Case study
Building the world's first AI-native fashion and retail model
Partnering with a major global fashion retailer to transform product development with AI, increasing speed and reducing waste
Our client is a major global retailer with a multi-brand fashion and apparel business
Challenge
How might AI drastically improve the speed, efficiency and sustainability of the fashion industry?
A large global fashion retailer approached us with an opportunity to help them reimagine the way fashion and apparel gets designed, bought and made in the autonomous age.
The retailer had identified a need to better stay ahead of culture, capture share of the lucrative Gen Z market, increase speed to market and develop a leapfrog model that out-competed SHEIN while reducing waste.
Intrigued by our work in the space of Autonomous Innovation, they wanted to explore the opportunity of building a working prototype of an Autonomous Innovation Engine together.
Strategy
Creating a system to outperform existing speed, accuracy and automation
Together, we created an AI-powered engine that could outperform current systems in speed, accuracy, and automation. Prioritizing feasible production garments over sheer volume, harnessing real-time data and AI Agents, we designed the engine to ensure new garment concepts meet time, resource, and brand identity criteria. This, paired with a custom set of synthetic personas focused on their priority Gen Z consumers, allowed us to generate more efficient, more on trend collections and prioritize them based on purchase intent.
Outcome
A flexible AI-powered model that is so much more than just a tool
When testing the designs generated from our engine against the best-sellers from our client’s existing apparel line of business with real-world consumers, we found that the results were not only comparable – but favorable – in terms of desirability, trendiness and brand cohesion. This demonstrates AI’s potential to rival human ingenuity in the design process, improving efficiency and demand prediction – leading to reduced waste.
Reducing a 6-month design process to a few clicks.
Challenge
How might AI drastically improve the speed and efficiency of the fashion pipeline?
A large global retailer approached us with an opportunity to help them reimagine the way products get made through the power of Artificial Intelligence.
This retailer had identified a need to better stay ahead of culture, capture the lucrative Gen Z market, increase speed to market, and become the go-to destination for trendy shoppers without the excess waste.
Intrigued by our work in the space of Autonomous Innovation, they wanted to explore the opportunity of building a working prototype together.
The big idea
Given the rapid rate of widespread interest and adoption, Gen AI is quickly disrupting the design process across industries.
As a global retailer looking to stay competitive, our client had the chance to not only leverage AI for its efficiencies but to embrace it as a means of leapfrogging the competition in ways only a business operating at a cross-category portfolio level can.
The Autonomous Innovation Engine operates according to three basic principles:
- Design garments based on need, not season.
- The ability to think and act niche at scale.
- Do fast fashion, sustainably.
Learning and adapting to real-time internal and external data
Generating new solutions (product, services, features, experiences) based on real-world learning
Developing new solutions (products, services, features, experiences) that are ready for launch)
Testing and assessing new solutions in synthetic environments before building and launching
Selling, distributing, marketing and managing solutions in real-world environments
Through this Autonomous Innovation engine, our ambition is that the retail industry no longer needs to rely on human intuition and bias - but is empowered to challenge the way things have always been done and offer up true value to the customers, through an approach we are calling “just on trend” production.
Through an intuitive interface, design teams have the opportunity to identify cultural trends in an instant, and filter through the ones that make sense for their brand and business objectives. The system then prioritizes relevant cross-cultural meta themes to activate against, and navigates the user through a series of AI-generated selections — from moodboard generation to custom-designed collections.
The best part is that users don’t have to be overwhelmed by an excessive amount of choice and possibility, as the system itself can auto-assort and advise the most-likely-to-be-purchased garments based on a combination of AI Agents and synthetic consumers. AI Agents replicate internal roles to ensure cohesion of the designs, and synthetic consumers help rank desirability of a given design.
In just a few clicks, the user can generate a garment design, but also the full marketing suite — from messaging, to pricing, and even marketing materials, product descriptions and packaging design to accompany it.
Key engine functionalities
- Early anticipation of emerging consumer trends, with always-on social listening
- More diverse inspiration and concept possibilities
- Reduced complexity in synthesizing and matching data, resources, and suppliers
- Tailored product recommendations across time, business, market, and function-related constraints
- Greater rigor and rationale for decision making
- A constant source for the voice of consumer without the effort of traditional research methods
Strategy
Creating a system to outperform existing speed, accuracy and automation
Together, we created an AI-powered engine that could outperform current systems in speed, accuracy, and automation. The unlock was to shift our focus from how many designs were generated, to how many designs could be produced within the given constraints of the system.
This could all be made possible through a team of AI Agents tasked with ensuring that the engine would only generate garments that could be achieved within a desired time frame – based on up-to-the minute data on resources and facilities. At the same time, agents would also ensure that designs are filtered according to brand identity and cohesiveness. This, paired with a custom set of synthetic personas, would allow us to not only generate on trend collections but also prioritize them based on likeliness to purchase.
There are a lot of shiny widgets in AI. This is actually practical for us.
Fashion technology leader, major global retailer
Through this Autonomous Innovation engine, our ambition is that the retail industry no longer needs to rely on human intuition and bias - but is empowered to challenge the way things have always been done and offer up true value to the customers, through an approach we are calling “just on trend” production.
Outcome
A flexible AI-powered model that is so much more than a tool
When testing the designs generated from our engine against the best-sellers from our client’s existing apparel line of business with real-world consumers, we found that the results were not only comparable - but favorable - in terms of desirability and trendiness. This demonstrates AI’s potential to rival human ingenuity in the design process while improving efficiency and sustainability - and points to an opportunity to rethink the entire development process, not just offer a new tool for designers.
From 6 months to a few minutes
Reducing the back and forth of the design process by streamlining the system and increasing speed of decision making.
100s of garments per trend
The ability to generate hundreds of on-trend designs at the click of a button - and then modify and assort them into collections.
Better performing products
Consumers rate the designs generated by our engine as highly favorable, both in terms of desirability and trendiness, compared to the best-sellers from our client’s current apparel line.
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