Artificial Intelligence promises a major advancement for the fashion industry. The industry is one of the biggest in the world. With its 3 trillion dollar evaluation, the industry occupies nearly 2% of the global economy output. Additionally, the competition for the next-big-thing in the fashion industry is heating up. Amazon acquired Zappos, a leading brand for $1 bn in 2010. Along with this acquisition, and new openings by Walmart, Target and Other major retailers, fashion industry players require innovation to gain a competitive edge. Moreover, AI promises to bring transformation to fashion industry in manufacturing, designing, sales, and consumer-use as well. AI technology today promises to build higher quality products, and also help minimize errors in prediction of next-big fashion trends. The rising investment in the field is expected to drive significant growth for the AI in fashion market in the near future.
Global AI in Fashion Market: Notable Developments
In 2018, the global fashion industry produced over $150 bn dollar garments. Moreover, out of these, $50 bn went unsold. Additionally, $50 bn were sold through discounts. For the very reason of being able to predict user-preferences, and the next-big-fashion trend, major brands like H&M to Tommy Hilfiger have invested in AI technologies.
The major Chinese wholesaler AliBaba also opened a fashion AI store in 2018 with integration of various technologies like intelligent garment tags, smart mirrors, and omnichannel integration. The rising costs of unsold stocks, the unpredictable nature of consumer tastes, and growing investment by major brands are key drivers on the horizon.
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University of Texas partnered with researchers from Cornell Tech and Georgia Tech to come with an Artificial Intelligence systems which can offer quick suggestions to users to make their appearances more fashionable. The App was trained using more than 10,000 images and according to user reports, the app provides trendy and easy choices.
The app is known to face internal pressures as data sets can be biased to reflect specific traits. For example, one of the major concern regarding this app it tends to reflect fashion apparels more common in North America, where internet use has been widespread since the 1990s. Additionally, body shapes of specific origins can also be more favored in the long run.