How Lowe’s Uses AI to Modernize Home Improvement

Retail AI isn’t winning because it’s flashy. It’s winning because it fixes the most expensive problems in commerce: out-of-stocks, slow customer support, confusing product discovery, and inefficient labor planning. Home improvement makes those problems even harder—catalogs are huge, projects are complex, and customers often need guidance, not just a checkout button.

That’s why Lowe’s AI story matters for anyone building technology and digital services in the United States. When a retailer at Lowe’s scale invests in AI, it’s not a science project. It’s a blueprint for how U.S. enterprises are turning AI into better customer experience, smoother operations, and measurable growth.

This post is part of our “AI in Retail & E-Commerce” series, where we focus on practical uses of artificial intelligence like personalization, demand forecasting, and automation. Lowe’s is a strong example because home improvement sits at the intersection of commerce + service—and that’s where AI tends to pay for itself fastest.

Why home improvement retail is a perfect AI use case

Home improvement retail is “high-intent,” but not always “high-confidence.” Customers often arrive knowing the outcome they want (fix the leak, redo the kitchen, build a deck) without knowing the exact parts, measurements, compatibility constraints, or local code considerations. That gap creates friction in every channel: web, app, phone, and in-store.

AI helps because it can translate goals into shopping actions—and it can do it at scale.

The core pain points AI can actually solve

A lot of retailers start with “chatbots” and stop there. The smarter approach is to map AI to operational pain.

Here are the problems home improvement retailers can’t ignore:

• Product discovery is messy: customers search “door handle” but need the right backset, finish, and lock type.
• Inventory is lumpy and seasonal: demand spikes around storms, spring projects, and holiday hosting.
• Assortment complexity is huge: thousands of SKUs with similar names and subtle differences.
• Fulfillment matters: bulky items, delivery windows, jobsite drop-offs, and returns.
• Customer support is project-based: a single “issue” often spans multiple products and steps.

AI—especially when connected to high-quality product data, inventory signals, and order history—can reduce that friction in ways that show up directly in conversion rate, margin, and customer satisfaction.

Where Lowe’s-style AI shows up in the customer experience

The most valuable retail AI experiences don’t feel like “AI.” They feel like the store finally understands what you’re trying to do.

Smarter search and project-based shopping

Search is still the front door of e-commerce. In home improvement, it’s also where most carts die.

AI-powered search improves results in three concrete ways:

1. Intent detection: understanding that “replace shower cartridge” is a task, not a SKU.
2. Attribute awareness: recognizing constraints like size, finish, voltage, compatibility, and brand equivalencies.
3. Guided refinement: prompting customers with the right follow-ups (dimensions, model number, material) without sending them to a separate help article.

When done well, this becomes project-based shopping: the experience shifts from “find an item” to “complete the job.” For retailers, that often translates into larger baskets and fewer returns—because customers buy the right parts the first time.

Personalization that respects the mission

Retail personalization has a bad reputation because it often devolves into “you clicked once, so here are 30 irrelevant recommendations.” Home improvement customers behave differently:

• They buy in bursts (a bathroom remodel month, then nothing for weeks).
• They need sequences (primer before paint, anchors before shelves).
• They care about compatibility and quality, not just price.

A better personalization model focuses on:

• Next-best step recommendations (what typically comes after this purchase)
• Accessory and replenishment timing (filters, blades, batteries)
• Local relevance (weather-driven items, regional code norms, delivery availability)

That kind of personalization feels helpful instead of creepy—and it’s one of the most reliable ways to increase repeat purchases in retail e-commerce.

AI in customer support: faster answers, fewer escalations

Customer support is one of the most immediate “AI pays for itself” areas in U.S. retail. The win isn’t replacing people; it’s reducing avoidable contacts and speeding up the rest.

A well-designed AI assistant can handle:

• Order status, delivery scheduling, and return eligibility
• Product Q&A grounded in specs and manuals
• Store services questions (installation, rental, pickup)
• Troubleshooting flows (common errors, missing parts)

The bar is higher now: generative AI has to be accurate, consistent, and policy-aware. In practice, that means connecting the assistant to approved knowledge sources, restricting answers when uncertainty is high, and handing off cleanly to human agents with context.

A retail AI assistant is only as good as its ability to say “I don’t know” and route the problem correctly.

How AI improves store and supply chain operations (where ROI gets real)

Customer experience gets the attention, but operations is where AI compounds. This is especially true for home improvement, where inventory and labor decisions are expensive and highly visible.

Demand forecasting and inventory optimization

AI forecasting can outperform basic time-series methods because it can ingest more signals:

• Weather forecasts (storm prep, freeze protection, heat waves)
• Regional seasonality (gardening vs. snow removal timing)
• Promotion calendars and price changes
• Online browsing trends (early intent signals)
• Local events and contractor activity patterns

Better forecasts drive three outcomes that finance teams care about:

• Higher in-stock rates on high-demand SKUs
• Lower safety stock where demand is stable
• Fewer markdowns on seasonal and bulky products

If you’re trying to justify an AI program internally, start here. Inventory errors are measurable, painful, and frequent.

Workforce planning and task automation in stores

Stores don’t just sell products—they do picking, staging, returns, customer assistance, and often service coordination. AI can improve labor planning by predicting:

• Foot traffic by hour and department
• Online order volume for pickup and delivery
• Service demand (rentals, installs)

Then it can translate predictions into staffing guidance and task prioritization. The best systems don’t pretend to “optimize” humans; they give managers usable recommendations they can override.

Shrink reduction and fraud detection

Shrink (loss from theft, damage, and process gaps) remains a major U.S. retail problem. AI can help by detecting patterns across:

• Unusual return behavior
• High-risk SKU movement
• Transaction anomalies
• Inventory discrepancies by location

This is a sensitive area: overly aggressive models create customer friction and associate distrust. The practical approach is to focus on decision support and targeted interventions rather than blanket restrictions.

The real foundation: data quality, governance, and evaluation

Most companies get this wrong: they buy an AI tool before fixing the data it needs.

If Lowe’s (and peers) are making AI work, it’s because the organization treats AI like a product—with inputs, evaluation, and accountability.

What “AI-ready” retail data looks like

Home improvement retail depends on product truth. If attributes are missing or inconsistent, AI assistants hallucinate, search fails, and recommendations become noise.

A strong retail data foundation includes:

• Clean product attributes (dimensions, materials, compatibility, power requirements)
• Consistent taxonomy (categories and subcategories that reflect how customers shop)
• Real-time inventory signals per store and DC
• Order and fulfillment events (picked, packed, staged, delivered)
• Knowledge content governance (manuals, FAQs, policy docs with version control)

Model evaluation that matches retail reality

Retail AI fails when it’s judged by vanity metrics. You want evaluation tied to outcomes:

• Search: click-through rate, add-to-cart rate, “zero results” rate
• Recommendations: attach rate, basket size, return rate
• Support: containment rate, first-contact resolution, time-to-resolution
• Forecasting: forecast error (MAPE), in-stock rate, markdown rate

For generative AI, you also need safety and reliability measures:

• Hallucination rate on known-answer tests
• Policy compliance rate
• Escalation accuracy (did it route correctly?)

What other U.S. retailers can learn from the Lowe’s pattern

Lowe’s isn’t interesting because it uses AI. It’s interesting because the use cases are practical and connected—customer experience, operations, and support reinforcing each other.

Here’s a playbook I’ve found works for mid-market and enterprise retailers alike.

A pragmatic 90-day AI roadmap (that leads to real pilots)

Days 1–30: Pick one measurable workflow

• Choose a single high-volume pain point (search relevance, support deflection, forecasting for one category)
• Define success metrics and guardrails
• Audit data quality for that workflow

Days 31–60: Build the smallest useful version

• Integrate only the data sources that are essential
• Keep human override in place
• Establish evaluation with weekly reporting

Days 61–90: Expand carefully, then automate

• Extend to more categories, regions, or intents
• Improve policies and escalation logic
• Add monitoring for drift and failures

The stance I’ll take: if you can’t measure it weekly, you’re not running an AI program—you’re running a demo.

“People also ask” (answered plainly)

How is AI used in retail e-commerce today?
AI is most commonly used for personalization, product search, demand forecasting, inventory optimization, customer support automation, and fraud detection.

Does AI in retail replace store associates?
No. The best implementations reduce repetitive work and improve guidance so associates can focus on higher-value customer help, complex questions, and service coordination.

What’s the biggest risk with generative AI in retail?
Wrong answers that sound confident—especially around compatibility, safety, and return policies. That’s why grounding, testing, and clear escalation matter.

What this means for the digital economy (and your next step)

AI in U.S. retail is becoming infrastructure. It’s how companies scale customer communication, keep shelves stocked, and make e-commerce feel less like a catalog and more like a helpful assistant—especially in high-consideration categories like home improvement.

If you’re responsible for digital services, customer experience, or operations, the next step is straightforward: pick one workflow where friction is obvious, connect the data that workflow needs, and commit to weekly measurement. That’s how you move from “we’re experimenting with AI” to “AI is powering growth.”

Where could AI remove the most friction in your customer journey—search, support, inventory, or fulfillment?