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

The Fashion industry’s traditional tastemakers – the buyers who instinctively knew what consumers craved – are now increasingly relying on artificial intelligence to navigate a landscape of razor-thin margins and relentless demand. But it’s not a replacement for human judgment, according to industry experts.

Data-driven decisions: a new era for retail

For decades, Fashion buyers have operated with a remarkable ability to anticipate trends, a sixth sense honed by observing consumer behavior. Now, fueled by unprecedented access to data – encompassing search patterns, click-through rates, regional preferences, and global product performance – AI is fundamentally reshaping how assortments are built, refined, and scaled. Forget solely relying on past sales figures or gut feeling; buyers are accessing real-time signals about what shoppers are actively seeking, both domestically and internationally.

“AI is more of a tool that extends their reach,” explains Rich Shepherd, VP of product at Lyst. “The best buyers still lead with instinct – AI just gives them a clearer view of where that instinct might resonate most strongly.”

Tapestry’s data fabric: a centralized approach

Companies like Tapestry, the parent company of Coach, Kate Spade, and Stuart Weitzman, are investing heavily in centralized data repositories – what they call “proprietary data fabrics” – to unlock the potential of AI. Fabio Luzzi, Tapestry’s chief data and analytics officer, emphasizes the importance of seamless data sharing. “It makes the digitization of processes very easy, as well as the ability to use AI across multiple steps in the value chain,” he states. This allows for quicker identification of regional variations in demand, such as a sudden surge in a particular silhouette in the American Southwest, followed by a drop-off in the Northeast – a discrepancy that previously took weeks to surface through traditional sell-through reports.

Coach’s buying teams are already utilizing this real-time data to adjust allocations proactively, mitigating potential stock imbalances before products even hit stores. This shift represents a significant efficiency gain, freeing up valuable time for merchandisers to focus on more strategic initiatives.

Beyond the known: navigating cultural trends

However, the picture isn’t entirely automated. Farfetch’s chief technology officer, Luis Carvalho, notes that AI is particularly effective in analyzing trends that aren't rooted in historical data – those driven by cultural timing, editorial context, or early signals. “We believe in empowering our customers’ individual style, not dictating it,” he argues. Lyst, a Fashion aggregator, is leveraging AI to move beyond basic catalog ranking and offer more sophisticated style-level recommendations, anticipating what customers might desire even before they know it themselves.

Miyon Im, VP of product design and editorial at Lyst, highlights this evolution: “Before, merchandising was just about what the first six products you’d see in a feed were. But with AI, we can get more sophisticated – around styling, outfitting, or even event-based suggestions.”

Human intelligence remains paramount

Despite the growing influence of AI, experts caution that machine learning models are only as reliable as the data they’re trained on. Biases embedded in historical sales data – regarding sizing, representation, geography, and taste – can easily be perpetuated rather than corrected. Julie Gilhart, a former Barneys New York buying executive, contends that “the real magic comes from human intuition; the instinctive sense that data alone cannot replicate.”

Ultimately, the future of Fashion buying lies in a synergistic partnership between data-driven insights and human expertise. Brands that successfully balance these two elements will gain a decisive competitive advantage. The key isn’t to replace human judgment, but to augment it with the precision and scale of AI.