How Lowe’s Uses AI to Improve Retail Experiences

Most retailers talk about AI like it’s a side project. Lowe’s treats it like infrastructure.

That difference matters in home improvement, where customers don’t just “shop”—they plan projects, compare materials, calculate quantities, coordinate delivery, and often need help mid-build. If you’re running digital services in the U.S. retail economy, the lesson is straightforward: AI in retail works best when it’s designed to remove friction across the entire customer journey, not just to answer a few support chats.

This post is part of our AI in Retail & E-Commerce series, focused on how companies use AI for personalization, forecasting, and customer experience improvements. Lowe’s is a particularly useful example because it sits at the intersection of physical stores, complex inventory, and high-intent customer questions—and that’s where AI has to be practical to be worth the cost.

Why home improvement retail is a perfect AI stress test

Home improvement is “high complexity retail,” and AI either proves itself here or it doesn’t.

Unlike many e-commerce categories, customers routinely need guidance that’s contextual: what fits their home, what complies with local codes, what’s compatible with an existing tool, what to do if something doesn’t fit, and how much to buy. This creates three persistent problems that AI is well-suited to solve when implemented thoughtfully:

• Decision fatigue: Too many options with subtle differences (paint finishes, lumber grades, appliance sizes).
• Project ambiguity: Shoppers know the outcome they want, not the exact items they need.
• Operational friction: Inventory, substitutions, delivery windows, returns, and store pickup all create failure points.

The reality? If your digital experience can’t turn “I’m redoing my bathroom” into a confident cart, you lose to whoever can.

Where Lowe’s AI efforts tend to concentrate (and why it works)

Lowe’s AI strategy is most effective when it’s applied to repeatable moments: search, product discovery, assistance, and store operations.

While we couldn’t access the original source page content directly due to a 403 restriction, the headline (“Lowe’s leverages AI to power home improvement retail”) matches a broader pattern playing out across U.S. retailers: AI becomes valuable when it’s embedded into core workflows, not bolted on as a demo.

Here are the AI areas that typically create measurable impact in home improvement retail, and why they’re worth prioritizing.

AI-powered customer support that doesn’t feel like a chatbot

Done right, AI customer service reduces cost and improves satisfaction.

The most common failure mode is treating AI like a scripted FAQ. In home improvement, customers ask messy questions: “What size breaker do I need for this heater?” or “Why is my new faucet leaking?” These aren’t simple lookups—they require clarification, follow-up questions, and safe guidance.

A strong model for AI customer support in retail includes:

• Guided troubleshooting flows (ask 2–4 clarifying questions before suggesting steps)
• Product-aware answers grounded in specs, manuals, and compatibility notes
• Escalation rules for safety-critical topics (electrical, gas, structural) and refunds/charge disputes
• Conversation memory that persists across channels (web, app, phone support)

If you’re generating leads from digital services, there’s a practical upside: AI support can capture project intent (“deck build,” “kitchen remodel”) and route the customer to higher-value services like installation, design consultation, or pro contractor programs.

Personalized product discovery for project-based shopping

In retail and e-commerce, personalization is only useful when it reduces work for the customer.

Home improvement shoppers don’t want “recommended for you” in the abstract. They want “recommended for this project.” That’s why the best personalization strategies here are context-first:

• Recommend the next item in a workflow (primer → paint → rollers → painter’s tape)
• Bundle complementary parts (mounting kits, connectors, fittings)
• Adjust recommendations by constraints (apartment vs. house, room size, budget range)

This is where AI-powered search and product discovery outperform traditional filters. Instead of forcing people to translate their needs into category trees, the system translates intent into a structured plan.

One stance I’ll take: retailers should measure personalization by time-to-confidence, not just conversion rate. If AI reduces the number of tabs opened, comparisons made, and returns caused by wrong-fit purchases, it’s doing its job.

Smarter inventory and fulfillment decisions

For U.S. retailers, AI inventory management isn’t glamorous—but it’s often the highest ROI.

Home improvement has awkward inventory characteristics: bulky items, long lead times, seasonal spikes, and regional demand differences (hurricane prep in one area, snow equipment in another). AI helps when it predicts demand at the right granularity:

• Store-level demand forecasting (not just national averages)
• Substitution and replenishment logic that accounts for delivery promises
• Dynamic safety stock for high-variability SKUs

Why customers notice: fewer “out of stock” dead ends, fewer order cancellations, and more accurate pickup readiness times.

And on the business side, AI reduces two expensive problems:

1. Overstock (cash tied up, markdowns, warehouse overflow)
2. Understock (lost sales, customer churn, labor wasted on apologies)

Employee-facing AI that improves service on the floor

Some of the best AI in retail never faces the customer directly.

Store associates are frequently asked the same project questions customers ask online. Employee-facing AI can act like a “second brain” that surfaces:

• Product location and real-time inventory
• Compatibility guidance (which connector fits which pipe)
• Step-by-step project instructions and install tips
• Upsell suggestions that actually help (the right anchors, not random add-ons)

In home improvement, the floor experience still drives trust. If AI helps associates answer correctly the first time, it reduces returns and builds loyalty.

What “good AI integration” looks like in U.S. digital services

The strongest pattern across AI adoption in the U.S. digital economy is simple: AI succeeds when it’s connected to systems that matter.

If you’re designing AI for a retail organization (or selling AI services into one), the architecture matters as much as the model. “Connected” typically means:

• Product catalog and attribute data (PIM)
• Inventory and order management (OMS)
• Customer profiles and loyalty history (CDP/CRM)
• Knowledge bases: manuals, returns policy, service terms
• Store operations data: staffing, pickup queues, delivery constraints

Without this, AI produces confident-sounding answers that aren’t operationally true. That’s how you get the classic failure: “Yes, it’s available for pickup in 2 hours,” followed by a cancellation email.

Snippet-worthy truth: In retail, AI accuracy isn’t just about correct language—it’s about correct inventory, policies, and promises.

Practical lessons other retailers can steal from Lowe’s approach

If you want AI to drive leads and revenue, don’t start with “where can we use AI?” Start with “where do customers get stuck?”

Here are four practical moves that consistently work for AI in retail and e-commerce, especially for project-based categories.

1) Prioritize journeys, not features

Pick one journey and make it noticeably better end-to-end.

Good starting points:

• “Find the right part” (compatibility + fit)
• “Plan a project” (materials list + quantity estimates)
• “Buy online, pick up in store” (accuracy + readiness)

A single improved journey can outperform a dozen scattered AI widgets.

2) Treat your product data like fuel

Personalization, AI-powered search, and recommendations all rise or fall on data quality.

If your catalog has missing dimensions, inconsistent naming, or weak compatibility metadata, AI will guess. And guessing creates returns.

An actionable checklist I’ve found useful:

• Normalize units (inches vs. mm) and enforce attribute standards
• Add compatibility fields where it matters (thread size, voltage, fittings)
• Capture “project role” tags (primer vs. topcoat, rough-in vs. finish)

3) Build guardrails for high-risk advice

Home improvement has safety implications. Your AI must know when to stop.

Retailers should implement:

• Refusal or escalation for electrical/gas/structural advice
• Clear boundaries between product guidance and professional instruction
• Audit logs for responses used in customer disputes

This is especially relevant in the U.S. market, where liability and consumer protection expectations are high.

4) Measure outcomes that finance teams trust

If you want AI to keep funding, report metrics that map to cost and revenue.

Strong KPI options:

• Reduction in contact center handle time
• Increase in self-serve resolution rate
• Fewer returns due to wrong-fit purchases
• Higher BOPIS completion rate (order placed → successfully picked up)
• Increased attachment rate for project bundles (with lower return rates)

People also ask: what does AI actually do in retail?

AI in retail typically improves four core functions:

1. Search and discovery (understanding intent and ranking better results)
2. Personalization (context-aware recommendations and bundles)
3. Forecasting and inventory management (store-level demand prediction)
4. Customer support (faster resolution with accurate, policy-aware answers)

The most profitable implementations connect these functions so the customer experience and the operations layer stay aligned.

What this means for 2026 planning (and why timing matters)

Late December is when retail teams are mapping budgets, resetting priorities, and deciding which experiments become real programs. If AI is on your roadmap for 2026, Lowe’s example points to a clear stance: stop treating AI as innovation theater and start treating it as a service layer across digital and store operations.

For lead generation, there’s another angle: AI doesn’t just reduce costs—it can also identify intent signals (project type, timeline, budget) that feed higher-margin services like installation, design help, and pro accounts. That’s where AI stops being “tech spend” and becomes a growth engine you can defend.

So here’s the forward-looking question worth asking internally: where do your customers still need a human because your digital experience can’t translate intent into action? That gap is your best AI opportunity.