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AI Impact on Fashion

Aug 21, 2023
AI Impact on Fashion

Generative AI has the potential to affect the entire fashion ecosystem. Fashion companies can use the technology to help create better-selling designs, reduce marketing costs, hyperpersonalize customer communications, and speed up processes. It may also reshape supply chain and logistics, store operations, and organization and support functions

Foundation models and generative AI can bee used across the fashion value chain.

Merchandising and product:

• Customize products for individual consumers at scale (for example, eyeglasses based on facial topography). — Supply chain and logistics: • Support negotiations with suppliers by compiling research. • Augment robotic automation for warehouse operations and inventory management through real-time analytics (for example, insights enabled by augmented reality, or AR). • Tailor product return offers based on individual consumers.

• Fashion Design: Generative AI models can assist fashion designers in generating new and innovative design concepts. Designers can input certain parameters or styles, and the AI can produce a range of design possibilities, helping designers explore new directions and ideas.Convert sketches, mood boards, and descriptions into high-fidelity designs (for example, 3-D models of furniture and jewelry).

• Enrich product ideation by collaborating with AI agents that generate creative options (for example, new ideas, variations) from data (for example, past product lines, inspirational imagery and style).

• Textile Patterns: Generative AI can be used to create intricate and unique textile patterns. By training on a vast dataset of existing patterns, AI models can generate new patterns that blend different styles, colors, and shapes, inspiring textile designers.

• Personalized Clothing: AI algorithms can analyze a person's preferences, body measurements, and style to create personalized clothing recommendations. This can enhance the shopping experience and help customers find items that match their individual tastes.

•Virtual Try-On: Generative AI can simulate how different clothing items would look on an individual using augmented reality. This technology allows customers to virtually try on clothes before making a purchase, improving the online shopping experience.

•Fashion Photography: AI-powered image generation can be used to create high-quality fashion images for advertising campaigns and lookbooks. These images can showcase clothing in various contexts and settings without requiring expensive photoshoots.

•Style Transfer: Style transfer algorithms use generative AI to blend the artistic style of one image with the content of another. This can be applied to fashion by transferring the visual style of famous artworks onto clothing designs or accessories.

•Fashion Trend Prediction: Generative models trained on historical fashion data can help predict upcoming fashion trends. By analyzing patterns and styles from the past, these models can forecast potential trends that might emerge in the future.

•Accessory Design: Generative AI can assist in designing accessories such as jewelry, handbags, and shoes. By learning from existing designs, AI models can generate new and unique accessory concepts.

•Virtual Fashion Models: Brands and designers can use generative AI to create virtual models that showcase their clothing lines. These virtual models can be customized to match the brand's aesthetic and used in marketing materials.

 

Marketing:

• Identify and predict trends to improve targeted marketing from unstructured data (forexample, consumer sentiment, in-store consumer behavior, omnichannel data).

• Automate consumer segmentation at scale to tailor marketing initiatives.

• Generate personalized marketing content based on unstructured data from consumer profiles and community insights.

• Collaborate with AI agents to accelerate content development and reduce creative blocks for in-house marketing teams. — Digital commerce and consumer experience:

• Structure and generate sales descriptions based on past successful sales posts.

• Personalize online consumer journey and offers (for example, web pages, product descriptions) based on individual consumer profiles.

• Tailor virtual product try-on and demos to individual consumers (for example, clothing try-on, styling recommendations).

• Enhance intelligent AI agents (for example, conversational chatbots, virtual assistants) and self-service to address advanced consumer inquiries (for example, multilingual support).

•Collaboration with Artists: Fashion brands and artists can collaborate to create unique pieces using generative AI. The AI-generated elements can be incorporated into clothing, prints, or accessories, resulting in limited-edition items.

Store operations:

• Optimize store layout planning by generating and testing layout plans under different parameters (for example, foot traffic, local consumer audience, size).

• Optimize in-store labor to avoid bottlenecks such as gaps in staff allocation and theft detection through real-time monitoring of video data.

• Support AR-assisted devices to better inform workforce in real time on product (for example, condition, assortment, inventory, recommendations).

— Organization and support functions:• Coach sales associates to sustain successful “clienteling” relationships via real-time recommendations, feedback reports, and high-value consumer profiles. • Develop individualized training content for employees based on role and performance.

• Enable self-serve and automate support tasks (for example, HR tickets, accounting for large documents, review of legal documents).

These are just a few examples of how generative AI intersects with the fashion industry. As AI technology continues to advance, we can expect even more innovative applications that revolutionize how fashion is designed, produced, and experienced.

 

Several brands and designers have embraced AI in the field of fashion design. Here are a few examples:

H&M: The global fashion retailer H&M has used AI algorithms to analyze fashion trends and customer preferences. They have employed AI to determine which designs are likely to be popular, helping them create more targeted and market-responsive collections.
Stella McCartney: Stella McCartney collaborated with Google in the past to use machine learning to analyze and optimize their supply chain. This helped them reduce waste and make more sustainable decisions in their production processes.
The Fabricant: The Fabricant is a digital fashion house that exclusively produces virtual clothing. They use 3D design and generative AI to create unique and imaginative digital clothing pieces that are sold as digital fashion items.
IBM and Tommy Hilfiger: IBM and Tommy Hilfiger collaborated on a project called "Reimagine Retail" which involved using AI to help design personalized, sustainable clothing. They used AI to predict fashion trends and customer preferences to create customized designs.
AnamXR: AnamXR is a company that uses AI to create virtual models and fashion shows. They work with brands to produce virtual fashion shows and presentations, saving resources and providing an innovative way to showcase new collections.
Brooks Brothers x IBM Watson: Brooks Brothers worked with IBM Watson to create the "Made-to-Measure" program, which uses AI to help customers design and personalize suits, shirts, and other garments. The AI takes into account the customer's preferences and measurements to suggest suitable designs.
Gucci x Snap: Gucci collaborated with Snap Inc. to create virtual sneakers that users could try on using augmented reality. This combined fashion and technology to engage customers in a unique way.
Rochambeau x IBM Watson: Rochambeau, a luxury fashion brand, partnered with IBM Watson to create a "cognitive dress" that changed color based on social media reactions and emotions. This demonstrated how AI could be integrated into the fabric of a garment.
These examples showcase the diverse ways in which brands and designers have integrated AI into various aspects of fashion, from design and production to marketing and customer engagement. As AI technology continues to evolve, more fashion brands are likely to explore innovative ways to leverage its capabilities.

Posted by Abiteks

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