Retail Tech Is Moving Faster Than Stores Can Adapt

Half of retailers say they can’t keep up with the pace of technology change. That’s not a headline designed to shock—it’s a plain-English description of what many teams feel every Monday morning.

And here’s the bit that should make Irish retailers sit up: stores are still where the money is. Even with e-commerce growth, the majority of retail revenue still happens in physical locations. So when store tech falls behind, you don’t just have an “IT problem.” You have a growth problem.

The same research that flagged the “can’t keep up” issue also found that 49% of retailers struggle to quantify ROI on in-store tech, and 37% feel conflicted—worried new tools will help, but also worried they’ll distract staff and frustrate shoppers. I’ve seen this pattern up close: the fear isn’t really about AI, mobile POS, or new kiosks. It’s about making a bet you can’t measure.

This post is part of our AI in Retail and E-Commerce series, and it’s written for a specific audience: retailers who want practical ways to modernise without turning their stores into a technology demo.

Why retailers are falling behind (and it’s not a motivation issue)

Retailers fall behind because store technology is trapped between customer expectations and legacy infrastructure. The gap widens every year.

One stat from the report says it all: 27% of retailers are running point-of-sale systems that are five years old or more. Compare that to consumer behaviour—many shoppers replace their phones every 2–3 years, and some every year. Customers don’t consciously think, “This retailer’s POS is old.” They feel it as:

• Slower queues and awkward checkout flows
• Loyalty points not applying correctly
• “Out of stock” online but “somewhere” in-store
• Associates who can’t see order history or product info

The real culprit: change fatigue plus fragmented systems

Retail teams aren’t ignoring innovation. They’re overwhelmed by it. A typical environment includes:

• Separate systems for POS, e-commerce, loyalty, promotions, inventory, workforce scheduling, and CRM
• Different data definitions across channels (the classic: “What counts as an active customer?”)
• Vendor contracts that make upgrades slow and expensive

When tech stacks look like that, every “simple improvement” becomes a project. That’s why modernisation often stalls.

The myth: “We need more tech”

Most companies get this wrong. They keep adding tools instead of fixing the operating model.

If you want stores that keep up, the goal isn’t more technology. It’s fewer, better-connected capabilities—and AI can play a central role because it can unify decision-making across messy data.

Why ROI feels impossible to measure in-store

In-store ROI is hard to prove when your metrics don’t match how customers actually shop. Shoppers don’t move in straight lines anymore. They browse on mobile, check stock online, visit a store, order for home delivery, then return in a different location. That’s normal.

So if you measure store tech with narrow metrics—like “checkout time saved” or “kiosk usage”—you’ll miss the bigger wins:

• Higher conversion because associates can find the right product faster
• Fewer lost sales from stock-outs through better inventory accuracy
• Lower returns because customers get better guidance at purchase
• Higher lifetime value because loyalty and personalisation work across channels

What to measure instead: a practical in-store tech scorecard

If your leadership team is stuck in ROI debates, start with a scorecard that mixes financial and operational metrics. Here’s one that works well:

1. Sales impact

   • Conversion rate (by store, by category)
   • Basket size
   • Assisted vs unassisted sales mix

2. Availability and fulfilment

   • On-shelf availability
   • Click-and-collect readiness time
   • Order cancellation rate due to inventory errors

3. Customer experience

   • Queue time or time-to-serve
   • Net Promoter Score (or a simpler post-visit rating)
   • Returns rate (especially for fit/compatibility-heavy categories)

4. Labour and productivity

   • Time spent on non-selling tasks
   • Staff turnover (tech pain increases churn)
   • Training time to competence

A useful stance: if you can’t measure it in 90 days, you’ve scoped it wrong.

That doesn’t mean every initiative must “pay back” in 90 days. It means you should see leading indicators quickly.

Where AI actually helps stores keep pace (without annoying shoppers)

AI helps retailers keep pace by reducing decision friction—what to stock, what to recommend, what to prioritise, and what to fix first. The best uses of AI in retail and e-commerce aren’t flashy. They’re practical.

AI-driven personalisation that works across channels

Personalisation fails when it’s trapped in e-commerce only. Irish shoppers might browse online and buy in-store the same day—especially in December, when urgency is high and delivery cut-offs are real.

What “good” looks like:

• Associates can see preference signals (size, style, repeat purchases) in a privacy-safe way
• Recommendations reflect local store inventory, not generic online stock
• Promotions are consistent, so customers don’t feel punished for choosing store over site

A simple example: if a customer regularly buys cruelty-free skincare online, the next in-store interaction shouldn’t start from zero. AI can summarise preferences and surface compatible products the store actually has.

AI for inventory accuracy and demand signals

Most store experience problems trace back to availability:

• Item not where it should be
• Counts wrong
• Replenishment too slow

AI can help by detecting patterns humans miss:

• Predicting demand spikes by store (weather, local events, payday patterns)
• Flagging likely inventory errors (sales velocity doesn’t match stock on hand)
• Prioritising cycle counts so staff focus on the highest-impact checks

This matters because stores are still the primary growth engine, but they can’t grow if the shelf isn’t trustworthy.

AI for staff enablement (the underrated win)

The “will tech distract associates?” fear is valid. Bad tech does distract them.

The fix isn’t banning new tools. It’s choosing tools that:

• Reduce screen taps
• Provide short, confident answers
• Fit into how staff already work

Well-designed AI copilots can support:

• Quick product comparisons
• Warranty/returns guidance
• “What’s the closest alternative we have in stock?”
• Step-by-step troubleshooting for devices and setups

If you want customer experience to improve, make the associate’s job easier first.

A 90-day plan for Irish retailers: modernise without ripping everything out

The fastest path to modern store tech is iterative: stabilise the core, connect the data, then layer AI where it reduces pain immediately. Here’s a realistic 90-day approach I’d stand behind.

Days 1–15: Pick one store journey that’s visibly broken

Choose a journey where shoppers feel friction today. Common candidates:

• Click-and-collect pickup
• Returns and exchanges
• Assisted selling in high-consideration categories (electronics, beauty, footwear)

Write a one-page “definition of done.” Make it concrete (e.g., “reduce pickup wait time from 8 minutes to 3 minutes”).

Days 16–45: Fix the data and workflow before the AI model

AI won’t save a broken workflow. Make sure you have:

• A single customer identifier (even if it’s loyalty-led)
• A clean product catalogue with consistent attributes
• Near-real-time inventory feeds to the store-facing experience

This is where many programmes quietly fail. Teams chase fancy features while the foundation stays shaky.

Days 46–90: Pilot AI in a controlled, measurable way

Now introduce AI where it supports the chosen journey:

• Personalised recommendations constrained by store inventory
• Associate copilot for product guidance and alternatives
• Anomaly detection for stock and fulfilment exceptions

Keep the pilot tight:

• 3–10 stores
• One category (or one service desk function)
• Weekly KPI review
• Clear escalation path when the system is wrong

A strong pilot isn’t “AI everywhere.” It’s one problem solved end-to-end.

Omnichannel isn’t optional in 2026—stores are the hub

Omnichannel retail works when stores act as both experience centres and fulfilment nodes. That’s especially true for Ireland, where geography and delivery expectations create real-world constraints. Customers want options:

• Buy online, pick up locally
• Reserve in-store
• Return anywhere
• Get accurate local availability

If your store tech can’t support those basics, the shopper will still buy—just not from you.

AI supports omnichannel experiences by making the system smarter about trade-offs:

• Which store should fulfil the order (cost + speed + inventory risk)
• Which items are likely to be returned (and why)
• Which customers need proactive service to prevent churn

The retailers that win won’t be the ones with the most tools. They’ll be the ones whose tools agree with each other.

What to do next if you feel behind

Half of retailers can’t keep up with the pace of tech change. If that feels familiar, you’re not failing—you’re bumping into a model that no longer fits.

Here’s the stance I’d take going into 2026: stop treating store technology like a multi-year makeover. Treat it like a product you ship improvements to every month.

If you’re planning your next steps in AI in retail and e-commerce, start small but pick a problem that matters to revenue: availability, assisted selling, click-and-collect, or returns. Build the measurement into the work from day one. Then scale what actually moves the numbers.

What’s the one store journey your customers complain about most—and are you measuring it closely enough to justify fixing it fast?