Retail AI in 2026: What Irish Retailers Must Fix Now

A retail AI stack that can’t answer one basic question — “What’s the true stock position for this SKU across store, web, and supplier?” — won’t survive 2026.

That’s the blunt reality behind the forecasts: AI spend is accelerating (92% of U.S. retailers planned to increase AI investment in 2025), the retail AI market is projected to reach $52.45B by 2030, and shoppers are already changing how they browse and buy. What’s different about 2026 is that AI won’t just help retail teams do work faster; it will increasingly decide what work gets done, when, and why.

For Irish retailers and e-commerce teams, this matters for a simple reason: the market is small enough that customer experience spreads quickly — and competitive pressure from UK and EU brands is constant. If your personalisation feels generic, your chatbot feels cold, or your pricing lags, people won’t “wait for you to catch up.” They’ll just switch.

This post is part of our AI in Retail and E-Commerce series, focused on practical AI adoption for customer behaviour analysis, personalised recommendations, pricing optimisation, and omnichannel experiences.

1) Agentic shopping is coming — and it changes discovery

Answer first: In 2026, more shoppers will outsource product research and even purchasing to AI agents, which means retailers must optimise for “AI-led discovery,” not just traditional SEO and on-site search.

The RSS piece highlights a key behavioural shift: consumers are getting comfortable using AI tools during the shopping journey, and predictions point toward intelligent agents that plan, compare, and complete purchases. That’s not a minor UX tweak — it changes how brands get discovered.

What “AI-led discovery” looks like in practice

Instead of a shopper searching “waterproof jacket men Ireland” and clicking around, an AI agent will:

• infer constraints (budget, delivery deadline, fit preferences, returns tolerance)
• compare across retailers and marketplaces
• favour products with clear specs, trustworthy reviews, and predictable fulfilment
• choose based on total experience (availability, shipping, returns), not just price

If your product data is thin or inconsistent, the agent can’t “understand” you.

What Irish retailers should do now

A lot of teams hear “agents” and jump straight to building one. I’d start elsewhere:

1. Fix your product information management (PIM) discipline

   • consistent titles, materials, sizing, care instructions
   • structured attributes (not just marketing copy)
   • variant-level detail (colourways, fits, bundles)

2. Make your policies machine-readable and human-clear

   • delivery pricing, cut-off times, and returns rules should be unambiguous
   • avoid hiding critical info behind vague FAQs

3. Treat reviews and Q&A as conversion assets, not clutter

   • encourage photo reviews
   • answer product questions quickly
   • summarise common concerns (fit, durability, true-to-size) on PDPs

A useful stance: agents will reward clarity. If your merchandising is built on cleverness, you’ll lose to the retailer who’s simply explicit.

2) Retail media is becoming the “operating system” of retail

Answer first: Retail media is shifting from an ad placement channel into a unified engine that ties media, merchandising, and commerce data together — and that forces a rethink of e-commerce, measurement, and attribution.

One of the strongest predictions in the source is that keyword-based strategies will fade in AI-driven environments. As AI-style conversations become normal on retail sites, product discovery won’t be a list of blue links plus sponsored slots. It will be recommendations generated from intent, context, and constraints.

Why this matters for omnichannel in Ireland

Omnichannel isn’t a buzzword in a country where:

• customers expect click-and-collect to work without drama
• store staff get asked to “check online stock” multiple times a day
• delivery expectations are shaped by large marketplaces

If your systems split “marketing performance” from “availability reality,” AI will surface the mismatch.

Practical moves that pay off

• Unify commerce events: ensure product views, add-to-cart, store inventory checks, returns, and customer service contacts can be tied to the same customer and SKU.
• Measure incrementality, not vanity: retail media success should be evaluated on incremental revenue and margin, not just ROAS.
• Connect promotions to supply: if you can’t model promo uplift against stock and lead times, you’re basically running AI with one eye closed.

Snippet-worthy line you can repeat internally: “Retail media without inventory context is just expensive guessing.”

3) The human support “swing back” is real — and it’s predictable

Answer first: AI-only customer service is already eroding trust; the winning model for 2026 is AI for speed + humans for empathy and exceptions.

The source cites consumer preference data from a hosting provider’s survey: 93% prefer human interaction, 84% say humans are more accurate, and 80% believe AI is used more to cut costs than improve service. Whether the exact figures hold across every retail segment, the direction is right: customers punish brands that hide behind bots.

Here’s what most companies get wrong: they implement a bot, reduce headcount, and then act surprised when complaints rise.

A better model: “AI triage, human resolution”

AI can:

• instantly fetch order status
• handle returns label creation
• answer product availability questions
• summarise prior interactions

Humans should:

• handle delivery failures and goodwill credits
• resolve complex returns and damaged goods
• manage high-value customers and loyalty issues
• intervene when sentiment drops or the customer repeats themselves

What to build (and what not to)

Build:

• handoff rules (e.g., 2 failed intents → human)
• sentiment detection that escalates before the customer explodes
• brand-safe tone (your “voice” can’t be whatever the default model feels like today)

Don’t build:

• a chatbot that refuses to answer pricing, returns, or stock questions
• a support flow that traps customers in loops

A line I use with teams: If your chatbot makes refunds harder, it’s not automation — it’s friction.

4) Data connection is the real competitive advantage

Answer first: The biggest differentiator in retail AI isn’t access to models; it’s whether your data is connected well enough to drive accurate decisions.

The article’s experts repeatedly come back to the same constraint: fragmentation. That’s true in Ireland too, especially for retailers with:

• separate EPOS and e-commerce platforms
• multiple store groups or franchises
• suppliers providing inconsistent product feeds
• loyalty data sitting in a different system from customer service

You can’t personalise, optimise pricing, or forecast demand reliably if your systems don’t agree on the basics.

The minimum viable “connected data” foundation

If you’re planning 2026 initiatives, aim for these four connected views:

1. Customer view: identity resolution across web, app, loyalty, and in-store (where possible and permitted)
2. Product view: consistent SKU/variant mapping across PIM, ERP, website, and marketplaces
3. Inventory view: near real-time availability with clear rules for safety stock and reservations
4. Experience view: behavioural events + service interactions + returns reasons tied to orders

This is where customer behaviour analysis stops being a dashboard and starts being a decision engine.

A practical ROI path (that doesn’t require perfection)

• Start with one category (e.g., footwear) and one KPI (e.g., returns rate).
• Use AI to identify drivers (size inconsistency, material confusion, poor imagery).
• Fix the upstream cause (better size guides, attribute completeness, richer media).
• Then automate the detection and repeat.

Retail AI returns come from closing loops, not launching pilots.

5) In-store and digital signage AI will quietly matter a lot

Answer first: AI-powered digital signage and in-store personalisation will be a major differentiator because it connects physical experience with behavioural data and operational reality.

The source points to screens becoming “self-optimising” systems that personalise content and monitor uptime. In Ireland, where high streets and shopping centres still matter, this is a big opportunity — if you treat it as part of omnichannel, not as “TV screens on walls.”

Where signage AI actually earns its keep

• Queue-aware messaging: detect queue length and promote self-checkout, app scan-and-go, or service desk routing.
• Localised merchandising: show offers based on store-level overstock, weather-driven demand, or local events.
• A/B test in the real world: run two creatives across matched stores and measure sell-through uplift, not just “engagement.”

The trick: signage should connect to inventory and pricing rules so you don’t advertise what you can’t fulfil.

A 90-day plan for Irish retailers preparing for 2026

Answer first: The fastest route to readiness is not “build an AI agent.” It’s tightening data, experience, and governance so agents (yours and others’) can work with you.

Here’s a straightforward 90-day plan that fits mid-market realities:

1. Weeks 1–2: Pick two outcomes you’ll measure in euros

   • examples: reduce returns by 10%, increase full-price sell-through by 5%, reduce “where is my order” contacts by 20%

2. Weeks 3–6: Fix one data junction

   • connect inventory + e-commerce availability rules
   • or connect returns reasons + product attributes
   • or connect customer service tags + loyalty tier

3. Weeks 7–10: Deploy one customer-facing improvement

   • better on-site search with intent understanding
   • proactive delivery updates
   • personalised recommendations limited to one category (so you can validate)

4. Weeks 11–13: Put guardrails in writing

   • tone of voice for bots
   • escalation rules
   • measurement plan and “stop doing” list

If you do just that, you’ll be ahead of the retailers still arguing about which model to use.

What to do next (if leads are your goal)

If your 2026 plan includes personalised recommendations, pricing optimisation, customer behaviour analysis, or omnichannel experience improvements, the next step is a readiness check — not a tech shopping spree.

I’d start by mapping your top five customer journeys (browse → buy → deliver → return → support) and asking one question: Where does data break, and what does it cost us per month? That’s where AI pays back fastest.

Retail AI in 2026 will reward the retailers who build trust: accurate stock, honest delivery promises, helpful support, and personalisation that feels like it came from understanding — not surveillance. What part of your customer journey would you be comfortable letting an AI agent run tomorrow?