Walmart's Trend-to-Product: how AI cut fashion development timelines
Walmart says its in-house Trend-to-Product tool can shorten apparel development by up to 18 weeks. Here is what the tool does, what is company-reported and what other retailers can learn.
KEY TAKEAWAYS Summary by the editors
- Walmart unveiled Trend-to-Product, a proprietary AI and generative AI tool for its private-brand designers and merchants, at its investment community meeting in Dallas on 9 April 2025.
- According to Walmart, the tool can shorten the fashion production timeline by as much as 18 weeks, taking a process of about six months down to six to eight weeks.
- Trend-to-Product gathers trend signals from the web and social media, generates mood boards with collection names, colours and textures, and produces a tech pack that tells suppliers how to make each item.
- Walmart says humans remain in charge of major decisions: designers and merchants refine the AI-generated mood boards, review sell-through data and create the final pieces.
- The timeline figures are Walmart's own claims and have not been independently verified; the company has said it does not pursue speed for its own sake and that not every item needs six-week delivery.
Walmart's Trend-to-Product is a proprietary AI tool that helps the retailer's private-brand teams turn online trend signals into finished apparel faster. Walmart says it can cut the fashion production timeline by as much as 18 weeks, bringing a process that traditionally took about six months down to six to eight weeks. The figures are company-reported, but the case shows clearly where generative AI fits into product development and where people still decide.
What is Walmart's Trend-to-Product tool?
Walmart presented Trend-to-Product publicly at its investment community meeting in Dallas on 9 April 2025, according to Axios. The retailer describes it as a trend-sensing design tool that uses AI and generative AI to analyse and synthesise global data and trends, including information from the internet and from tastemakers. Axios reported that the tool was developed in-house over the preceding 18 months and is proprietary, rather than a licensed off-the-shelf product.
The tool is aimed at designers and merchants working on Walmart's own brands. Axios reported that it had already been used to create items for No Boundaries, a private label Axios described as a $2 billion brand, with products released in February 2025. Andrea Albright, Walmart's executive vice president of sourcing, described apparel as one of the company's hardest categories to bring to life, which is why it was chosen as the first test.
How does Trend-to-Product work, step by step?
Just Style and Retail Brew, both drawing on Walmart's announcement, describe a three-stage workflow in which AI handles research and documentation while people handle judgement:
- Trend sensing. The system gathers signals from the web, including social media posts and runway and red carpet videos, and combines them with Walmart's internal data.
- Mood boards. Generative AI produces mood boards with collection names, colours, textures and ideas, replacing much of the manual research that designers previously did.
- Human refinement. Designers and merchants refine the mood boards, review sell-through data and apply their own experience to create the final pieces.
- Tech pack. The tool then generates a fashion tech pack that tells suppliers how to make each item.
According to Just Style, running the tool itself takes about one hour, and Retail Brew reported Walmart's claim that ideation now takes under an hour. Walmart's CTO for Walmart International, Vinod Bidarkoppa, told Axios that some steps went from days and weeks to minutes.
| Stage | Before (Walmart's description) | With Trend-to-Product | Who decides |
|---|---|---|---|
| Trend research | Manual research on names, colours and textures | AI gathers web, social and runway signals | Designers select relevant signals |
| Concept | Hand-built mood boards | Generated mood boards in about an hour | Designers and merchants refine |
| Range decisions | Experience and historic data | Sell-through data reviewed alongside AI output | Merchants |
| Supplier brief | Manually written tech pack | AI-generated tech pack | Product development teams check |
| Overall timeline | About six months | Six to eight weeks, up to 18 weeks shorter | Sourcing chooses speed per category |
How much faster is Walmart's fashion development with AI?
Walmart's headline claim is a reduction of up to 18 weeks. Just Style reported that designing and producing a clothing collection traditionally takes about six months and that, with the tool, collections reach shelves and online within six to eight weeks. Bidarkoppa summarised it to Axios as six months becoming six weeks.
Two caveats matter. First, Retail Brew noted that these figures are company claims and that the coverage gives no technical detail on how accurate the outputs are. Second, Walmart itself has framed speed as optional. Albright told Axios that the company does not chase speed for its own sake, that not everything needs six-week delivery to stores, and that sourcing countries vary by category and required speed, with tariffs among the factors. She also said the approach differs from fast fashion and does not compromise on responsible sourcing.
What role do designers and merchants still play?
Walmart is explicit that people stay in charge of major decisions. Jen Jackson Brown, senior vice president for apparel brand and design at Walmart US, said the tool lets private-brand design and product development associates spend less time chasing trends, according to Retail Brew and Just Style. The design judgement, the selection of what fits the brand and its customer, and the check against sell-through data remain human tasks.
This division of labour reflects a wider limit that Retail Brew raised. Tucker Marion, an associate professor at Northeastern University, said that models trained on a brand's own data and design DNA do not yet fully exist. In practice, trend-sensing AI surfaces what is happening in the market; it does not know on its own what a particular label should make.
What data does a trend-to-product workflow need?
Walmart's description points to the inputs any retailer would need to replicate the approach:
- External trend signals: social media, runway and red carpet imagery and wider web content, collected with attention to copyright and platform terms.
- Internal performance data: sell-through history by style, colour and size so that teams can test AI suggestions against what actually sold.
- Structured product data: consistent attributes for fabrics, colours and construction, so that generated tech packs are usable by suppliers.
- Sourcing constraints: lead times, capacity and costs by country, since speed depends on where and how a product is made.
Will Walmart use Trend-to-Product beyond fashion?
Yes, according to the company. Axios reported that Walmart plans to extend the tool beyond apparel, with trending seasonal items and general merchandise as priorities. Albright told Just Style that the company wants to bring AI to the first mile of product creation and suggested uses such as lipstick shades or food flavour combinations.
What can other fashion retailers learn from Walmart?
The case offers practical lessons for private-label and own-brand teams. AI is applied to the slowest, most research-heavy part of development, while decisions on what to make stay with designers and merchants. The output is tied to internal sell-through data rather than to trend signals alone. And speed is treated as a sourcing choice per category, not as a universal target.
The limits are equally instructive. Walmart built the tool in-house over 18 months, which assumes significant engineering capacity and data access. The published results concern timelines, not commercial outcomes, so retailers evaluating similar tools should define their own measures, such as full-price sell-through, markdown rates and sample counts, before claiming success.
Frequently asked questions
What is Walmart Trend-to-Product?
Trend-to-Product is a proprietary Walmart tool that uses AI and generative AI to analyse online and social trends, create mood boards and generate tech packs for private-brand apparel. Walmart unveiled it publicly in April 2025.
How much time does Walmart's AI save in fashion development?
Walmart says the tool can shorten the production timeline by as much as 18 weeks, from about six months to six to eight weeks. These are company-reported figures and have not been independently verified.
Does AI design Walmart's clothes?
Not on its own. The AI proposes mood boards and concepts, but Walmart says designers and merchants refine them, review sell-through data and create the final pieces, with humans in charge of major decisions.
Which Walmart brands use Trend-to-Product?
Axios reported that the tool was used to create items for No Boundaries, Walmart's private label, with products released in February 2025. Walmart plans to extend it to seasonal and general merchandise.
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