Ten years’ worth of runway fashion images and look books from specific designers were analyzed and distilled, along with real-time social-media buzz from Twitter, Instagram and Pinterest. He accelerated the information gathering phase of his designs by 600% compared to his previous season. For years, JASONGRECH’s designs were typically fashioned in dark color schemes. But when using Watson’s visual-recognition technology to identify and categorize consumers’ opinions on colors, patterns, fabrics, and more, JASONGRECH predicted the industry’s shifting preference towards pastels. He incorporated these colors into his designs—an element he would not have considered before—and it became a focal point of his line. The information gathering and analysis process took about four days, as opposed to the 28 days typically required via manual efforts. And, more importantly, sales spiked significantly.

In another example, Indian designer duo Falguni & Shane Peacock worked with Watson to inspire the design of their brand’s new collection, which is based on the history of Bollywood fashion. They analyzed 600,000 images spanning London, Paris, Milan, and New York fashion weeks for insights into high-end couture and coupled it with 8,000 Bollywood images from social-media sites and images from Bollywood posters from the past four decades. This data combined with an analysis of 100,000 print swatches allowed them to generate novel prints with the trendiest colors of the season. The result? They created three unique dresses based solely on Watson’s insights that became signature designs of their latest fashion collection.

Cognitive technologies don’t replace the creative process; they are proving to enhance and accelerate it. While trends fade, new creative ideas inspired by unique combinations of those trends are timeless.

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