Artificial intelligence is changing home-improvement retail as a connected operating system, not a single chatbot. At Lowe’s and The Home Depot, disclosed applications span online search, recommendations, product compatibility, project-cart building, catalog and sourcing decisions, demand planning, freight and delivery routing, post-purchase support, and tools for store associates.
Those systems can increase completed project baskets, improve product availability, lower fulfillment friction, and help employees answer more questions. However, neither company’s public disclosures isolate an AI-attributable increase in revenue, operating margin, or earnings. The most accurate conclusion is that AI creates several plausible profit levers while the realized financial effect remains undisclosed.
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What AI is doing in a home-improvement retailer
Home-improvement shopping is unusually well suited to decision-support software. A customer may know the outcome they want—repair a leaking pipe, build a deck, replace a light fixture—but not the exact parts, quantities, tools, codes, or installation steps. A retailer also has to coordinate millions of products, store inventory, suppliers, delivery capacity, and customer-service interactions.
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AI can connect those pieces. Earlier analytical models already support several functions, while newer generative systems let shoppers and employees ask questions in ordinary language. The practical change is not that a machine replaces every retail decision. It is that more decisions can be made with product, inventory, project, and logistics data in the same workflow.
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Discovery and project-basket building
Search models can interpret phrases such as “quiet bathroom fan for a 100-square-foot room” more effectively than a literal keyword match. Recommendation systems can surface complementary products, and compatibility logic can warn that a fitting, bulb, battery, or mounting system does not match the selected item. Cart-building tools can turn a single product request into a fuller project basket.
These capabilities address a costly failure mode in home improvement: a customer buys one component, discovers an incompatibility at home, and must make another trip or return the item. Better guidance may reduce that friction and increase the chance that the first order contains what the project requires.
Merchandising, catalog, and sourcing
Retailers maintain detailed product catalogs with dimensions, materials, certifications, compatible accessories, regional availability, and installation information. AI-assisted catalog processing can help organize that data and identify relationships among products. Sourcing engines can use demand, supplier, and availability signals when deciding where products should come from.
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Demand planning and inventory
Demand-planning systems estimate what customers will want, where they will want it, and when demand may change. In home improvement, weather, seasonality, housing activity, local projects, and promotions can all affect demand. Better forecasts can help a retailer place inventory closer to likely demand and reduce the risk of both empty shelves and excess stock.
This is a mechanism for improving sales and inventory economics, not proof that AI has already produced a particular improvement. Public disclosures do not provide an AI-only change in in-stock rates, markdowns, or inventory turns.
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Freight, delivery, and post-purchase service
Once an order is placed, freight-flow and delivery-route optimization can sequence shipments, assign capacity, and reduce unnecessary handling or travel. Post-purchase assistants can answer status, installation, return, or product-use questions using order and catalog information.
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What Lowe’s has announced
In a December 11, 2024 strategy release, Lowe’s said it had created “a framework to harness the new power of generative AI through a standardized development process, designed to enhance the customer experience and unlock productivity.” The wording describes a governance and development approach rather than one standalone product.
Lowe’s said earlier AI models already power online search, product recommendations, sourcing engines, and demand-planning tools. It also said pilots were intended to empower frontline associates and improve the in-store experience. Lowe’s named NVIDIA, OpenAI, and Palantir as leading technology platforms it can work alongside.
Chief executive Marvin R. Ellison said: “By leveraging leading technology solutions, we’re creating a best-in-class omnichannel shopping experience for all generations of homeowners.” That is a management objective, not a reported measurement of AI-generated profit.
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What The Home Depot has announced
The Home Depot’s investor-conference materials describe AI investments across search, recommendations, product compatibility, cart building, catalog data, sourcing logic, delivery-route intelligence, and post-purchase support. The breadth matters because a recommendation engine has a larger operational effect when it can also see inventory, delivery options, and order history.
The company said it launched Magic Apron, an on-site generative-AI experience, in October 2024. A customer-facing assistant can help translate a project question into products or instructions, but the public materials do not establish a specific conversion lift, basket increase, or labor saving from Magic Apron.
The Home Depot’s investor-relations page reports fiscal 2025 net sales of $164.7 billion and earnings of $14.2 billion, with more than 2,300 stores across the United States, Canada, and Mexico. Those are company-wide fiscal 2025 results and network figures; they must not be presented as AI-generated results.
How AI could change profits
The following are evidence-based mechanisms inferred from the disclosed use cases. They show where profit could move, not a measured attribution of past earnings.
| Profit lever | How the system can affect economics | What would need to be measured | Publicly established AI result |
|---|---|---|---|
| Revenue conversion | Search, recommendations, compatibility guidance, and project-cart building can reduce decision friction and help customers complete a basket. | Conversion, average order value, attachment rate, returns, and repeat visits for exposed versus comparable shoppers. | Not stated by either retailer. |
| Inventory and merchandising | Sourcing and demand-planning models can improve availability and reduce overstock or markdown exposure. | In-stock rate, inventory turns, substitutions, markdowns, and gross margin by category. | Not stated as an AI-attributable result. |
| Fulfillment cost | Freight-flow and delivery-route optimization can reduce handling, miles, time, or labor per order. | Cost per delivery, miles per shipment, touches per order, on-time rate, and damage or re-delivery rate. | Not stated by either retailer. |
| Service productivity | Associate and post-purchase assistants can let each employee handle more questions and provide more consistent answers. | Questions resolved per labor hour, escalation rate, resolution time, returns, and customer outcomes. | Not stated by either retailer. |
| Strategic reach | Marketplace and omnichannel initiatives can widen assortment and access; AI can improve discovery and matching within that network. | New-customer acquisition, marketplace conversion, fulfillment economics, and incremental contribution margin. | No standalone AI contribution disclosed. |
A retailer could improve revenue while losing money if model costs, fulfillment complexity, errors, or returns rise faster than sales. Conversely, a modest sales improvement could be valuable if it comes with lower service or delivery cost. That is why a credible financial assessment needs both revenue and cost measures.
Lowe’s and The Home Depot: what can actually be compared
Public announcements support a comparison of emphasis and deployment, but not a definitive ranking of AI profit performance.
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| Comparison axis | Lowe’s public emphasis | The Home Depot public emphasis | What the disclosures establish |
|---|---|---|---|
| Breadth of use cases | Search, recommendations, sourcing, demand planning, freight-flow optimization, customer service, and associate pilots. | Search, recommendations, compatibility, cart building, catalog, sourcing, delivery routes, and post-purchase support. | Both describe coverage from discovery through operations. |
| Customer-facing deployment | Generative-AI framework and pilots intended to improve the in-store experience. | Magic Apron launched on-site in October 2024. | Home Depot has named a specific customer-facing launch; Lowe’s has emphasized a standardized process and pilots. |
| Store and network integration | Frontline-associate tools and freight-flow work across a U.S. store network. | Digital and supply-chain applications connected to a network of more than 2,300 stores in three countries. | Neither disclosure gives adoption by store, order, or employee. |
| Workforce objective | Empower associates and unlock productivity. | Improve customer and operational workflows across the digital and supply-chain stack. | Objectives are stated; quantified labor results are not. |
| Financial transparency | No standalone AI revenue, margin, or earnings impact published. | No standalone AI revenue, margin, or earnings impact published. | The materials do not support a winner on AI profit performance. |
What changes for retail employees
The disclosed direction points to task redesign more clearly than to a specific head-count outcome. Store associates may spend less time looking up specifications and more time validating recommendations, handling exceptions, and advising customers on complex projects. Contact-center employees may receive suggested answers or a summarized order history before taking over a conversation. Merchandising and supply-chain teams may review model outputs instead of building every forecast or route manually.
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- Verification: checking that a recommendation fits local codes, product constraints, and the customer’s actual project.
- Exception handling: resolving cases where inventory, delivery, substitutions, or customer information is incomplete.
- Feedback: flagging incorrect catalog relationships, unsafe advice, or recurring unanswered questions.
- Customer trust: making clear when an answer is automated and when an employee or specialist should provide the final guidance.
Nothing in the cited announcements establishes layoffs, a particular staffing ratio, or a guaranteed reduction in labor expense. Productivity gains could instead allow a retailer to serve more customers with the existing workforce or redirect employees toward higher-value help.
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Incorrect or incomplete product relationships
A system that recommends an incompatible part can create returns, project delays, safety issues, and extra service contacts. Compatibility data therefore needs testing and human escalation, especially for electrical, structural, plumbing, and code-sensitive projects.
Generative answers need operational controls
A fluent answer can still be wrong. Retailers need current product data, clear boundaries on what the assistant may answer, logging, escalation paths, and a way to correct recurring errors. An answer that cannot be traced to the relevant product or order data is difficult for an associate to trust.
Integration can be harder than the model
Search, inventory, supplier, delivery, and customer-service systems often use different identifiers and update schedules. A powerful model cannot compensate for stale stock data or a catalog that does not describe compatibility accurately.
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Costs and measurement can be obscured
Cloud inference, data preparation, integration, employee training, monitoring, and quality assurance all affect the business case. Management must compare the incremental contribution from an AI-assisted interaction with the cost of running and supervising it. Company-wide sales or earnings cannot prove that AI caused the change.
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How a retailer should test whether AI is working
A disciplined rollout would measure a defined customer or operational outcome against a comparable baseline. Useful tests include:
- Define one job to be improved. For example, measure complete project-basket creation, not vague “engagement.”
- Choose a controlled comparison. Compare exposed shoppers, orders, or stores with similar unexposed groups where practical.
- Track quality as well as speed. Include returns, substitutions, escalations, complaints, delivery failures, and safety-related corrections.
- Calculate contribution, not just activity. A high usage rate is not a profit result unless incremental gross profit exceeds technology, labor, and fulfillment costs.
- Review results by category and customer type. A model may help routine replenishment while requiring specialist review for complex projects.
- Expand only after exception rates are acceptable. Store and contact-center pilots should have an explicit rollback path when data or recommendations fail.
What shoppers are likely to notice
Customers may encounter more natural-language search, recommendations for compatible accessories, project checklists, and automated order-support answers. The visible experience could be a faster path from “I need to replace this” to a set of products that can actually be purchased and delivered together.
The less visible changes may matter just as much: improved replenishment, better substitutions when an item is unavailable, more coordinated delivery windows, and an associate who can retrieve accurate product information without leaving the customer. These outcomes depend on the retailer connecting the AI layer to reliable store and supply-chain data.
What is known—and what remains unknown
It is established that Lowe’s and The Home Depot are deploying or developing AI across customer discovery, merchandising, fulfillment, and service. Lowe’s has publicly described a standardized generative-AI framework, earlier models in search and planning, and associate-oriented pilots. The Home Depot has publicly described a broad digital and supply-chain application set and launched Magic Apron in October 2024.
It is not established from those disclosures how much incremental revenue, margin, earnings, labor saving, inventory reduction, or customer satisfaction either company has obtained specifically because of AI. Lowe’s 1,748 U.S. stores as of January 31, 2025 and The Home Depot’s more than 2,300 stores and fiscal 2025 results describe company scale, not AI performance.
The defensible forecast is therefore conditional: AI can improve profits when better decisions increase completed demand or reduce the cost of serving it, and when error-control and technology costs remain below those gains. Investors and customers should treat percentage claims about AI-driven profit growth skeptically unless a retailer reports the measurement method, comparison group, period, and costs included.
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