IKEA x Best Buy Shop-in-Shop: AI-Powered Omnichannel Wins

A 10-store pilot doesn’t sound like much—until you remember how retail actually changes. It doesn’t change through grand announcements. It changes through small, measurable experiments that reduce customer friction.

That’s why the news that IKEA is opening small shops inside Best Buy locations across 10 stores in Florida and Texas is worth paying attention to. Not because a couple of well-known brands are sharing floor space, but because shop-in-shop is becoming the testing ground for omnichannel retail, and AI is the tool that can make these partnerships pay off (or expose why they won’t).

For retailers and e-commerce teams—especially those watching the AI in Retail and E-Commerce space closely—this pilot is a clean case study: two brands, two purchase journeys, one customer, and a lot of operational questions that AI can answer faster than humans can.

Why the IKEA–Best Buy pilot makes sense (and why it’s hard)

Answer first: Shop-in-shop works when it reduces decision fatigue and makes “I want that” easier to act on. It fails when the experience feels disjointed, inventory is unclear, or staff can’t support the new category.

On paper, IKEA inside Best Buy is logical. Best Buy sells big-ticket electronics that people increasingly want integrated into their homes—think home office setups, smart lighting, home networking, and entertainment systems. IKEA brings the context: rooms, layouts, storage, and the “how it fits” story.

But here’s what most companies get wrong about shop-in-shop: they treat it like a merchandising project, not a data project. Endcaps and displays matter, sure. The real determinant is whether the partnership can answer three questions consistently:

1. Can customers find the right solution quickly? (Not just a product.)
2. Can they buy it without uncertainty? (Price, availability, delivery, returns.)
3. Can the retailer learn from every interaction? (To improve week by week.)

The reality? A shop-in-shop is a forced marriage between:

• Different POS and promo rules
• Different inventory and delivery models
• Different product education needs
• Different brand voices

That friction is exactly where AI for customer behavior analysis and omnichannel measurement becomes practical—not theoretical.

Shop-in-shop is omnichannel retail in miniature

Answer first: A shop-in-shop pilot is an omnichannel stress test because it blends physical discovery, digital research, service, fulfillment, and post-purchase support in one footprint.

When someone buys a desk lamp, a slightly confusing in-store experience is survivable. When someone buys a desk, a monitor, a mounting arm, cable management, and lighting together, confusion kills conversion.

The customer journey you’re really managing

A typical journey for this pilot likely looks like this:

• Customer sees a room vignette (IKEA influence)
• They compare options on their phone (Best Buy influence)
• They ask a staff member one question (the make-or-break moment)
• They decide whether to buy now or later
• They choose delivery vs. carry-out
• They discover assembly or setup complexity at home

Omnichannel success depends on continuity across that journey. If the experience breaks at any point—“Is this in stock?”, “Can I return it here?”, “Will this fit my space?”—you lose trust.

Where AI fits immediately (without boiling the ocean)

You don’t need humanoid robots to improve this. You need a smarter feedback loop.

AI can help by:

• Detecting the highest-friction moments (from chat logs, associate notes, returns reasons)
• Mapping product adjacency (what people buy together, what they try to buy together)
• Personalizing recommendations based on intent signals (room type, budget, device ecosystem)

A good omnichannel strategy isn’t “online + offline.” It’s one set of decisions supported everywhere.

What AI can do inside the store (that typical analytics can’t)

Answer first: Traditional analytics counts transactions. AI explains behavior: what shoppers considered, where they hesitated, and what nudged them to buy.

For a shop-in-shop partnership, the store becomes a live laboratory. The question is whether you can observe it in a way that respects privacy and still yields usable insights.

1) Behavior analytics without creepiness

Retailers don’t need to identify individuals to learn. They need patterns.

Using privacy-aware approaches (aggregated signals, anonymized event data), AI can help quantify:

• Dwell time by zone (Are shoppers stopping at the vignettes or walking past?)
• Pathing trends (Do shoppers reach IKEA first or only after browsing TVs/laptops?)
• Assisted vs. unassisted conversions (How often an associate interaction changes outcome)

If the IKEA section is acting as a “solution hub,” you should see longer dwell time and higher attachment rates to complementary items.

2) Smarter bundling: from “related items” to “complete setups”

Most recommendations are lazy: “people also bought.” What you want in this scenario is setup completeness.

AI-driven recommendations can be tuned to answer:

• For a home office display, what’s the minimum viable bundle?
• What accessories reduce returns? (cable length, mounting compatibility, desk depth)
• What items reduce support calls? (surge protection, correct adapters)

A memorable rule: Bundling isn’t upsell. It’s risk removal.

3) Demand forecasting at the category boundary

Shop-in-shop creates a forecasting challenge because demand shifts are cross-category:

• A compelling IKEA office display may spike monitor arms
• A laptop promotion may spike desks and task chairs

AI forecasting models are better than spreadsheet trend lines at capturing these relationships because they can incorporate:

• Promotions and price changes
• Local store demographics
• Seasonal intent (January “reset” purchases are real)
• Weather and regional patterns (relevant for Florida/Texas footfall variability)

This matters in December 2025 because shoppers are straddling holiday gifting and post-holiday home upgrades. If you understock the “boring” parts (cable management, brackets, small storage), you lose the entire basket.

The operational realities that make or break the pilot

Answer first: The success of IKEA-in-Best Buy will hinge less on aesthetics and more on four operational decisions: inventory visibility, staff enablement, fulfillment, and measurement.

Inventory: “In stock” has to mean something

Nothing torpedoes omnichannel trust faster than unclear availability.

At minimum, the pilot needs:

• Unified availability messaging at the shelf and online
• Clear rules for “store stock vs. ship-to-home”
• A way to prevent customers from discovering constraints at checkout

AI can support this by flagging items with high intent-to-buy but frequent out-of-stock events, so teams prioritize replenishment where it lifts revenue.

Staff enablement: one associate can’t know everything

Best Buy associates know electronics. IKEA products require different knowledge: dimensions, materials, assembly complexity, room planning.

The fix isn’t a three-ring binder. It’s associate copilots—internal AI tools that answer:

• “Will this monitor arm fit this desk thickness?”
• “What’s the best alternative in stock right now?”
• “What’s the simplest bundle under €X/$X for a student setup?”

If you’re building AI in retail, this is one of the highest ROI applications because it improves conversion and reduces misinformation-driven returns.

Fulfillment and returns: the customer doesn’t care whose box it is

Shop-in-shop shoppers assume the experience is unified. When returns are complicated—“You bought this here, but you must return it there”—they stop believing the partnership.

The pilot should be evaluated on:

• Return rate differences vs. baseline
• Time-to-resolution for service issues
• Customer satisfaction on “ease of return” and “delivery clarity”

AI helps here by categorizing return reasons from unstructured text, then pinpointing which display setups and bundles are causing confusion.

Measurement: a pilot without a scoreboard is theatre

If you can’t measure it, you can’t scale it.

A practical measurement stack includes:

• Attachment rate (e.g., electronics basket size with IKEA exposure vs. without)
• Zone conversion (engaged with shop-in-shop → purchased same day)
• Assisted conversion rate (associate interaction impact)
• Cross-channel halo (did in-store exposure lift online orders later?)

AI is useful because it can connect messy signals—POS, loyalty, web sessions, customer support—into a coherent model of incremental lift.

If your pilot dashboard only tracks sales in the IKEA footprint, you’ll miss the whole point.

Lessons for Irish retailers watching this trend

Answer first: Irish retailers can borrow the mechanics of this pilot—shared footprints, shared data, shared fulfillment—even if they don’t have U.S.-scale store networks.

This post is part of our AI in Retail and E-Commerce series, and the pattern is showing up everywhere: customers want the confidence of physical retail with the convenience of e-commerce.

Here are three ways I’ve seen retailers in Ireland apply similar thinking:

1) Use “micro-experiences” instead of new stores

Rather than opening new locations, create:

• Store-within-store partnerships
• Pop-ups inside complementary retailers
• Dedicated “solution corners” (home office, smart home, gifting)

Then instrument them. If it can’t be measured, it’s just decor.

2) Make personalization physical

Personalization shouldn’t end at the store door.

Simple, effective tactics:

• QR-based journeys tied to room setups (capture intent, not identity)
• Recommendation flows based on use case (small apartment, student, remote worker)
• AI-driven “next best product” prompts for associates, not customers

3) Treat data-sharing like a product requirement

Partnerships die when data lives in silos.

Before you launch, define:

• What data you will share (aggregated, privacy-safe)
• Who owns the customer relationship at each touchpoint
• How you will attribute revenue across channels

If you’re serious about omnichannel experiences, this isn’t legal fine print—it’s the operating system.

Quick FAQ: what leaders usually ask first

How do you know if shop-in-shop is working?
You’re looking for measurable lift in total basket size, attachment rates, and repeat visits—plus fewer service issues.

Is AI necessary for this kind of partnership?
If you want to scale it, yes. Humans can manage a pilot. Scaling requires automated insight into behavior, inventory, and operational friction.

What’s the biggest risk?
A mismatched customer promise: beautiful displays paired with confusing availability, delivery, or returns.

What to do next if you’re planning your own pilot

This IKEA–Best Buy pilot is a reminder that omnichannel retail doesn’t need to be abstract. It can be a 500-square-foot test with a ruthless measurement plan.

If you’re considering a store-within-store concept (or even a “solution zone” inside your own shops), start with these next steps:

1. Define the customer promise in one sentence (what gets easier for them?)
2. Pick 5–7 metrics that reflect the whole journey, not just one category’s sales
3. Deploy AI where humans struggle: forecasting, bundle logic, and service triage
4. Run the pilot like a product team—weekly iteration, not quarterly post-mortems

The most interesting question isn’t whether IKEA can sell inside Best Buy. It’s whether the pilot proves a repeatable model for AI-powered omnichannel experiences—the kind that feel coherent to customers and profitable to operators. If it does, expect more partnerships like this to follow fast.