The fashion industry has always adopted new technologies, from the invention of sewing machines to the rise of e-commerce, with innovation continuously driving it forward.
Now, AI is leading the next big leap & evidently so.
Brands can now predict demand at a product SKU level from one street to another, optimise inventory, and make hyper-personalised recommendations instantly.
If you’re a fashion retailer, it has become a necessity to adopt such technologies.
In this article, we’ll explore how AI is revolutionising the fashion industry, from trend forecasting to personalised shopping experiences and more.
AI in fashion: 8 major trends to look out for
1) AI in supply chain management
Demand forecasting is the backbone of effective supply chain management.
Trends can vary not just from city to city but even from one street to another. This makes it easy to miscalculate demand, leading to overstocking of certain products while understocking others.
Overstock results in higher storage costs, markdowns, and excess inventory, while understocking can lead to lost sales and dissatisfied customers.
By analysing location demographics, combined with external datasets, existing sales data, and predictive analytics, fashion retailers can accurately forecast demand and stock the right products in the right quantities.
This AI-driven approach ensures better inventory management, reduced waste, and improved profitability.
2) Real-time trend prediction
As you very well know, trends change rapidly and if your store isn’t adapting to the new trends regularly, it could lead to dead stock and other inventory management issues that could be costly.
Traditionally, trend forecasting relied on historical sales data, intuition, and seasonal runway shows, along with celebrity influence on television.
While these methods provide some insight, they often lag behind real-time consumer preferences and fail to capture real-time shifts in demand.
For example, viral social media trends can drive instant consumer interest in specific styles but it’s challenging for brands to keep up using conventional forecasting techniques.
AI in fashion, however, is changing the game.
With AI-powered real-time trend analysis, brands can track emerging hyperlocal trends down to a street level.
Our location intelligence platform leverages AI and vast external datasets to analyse social media buzz, online searches, and real-time purchase behaviour with demographic insights, allowing you to predict trends and demand at a street level for individual product SKUs.
To give you an example, our AI-powered models have predicted that the oversized clothing trend is expected to come down in 2025 and beyond:
It’s clear that stocking oversized clothing is still a viable option but at a much lower scale than before.
So, retailers who are looking to stock should analyse demand before doing so.
You can book a 15-minute demo call with us to understand more about how we utilise various datasets to analyse trends and how we can help you as well.
3) Generative design
Generative AI is redefining creativity within the fashion sector by enabling the development of new designs and improving the existing ones.
Tools such as ClothingGAN and AiDLab generate detailed designs and 3D models for items ranging from apparel to jewellery, reducing resource dependence for design.
AI can also suggest fabric choices, colour palettes, and styling recommendations, making the design process more efficient.
For instance, brands like H&M have experimented with AI-generated fashion, using machine learning to ideate new styles for their fashion lineup.
With such tools, you can generate numerous design alternatives that meet your particular criteria.
4) AI fashion models
The rise of AI-generated fashion models is transforming the way brands showcase their products on their websites and advertisements.
Deep Agency and Botika are such companies that help you create your own customisable fashion AI models you can show for your brand.
These digital models, powered by AI and deep learning, can be customised to represent different body types, ethnicities, and styles – helping brands reach a wider audience.
Traditionally, fashion brands relied on professional photoshoots, which involved hiring models, photographers, stylists, and makeup artists. It can be an expensive and time-consuming process.
With the advent of AI in fashion, you can “create” AI-backed fashion models without the logistical challenges of traditional shoots.
5) Virtual try-on
Trying on clothes, shoes, or accessories before buying has always been a key part of the shopping experience. But with the rise of online shopping, customers often struggle to gauge how an outfit or accessory will look on them.
AI in fashion has found a way. Virtual try-on tools use artificial intelligence (AI), augmented reality (AR), and computer vision to let shoppers see how products will look on them in real time.
By simply using their phone or computer camera, customers can try on clothing, sunglasses, makeup, or even sneakers without setting foot in a store.
Eyewear brand Lenskart has successfully leveraged AI-powered virtual try-on technology. Their 3D Try-On feature allows customers to scan their faces and virtually try different frames to find the perfect fit before making a purchase.
| Fact byte: GeoIQ helped Lenskart expand 1000+ stores faster with location intelligence
7) Sustainable manufacturing practices
Not only for business growth, AI contributes to eco-friendly manufacturing as well by optimising production workflows, minimising waste, and promoting the use of sustainable materials.
AI can also analyse the lifecycle of materials, suggesting the most sustainable options and helping brands transition to circular economies.
Additionally, Stella McCartney collaborates with Google Cloud’s AI-powered tool, which assesses the environmental impact of various raw materials.
This helps the brand reduce water consumption and carbon emissions by making data-driven decisions about its fabric choices.
8) AI-driven customer service & chatbots
Customer support is growing beyond human-driven interactions.
AI-powered chatbots and virtual stylists are now capable of handling everything from style advice to returns processing.
These AI assistants analyse customer preferences, browsing history, and past purchases to provide hyper-personalised recommendations and styling tips.
For instance, H&M’s chatbot helps customers find outfits based on their personal style and upcoming events, and Sephora’s virtual assistant gives personalised makeup recommendations based on skin tone and past purchases.
Integrating AI into customer interactions drastically improves the shopping experience and minimises the time spent on making purchasing decisions.
Conclusion
AI in fashion has already started changing the way fashion retailers operate, whether it’s online or offline.
These 7 trends are the beginning of a larger transformation that will continue to reshape the industry.
At GeoIQ, we’re doing our part by helping brands predict trends and demand at the street and product SKU levels.
Book a 15-minute demo with us and let us help you in your journey!