An assistant can compare price and material. However, he can only recommend a brand if it creates a clearly identifiable difference. Handwriting, attitude, origin, craft, community, collaborations, cultural relevance. Everything that makes a product more than just a list of features.
So maybe the shift isn’t that big after all, the task remains essentially the same: Be a clearly defined brand. Only, and this is new, for two target groups: For the people who feel. And the machine that sorts. Anyone who only knows one of the two will be removed from the watch list.