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UX Design|Design Systems|Workflow Transformation|AI Enablement

Walmart Picker Images 

& AI-Powered Workflow

Walmart Color Picker images are a mandatory asset for selling color cosmetics on Walmart.com. Without them, PDP creative cannot go live and products cannot be sold online.

Beyond ecommerce compliance, these images help Walmart associates quickly locate the correct SKU when fulfilling pickup and delivery orders.

WALMART PICKER IMAGES BEFORE

WALMART-STORE PICKING IMAGES-0071249705704-AV83.jpg
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Key Challenges:​

  • ​Inconsistent layouts across brands

  • Unclear shade communication

  • Information overload

  • Packaging shown inconsistently

  • Slow product recognition during order fulfillment

Opportunity:
 
Create a standardized, highly scannable system that improves recognition while scaling across all L'Oréal CPD brands.

Designing for Recognition

Rather than optimizing for aesthetics, I optimized for speed and accuracy.















BEFORE

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EXPLORATION

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FINAL ASSET

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After testing multiple layouts,
I identified the information
Walmart associates needed most:​

​

  • Brand Logo

  • Product Name

  • UPC Verification

  • Shade Number

  • Shade Name

  • Visual Indicator Showing Exact Shade Location

The final design minimizes cognitive load and enables quick product verification.

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The framework standardized typography, spacing, sizing,
UPC placement, and shade communication across thousands
of product variations.

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The Real Bottleneck​

As the project scaled, I discovered the biggest challenge wasn't the template.
It was the briefing process.


Before


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Teams received large Salsify exports containing:

  • multiple image URLs

  • product descriptions

  • packshots

  • texture assets

  • shade references

Production teams had to interpret information, make assumptions, and manually determine what data was actually required.

Redesigning the Brief

I redesigned the briefing process around the exact information needed to generate a Walmart Picker image.


After

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The new brief surfaces only critical inputs:

  • brand

  • product Name

  • UPC

  • shade Number

  • shade Name

  • HEX Code

  • product Image

  • naming Convention

  • standardized language for shade location 

Instead of asking teams to interpret data, the system clearly defines what information is required and where it will appear.

Impact 

& Takeaways

For Walmart Associates:

  • Faster product recognition

  • Better shade identification

  • Reduced search time

  • More consistent experience

For L'Oréal

  • Standardized assets across CPD brands

  • Clear design governance

  • Scalable production framework

  • AI-ready workflow foundation

Ai Changed
How I Think 
About Design

One of the biggest lessons from this project was realizing that 
successful AI workflows start long before generation.


Most people focus on outputs. I focused on inputs.
 

The redesigned brief effectively became a prompt.
Better inputs → fewer assumptions → more consistent outputs → greater scale.
 

What began as a design project evolved into workflow design and AI enablement.

​

Great design isn't just about creating better assets. It's about designing systems that make better outcomes inevitable.
 

This project transformed both the shopper image experience and the production process behind it.

​

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