Google Flow AI Tools Transform Fashion Week Planning for Jane Wade and Sergio Hudson
Google Flow tools helped Jane Wade style looks virtually and Sergio Hudson plan a budget conscious runway for New York Fashion Week, without any coding.
Summary
On September 18, 2026, Google detailed how its Envisioning Studio, supported by Google Labs, worked with designers Jane Wade and Sergio Hudson before New York Fashion Week. Engineers used Google Flow, Google’s AI creative studio, to build two custom tools. Wade’s Styling Suite placed garments, hair, makeup, accessories and shoes on digital models, helping her balance complete looks and identify missing pieces before producing physical samples. In-person casting and fittings typically take design teams up to three full days.
Hudson’s Runway Visualization simulated his venue, lighting, props and model paths within a tight studio budget. It reduced production back-and-forth that previously raised costs because each revision required a new 3D rendering. Both collaborations influenced shows presented at New York Fashion Week. Google now lets users create bespoke Flow tools and workflows by describing them in natural language, with no coding experience required, positioning the approach as a way to move fashion AI beyond theoretical testing while keeping designers in control.
Positives
- Styling Suite let Jane Wade test complete runway looks digitally before producing physical samples.
- Up to three full days of casting and fittings could be reduced through virtual styling.
- Runway Visualization helped Sergio Hudson test venue layouts, lighting, props and model paths within his budget.
- Natural language lets Google Flow users build bespoke design tools without coding experience.
- Both custom tools influenced shows presented at New York Fashion Week.
Risks & concerns
- Each runway design revision previously required a new 3D rendering, increasing Sergio Hudson’s production costs.
- In-person casting and fittings can consume up to three full days for a design team.
- Many fashion AI projects remain confined to theoretical testing rather than established production workflows.
- Google provided no specific savings, budget figures or performance measurements for either tool.