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Walmart

Document Processing · Enterprise Knowledge Search

Walmart wanted to give associates a consumer-grade digital experience and reduce time spent on drafting, summarization, and ideation so employees could focus on higher-value human work across its global business.

11 countries in 2024
25,000 associates in our international markets
50,000 campus associates in the U.S.
a 50% increase in the user base

Reported impact

Business outcome

The tool saves time for associates, speeds up drafting and summarization, supports multilingual use across international markets, and frees employees to focus on strategy, idea generation, and relationship-building.

  • Productivity Improvement
  • Scalability

Solution

Walmart built My Assistant as an internal generative AI workspace for associates and rolled it out first to 50,000 U.S. campus employees before extending access across international markets. The employee-facing serving layer is the My Assistant application, which accepts prompts in associates' native languages and returns first drafts, large-document summaries, and topic-specific thought starters. The system is grounded in Walmart-specific knowledge, indicating an internal knowledge base is loaded into the assistant to tailor responses to company context, although the article does not identify the upstream data sources, storage services, retrieval method, or external model provider. Walmart says its existing technology foundation let the team move from vision to viable in about 60 days, then improve the tool using associate feedback as it expands globally. The implementation described is therefore a multilingual enterprise GenAI assistant embedded in internal employee workflows.

Data flow

Associates submit prompts to My Assistant in their native language; the tool uses Walmart-specific knowledge to generate first drafts, summarize large documents, and provide thought starters, then returns the output directly to associates for strategy, idea generation, and relationship-focused work. The article does not disclose the underlying storage, retrieval, or model orchestration layers.

Solution architecture

1 components · 1 layer
  1. Serving
    • My Assistant Internal generative AI tool that helps associates create first drafts, summarize large documents, and generate thought starters in their native language.

Architecture clues

  • First-party internal associate tool rather than a public customer-facing chatbot
  • Grounded in Walmart-specific knowledge
  • Rolled out to U.S. campus associates before international expansion
  • Supports native-language interaction for associates in multiple countries
  • Walmart iterates the tool using associate feedback

Evidence from the source

"Associates in these countries will be able to interact with the tool in their native language, and the tool will still be loaded with the Walmart-specific knowledge that drives an intuitive, efficient experience."
"Most importantly, it frees up time so they can focus on the tasks that are distinctly human, like generating new ideas, crafting strategy and building relationships."
"Our U.S. associates are already using the tool to get to first drafts faster, summarize large documents in seconds and spark creativity with thought starters on a variety of topics."
"We built My Assistant to help Walmart associates spend more time on the things that matter."
"We expect 25,000 associates in our international markets to have access to the tool this year – a 50% increase in the user base."
"We first introduced this tool, known as My Assistant, to 50,000 campus associates in the U.S. last year."
Transparency Evidence & methodology 90/100 reference value · 94% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Walmart

    Walmart customer story

    First-party
    https://corporate.walmart.com/news/2024/01/09/walmarts-expanding-one-of-a-kind-associate-genai-tool-to-11-countries-in-2024
    Source type
    Customer-published story
    Published
    Jan 9, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jan 9, 2024 · Walmart · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.