Reported impact
Business outcome
Walmart reduced the research and design phase from weeks to minutes and shortened the overall apparel launch process by around 18 weeks, helping teams bring on-trend products to shelves and online faster while letting designers spend more time creating.
- Customer Experience
- Productivity Improvement
- Speed to Market
Solution
Walmart built Trend-to-Product, an internal generative AI workflow for its private-brand apparel teams, to compress the front end of fashion development. The system ingests trend signals from runways, social media, and external trend services, then combines that research with Walmart sell-through data during human review. Within Trend-to-Product, generative AI and large language model capabilities turn the source material into draft mood boards, collection names, color and texture directions, and style ideas in minutes rather than weeks. Designers and merchants then refine the AI-generated outputs and use their own expertise to select the final pieces for the collection. In the last step, Trend-to-Product generates a fashion tech pack that gives suppliers exact production instructions, allowing Walmart to move approved concepts into manufacturing and onto shelves and online channels much faster. The source does not name any underlying cloud, storage, or model vendors.
Data flow
Trend-to-Product pulls trend inputs from runways, social media, and trend services, uses generative AI/LLM processing to produce mood boards and collection concepts, and is then reviewed with Walmart sell-through data by designers and merchants. After refinement, the tool outputs a fashion tech pack for suppliers, who manufacture items that are then launched in stores and online.