15 Great Applications of AI in Fashion and Apparel Industry

With the great level of complexity and uncertainty, the fashion industry has been facing big challenges. Competition, customer expectations, technology up-gradation, social and economic changes are the main factors making Artificial Intelligence important for the fashion industry. Artificial Intelligence is key for businesses today. The fashion and apparel industry is implementing unique features that come from various applications of AI (Artificial Intelligence). 15 great applications of AI in Fashion and Apparel Industry can change the business of fashion and customer experiences.


1. Supply Chain & Demand Projection

H&M announced that they were sitting on $4.3B of inventory, which is a lot of money that is sitting and being wasted. Using AI, it can use prediction analysis that which type of clothes, might not sell and create a combination of discounts, free shipping, product combo’s etc. to clear out the inventory that is most likely not to sell after a certain time period. Rue La La collaborated with MIT has developed a system that helped them predict the demand for products in their flash sales and accounted for data sources including brand information, product type, color, price and a range of other factors. This enabled them to optimize prices and generate a 10 percent increase in revenue without the extra burden of unused inventory or supplier costs. A German e-commerce player Otto uses deep learning to analyze billions of transactions and is 90 percent accurate in forecasting what it will sell in the next 30 days. This insight allows it to order a couple of hundred thousand items each month from vendors with no human intervention. It has also cut surplus stock by 20 percent and reduced costly returns by 2 million items each year.


  • More accurate inventory planning
  • High sell-through
  • Higher profit
  • Can help to avoid dead stock


2. Sorting Orders at Warehouse

When you order a product on GAP or Amazon or any other online retailer, there are actual people on the other side picking and sorting products for you. It is a huge cost for because retailers have to pay them, it takes a longer time to sort products and most warehouses can’t function 24/7. AI Solution: GAP is testing and working with Kindred AI to train robots to pick and sort its products. Imagine! Robots pick items of different sizes, puts them together in a nice box and drone brings it to your door.


  • AI robots can be more efficient
  • Time saving
  • Cost saving
  • Organized stocks


3. Inventory Planning and Control

Today human beings go visit the racks and check if items need to be restocked. AI Solution: Cameras and sensors can automatically sense what is missing and needs to be restocked and can pre-order it for you. And they can pair the understanding of the seasons, sales data, etc to make sure they only order the item that is more likely to sell. And instead of getting new items manufactured, they can check if that item or similar item exists at other locations but its probability of selling at that location is low it, it can order it from there or ship it to the customer from there.


  • More accurate inventory planning
  • Quick Response to market demand
  • Time and Cost saving
  • Minimum deadstock
  • Higher Customer Satisfaction


4. Trend Forecasting

Trend forecasting is big business. There are companies such as WGSN do that. They have employees that go thru thousands of images, look for patterns, new trends and sort them out to do trend forecasts. AI: Analyzing which images, colors, style of photos didn’t exist before but are getting high engagement. Combine that with data from hashtags, location, influencer, color distribution etc. AI can help analyze & predict upcoming trends.


  • More accurate trend prediction
  • Effective for Sourcing
  • Efficient Marketing and Supply Chain Management
  • Optimum Utilization of Resources
  • Higher Customer Satisfaction


5. Sales Forecasting

Sales Forecasting is the process of estimating the number of sales for your business over a future period of time. This forecast period can be monthly, quarterly, half-yearly, or yearly. Usually, sales forecasts are based on past sales data, industry-wide comparisons, and current economic trends. It is easier to come up with a sales forecast if you have a good amount of data in hand. Today, companies use Data Analytics and Artificial Intelligence to forecast sales and revenue.


  • More accurate budget planning
  • Effective for resource planning
  • Helpful to make marketing plans based on sales projections
  • Competitive advantages


6. Clothes Designing

Amazon is developing AI that combines hundreds of thousands of designs, fabrics etc to create something new that it interprets that could sell. Myntra’s brand Moda Rapido is powered by AI and works without human intervention to offer computer-generated designs, including T-shirts, jeans, kurtas, and shoes. The system is fed data from various sources, including customer data, social media, fashion publications, etc., and creates thousands of combinations of designs, then hones in on what would sell well. It now has the highest gross margins compared to all other 14 brands under the Myntra portfolio.


7. Programming Based Photography

When you create a new clothing line or launch a new collection, you have to take photos of all items to be posted on your website. It can be extremely cost-sensitive because it involves models, photographers, makeup artists, etc and you have to manage their schedules and re-shoots can be a pain in the ass. There are AI companies that are enabling retailers to shoot images by putting clothes on mannequins and then replacing the mannequin with the 3D Image of the actual model. They did


8. Dynamic Websites and Apps

So currently when you’re doing traditional testing to find out which version of the website is going to give you the most number of conversions, we show different variations of the website to a certain set of customers and see which one converts the best. Since this is very limited you can only show two maybe five or six different sets of visitors and each one has to be modified. But imagine having an AI solution where each user that visitors visit the website and see the website content, its images, its wording based on what that person will more likely to respond to. So the AI company sentiment technologies work with Skechers to improve recommendations based on analyzing which image of shoes and individual customer prefers to curate a gallery tailored to that shopper’s unique style and preferences.


9. Product Testing Prior to Purchase

Barbie Parker allowed users to pick up to five different styles and using AI they can understand what style of sunglasses work for what type of consumer. So, they could send you a box with five different styles and you try those styles at home and then you send back all of these glasses and you pick the one that actually you like the most. Barbie Parker has experimented with AI technology that you can take an image and you can apply different glasses on your face and see which one you like the best and then you can order specifically from there. This way they don’t have to send you the actual five frames at home and he saves a lot of money on shipping and actually breaks those glasses. Another company called Modi Face allows people to look into the camera take an image and they can test different types of hair colors and hairstyles virtually and see how they would look. So, this way it saves a huge amount of effort and time for the user so they don’t have to go out and try the color and then find out if they actually like it or not. This way they can actually see using the app that if the color will actually look good on them or not and they can pick accordingly.


10. Personalized Clothing Recommendation

Stitch Fix is a prime example the algorithms analyze returns and buys from its customers and then sends them clothes that they’re more likely to buy. This data is important for brands because before they didn’t know which type of activity happened in the fitting rooms and they didn’t know which eye product customer tried and they did not like. Today personalized recommendation websites are doing extremely well.


11. Virtual Fitting Rooms

We have many retail companies who are testing with different types of virtual fitting rooms so users can actually go through and stand in front of the mirror and they can test different clothing items without actually trying them on. The same feature is being experimented with by many eCommerce companies through web and mobile apps. This technology provides a great solution to fitting and trails difficulties in store.  

12. Voice Ordering

We have Alexa we have Google home where they can watch the items whichever you want to order and imagine like Google home and like that they know exactly where you live they know who you are they have information on your credit card they have your home address so you can literally sit an order through the voice that hey Alexa send me this particular item or Alexa send me this item from gap or guests with this style and they will be able to charge your credit card because they know who you are they can send it to your home because they have your shipping address they know who you particularly are so they can also say that this customer viewed these items and actually ordered this particular product so voice ordering is will be huge as it comes


13. Out of Stock Items

Pickle AG is another example it collaborative with bond pricks to make personalized recommendations for out of stock items based on visual similarities reducing the dropout rate of out-of-stock items by 43%.


14. Chatbot

The great service a bot offers can turn users into repeat customers. Not only can a chatbot answer questions in an engaging way, but it can also be set up to provide a fully personalized shopping experience. From there, customers can click through to your store and make a purchase.


  • Improved the customer experience
  • Helpful to increase sales
  • Time and money-saving
  • Customized business solution
  • Competitive advantages


15. Clothing Insurance

The problem today is that fashion designers and retailers buy insurance from larger companies like Allstate nationwide and it’s usually calculated by the national average. But, imagine an AI solution that creates specific insurance just for you based on who your customer is, how many returns you get, how many collections per year you do. And if you decide to stick or skip a collection or reduce the number of returns which means your shipping clothing lesser than before then it can reduce your insurance accordingly. So, this means your clothing insurance becomes dynamic based on how many actions or activities you’re taking a month afterward.

Hopefully, this article will be helpful for you in your business and career in the fashion and apparel industry. please do write your feedback and suggestions in the below comment section.

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