Costco’s Record Q1 Shows What Retail AI Can Fix

$67.31 billion in quarterly revenue is a loud signal. Costco just posted record-breaking Q1 sales for fiscal 2026, with earnings of $4.50 per share versus estimates of $4.27. The headlines focused on “fun facts” (pizza, pies, Black Friday online records). I think the more useful story is what those facts really represent: demand patterns that were predicted well, stocked well, priced well, and fulfilled well—across channels.

If you’re running retail or e-commerce in Ireland (or selling into Ireland), this matters for a practical reason: you’re dealing with the same forces Costco is—holiday volatility, margin pressure, and shoppers who bounce between store, site, and app without thinking twice. The difference is you probably don’t have Costco’s scale to absorb mistakes.

This post is part of our AI in Retail and E-Commerce series, where we focus on how retailers use AI for customer behaviour analysis, personalized recommendations, pricing optimization, and omnichannel experiences. Costco’s quarter is a useful case study because it shows what happens when execution is tight—and what other retailers can copy with the right AI foundation.

What Costco’s Q1 numbers really say about demand

Costco’s “fun facts” are only fun if you ignore the operations behind them. They’re actually demand signals—high-frequency, high-stakes proof that the company understood what shoppers would want, when they’d want it, and how they’d buy it.

Here’s what was reported from the quarter:

• 358,000 pizzas served at U.S. food courts, up 31% over 2024
• 4.5 million pies sold in the three days before Thanksgiving
• $250M+ in non-food online orders on Black Friday (record for Costco’s U.S. e-commerce business)

These aren’t random spikes. They’re repeatable patterns with a short window for error.

The hidden challenge: planning for “peaky” demand

Holiday demand isn’t just higher; it’s spikier. That means:

• Forecast errors get expensive fast (stockouts cost revenue; overbuys cost cash)
• Fulfilment capacity becomes a bottleneck (delivery slots, pick/pack labour, carrier constraints)
• Pricing and promotions can backfire (discounting into stockouts is self-sabotage)

The lesson: you don’t need Costco’s membership model to benefit from Costco-style demand planning. You need the ability to forecast at a more granular level than “December will be busy.” AI is how mid-market retailers do that without hiring a small army of analysts.

AI demand forecasting: the most direct path to fewer stockouts

If you want one area where AI earns its keep quickly, it’s demand forecasting for retail—especially in Q4 and early Q1 when shoppers are still in gift-card mode and returns reshape inventory.

A solid AI forecasting setup does three things better than traditional spreadsheets:

1. Learns from many signals at once (sales, promos, web traffic, weather, local events, lead times, competitor pricing)
2. Forecasts at the level you actually operate (SKU-store-day, not category-month)
3. Quantifies uncertainty so you can plan safety stock and replenishment rules realistically

What to copy from Costco (without copying Costco)

Costco can sell 4.5 million pies because it can plan ingredients, labour, and throughput. For most retailers, the parallel is:

• Know which SKUs become “event products” (the items people buy because it’s a moment)
• Pre-position stock by region/store based on local demand patterns
• Adjust replenishment cadences and reorder points before the surge

AI helps you identify event products early by clustering products with similar seasonal behaviour and detecting leading indicators—like a sharp uptick in product page views or “add to cart” events that don’t immediately convert.

If you sell in Ireland, this is especially relevant around the late-November/December run, St. Stephen’s Day sales, and January clearance. The calendar differs from the U.S., but the mechanism is identical: peaks happen, and the winners plan them.

Pricing optimization: protect margin while staying competitive

Retailers often treat pricing as a blunt instrument: raise prices when costs rise, discount when inventory builds, hope it averages out. That approach falls apart during holiday volatility and tariff-driven cost swings.

Costco’s quarter also sits next to a very real pressure point: the company is suing the federal government over emergency tariffs, arguing it was improperly required to pay certain import costs. Whether you agree with the legal stance or not, the retail implication is straightforward: cost volatility is back on the table.

What AI pricing does that rules-based pricing can’t

AI pricing optimization isn’t about “charging the most.” It’s about making pricing decisions that reflect demand and constraints in real time:

• Elasticity-aware pricing: how sensitive demand is to a price change for this SKU, this customer segment, this week
• Promo efficiency: which discounts drive incremental profit vs. subsidising purchases that would’ve happened anyway
• Competitive context: monitoring market price ranges without blindly matching the lowest number
• Constraint-aware decisions: don’t promote products you can’t fulfil; don’t chase volume that crushes margin

A practical stance: if your pricing team is still arguing about price changes using last year’s averages, you’re not “being careful”—you’re reacting too slowly.

A simple pricing play retailers can run in January

January is a stress test: returns, gift cards, clearance, and slower footfall.

A strong AI-driven approach is to split inventory into three buckets:

1. Core winners (keep price integrity; avoid unnecessary promos)
2. Seasonal leftovers (markdowns based on sell-through probability, not gut feel)
3. Supply-constrained items (price to manage demand and availability)

That’s how you protect margin while still moving stock.

Omnichannel execution: why Costco’s online record matters

The record $250M+ in non-food online orders on Black Friday is the omnichannel tell. Costco is still a store-led business, but its e-commerce performance shows something many retailers underestimate: online volume spikes are operational problems before they’re marketing wins.

Where retailers usually break during peak

In my experience, peak failures cluster in four places:

• Inventory accuracy: website says “in stock,” store says “not here”
• Order routing: the wrong node fulfils the order (higher cost, slower delivery)
• Customer service overload: “Where’s my order?” tickets explode
• Returns handling: returned inventory doesn’t get back into sellable stock fast enough

AI supports omnichannel in a very unglamorous but profitable way: it makes better decisions about where an order should be fulfilled from.

AI decisioning for fulfilment (what to implement first)

If you’re building toward an AI-powered omnichannel experience, prioritise:

• Real-time available-to-promise (ATP): accurate stock + capacity, not just “on hand”
• Intelligent order routing: choose ship-from-store vs. warehouse based on cost-to-serve and promised delivery date
• Pick/pack labour forecasting: staffing plans driven by predicted order volume and basket complexity

This is where “AI in e-commerce” becomes tangible: fewer split shipments, fewer late deliveries, fewer cancellations.

Customer behaviour analysis: make “fun facts” useful

Pizza counts and pie counts are charming, but you can turn that thinking into a serious advantage: treat every behaviour as a data point that can improve the next decision.

Customer behaviour analysis with AI typically means:

• Predicting who is likely to buy again (and what they’ll buy)
• Detecting when a shopper is price-sensitive vs. convenience-sensitive
• Personalizing recommendations without becoming creepy

Personalization that actually helps (and doesn’t annoy)

Retailers love the idea of personalised recommendations. Shoppers love them only when they’re relevant and timely.

Three recommendation patterns that work well for retail and grocery-adjacent categories:

1. Mission-based bundles: “hosting,” “weeknight dinners,” “back-to-school” kits
2. Replenishment nudges: reminders driven by typical consumption cycles
3. Substitution intelligence: if an item is out of stock, suggest the closest alternative based on behaviour, not category labels

If you’re operating in Ireland where loyalty schemes and privacy expectations are high, the win is building trustworthy personalization: explain value (“faster restock alerts” or “better size suggestions”), keep controls obvious, and avoid over-targeting.

A practical AI roadmap: 30–60–90 days for retailers

Retail teams don’t need a grand AI transformation plan to get started. They need a sequence that produces measurable results.

First 30 days: fix the data you already have

• Audit product, inventory, and transaction data for gaps and inconsistencies
• Define a single view of: SKU, store, channel, timestamp
• Pick 3–5 KPIs you’ll commit to improving (stockout rate, forecast error, margin, on-time delivery, return-to-stock time)

Next 60 days: pilot one high-impact use case

Choose one:

• AI demand forecasting for your top 200 SKUs
• Markdown optimization for seasonal inventory
• Order routing optimization for ship-from-store

Keep the pilot narrow, but instrument it properly. If you can’t measure it, it becomes theatre.

By 90 days: operationalise (or stop)

• Integrate the model outputs into planning workflows (not just dashboards)
• Set decision rights: who approves price changes, buys, replenishment overrides
• Create a feedback loop: forecast vs. actual, recommendation performance, fulfilment SLA adherence

Most companies get this wrong by launching three pilots at once and operationalising none. Pick one, win, expand.

What Costco’s quarter should push you to do next

Costco’s Q1 performance wasn’t magic. It was the result of being relentlessly good at the basics: predicting demand, protecting value, and delivering across channels even when volumes spike.

For retailers and e-commerce teams in Ireland, the takeaway is clear: AI isn’t a side project. It’s how you make fewer expensive decisions during the moments that define your year. Demand forecasting, pricing optimization, customer behaviour analysis, and omnichannel decisioning are the four pillars that translate “records” into repeatable performance.

If you’re planning for 2026 growth, start with one question: Which operational decision is currently driven by guesswork—and what would it be worth to replace it with a reliable prediction? That’s usually where your first AI use case should live.