The rise of the Silicon Valley has given birth to various technological game-changers that have essentially become a part of our day-to-day activities. Be it social media or cab-hailing apps, technology has eased virtually everything for humans. From the consumers’ end, you get several options and tools to choose from. On the business side of it, people know what to offer to these consumers.

Fashion has certainly seen a makeover and the days of guessing seem to be long gone. Artificial intelligence (AI) has provided all sorts of tools and data to make a retail brand successful. You don’t have to guess if a certain style will be popular or not, AI can do that for you. Be it trend forecasting, sales predictions or improving the efficiency of fabric cutting, AI can serve all the outputs on a silver platter for the buyers to make informed decisions for their customers without spending on blind risks and waste buffers.

Sure, there are some key advantages when adopting AI-based solutions for a fashion brand. The already saturated fashion market is filled with competitors that are hungry for customers and to stand out in the crowd, any business advantage is worth investing in. This is why many brands have invested heavily in AI-based planning technology that predicts product success rate without actually pushing the product into the market.

AI is able to do so because of two reasons. It runs on machine learning, and for our case, let us consider that the AI has been successfully trained to predict bestsellers for a brand. It is able to do so from two sources of data: historical and external. Historical data enables AI to find trends and patterns in the success or failure of products from past seasons. External data is obtained from sources which are outside of a brand’s control. Be it finding trends in the catalogue of a particular e-commerce portal or the frequency of restocking of a particular style, depending on requirements, AI can now successfully predict which styles will fail or which ones succeed.

However, just like with any other piece of technology, AI can compute transactional data and not qualitative or comprehensive data. Let’s extend the case of predicting successful styles for a few years or even a decade. As the AI relies on historical data, as we proceed with more seasons, the entire collection may look like a cousin of all bestsellers from the past collections. This is in no way bad because according to the AI, the brand will make money from the collection which satisfies the entire purpose of AI. However, once this point is reached, the collections will no longer see new designs. Repeat that with every brand present in the market and there is a handful of designs available for a customer to purchase. Doesn’t sound interesting, does it?

The reason why humans are hooked on to things like consumer technology, or even fashion for that matter, is because as humans we like innovation. We are biologically trained to find innovative solutions over traditional problems to be more attractive than the generic ones, and that includes mundane tasks like finding the right clothes to wear. If it weren’t for different demographics with different tastes, everyone in the world would wear a black tuxedo to a red carpet or a white T-shirt with blue denim on a casual outing. Such a condition would be equivalent to the concept of uniforms in a school and we all know how desperately everyone wished to wear informal clothes. Personal expression binds us to the choices of clothing that we pick from a retail store which is why a store cannot have only bestsellers.

This brings us back to the question of whether AI should belong in the retail side of fashion. The answer is yes, and no. AI is bound to help understanding of a brand’s customer. It can help you decide what to keep and what to put aside. However, AI cannot fully take charge of a brand’s merchandise. This is why a balance of machine and humans creates a line of merchandise that is both commercially successful yet appears interesting.

To dive deeper into the trend system of fashion, trends aren’t controlled by machines. Fashion trends are popularised by the increase in the frequency of people adopting it. The stimulus can be different but the outcome remains more of the same which further influences other people in adopting those trends. By definition, a trend goes in and out of fashion, and thus, is a part of keeping things fresh and interesting for consumers. A fashion product’s lifecycle is short and the investment is even shorter which justifies the trend culture. So to steer into the market successfully, AI needs to come with an option of manual driving along with autopilot.