How fashion buyers and merchants are embracing the age of AI
Fashion buyers have long served as the industry’s quiet tastemakers, the people who sense a desire before it becomes a thing. But now, facing the pressure of low margins and accuracy, they are meeting these demands with the help of AI.
With the ability to process vast amounts of previously secret data – search behavior, click patterns, regional preferences and product performance across markets – AI is rapidly moving beyond simple sales forecasting. Buyers and merchants say they are now reshaping the way they create, refine and scale their assortment, as decisions become more data-driven than ever.
Instead of relying solely on past sell-through or personal intuition, buyers can access real-time signals about what shoppers are looking for, clicking on, and saving globally. “AI is a tool that expands their reach,” says Rich Shepherd, vice president of product at Lyst. “The best buyers still move with instinct – AI just gives them a clearer view of where that instinct might resonate most strongly.”
From luxury groups to global e-commerce platforms, a new model is emerging: AI-powered recommendation systems and pattern-surfacing tools that analyze data, while human buyers interpret those insights and make strategic decisions. The balance between the two is becoming a competitive advantage.
Real-time demand insights
Tapestry, the parent company of Coach, Kate Spade and Stuart Weitzman, uses AI behind the scenes to help shoppers make better decisions about what to order, how much to stock, and where to allocate inventory.
“We always understood that to digitalize this process and scale rapidly, we needed to create the ability to easily host and share data across the business,” says Fabio Luzzi, chief data and analytics officer at Tapestry. The company invested in building a centralized data repository—what Luzzi calls its “proprietary data fabric”—that makes it easier to model data around customers, locations, and supply chains. “This makes the digitalization of processes much easier, as well as the ability to use AI at multiple stages in the value chain.”
Coach’s buying teams are already using shared data sets to compare regional purchasing patterns in real time, adjusting depth and allocation before products hit stores. These insights reveal demand with greater accuracy than historical sell-offs alone.
In practice, a team member could open a live, shared dashboard that would show a particular silhouette over-indexing in the southwest US, while underperforming in the northeast – information that had previously come via a sell-through report weeks later. This signal allows them to adjust the allocation before the stock is committed rather than placing the stock in the wrong warehouse. Luzzi positions AI as an embedded decision-support system in design, inventory, and pricing, accelerating analysis and interactions while leaving the final product and business decisions to human teams. He says this is freeing up buying and selling teams time so they can focus on more strategic work.
