Ai’s quiet takeover: how retail buyers are trading instinct for data

Fashion buyers, traditionally the industry’s silent tastemakers, are increasingly relying on artificial intelligence to predict trends and optimize assortments – a shift poised to reshape the entire retail landscape.

The data-driven revolution

For years, these buyers have possessed an uncanny ability to sense what consumers will desire before it even manifests. Now, facing shrinking margins and the demands of precise forecasting, they’re embracing AI, leveraging vast troves of data – from search behavior and click patterns to regional preferences and market performance – to build, refine, and scale their product selections. It’s about moving beyond guesswork and embracing a truly data-led approach.

From intuition to insights

AI isn’t simply predicting sales; it’s fundamentally altering how buyers operate. Systems like Tapestry’s “proprietary data fabric” – a centralized repository of customer, location, and supply chain information – are providing real-time demand signals, allowing teams to adjust inventory allocation and buying strategies with unprecedented speed and accuracy. Previously, insights arrived weeks after the fact, relying solely on historical sell-through. Now, a simple dashboard can reveal that a particular silhouette is surging in the Southwest US while lagging in the Northeast – information that directly informs immediate action.

Balancing the algorithm and the eye

While AI offers undeniable power, experts caution that human intuition remains paramount. As Julie Gilhart, a former Barneys New York buying executive, puts it: “The best buyers still lead with instinct – AI just gives them a clearer view of where that instinct might resonate most strongly.” It’s not about replacing the human element, but augmenting it.

Personalized pathways, not dictated trends

Farfetch, for instance, is moving beyond simple catalog ranking to offer style-level recommendations, tailoring product suggestions to individual shoppers’ tastes and preferences. Lyst’s merchandisers are now receiving “data briefs” highlighting emerging trends – a sudden spike in a particular color or texture – and interrogating the context behind the data before incorporating it into merchandising strategies. The McQueen skull scarf resurgence, triggered by celebrity sightings, exemplifies how AI can surface previously unnoticed signals.

The new merchandiser: data translator

This shift is creating a new critical role: the merchandiser who can translate analytical signals into creative strategies. Companies like Coach are automating routine replenishment processes, freeing up buying teams to focus on the more complex questions – what the customer doesn’t know they want – and leveraging their judgment to guide product development. The time saved is significant, redirecting resources toward higher-stakes decisions.

A measured advance

However, the promise of AI isn’t without limitations. Machine learning models are only as reliable as the data they’re trained on, and inherent biases in historical sales data – relating to sizing, representation, and geographic preferences – can be inadvertently amplified. Ultimately, brands that successfully integrate AI must prioritize human judgment and creativity, using technology to enhance, not replace, the art of Fashion.