A Gen-Ai framework to assist navigating business challenges

Specifications

Design Direction
UX/UI
Research
Gen AI

UX
Walmart

A Gen-Ai framework to assist navigating business challenges

Specifications

Design Direction
UX/UI
Research
Gen AI

the context

Walmart Marketplace is an e-commerce platform where third-party sellers list products on Walmart.com alongside Walmart's inventory, accessing 120 million monthly visitors plus fulfillment services, advertising tools, and customer support.

The challenge

The data team is on track to debut an alpha Gen AI chatbot for Walmart sellers, but the current solution sits outside Seller Center, separate from where sellers manage their business. With a three-week launch window, resolving this disconnect becomes critical.

Data Science presented a working version, ready to launch.

The hypotheses

To expand the experiment, we’ll run a beta with a small group of sellers using the Gen AI assistant directly within Seller Center. This will help us understand how they interact with the tool, and these insights will guide future research and experience improvements.

Partners aligned on this first Smart Assistant iteration, released to an initial group of 300 sellers.

The research

the goal

To understand how sellers experienced the assistant, what aspects they found valuable, and what improvements they believed would strengthen its usefulness.

Theme 1: What they valued

- Fast, clear answers
- Humanlike, trustworthy tone
- Confidence in Walmart-sourced accuracy
- Useful insights beyond documentation

Theme 2: Gaps & Opportunities

- Personalization based on catalog & performance
- More actionable guidance, not just explanations
- Unified, instant support to avoid slow tickets
- Clearer understanding of what the assistant can do

Theme 3: What they wanted next

- Alerts for inventory, pricing, or shipping issues
- Catalog automation (setup, fixes, pricing updates)
- Growth recommendations and performance insights
- Shipment workflows and label automation
- Lightweight analytics and benchmark-style insights

The vision

Designed for Seller Center users, this tool supports them on their path to success

Accurate assistance for business challenges.

Timely help with a strategic lens

Helping navigate complexity

The framework

It enables the assistant to become a strategic business partner for sellers and helps them thrive in a highly competitive environment.

An idea led framework

By studying months of data and interactions, we shaped the framework around sellers’ natural behaviors, allowing for adaptive problem solving that turns simple questions into actionable business insights.

Follow up questions

AI’s follow-up questions aren’t pre-programmed responses but emerge from the same neural network patterns that generate the content itself.

How follow-up questions work:

  They emerge from two primary conversational patterns that mirror human dialogue strategies:

– Deepening
– Broadening

Deepening

Deepening follow-up questions let a Gen AI assistant refine context as a conversation progresses. Each response invites more detail, narrowing ambiguity and revealing intent, so answers shift from surface-level guidance to sharper, more relevant insight.

Broadening

Broadening follow-up questions help a Gen AI assistant expand context when needed. By opening the conversation to related factors, constraints, or goals, the assistant uncovers missing context and delivers guidance with wider relevance and stronger strategic insight.

Example conversation

UI rationale for key components

Key screens of the experience.

A prominent ingress

Predictable navigation helps sellers quickly understand where to find information and how to use Smart Assistant, lowering the learning curve and driving adoption.

Entry points & wayfinding

Giving the user intuitive navigation pathways that build user trust while reducing cognitive load can help them discover valuable insights without getting lost in complex interfaces.

Conversation UI

Clear visual distinction between conversation elements combined with optimized content structure improves information processing speed and readability.

Suggested components

Create a guided interaction flow that reduces input friction, deepens exploration, and simplifies decision pointsInclude suggested prompts and quick actions to help users begin without friction.

The next iteration presents the assistant as a copilot on desktop.

Next project

Balmoral Advisors