India is a country of fashion-loving millennials who love to wear the latest colors and styles. To capitalize on this strong market, however, fast fashion retailers such as my company, Bestseller India, have several challenges to overcome.

The first is common to all retailers: how to offer consumers the right merchandise in the right store at the right time. But we also face a challenge unique to India, a country so large and diverse it’s more like a continent.

India’s many states and hundreds of religions, cultures and dialects within our cities mean that consumer tastes in color, styles, dressing up and what to wear on different occasions can change every few kilometers. Our goal is to offer consumers the fashions they want at affordable prices in stores nearest them. To do so, we must crack the code of this complex demand so we can tailor our products to fulfill consumer needs.

That’s why we’re working with IBM to transform Bestseller into a cognitive enterprise. Together, we’re developing an IBM Watson AI engine to help us predict the next big trend and the most relevant styles, colors and size ratios. Higher relevancy means a sharper, better selling assortment, helping us meet consumer expectations while becoming more efficient.

A common problem for fashion retailers

High-tech solutions aren’t yet mainstream in the world of fashion, but we believe they should be. We first became convinced of Watson’s power after a visit to the IBM Labs in Bangalore. Bearing data about our brands, we presented a problem to solve: of two similar products, why did one sell and the other didn’t? IBM developed a proof-of-concept solution that gave us the answer. Clearly, AI could help our company succeed.

AI has applications throughout the retail business, from the customer experience to omnichannel marketing to the supply chain to product design and assortments. At present, we’re focused on empowering our planners and buyers who, for years, made product decisions from the gut. AI can deliver insights that far surpass the capabilities of human instinct, the mind and typical spreadsheet analysis.

With Watson mining deeply into big data, we can forecast which products will sell today and tomorrow across the great expanse of India. Then, when consumers walk into a store, they’ll be more likely to find what they’re looking for. And operationally, as we produce more hits and fewer misses, we can reduce our assortment size and increase profits.

Use cases to overcome culture shock

As promising as AI is for fashion retailers, it’s not all clear sailing. There’s concern about lack of demographic data for AI to leverage. In some parts of the world, data is available that can predict the fabrics people prefer across different markets. Such data is scarce in India today.

Still, here’s a key idea I’d like to get across: don’t make the mistake of believing that yesterday’s business model and technology will survive tomorrow. Consumers are changing faster than we think and we need to change with them.

AI is an essential tool, but the even best technology won’t help if you don’t prepare employees to accept it. As they say, the proof of the pudding is in the eating. People may resist AI unless you develop use cases that show positive results.

At Bestseller India, our first Watson project is a virtual assistant that can answer employee questions. Its usefulness has made it quite popular. People become enthusiastic when they see how AI can improve their job performance, help support colleagues and make a difference in their work lives.

Watch Vineet Gautam discuss AI’s promise in fashion retailing:

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