Fashion buyers have long been the industry’s quiet trendsetters—the ones who can spot a desire before it even takes shape. But now, with tighter profit margins and the need for pinpoint accuracy, they’re turning to AI to meet these challenges.
By processing huge amounts of data that used to be locked away in separate systems—like search behavior, click patterns, regional preferences, and product performance across different markets—AI is quickly moving beyond just forecasting sales. Buyers and merchandisers say it’s now changing how they build, fine-tune, and scale their product selections, as decisions become more data-driven than ever.
Instead of relying only on past sales or gut feelings, buyers can tap into real-time signals about what shoppers are searching for, clicking on, and saving around the world. “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 picture of where that instinct might work best.”
From luxury brands to global e-commerce platforms, a new approach is taking shape: AI-powered recommendation systems and tools that spot patterns in data, while human buyers interpret those insights and make strategic calls. Balancing the two is becoming a key competitive edge.
Real-time demand insights
Tapestry, the parent company of Coach, Kate Spade, and Stuart Weitzman, uses AI behind the scenes to help buyers make smarter decisions about what to order, how much to stock, and where to send inventory.
“We always knew that to digitize this process and scale quickly, we needed to build a way to easily share data across the business,” says Fabio Luzzi, chief data and analytics officer at Tapestry. The company invested in a central data hub—what Luzzi calls its “proprietary data fabric”—which makes it simple to model data around customers, locations, and supply chains. “It makes digitizing processes easy, as well as using AI across many steps in the value chain.”
“The best buyers still lead with instinct—AI just gives them a clearer picture of where that instinct might work best.”
Coach’s buying teams are already using shared data sets to compare regional buying patterns in real time, adjusting how much they order and where to send products before they hit stores. These insights reveal demand earlier and more accurately than just looking at past sales.
In practical terms, a team member might open a live, shared dashboard that shows a certain style is selling well in the southwestern US but not in the northeast—information that used to arrive weeks later through sales reports. That signal lets them adjust where stock goes before it’s locked in, instead of having it sit in the wrong warehouse. Luzzi sees AI as a built-in decision-support system for design, inventory, and pricing, speeding up analysis and interaction while leaving final product and merchandising choices to human teams. He says this frees up time for buying and merchandising teams to focus on more strategic work.
At Coach, core leather goods and seasonal basics—categories with years of sales data behind them—are increasingly handled through automated restocking models. The system flags when to reorder, adjusts quantities by store, and moves stock between regions without manual input. The time saved is significant: merchants who used to spend most of the buying cycle managing the predictable parts of their assortment can now focus on categories where data offers less certainty and human judgment matters more.
More time for merchandising and trends
For trend-driven or new items, it’s a different story. Their sales depend on cultural timing, media buzz, and early signals that historical data alone can’t yet predict.In practice, this means buyers are spending less time making decisions about familiar products and more time tackling the tougher question of what customers don’t yet know they want — the part of the job that requires taste, not just analysis, according to Farfetch chief technology officer Luis Carvalho.
“We believe in empowering our customers’ individual style, not dictating it,” Carvalho says. Farfetch’s personalization engine refines what shoppers see based on style signals rather than just popularity. “Advances in AI — from data processing to predictive modeling — help us navigate huge amounts of information and connect each customer to the most personalized products across our network.”
These advances include AI’s ability to process billions of signals — like search behavior, click patterns, product metadata, and regional buying differences — at a speed that human teams can’t match. As AI capabilities have grown, fashion aggregator marketplace Lyst has moved from broad catalog rankings to style-level recommendations, matching products to individual shoppers based on taste, price sensitivity, and occasion.
The McQueen skull scarf has recently made a comeback in fashion trends, following several celebrity sightings and Charli XCX wearing one during her 2025 Glastonbury performance.
Photo: Shoot Digital for Style.com/ Getty Images
“Before, merchandising was just about deciding the first six products you’d see in a feed,” says Miyon Im, VP of product design and editorial at Lyst. “But with AI, we can get more sophisticated — around styling, outfits, or even event-based suggestions. If we can use AI to create something like an office party recommendation, where every piece feels right because we understand your taste, preferences, and price sensitivity, that’s the future.”
In practice, this means Lyst’s merchandisers receive regular data briefs, including items that are gaining unusual traction in searches or saves. They then examine these for fashion context before making any recommendation. When data shows spikes in certain colors or textures, it’s not immediately published as a trend. A human has to ask why: was it a runway moment, a celebrity sighting, or a cultural reference? Only then is it added to the merchandising.
Balancing data with intuition
Executives say that, for now, AI’s potential comes with structural limits. Machine learning models are only as reliable as the data they’re trained on, and fashion’s historical biases — in sizing, representation, geography, and taste hierarchies — can easily be reinforced rather than corrected. If past sales were skewed toward narrow size ranges or specific demographics, those exclusions don’t disappear in machine learning models — they scale up. Experts say AI still can’t replace the cultural intelligence, intuition, and storytelling instincts that shape fashion.
“AI is here, and it’s an incredible tool to enhance your work,” says Julie Gilhart, a fashion consultant who spent 18 years overseeing buying decisions at Barneys New York. “But the real magic comes from human intuition — the instinctive sense that data alone can’t replicate. The brands that get it right will let creativity lead, with AI enhancing the vision rather than replacing the human touch.”
As brands adopt more data-driven tools, Gilhart says a new role is emerging: merchandisers who can turn analytical signals into creative strategies. “You have to be curious,” Shepherd says. “You don’t need a computer science background, but you need to understand how the technology works to solve problems for users and partners.”
Frequently Asked Questions
Here is a list of FAQs about how fashion buyers and merchandisers are adjusting to the age of AI
BeginnerLevel Questions
1 What exactly is AI doing in fashion buying and merchandising
AI is analyzing huge amounts of datalike past sales social media trends and weather forecaststo predict what customers will want to buy It helps decide how much stock to order which styles to push and when to put items on sale
2 Is AI going to replace fashion buyers and merchandisers
No AI is a tool to make their jobs easier not to replace them It handles repetitive numbercrunching and patternspotting freeing up buyers and merchandisers to focus on creative decisions negotiating with suppliers and building brand stories
3 How does AI help with predicting fashion trends
AI scans millions of images from social media runway shows and street style photos It identifies emerging colors silhouettes and patterns much faster than a human team could giving buyers a head start on trend forecasting
4 Whats the main benefit of using AI for a merchandiser
The biggest benefit is accuracy AI can forecast demand for specific sizes colors and stores with much less waste This means fewer markdowns on unsold clothes and fewer out of stock notices on popular items
5 Do I need to be a tech expert to work with AI as a buyer
Not at all Most AI tools are designed with userfriendly dashboards You need to understand the questions to ask the AI not how to code it The key skill is learning to interpret the AIs recommendations and trust them based on your market knowledge
AdvancedLevel Questions
6 How is AI changing the traditional buying calendar
AI enables agile buying Instead of placing all orders months in advance buyers can now use AI to test small batches in realtime then reorder bestsellers instantly based on live sales data This shifts the calendar from rigid seasonal drops to a continuous responsive flow of new products
7 Can AI help with sustainability goals in merchandising
Yes significantly AI optimizes inventory levels to reduce overproductionthe fashion industrys biggest waste problem It can also predict which materials will have