The Future of Fashion: Generative AI could add $150 billion in the Next 3 to 5 years
International firm McKinsey wrote an interesting piece on the future of Fashion with Generative AI – Here is some of the highlights of the piece.
While the fashion industry has experimented with basic AI and other frontier technologies—the metaverse, nonfungible tokens (NFTs), digital IDs, and augmented or virtual reality come to mind—it has so far had little experience with generative AI. True, this nascent technology became broadly available only recently and is still rife with worrisome kinks and bugs, but all indications are that it could improve at lightning speed and become a game changer in many aspects of business.
In the next three to five years, generative AI could add $150 billion, conservatively, and up to $275 billion to the apparel, fashion, and luxury sectors’ operating profits, according to McKinsey analysis. From codesigning to speeding content development processes, generative AI creates new space for creativity. It can input all forms of “unstructured” data—raw text, images, and video—and output new forms of media, ranging from fully-written scripts to 3-D designs and realistic virtual models for video campaigns.
Generative AI use cases in fashion
Foundation models and generative AI can be used across the fashion value chain.
Merchandising and product:
* 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).
* 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.
* Identify and predict trends to improve targeted marketing from unstructured data (for example, 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).
* 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).
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 (see sidebar, “Generative AI use cases in fashion”).