Google brought its AI tools to New York Fashion Week, partnering with designers Jane Wade and Sergio Hudson to test two fashion-specific applications built through its Flow AI creative studio. The tools target pre-show styling and runway visualization, two of the most time-consuming and expensive parts of fashion production.
The project came out of Google's Envisioning Studio, which previously worked with A24 on film production workflows. "Our core premise is that technology must be built with creatives, not simply thrown over the wall at them," said Yeawon Choi, the user experience designer who led the project. "We co-design with the creative that will use these tools because we want them to be helpful."
An AI styling suite for small teams
Jane Wade's studio previously relied on hand-sketching for every styling decision. The AI styling suite she co-developed with Google digitized that process, letting her team visualize garments in different colorways, fabrics, and styling combinations before committing to production. For a designer who is also her own full-time pattern-maker, the savings are concrete.
"Just being able to visualize a look before committing to cutting it could easily save a thousand dollars at the factory," Wade said. The tool's memory allowed her to upload previous collections, brand photography, and beauty lookbooks to train it on her aesthetic.
Wade also used the suite to create pre-fitting styling visualizations. She and her stylist could layer accessories, eyewear, and beauty direction onto images of real cast members taken during fittings, generating full looks, headshots, and detail shots. She stressed that the tool supplements rather than replaces physical fittings - every look still gets tried on a model.
The suite also includes an automated model card generator, which replicates a complex Adobe Illustrator workflow in a simplified interface. Team members without deep Adobe skills can produce print-ready dressing cards at the correct specifications. Wade estimates the tools helped her avoid recutting around one style across the season.
For those working with AI for Creatives, Wade's approach offers a useful template: AI as process accelerator, not creative author. "Where I draw the line is for the final image - I want to pay real people, in a real studio, with a real photographer and stylist on set," she said.
Runway visualization on a budget
Sergio Hudson's challenge was staging. His AI runway visualization tool simulated venue dimensions, lighting, mood, and prop placements within budget parameters he controlled. Instead of committing to expensive physical builds or outsourced 3D renderings, Hudson ran dozens of spatial iterations digitally.
"When every design revision or mock-up sent to production carries a real price tag, independent designers naturally tend to play it safe," Hudson said. "You default to familiar set-ups because experimenting carries financial risk." The tool let him reorder collection looks and change set elements without paying for costly last-minute alterations on site.
Hudson said the tool "protected" creativity rather than diminishing it, giving his team back bandwidth to focus on craft. Independent labels often struggle to match the sensory spectacle of larger houses with bigger budgets. For Hudson, AI visualization made risk-taking in staging financially viable.
The tool fell short on creating the "energy, environment and silhouette" of the runway, Hudson said, but he plans to keep refining it for next season's pre-production. Designers interested in similar applications may find relevant skills in AI Design Courses that cover visualization workflows.
Why this matters for creatives
The New York Fashion Week pilot shows a practical division of labor: AI handles iteration and visualization, while humans retain control over final creative decisions. Wade and Hudson both used the tools to reduce financial risk without ceding authorship. For independent creatives working with tight budgets, that's the model worth watching - use AI to explore options cheaply, then pay real people for the work that matters.
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