Views: 0 Author: Site Editor Publish Time: 2025-11-10 Origin: Site
Supply chain optimization and efficiency improvement— the fashion industry is undergoing profound changes while actively embracing AI technology. Behind the highly attractive data, fragmented AI application models and evolving consumer behaviors remain key tests for the accuracy of fashion industry decisions.
The global market size of generative AI is expected to grow from $7.9 billion in 2021 to $110.8 billion in 2030. Within the next three to five years, generative AI will add $150 billion to $275 billion in operating profits to the apparel, fashion, and luxury goods industry.
Although AI can accurately analyze trends and fit fashion currents, there is an essential difference between "following trends" and "creating trends." The irreplaceable core often lies in "humanistic breakthroughs" that "transcend data": Fashion design— from initial inspiration conception, pattern generation, and print design to later fabric processing— requires considerable complex human intervention. However, when every brand instinctively relies on data to decide design directions, fashion may lose its original "sense of surprise."
AI tools have not replaced creators but have empowered more potential creators unprecedentedly. Future fashion design may no longer be the exclusive domain of professionals; real consumers can directly participate in design and directly influence design paths.
"Personalized customization" will no longer be a luxury concept. Fashion will break through conventional category frameworks, embrace every real individual, and ultimately move towards a highly inclusive and diverse fashion future that cannot be simply classified.
AI-generated "humans" may face low recognition and convergent aesthetics. When the fashion industry extensively uses AI models for product display and creation, styles may lose individuality, and the authenticity of the produced content or products may be questioned. Therefore, even if AI models have reached a certain level of precision, the industry still needs to find another form of "authenticity" to retain its unique differentiation.
The World Artificial Intelligence Creator Competition launched the world’s first AI model beauty pageant "Miss AI," with two real humans and two AI models serving as judges. "Miss AI" takes the social media persona and content influence as the primary evaluation criteria, followed by appearance. This means that emotional expression and resonance with the public have become one of the means to establish deep "authentic" connections with the general public.
Building "authenticity" through algorithms relies directly on data, which is linked to the actual value of the technology.
In 2024, only 41% of generative AI pilot projects successfully entered the production stage, and more than half of generative AI implementations were terminated midway. From usage scenarios to actual outputs, from quantitative changes driven by technological outbreaks to qualitative changes in the industry, there is still a gap in implementation.
—— Janice Wang, CEO of Alvanon
AI has passed the stage of cognitive popularization, but against the backdrop of fragmented AI technology applications, enterprises need to form an effective "closed loop": When brands invest in organizing and integrating their data, AI applications will be more easily transformed into insights or outputs with sustainable commercial value.
In the future, even if AI can help apparel enterprises accurately allocate sizes and determine production batches in real time, the key still lies in solving the problem of the reliability of consumer data.
AI may enable brands to stock up more "smartly," but in terms of size recommendation, a precise understanding of individual "fit" needs still requires the goal setting of data algorithms. Otherwise, even the most advanced algorithms may lead to wrong decisions or biased results due to data quality issues.
Currently, most size recommendation tools in the apparel industry mainly focus on promoting conversion. However, accurately capturing shoppers' body data can provide a more comprehensive understanding of customers' real figures. By inputting "real data" into AI through machine learning, brands can better enhance product fit.
