When self-checkout tills first appeared in supermarkets, plenty of UK retailers still weren’t convinced. Now, they’re everywhere, yet they’re merely the visible portion of a much bigger shift. Behind the scenes, systems running inventory, orders and finance are finally starting to talk to each other, and AI is rewriting how retailers forecast demand, manage stock and spot shoplifters. That’s music to the ears of businesses facing rising costs, staffing headaches and customers who expect everything yesterday.
Now that these technologies are real, retailers must decide which ones make the most sense for their businesses. This article covers 10 emerging retail technology trends worth watching.
The State of Retail Technology in 2026
Technology investment in retail is picking up pace, and AI sits at the heart of nearly every trend. Nearly all (99%) of UK retailers reported some level of in-house AI expertise in a 2025 survey, and 73% say generative AI is handling basic customer service interactions. Physical shops remain the preferred shopping destination for UK consumers, but the pressure to build digital capabilities isn’t letting up. Shoppers now expect to engage retailers across multiple touchpoints, whether that’s social media, mobile apps, websites or the high street.
Retailers are also wielding technology to respond to the various economic pressures that haven’t eased, either. Rising labour costs are squeezing margins. Consumer confidence remains fragile. And the operational demands of omnichannel retail (inventory, fulfilment, returns) grow more complex by the year.
Technology investment alone doesn’t guarantee results. Legacy systems, siloed data and skill gaps are slowing the pace of technology adoption. The retailers moving fastest are those with flexible, API-first platforms that connect new capabilities to existing operations without incurring months of integration work. For those still running disconnected systems, getting everything to communicate remains the central challenge, a theme that recurs across nearly every trend described below.
10 Emerging Retail Technology Trends
AI underpins most of what’s changing in retail technology because its applications vary widely. The following 10 trends are being watched and invested in by retailers.
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Demand Forecasting
Getting demand forecasting right has always mattered, but AI is changing what “right” looks like. Traditional forecasting tools extend historical averages in a fairly linear fashion: take last year’s sales and adjust them for seasonality. AI-powered systems work differently. They ingest thousands of variables, including sales data, weather patterns, local events and supplier lead times, then generate forecasts that adapt quickly to changing conditions. For example, a heat wave forecast for a bank holiday weekend can trigger automatic stock adjustments for barbecues and sun cream, days before the temperature rises.
For midsize retailers, the barrier to entry is falling. Cloud-native forecasting solutions, for example, require less up-front infrastructure investment, and the range of external data sources these tools can tap into keeps growing. The feedback loop between forecasting and automated replenishment is tightening, too, which means less manual intervention needed to keep shelves stocked.
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Augmented Reality
Augmented reality (AR) tackles a problem every online retailer knows well: the uncertainty shoppers feel when they can’t see or touch a product before buying it. Virtual try-ons for fashion, beauty and home furnishings let customers visualise products on themselves or in their living rooms prior to purchasing. Furniture apps that show how a sofa would look in the customer’s space have gained traction in the UK. Fashion and beauty retailers are also building AR into their apps so customers can try before they buy.
AI is increasingly the engine behind effective AR experiences, powering the realism that makes virtual try-ons work. Computer vision interprets body shape and movement; machine learning improves fit recommendations based on returns data; and GenAI renders how garments will drape and move.
The commercial payoff is real. Retailers using virtual try-on technology are seeing higher conversion rates and fewer returns, which also makes reverse logistics easier. But challenges remain. Implementation can be costly, and customers don’t always trust AR’s accuracy for fit or colour. But, as hardware costs fall and social AR normalises the technology, smart fitting rooms and in-store AR navigation are becoming customers’ usual expectation.
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AI Agents
There’s an important distinction between the chatbots retailers have used for years and the AI agents now emerging. Chatbots follow scripts, whereas AI agents can actually do things, autonomously, across multiple systems, without waiting for a human to step in. In a retail context, an AI agent might monitor stock levels, spot that a product is running low, check supplier availability and raise a purchase order. There need be no human in the loop, unless the retailer wishes it.
Making this work requires unified data across inventory, CRM, POS and order management, which harks back to the integration challenge flagged earlier. This is where the connection between AI and ERP systems becomes central. Without clean, connected data, agents can’t function.
Marketing is the top use case so far, but retailers expect rapid expansion into customer service, inventory management and operations. Consumer trust matters here, too. When asked what would make them more comfortable with AI agents, UK shoppers put data privacy and security at the top of the list, a concern that surfaces across several of these technologies.
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RFID Tags and Smart Shelves
Radio-frequency identification (RFID) tags are replacing barcode scanners because they can identify and track products without line-of-sight scanning. A single reader can capture more than 100 tags at once, even through packaging. The big win is inventory accuracy. Item-level RFID tags can push retail inventory management accuracy to 95% and above. And the knock-on effects are substantial: fewer cancelled orders, better click-and-collect performance and replenishment cycles that shrink from monthly to daily.
The UK is an active market for RFID, particularly in fashion and sports retail. Leading apparel retailers have rolled out implementations covering entire product ranges. Smart shelf technology, which uses RFID and other sensors to monitor stock levels, is gaining ground among larger grocers. Tag costs have dropped below 5p per tag, lowering what was once a major barrier. Connecting RFID to ERP, warehouse management and POS systems takes planning, but the technology is on track to become standard infrastructure for apparel retailers.
AI’s adjacent role should surprise no one: RFID captures the data and AI interprets it. Smart shelves work best when RFID signals feed AI software that can detect anomalies, trigger replenishment and predict stock movement patterns.
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Loss Prevention Systems
Retail crime in the UK has reached levels that would have seemed unthinkable a decade ago. The British Retail Consortium’s Crime and Shrink Benchmark 2025 recorded more than 20 million thefts in 2023-24, costing £2.2 billion. Technology to combat theft is accelerating quickly. Computer vision systems use AI to analyse CCTV footage and highlight suspicious behaviour and shoplifting patterns. Body-worn cameras for staff have become standard in high-risk environments. AI-powered anti-theft tagging systems are being integrated with CCTV and access control to create layered deterrents. Self-checkout has become a particular focus, with computer vision at terminals catching scanning errors and product swaps.
The challenge is balancing security with customer experience. Theft deterrents can be a nuisance for legitimate shoppers. AI surveillance raises data privacy questions under UK General Data Protection Regulation, and facial recognition sits in legally murky territory. The retailers making progress tend to combine multiple technologies into integrated systems, rather than betting on any single solution.
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Personalised Recommendations
AI-powered personalisation is used in product recommendations, dynamic home pages, emails and search results tailored to individual shoppers. The commercial logic is sound. Consumers prefer brands that personalise, and they spend more on those brands because of it. But there’s a gap between what retailers think they’re delivering and what customers experience. In a 2024 US survey, for example, Deloitte found that 92% of retailers believed they personalised effectively but only 48% of consumers agreed. UK retailers are making strides by launching fully personalised home pages that adapt to individual behaviour, and visual AI to power search and discovery.
GenAI is adding new dimensions, too, in the form of AI-powered styling assistants that recommend outfits based on preferences and body shape. But, as these tools grow more sophisticated, privacy becomes a bigger consideration. The UK’s data protection framework creates specific obligations around using personal data for personalisation. In a 2025 UK survey, 66% of consumers said they understand retailers may use AI to recommend products, showing relatively high awareness and tolerance. But, as with AI agents and loss prevention, trust depends on transparency. Retailers need to communicate clearly about data usage and make opting out painless.
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Modernised Payments
Payment habits are shifting dramatically. According to UK Finance, cash has fallen below 10% of all payments, down from nearly half of transactions a decade ago. Contactless payments now account for 62% of card transactions. Mobile wallet adoption has crossed the 50% threshold for adults. For retailers, this means POS infrastructure needs to support the full range: contactless, mobile wallets and buy now pay later, all connected through integrated payment processing that ties into inventory, customer data and finance.
Buy now pay later is growing, particularly in fashion, and it’s no longer just a young person’s payment preference. Uptake among the 55-to-64 age group more than doubled in 2024. Regulation is coming, with the Financial Conduct Authority proposing creditworthiness checks that will reshape how these products work. Open banking is another piece of the puzzle, though consumer adoption remains cautious. AI-powered payment systems can boost sales by reducing incorrectly declined transactions, improving fraud detection and cutting processing costs.
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Robotic Process Automation (RPA)
RPA automates the structured, rule-based tasks that chew through back-office time: invoice processing, inventory reconciliation and report generation. Retailers have used RPA for years, especially in accounts payable, purchase-to-pay and compliance reporting. RPA can also help bridge between legacy platforms, automating data flows that would otherwise require manual work, making it particularly useful for retailers still dealing with integration challenges.
What’s emerging now is a partnership between RPA and GenAI. Traditional RPA deals only with structured tasks where inputs and outputs are predictable. GenAI can extend that to semi-structured work, such as invoices with inconsistent formats or information buried in unstructured documents. For retailers, the value is straightforward: automate the admin that eats up staff time, freeing staff for work that actually needs a human.
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Sentiment Analysis
AI-powered sentiment analysis systems scan customer reviews, social media posts and feedback data to track how customers truly feel about a brand. Retailers use sentiment analysis to catch product quality issues early, track how price changes land with customers and detect patterns from unstructured feedback. For example, a spike in negative reviews about a particular product can spark an investigation before returns pile up. Shifts in how customers talk about delivery times can shape logistics decisions. Social monitoring can prevent emerging issues from becoming public relations problems.
The technology works best when it’s connected to systems that can act on what it finds; an example of why unified data matters. Linking sentiment signals to customer data platforms can guide personalisation, merchandising and operations. Retailers juggling large catalogues and multiple channels can use sentiment analysis to keep a finger on the pulse of changing consumer preferences.
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Omnichannel Retail
The idea of omnichannel retail (interconnecting every channel to create a single, unified customer experience) has been around for more than a decade. Delivering it is still difficult. But AI is finally making omnichannel retail achievable by unifying customer data, orchestrating fulfilment decisions in real time and enabling agentic workflows that coordinate multiple systems without human intervention. For example, AI agents are tracking customer conversations across channels, safeguarding context so that it isn’t lost when a shopper moves from web chat to phone to store.
Still, the gap between ambition and execution is the defining challenge. Consumer expectations, meanwhile, keep rising. Click-and-collect is assumed. Free returns are standard. Same-day delivery is a baseline for many shoppers.
Click-and-collect is a particular strength for UK retail, thanks to a dense store network and customers who value convenience. RFID plays a supporting role by providing the inventory accuracy that makes click-and-collect reliable. But shops not designed with collection in mind create friction. Returns processes vary wildly between channels. Inventory visibility, knowing what’s where across stores, warehouses and goods in transit, is patchy for retailers that lack the right infrastructure. The answer, increasingly, is unified commerce: not just connected channels, but one platform running the entire operation so data flows freely and customers get consistent experiences wherever they shop. Retailers who get there first will have a real edge.
Modernise Your Retail Operations with NetSuite ERP
Disconnected systems are the roadblock preventing retailers from fully embracing emerging technologies that provide AI-powered forecasting, real-time inventory visibility and true omnichannel operations. NetSuite Retail ERP removes that barrier by bringing inventory, orders, procurement, warehouse management and financials together on a single cloud-based platform. For retailers who use multiple AI systems, NetSuite’s AI Connector Service, built on the model context protocol (MCP), connects those external AI tools directly to NetSuite data while respecting permissions and security controls. For retailers exploring agentic AI, this means custom agents can reason over NetSuite data, look up inventory, analyse sales patterns, or even create orders—all without building bespoke integrations.
If stock runs low, NetSuite can trigger replenishment. Incoming orders route automatically to the best fulfilment location. For retailers selling across multiple channels, everyone works from the same data, meaning no more reconciling spreadsheets or waiting on integration projects before adopting new capabilities. And because NetSuite scales with the business, growth never forces a retailer to rip and replace its infrastructure.
The technologies reshaping retail share a common thread: they close the gap between what customers expect and what operations can deliver. AI-powered forecasting ends stockouts. RFID provides the inventory accuracy omnichannel fulfilment demands. Personalisation meets rising expectations for relevant experiences. Unified commerce platforms tie everything together. For retailers managing thin margins and rising costs, this is how you build the operational muscle to compete.
Emerging Retail Technology FAQs
What are the new technologies in retail industry?
Among the most common new retail technologies are AI-powered demand forecasting, augmented reality for virtual try-ons, AI agents that handle tasks across systems autonomously, RFID for inventory accuracy, computer vision for loss prevention and unified commerce platforms connecting all sales channels.
How is AI being used in the retail industry?
Retailers are using AI across the board. In demand forecasting, it analyses sales data, weather, events and social signals to predict what customers will buy. In personalisation, it powers product recommendations and dynamic experiences based on individual behaviour. AI agents handle customer enquiries and automate replenishment. Computer vision systems spot theft. Large language models generate product descriptions and field customer questions.
How can data analytics benefit UK retailers?
Data analytics helps retailers understand customer behaviour, sharpen inventory levels and spot operational inefficiencies. Real-time analytics speeds up decisions on pricing, promotions and fulfilment. Sentiment analysis reveals customer feedback trends that would take ages to review manually. Integrating analytics with operational systems lets retailers act on what they learn, rather than just report on it.
