Fashion buyers have long acted as the industry’s quiet tastemakers, the people who can sense desire before it’s formed. But now, facing tighter margins and the pressure of precision, they’re meeting these demands with the help of AI.
With the ability to process vast amounts of previously siloed data — search behavior, click patterns, regional preferences, and product performance across markets — AI is rapidly moving beyond simple sales forecasting. Buyers and merchandisers say it’s now reshaping how they build, refine, and scale assortments, as decisions become more data-led than ever.
Instead of relying solely on past sell-through or personal intuition, buyers can access real-time signals about what shoppers are searching for, clicking on and saving globally. “AI is more of a tool that extends their reach,” says 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.”
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, parent company of Coach, Kate Spade, and Stuart Weitzman, uses AI behind the scenes, helping buyers to make smarter decisions about what to order, how much to stock, and where to allocate inventory.
“We always understood that to digitalize this process and scale fast, we had to build a capability to host and share data easily 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” — which makes it easy to model data around customers, locations, and supply chains. “It makes the digitization of processes very easy, as well as the ability to use AI across multiple steps in the value chain.”
Coach’s buying teams are already using shared data sets to compare regional buying patterns in real time, adjusting depth and allocation before products hit stores. These insights reveal demand earlier, with more precision than historical sell-through alone.
In practical terms, a member of the team might open a live, shared dashboard, which will show a particular silhouette over-indexing in the southwest US while underperforming in the northeast — information that previously arrived weeks later via sell-through reports. That signal allows them to adjust the allocation before stock is committed, rather than having it sit in the wrong warehouse. Luzzi positions AI as an embedded decision-support system across design, inventory, and pricing, accelerating analysis and interaction while leaving final product and merchandising judgments with human teams. He says this is freeing up buying and merchandising teams’ time so they can focus on more strategic work.
