Lowe’s AI Puts Project Expertise in Every Customer’s Hand

Most home improvement projects don’t fail because people can’t swing a hammer. They fail because the plan is fuzzy: the wrong materials, one missing part, a measurement mistake you notice only after the drywall’s up. Retailers have tried to solve this with how-to content and in-store advice for decades. The problem is scale—expert help is expensive, and demand spikes at the exact moments you can’t staff for.

That’s why Lowe’s partnership with OpenAI—and the launch of Mylow and Mylow Companion—is a big deal for the AI in Retail & E-Commerce story. This isn’t AI as a novelty. It’s AI-powered customer service applied to a practical, high-stakes category where guidance is part of the product.

If you’re a retailer, a digital services leader, or a brand trying to turn support into growth, Lowe’s move is a clear signal: generative AI is becoming the new front line of customer communication and associate enablement—especially in complex purchase journeys like home improvement.

What Lowe’s built: Mylow vs. Mylow Companion

Answer first: Lowe’s is using generative AI to provide project guidance for shoppers and real-time support for store associates, so expertise is available even when a human expert isn’t.

From the RSS summary, Lowe’s partnered with OpenAI to create two AI-powered tools:

• Mylow: built for customers who need help planning and completing home improvement projects.
• Mylow Companion: built for store associates who need fast, accurate product and project help while serving customers.

This split matters. Retail AI often focuses on either customer-facing chat or internal productivity. Lowe’s is doing both, which is how you actually scale service without breaking the in-store experience.

Why home improvement is the perfect “hard mode” use case

Home improvement retail is messy in a way that apparel or electronics isn’t:

• Purchases are interdependent (primer before paint, anchors matched to wall type, grout width matched to tile spacing).
• Customers frequently start with incomplete requirements (“I want a shower niche” is not a bill of materials).
• Mistakes are costly—returns are bulky, time is wasted, and trust drops.

In other words, if AI in retail works here, it’s likely to work anywhere.

The real win: AI that scales customer communication

Answer first: Mylow-style tools scale high-quality customer communication by turning scattered expertise into consistent, on-demand guidance.

Most retailers already have the raw material: product data, how-to articles, installation guides, and associate know-how. The issue is that customers can’t navigate it when they’re stressed, mid-project, and standing in an aisle.

Generative AI changes the interface. Instead of forcing people to search keywords, it supports natural language shopping:

• “I’m mounting a TV on drywall but don’t hit studs—what anchors do I need?”
• “How much flooring should I buy for a 12x14 room with two closets?”
• “What’s the difference between interior and exterior caulk for a bathroom?”

When retailers get this right, customer support stops being a cost center and starts acting like a sales assistant that:

• reduces decision friction,
• improves basket completeness,
• and prevents project-killing mistakes.

A practical example: the “one missing part” problem

Here’s what I’ve found in real-world retail journeys: customers rarely under-buy the main item. They under-buy the connective tissue—fasteners, adapters, prep materials, safety gear.

An AI assistant can proactively suggest what humans typically ask about:

• For painting: patch compound, sanding blocks, painter’s tape, drop cloths, primer selection.
• For a faucet install: supply lines, shutoff valves, plumber’s tape, basin wrench.
• For floating shelves: stud finder, level, anchors rated for load, drill bit sizes.

That’s not just helpful. It’s measurable revenue protection.

Associate enablement: why “AI in the aisle” matters

Answer first: Mylow Companion can turn every associate into a more confident helper by providing instant project context, product fit guidance, and troubleshooting prompts.

Retailers are dealing with a staffing reality: turnover is persistent, product catalogs are huge, and customers expect expert-level guidance anyway. An associate might be great at paint but unsure about smart thermostats. Another might know plumbing basics but not flooring transitions.

An AI assistant for store associates can help in three ways:

1. Faster answers under pressure

   • Instead of walking a customer to another department or searching internal systems, associates can get a direct, usable response.

2. More consistent guidance

   • Service quality stops depending on who’s scheduled.

3. Training in the flow of work

   • Associates learn while solving real customer problems, which is often more effective than training modules.

A simple rule: if the associate has to leave the customer to “go look it up,” the retailer is losing momentum—and sometimes the sale.

This is also where the U.S. digital economy angle shows up clearly: AI-powered tools are becoming the infrastructure layer for service delivery, not just a chatbot bolted onto a website.

What retailers should copy (and what they shouldn’t)

Answer first: The play isn’t “add a chatbot.” The play is to connect AI to project workflows, product reality, and service accountability.

Plenty of retailers will try to mimic Lowe’s with a generic customer service bot. That’s the wrong lesson. The defensible part is project expertise operationalized.

Do copy: project-based experiences, not generic Q&A

Home improvement shoppers don’t want “answers.” They want a path:

• steps,
• materials,
• quantities,
• safety notes,
• and a check for compatibility.

If you’re building in retail, aim for project flows like:

1. Define project goal (replace, repair, upgrade).
2. Gather constraints (dimensions, surface type, budget, timeline).
3. Generate bill of materials.
4. Confirm product compatibility.
5. Provide step-by-step plan.
6. Offer “what can go wrong” troubleshooting.

Do copy: human handoff and accountability

AI should be allowed to say:

• “I need one more detail to recommend the right part.”
• “Let’s confirm the measurement.”
• “This looks like a code/safety issue—ask a licensed pro.”

And when confidence is low, it should escalate—either to a specialist, a service desk, or a scheduled consult.

Don’t copy: overly confident responses

In categories like electrical, plumbing, or structural work, hallucinated advice isn’t just annoying. It’s risky. The safer approach is:

• constrain responses to approved knowledge,
• cite product attributes and compatibility rules,
• ask clarifying questions early,
• and include safety guardrails.

How this fits the bigger “AI in Retail & E-Commerce” trend

Answer first: Lowe’s is showing the next phase of AI in retail: personalization and support aren’t separate—they’re one guided commerce experience.

In this topic series, we often talk about personalization, demand forecasting, pricing, and inventory optimization. Those are crucial. But for customer loyalty, guided commerce is where retailers can separate themselves.

Here’s the shift:

• Old model: search → filter → compare → hope you didn’t miss something.
• New model: describe the job → get a plan → get the right products → complete the project.

That new model drives multiple benefits at once:

• Customer experience: fewer stalled projects and fewer “I bought the wrong thing” moments.
• Conversion rate: less friction, more confidence.
• Average order value: more complete baskets, fewer forgotten essentials.
• Operational efficiency: fewer repetitive questions at service desks.

Seasonal relevance: why this lands in late December

Late December is when a lot of households plan January projects—closet upgrades, garage organization, bathroom refreshes, and energy-efficiency fixes after winter utility bills land. It’s also a common time for gift card redemption.

AI project assistants shine here because shoppers are often:

• starting from scratch,
• working within a budget,
• and trying to estimate quantities before they commit.

“People also ask” about AI project assistants in retail

Answer first: The best AI project assistants are accurate, workflow-based, and tied to real product constraints.

Can an AI assistant really help with DIY planning?

Yes—when it collects the right inputs (dimensions, surface type, environment) and returns an ordered plan plus materials. The value isn’t “tips.” It’s preventing rework.

Will AI replace in-store experts?

No. It reduces the load on experts by handling repetitive questions and basic planning. Experts still matter for edge cases, code-adjacent questions, and complex troubleshooting.

What should retailers measure to prove ROI?

Track outcomes that connect directly to revenue and service costs:

• project completion rates (proxy via repeat visits and product sequence purchases),
• return rates for project-linked SKUs,
• attach rates (the “forgotten essentials” line items),
• time-to-assist for associates,
• customer satisfaction on guided interactions.

What to do next if you’re building AI in retail

Answer first: Start with one high-friction project journey, instrument it end-to-end, and expand only after you can prove accuracy and impact.

If you’re considering an AI assistant for retail or e-commerce, borrow Lowe’s underlying strategy:

1. Pick one project category with heavy confusion (paint selection, faucet replacement, shelving, flooring).
2. Define the minimum viable “plan” the assistant must produce (steps + bill of materials + quantities).
3. Add guardrails (clarifying questions, safety prompts, escalation paths).
4. Equip associates too, not just customers—internal adoption is where efficiency shows up.
5. Measure attach rate, returns, and time-to-resolution before you chase vanity metrics.

Retail AI works when it reduces uncertainty. That’s the job.

Lowe’s decision to put project expertise into every hand is a preview of where U.S. digital services are heading: AI isn’t replacing the store. It’s making the store smarter, more consistent, and easier to shop—especially for complicated, real-life needs.

If this is the direction, the next question is straightforward: which customer journey in your business is still depending on “hope they figure it out”?