OpenAI Retail North America

Lowe's

Enterprise Knowledge Search · Marketing Personalization · Supply Chain Optimization

Lowe's needed to give DIY shoppers and store associates project-specific guidance across thousands of products and 1,700 stores while also improving merchandising, forecasting, and supply-chain decisions without making the experience feel impersonal.

1,700 stores
50+ machine learning models deployed across pricing, forecasting, and supply chain
Approximately 16 million customer transactions a week

Reported impact

Business outcome

Lowe's turned AI into a retail operating layer that helps customers and associates make better project decisions, supported more than 50 machine-learning models across core operations, and created the governance and training needed to scale adoption across the company.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Lowe's built a layered AI program around OpenAI's APIs and its own retail data to support how customers shop, how associates sell, and how teams run operations. On Lowe's.com, Mylow acts as a home-improvement advisor that turns natural-language questions into trusted project guidance and tailored product recommendations. In stores, Mylow Companion gives associates instant access to project steps, product details, and availability so they can answer complex questions in any department. These customer-facing experiences are grounded in Lowe's century of project expertise and linked to broader machine-learning deployments in pricing, forecasting, and supply chain. Around the tools, Lowe's created an AI governance committee, an AI Community of Practice, and a transformation office so adoption, training, and measurement could scale with the business rather than remain isolated experiments.

Data flow

Customer or associate questions enter Mylow or Mylow Companion, which use OpenAI's APIs plus Lowe's project knowledge, product catalog context, and availability signals to return guidance, recommendations, and next steps. The same operating model sits alongside Lowe's broader ML estate for pricing, forecasting, and supply-chain decisions.

Solution architecture

3 components · 2 layers
  1. Compute
    • OpenAI's APIs Provide the language-model layer that powers the customer and associate assistants built on Lowe's retail expertise and data.
  2. Serving
    • Mylow Acts as Lowe's online home-improvement advisor, turning project questions into trusted guidance and product recommendations.
    • Mylow Companion Gives store associates project steps, product details, and availability so they can help customers anywhere in the store.

Architecture clues

  • AI Transformation Office bridges business and technology adoption
  • AI governance committee and AI Community of Practice support rollout
  • Assistants use project guidance, product details, and availability to support customers and associates
  • OpenAI APIs power both Mylow and Mylow Companion

Evidence from the source

"Lowe's is using OpenAI's APIs to power Mylow ... OpenAI is also powering its Mylow Companion app for store associates"
"Mylow ... is like having a red-vested associate in your pocket, walking you through a project, recommending the right products, and helping you feel prepared to move forward."
"We've already deployed over 50 machine learning models in areas like pricing, forecasting, and supply chain."
"With Mylow Companion, associates can instantly access project steps, product details, and check availability."
Transparency Evidence & methodology 86/100 reference value · 89% 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 value86/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 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. OpenAI

    Lowe's customer story

    First-party
    https://openai.com/index/lowes-chandhu-nair/
    Source type
    Vendor-published customer story
    Published
    May 5, 2025
    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 May 5, 2025 · OpenAI · 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.