AI Holiday Shopping: What Irish Retailers Do Next

A single stat explains why holiday trading has started to feel different: 39% of holiday shoppers say they’ll use AI tools while shopping, and 68% of those users will buy directly through them. Pair that with 57% using social media for holiday shopping and you’ve got a new reality—customers are making decisions (and purchases) inside interfaces you don’t control.

For Irish retailers and e-commerce teams, this isn’t “another channel.” It’s a shift in how intent forms. People are asking ChatGPT-style assistants for “the best waterproof walking shoes under €120 that ship before Christmas,” then buying what’s recommended. They’re watching TikTok gift guides, then checking out without ever hitting Google.

This post is part of our AI in Retail and E-Commerce series, and it’s focused on practical steps. If you want more holiday revenue (and fewer January headaches), you need two things: AI-ready product data and omnichannel execution that holds up when discovery happens on social and inside AI assistants.

The new funnel: discovery happens in AI and social

Answer first: Holiday shoppers are compressing the journey—discovery, evaluation, and checkout are increasingly happening inside social platforms and AI assistants.

The report highlights three behaviors worth treating as one combined trend:

• 57% of shoppers use social media while shopping.
• 42% plan to checkout directly through social.
• Among those using AI tools (like ChatGPT, Gemini, Claude), 68% buy directly through them.

That last number is the kicker. “Buy through AI” doesn’t always mean the AI processed the payment. Often it means the AI influenced the choice so strongly that the shopper goes straight to the retailer or marketplace it suggested.

What this means for Irish retailers

If your product pages are built for keyword search only, you’ll underperform in AI-led discovery. AI assistants don’t just match keywords—they assemble answers. They look for:

• Clear product attributes (materials, sizing, compatibility, shipping cutoffs)
• Comparable options (“best for wide feet,” “best under €50,” “best for next-day delivery”)
• Trust signals (ratings, return policy clarity, warranty)

If that information is missing, inconsistent, or buried, AI tools and social creators will fill the gap with someone else’s product.

My stance: Most retailers don’t have a marketing problem here—they have a product information problem.

Gen Z is training the market to expect an AI shopping assistant

Answer first: Gen Z is normalising “ask AI first,” and everyone else follows once it saves time.

The data shows 73% of Gen Z plan to use AI across the journey, from discovery to purchase. This matters beyond Gen Z because holiday shopping is a high-pressure moment: tighter budgets, deadlines, and decision fatigue. When a tool reduces that friction, adoption spreads fast.

Build for “prompted commerce,” not just browsing

AI-led shopping starts with a prompt like:

• “Gift ideas for a 10-year-old who loves art under €30”
• “Best espresso machine for a small kitchen, quiet, ships before Christmas”

To show up in those answers, your catalogue needs structured signals that map to natural language:

• Use consistent attributes: age range, interests, recipients, occasions, sizes
• Add plain-English use cases: “fits cabin baggage,” “good for sensitive skin,” “works with iPhone 15”
• Maintain accurate stock and delivery promises (AI recommendations fall apart if you miss delivery)

If you’re running Shopify, Magento, or a custom stack, this is usually an enrichment workflow problem—one that AI can help solve (more on that below).

Price sensitivity is rising—so pricing optimisation can’t be blunt

Answer first: With 74% of shoppers spending the same or less than last year, you need AI-driven pricing and promotion planning that protects margin and targets the shoppers who are actually persuadable.

The report’s signals are consistent with what many Irish retailers have felt all year: people still want nice things, but they’re more deliberate. Shoppers are leaning on:

• Discounts/deals
• Gift cards
• Convenience wins (fast delivery, easy returns)

Here’s the trap: blanket discounting trains customers to wait and squeezes margin right when fulfilment costs spike.

A smarter approach to AI pricing optimisation

AI is most useful when it helps you answer specific questions:

1. Where do discounts create incremental sales vs. cannibalise full-price?
2. Which products are “price elastic” right now? (small discount = big volume change)
3. What’s the minimum incentive needed per audience segment?

Practical tactics Irish teams are using:

• Segmented promotions: email/SMS offers based on previous categories purchased and price sensitivity
• Bundle optimisation: pair a high-intent product with a high-margin accessory
• Markdown timing: AI forecasting to choose when to discount, not just how much

If your data foundation is solid, you can test this quickly: pick 20 SKUs in a category, run two promotional strategies for 10 days, and measure margin, conversion rate, and return rate.

Convenience wins holidays—especially around delivery anxiety

Answer first: Shipping confidence is now a conversion lever. 57% worry about delays (up from 48% last year), and 86% will pay for shipping.

Holiday shopping isn’t just about product appeal. It’s about certainty. When customers fear late delivery, they either:

• Pay more for faster shipping
• Switch retailers
• Buy gift cards

Use AI to reduce “will it arrive?” friction

You don’t need science fiction here. You need operational truth surfaced in the customer journey.

AI can help by:

• Predicting delivery promise accuracy by region, carrier, cut-off dates, and SKU handling time
• Triggering proactive messaging (“Order by Dec 20 for delivery before Christmas in Dublin”)
• Offering smarter alternatives when stock is tight (closest substitute, store pickup, digital gift)

One straightforward win: put a delivery confidence module on PDPs and checkout that updates by location and time. If your teams can’t reliably promise it, customers won’t trust it.

Social commerce is now a checkout channel, not just awareness

Answer first: If 42% plan to checkout directly in social, then your product feed, creative, and inventory accuracy are now core retail infrastructure.

The report calls out Facebook and TikTok as leading platforms for discovery and purchases, with YouTube and Instagram also strong. That’s not just “where ads run.” It’s where product research happens.

What Irish omnichannel teams should fix first

If you only do three things before your next peak period, do these:

1. Clean product feeds

   • Correct titles, variants, pricing, availability
   • Consistent imagery per variant
   • Attributes that match how people shop (recipient, occasion, style)

2. Align content to intent

   • TikTok/short video: “why this solves a problem” + price + delivery promise
   • Facebook: gift sets, bundles, family-oriented messaging
   • YouTube: comparison and “top 5” formats that answer specific use cases

3. Close the loop between social and onsite

   • Landing pages that match the video promise
   • Fast mobile checkout
   • Clear returns and shipping terms

Social commerce punishes inconsistency. If the ad says “€49 gift set” but checkout shows €59 due to variant mismatch, you’ll feel it in abandoned carts and nasty comments.

How to operationalise this: a 30-day AI action plan

Answer first: You can make meaningful progress in a month by focusing on product data, customer service automation, and omnichannel measurement.

Here’s a realistic plan I’d run with a mid-sized Irish retailer.

Week 1: Make your catalogue AI-readable

• Identify top 200 holiday SKUs (by revenue potential, not just last year’s sales)
• Standardise attributes: size, material, compatibility, recipient, occasion
• Add 3–5 “use case bullets” per SKU in plain English
• Ensure stock and delivery estimates are accurate and updated

Week 2: Add an AI shopping assistant where it matters

• Deploy a site assistant that answers:
  • “What’s the difference between A and B?”
  • “Will this arrive before Christmas?”
  • “What gift suits a €30 budget?”
• Connect it to live inventory and shipping rules
• Train it on your returns policy and store pickup options

Week 3: Personalisation that doesn’t creep customers out

• Personalise based on behaviour and context:
  • “Gifts under €50” for price-sensitive segments
  • “Arrives before Christmas in Cork” based on location
  • “Complete the set” bundles based on cart contents
• Keep it transparent: explain why an item is recommended (“Popular with buyers of…”, “Fits your budget”)

Week 4: Measure the omnichannel journey end-to-end

• Create a single view of performance:
  • Social clicks → landing page conversion → checkout completion
  • Assisted conversions from AI chat → add-to-cart → purchase
• Monitor operational metrics too:
  • Delivery promise accuracy
  • Returns by SKU and campaign
  • Customer service contact rate per 1,000 orders

If your measurement is fragmented, you’ll blame marketing for what is actually a fulfilment promise issue—or blame fulfilment for what is actually a poor product page.

Quick Q&A (the stuff teams ask in real meetings)

Do AI shopping assistants reduce customer service load?

Yes—when they can answer order status, delivery timelines, returns, and product comparisons accurately. If they can’t access live data, they create more tickets.

Will AI replace onsite search?

Not entirely. But it will reshape it. The best setups blend search + AI guidance: filters for precision, AI for “help me choose.”

Is this only for big-box retailers?

No. Big-box will win on scale, but Irish specialists win on clarity and expertise. AI helps you package that expertise consistently across product pages, chat, email, and social.

Where this is heading in 2026

Holiday shopping behaviour is turning into a year-round pattern: social discovery, AI-assisted decisions, and expectation of fast certainty on delivery and returns. Retailers who treat AI as a thin chatbot layer will get disappointed. Retailers who treat AI as a customer behaviour analysis and execution layer—grounded in clean product data—will keep more margin and earn repeat customers.

If you’re planning your next peak season, start with this: make it easy for AI tools (and humans) to understand your products, trust your delivery promises, and buy in two minutes on mobile. That’s the bar now.

What’s the one part of your journey you’d fix first—product data, delivery promise, or social checkout?