Fashion is always evolving, and various trends come back around time and time again, but emerging technology is presenting fashion with a brand new front, Artificial Intelligence (AI). Generative AI in fashion uplifts new trends, new frontiers, and a new scope of business and marketing in the fashion industry. Not to mention, artificial intelligence has proven results that help enhance the customer experience and could help companies commit to sustainable efforts more easily.
With the increase in productivity that AI allows in the design realm, AI could ring in billions of dollars within the fashion industry when harnessed productively. According to a 2023 article by McKinsey and Company, Generative AI: Unlocking the Future of Fashion, it was predicted that AI could generate around $150 billion and up to $275 billion in the apparel, fashion, and luxury sectors’ operating profits in the next 3 to 5 years.
In our view, generative AI is not just automation—it’s about augmentation and acceleration. That means giving fashion professionals and creatives the technological tools to do certain tasks dramatically faster, freeing them up to spend more of their time doing things that only humans can do.
McKinesy and Company
Quite ironically, AI use in fashion is said to be increasing personalization in the industry, although customization might be a better word for this preference-predicting resource for customers, as it certainly eliminates the step of a human person helping you, which could be considered either a pro or con. Here are the main ways in which AI is transforming the Fashion industry and the consequential pros and cons of AI as an entity in the fashion world.
Overview of the current ways in which Generative AI is used in fashion:
1. Creative Process: AI tools help curate new designs and increase efficiency in design steps.
2. Insights for Consumers and Target audience: Use of AI to analyze a surplus of data relating to customer preferences, which helps brands adapt to their customers’ specific demands. For example, products such as glasses could be tailored by design specifically to the topography of a customer’s face.
3. Marketing: New marketing strategies that also adhere to customers on a more personalized note based on personal preference, prior shopping experiences, and aptitude.
4. Supply Chain Efficiency: AI optimizes inventory overview and can forecast specific trends which ultimately eliminates surplus, reducing waste.
The Pros of Generative AI in Fashion
In addition to improving marketing, creation and product customization, and forecasting trends, AI also allows companies to offer 24/7 customer support, which can be great especially for small businesses that have fewer employees to be able to lower response time and free up more time for retail workers, which can especially help around the holidays. META’s automated responses in messenger that help direct customers to FAQs or other helpful customer service avenues or corporate fashion’s chatbots that link customers to a real agent after specifying the issue to help them in the best way possible saves time for both the company, and the customer who would otherwise be waiting on hold.
Additionally, the ‘personalization’ that AI allows can both help the customer and the company save time and resources. For example, Amazon fashion has suggested clothing items that pair well with what’s in your cart as a great sales technique for years, but now also offers your specific recommended sizes on fashion brands, based on other items you’ve purchased in the past, and summarizes the product reviews through AI. This helpful tool eliminates a step of having to figure out where and when to size up or down based on prior purchases and what brands and products generally run small, large, or true to size. Additionally, this tool helps companies save from having to pay shipping for returns, as well as helps them eliminate their carbon footprint.
In an effort to better stock individual stores with merchandise local clientele desires, H&M is using big data and Artificial Intelligence (AI) to analyze returns, receipts and loyalty card data to tailor the merchandise for each store. This is known as localization and can be trickier to execute for a global chain such as H&M that typically can leverage economies of scale with its global network of suppliers.
The Cons of Generative AI in Fashion
So, what are the downsides of AI in Fashion? One of the biggest may be the evident lack of authenticity that will persist in the AI world of fashion. AI can not only edit and alter models’ appearances, but can also adapt and shift the way models pose and present themselves, which takes away the personable nature and art of modeling. Additionally, with AI being able to adapt models’ features and what they’re wearing with a simple click, this can take away jobs from models as well as eliminate gigs for photographers that might have originally been requested for multiple sessions, that can now appear as if there were multiple photo sessions from just one base photo.
Beyond that, AI models have been emerging in the past couple of years, which can create unrealistic standards that affect body image for consumers, in addition to taking away jobs from models. It’s also likely that AI fashion designers will develop in the industry, according to Forbes, but it may be a while before this threatens the famed designers of our day, especially as AI devices work out various copyright kinks and in terms of legality.
Another con is the carbon emissions produced by AI generation, which are both costly and harmful to the environment. Even just saying thank you to AI interfaces such as ChatGPT is costing tens of millions of dollars in energy bills. In addition, its role in fashion could exacerbate the overconsumption problem, as brands prioritize profit over sustainability.
While there could be a noticeable shift in jobs being reallocated with the incorporation of Artificial Intelligence in the fashion industry, there may also be the creation of many new jobs due to AI. As AI tools launch and continue to develop, the human-in-the-loop method will be the best approach for companies to ensure that AI is adapting and learning in a way that will best inform them and customers, and not give misinformation. The human-in-the-loop method will keep people involved in the adaptation and validation of the learning process for AI, as humans moderate the data generated from AI. While it’s still underway, it cannot yet fall under the “pros” of AI in Fashion, but presents a promising projection for this movement economically.
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