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Joor Integrates Proprietary AI Tool into Shoppable Fall ‘26 Women’s Trend Report—Empowering Retailers to Buy into Key Seasonal Fashion Trends Like Never Before

Retail
Credits: JOOR
PRESS RELEASE
By Press Club

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Following the FW26 designer womenswear collections shown in New York, London, Milan and Paris, JOOR identifies six must-have fashion trends and matches these to shoppable styles curated from the millions of products on the JOOR platform using a new exclusive JOOR-developed AI tool.

As the fashion industry’s leading wholesale platform, JOOR leverages both its unique industry expertise and data-driven insights to define the key fashion trend statements as seen on the runways of the world’s premier fashion weeks. This human curation process is then complemented by the power of technology through JOOR’s new custom-built AI tool, designed to identify commercially available styles reflective of these important seasonal trends.

JOOR’s AI-powered tool leverages a proprietary algorithm to visually search millions of products on the JOOR platform and identify those that best embody the trends. Unlike traditional large language models (LLMs) which rely on text-led product descriptions, JOOR’s application digests and interprets embedded visual information using a fashion-specific vision-language model. It analyzes multiple images of each product to ensure it is surfacing the most accurate trend-right styles. Once identified, the relevant products are then curated into the On Trend: Women’s Fall 2026 digital shopping experience on JOOR, making it easy for retailers looking to buy into the hottest FW26 fashion trends.

The six main FW26 trends outlined in JOOR’s Fall 2026 Trend Report are:

  • Nouveau Tuxedo: classic black tie dressing is reimagined with new proportions and silhouettes
  • Moody Maroon: shades of deep burgundy, oxblood, and maroon saturated runways
  • Cocooned Confidence: elongated oval sleeves and bubble skirts create cocoon-like looks
  • Intarsia Knits: multicolored argyle, jacquard, and geometric patterns portray an eclectic take on cold-weather dressing
  • Lean Tailoring: sharply cut separates with an ultra slim fit usher in a new era in tailoring
  • Leather Bombers: flight jackets and classic bomber styles emerge as a cornerstone piece this season

Published bi-annually, JOOR’s Fashion Trend Report and correlating shoppable On Trend digital experience have become an indispensable guide and buying resource for JOOR’s community of over 675,000 retail buyers from the world’s leading fashion stores. The Fall 2026 edition offers unique trend insights from JOOR’s clients – including Khaite, Dries van Noten, Sacai, and Chloé, among others – matched with relevant shoppable styles from JOOR’s wider ecosystem of fashion brands.

Amanda McCormick Bacal, SVP of Marketing at JOOR, commented, “With our deep industry expertise and vast network of luxury fashion clients, JOOR has the unique ability to identify key seasonal trends and enable buyers to incorporate these trends into their assortments. Our new proprietary AI tool optimizes this process and ensures we are surfacing the best options for our global community of retail buyers. By combining human trend analysis with AI’s ability to accurately review millions of styles, JOOR’s On Trend: Women’s Fall 2026 digital event is a powerful resource to facilitate trend-right product discovery.”

Debuting on JOOR Passport on March 18th, the On Trend: Women’s Fall 2026 shoppable experience enables buyers to directly shop these prominent fashion trends from the collections of established power brands as well as upcoming new talent featured on JOOR.

JOOR Passport is a centralized digital destination facilitating curated brand discovery and providing global access to the world’s finest fashion trade shows and market events. Since its launch in 2020, JOOR Passport has hosted over 110 events, attracting more than 500,000 retail visitors across 174 countries. Over 11,300 brands have participated in JOOR Passport events, with nearly 1 million products sold through the platform.

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