The Tamesis
Fashion buyers have long been the industry’s quiet tastemakers, able to detect desires before they form. But now, faced with tighter margins and pressure for accuracy, they are meeting those needs with the help of artificial intelligence.
With its ability to process large amounts of previously siled data—search behavior, click patterns, regional preferences, and product performance across markets—AI is quickly moving beyond simple sales forecasting. Buyers and sellers say they are now reshaping the way they build, refine and scale assortments as decision-making becomes more data-driven than ever.
Instead of relying solely on past sales or personal intuition, buyers can get real-time signals on what shoppers are searching for, clicking on, and saving around the world. “AI is more of a tool to expand reach,” said Rich Shepherd, vice president of product at Lyst. “The best buyers still go by instinct—AI just gives them a clearer understanding of where instinct might resonate most strongly.”
From luxury goods conglomerates to global e-commerce platforms, a new paradigm is emerging: AI-driven recommendation systems and pattern-presenting tools that analyze data, while human buyers interpret the insights and make strategic decisions. A 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 artificial intelligence behind the scenes to help buyers make smarter decisions about what to order, how much to stock and where to allocate inventory.
“We’ve always understood that to digitize our processes and scale quickly, we had to build the ability to easily host and share data across the enterprise,” said Fabio Luzzi, chief data and analytics officer at Tapestry. The company invested in a centralized data repository — what Luzzi calls a “proprietary data fabric” — that makes it easy to model data around customers, locations and supply chains. “It makes it very easy to digitize processes and enable the use of artificial intelligence in multiple steps of the value chain.”
Coach’s buying team is already using shared data sets to compare regional buying patterns in real time, adjusting depth and allocation before products go to market. These insights reveal demand earlier and more accurately than historical sales alone.
In fact, a team member might open a live shared dashboard that would show a specific outline of the U.S. Southwest being over-indexed and the Northeast underperforming—information that previously arrived weeks later via sales reports. This signal allows them to adjust allocations before committing inventory, rather than placing it in the wrong warehouse. Luzzi positions AI as an embedded decision support system across design, inventory and pricing that accelerates analysis and interactions while leaving final product and sales judgment to human teams. This frees up procurement and sales teams’ time, allowing them to focus on more strategic work, he said.
