Generative AI is revolutionizing the fashion industry, bringing innovative solutions that can dramatically transform e-commerce operations. Indeed, “73% of fashion executives think gen AI is a 2024 priority for their companies”. Nevertheless, “only 5% believe they have the capabilities to fully leverage it” [BoF-McKinsey State of Fashion 2024]. This makes embracing generative AI a significant opportunity to outpace competitors.

To lead in this field, you need more than a superficial understanding of AI technologies. This article aims to offer you a clear overview of how generative AI is evolving in fashion.

What is Generative AI?

Using generative AI facilitates the creation of new content (text, images, videos, etc.) through generative models pre-trained on massive data sets. Its applications in fashion are vast.

Here’s an explicit overview of the different applications of generative AI along the value chain.

Generative AI overview

Now, it's time to explore each use case together to see how Gen AI can revolutionize the industry.

Automated fashion design

Generative AI is revolutionizing fashion design by speeding up the creation of new clothing lines. This technology automates the design process using Diffusion Models (DMs). It allows brands to quickly generate diverse and unique designs: creative teams input sketches and specify details such as fabrics, colors, and patterns into AI-driven platforms, which refine these ideas into detailed, realistic designs. It helps designers explore various styles and aesthetics more effectively.

Thus, hiring prompt engineers skilled in AI or partnering with companies offering this service is crucial for maximizing this technology's potential.

For example, IMKI partnered with The Kooples to launch a 100% AI-generated capsule collection, enabling the company to generate 10 times more creative proposals and develop products like jackets, handbags, dresses, and denim three times faster [Fashion Network].

Furthermore, generative AI boosts creativity in marketing campaigns, as exemplified by Moncler Genius at London Fashion Week 2023. Their collaborations with Maison Meta showcased AI-powered campaigns, highlighting the technology's potential.

Moncler Genius AI-powered marketing campaign with Meta

Automate your photoshoots: a lever to optimize your supply chain and adapt your offer

The challenge of visual content production for fashion brands has persisted for years, but recent advancements in generative AI promise to offer new solutions to this problem. Traditional methods, like Generative Adversarial Networks (GANs), fell short in achieving realistic renderings.

Enter diffusion models (DMs), a breakthrough technology capable of creating highly realistic images of clothing on diverse body types with superior control and quality.

The difference with GANs and diffusion models (DMs) rests in the neural network’s learning framework, rather than its architecture. Simply put, where GANs learn by using two neural networks, a generator and a discriminator, which compete against each other, DMs create images by starting with noisy images and refine them pixel by pixel.

This innovation, led by startups like Veeton offers a credible alternative to photoshoots. It is aimed at operational efficiency and cost-effectiveness for fashion retailers.

Veeton’s AI-generated fashion images

Indeed, it accelerates time-to-market and operational efficiency by offering brands the possibility to rapidly generate photorealistic images, boosting agility in this fast-paced industry.

Additionally, it boosts inclusivity by catering to diverse body shapes without relying on conventional photoshoots. Embracing diversity is increasingly essential in the fashion industry. According to Mintel's 2022 research, 41% of Gen Z prefer brands that reflect their identity.

Optimize your supply chain through inventory management and trends predictions.

AI is crucial in optimizing supply chains, predicting trends, and managing inventory. In a world where 73% of executives are struggling with unpredictable demand according to BoF-McKinsey State of Fashion 2024, imagine accurately predicting demand for a new season’s collection. In fact, AI is already helping manage inventory levels, reducing overstock and stockouts. The French Startup Ako utilizes advanced AI technology to revolutionize supply chain management in the fashion industry. It enhances operational efficiency by forecasting demand, optimizing inventory, and streamlining supply planning with real-time insights.

BoF-McKinsey State of Fashion 2024

Create personalized marketing campaigns

Moreover, generative AI can help fashion brands adjust their marketing campaigns to fit customer preferences, creating personalized marketing content with images featuring models that resonate with each customer, aiming for personalized connections and stronger customer loyalty. Veeton’s generated images can help you create and adapt your marketing campaigns to each customer thanks to high-quality production at scale.

Automate Your Product Listing to launch faster

AI automates product enrichment by creating product listings directly from images. Companies like YZR utilize this technology to streamline product uploads and enhance online visibility. Thanks to this technology, brands are able to improve their conversion rate by 30% thanks to high-quality product sheets.

Additionally, visual search and automated product tagging enhance customer experience in e-commerce. AI helps analyze visual data accurately, enabling customers to upload images and find similar products easily, driving sales and boosting engagement. ASOS's visual search tool, for example, has increased user engagement by 25% and boosted sales by 15%.

This also highlights the importance of maintaining a well-organized product database to prepare data for advanced analytics and AI-driven applications.

Personalized product recommendations

Generative AI also excels in personalized product recommendations. Consider a customer browsing your online store. With AI-driven recommendations, they see products that match their preferences, increasing the likelihood of a purchase.

Recommendation engines are algorithms designed to predict and suggest items that a user may be interested in based on their past interactions, preferences, and behavior. For example, retail giants like Amazon use NLPs to understand customer preferences and tailor recommendations accordingly.

Personalized recommendations enhance the shopping experience, driving higher conversion rates. Indeed, McKinsey noted that 35% of what shoppers buy on Amazon comes from product recommendations.

Enhance customer experience by offering a virtual fitting experience

Thanks to Gen AI, customers can try on various outfits virtually, boosting confidence in their purchases. Traditional methods use 3D avatars and GANs for virtual try-ons, but these technologies still need improvement. They struggle with photorealism, especially in depicting garment folds, shadows, and adapting to body shape and position. This can result in a less convincing experience and affect the brand's image. Companies must balance enhancing customer satisfaction with overcoming these technical limitations.

To conclude, while personalized product recommendations and product tagging solutions are mature technologies already in use on the market, some generative AI technologies are still in development but will bring sweeping changes to the industry, creating personalized marketing campaigns and automating photoshoots with the greatest potential.