“Seismic.” That’s how Tesco’s CEO describes the changes AI is making in retail. UK retailers appear to agree — 99% now report AI expertise in their businesses, and 73% say generative AI now handles most basic customer requests. The transformation is well underway: Sainsbury’s has launched a five-year programme to become the UK’s leading AI-enabled grocer, Morrisons has deployed AI cameras across its stores to monitor shelf availability, and Waitrose uses AI in demand forecasting to analyse why customers bought — not just what.

For retailers, the question is not whether AI will reshape operations — it’s how to capture AI’s benefits before competitors do. This article explores where the opportunities lie.

Why Are Retailers Looking to AI?

Retailers face a convergence of pressures that make AI adoption urgent rather than optional.

First, customer expectations keep rising. Shoppers want personalisation, speed, and integrated omnichannel experiences, regardless of what’s happening behind the scenes. And there’s a lot happening behind the scenes, starting with an additional £7 billion in retail labour payroll due to rises in National Insurance contributions and the National Living Wage, according to a British Retail Consortium (BRC) analysis. In a related BRC survey of 52 retail CFOs, 31% said that payroll increase will lead to greater use of automation technologies.

Second, the pace of stock loss growth is considered a crisis. Retail crime is surging, with shoplifting incidents up significantly year-on-year and many stores reporting that a large share of their losses come from sources they struggle to identify. AI systems that monitor self-checkout and flag suspicious transaction patterns are, in turn, becoming a core part of retailers’ loss-prevention stacks.

Third, Brexit has made supply chains permanently harder to manage. The customs paperwork, border holdups and rules-of-origin calculations that come with every EU shipment require a responsiveness that spreadsheets and manual processes can’t match. AI systems that automate compliance, predict disruptions and optimise inventory buffers help UK retailers navigate challenges that other nations don’t face.

How Is AI Impacting the Retail Industry?

Ken Murphy, CEO of Tesco, put it plainly at the FT Future of Retail 2024 conference: “AI will revolutionise how customers interact with retailers. It will be seismic. … AI will impact every facet of our business.” Retailers are looking to AI to improve customer relations, increase the efficiency of their operations, save money, deepen supply chain visibility and much more. Major UK retailers are already moving beyond pilots to enterprise-wide deployment. Sainsbury’s AI-powered demand forecasting, for example, is contributing to its £1 billion cost-saving programme while improving product availability. ASOS delivers billions of AI-personalised product recommendations daily.

Retailers say the shift to AI isn’t necessarily about replacing workers — it’s about changing what they do. In fact, 94% of UK retail leaders agree AI will enable more meaningful, value-added work — the highest confidence level of the five countries surveyed by Retail Economics and Eversheds Sutherland. Generative AI and chatbots are handling routine customer enquiries at many retailers, giving service teams more time for complex problems that benefit from a human touch. This may be why two-thirds of the UK retail leaders in the Retail Economics/Eversheds Sutherland survey expect AI to increase the proportion of higher-skilled, better-paid roles, and more than four out of five say it will make retail jobs more customer-centric.

Other promising use cases include AI-driven forecasting, which reduces waste by matching inventory more precisely to demand, and dynamic pricing, which adjusts prices to market conditions in the moment. In warehouses, AI optimises picking routes and predicts demand patterns, while supply chain visibility tools flag disruptions before they can ripple out to cause empty shelves. On the shop floor, automated stock monitoring frees store colleagues from manual inventory checks, giving them more time to help customers.

Crucially, the retailers investing now aren’t just solving current problems — they’re building AI experience and expertise that may define their competitiveness for years to come.

9 AI Use Cases in Retail

The applications of AI in retail span the entire value chain — from predicting what customers will want, to how products reach them and how they’re priced at the point of sale. Here are nine use cases where UK retailers are seeing meaningful impact.

  1. Demand forecasting

    Accurate demand forecasting has always mattered, but post-Brexit supply chain volatility has raised the stakes. When lead times are less predictable and buffer stock ties up capital, the cost of getting forecasts wrong — either through stockouts or waste — compounds quickly. AI-powered forecasting analyses historical sales alongside external factors like weather, local events and even why customers decided to buy certain items. That level of detail lets retailers spot demand shifts before they hit the sales reports. In grocery shops, where unsold perishables go straight to waste, even a modest improvement in forecast accuracy shows up on the margin line.

  2. Virtual agents

    AI chatbots now handle the questions customers ask most often — where’s my order, how do I return this, does it come in blue — without a person getting involved. For retailers watching their wage bills climb, the point isn’t just saving money. It’s freeing up service teams to deal with the problems that actually need a human: complicated returns, frustrated customers and situations where judgment and empathy matter. Virtual agent technology is maturing rapidly. AI virtual agents use natural language processing to better understand context and nuance, moving well beyond the scripted, frustrating chatbots of a few years ago. Motel Rocks, a UK fashion brand, deployed AI chatbots and saw a 43% reduction in tickets requiring human handling — and customer satisfaction actually improved.

  3. Supply chain optimisation

    AI systems help retailers manage supply chain complexity by automating compliance tasks, predicting potential disruptions and optimising inventory placement to buffer against uncertainty. Beyond customs concerns, AI can improve visibility across the entire supply network. AI-enabled real-time tracking combined with predictive analytics allows retailers to spot problems — a delayed shipment or a container sitting too long at port — before they disrupt sales. Tesco’s AI-powered supply chain platform, for instance, now tracks container movements among 3,000-plus locations, providing transparency that results in faster issue resolution and reduced dwell times.

  4. Personalisation

    Personalisation has moved from “remember the customer’s name” to anticipating what they want before they search for it. AI analyses browsing behaviour, purchase history and contextual signals to deliver product recommendations, tailored promotions and individualised content across channels.

    For UK retailers competing with global platforms that have spent years refining their recommendation engines, AI-powered personalisation helps level the playing field. The goal is to build the kind of relevance that earns long-term loyalty. Done well, personalisation feels helpful rather than intrusive, surfacing products customers genuinely want.

  5. Anomaly detection

    Anomaly detection uses AI to identify patterns that deviate from the norm, flagging potential fraud, theft, or operational errors before they escalate. Given the UK’s inventory shrinkage crisis, it has become essential. At self-checkouts, visual AI identifies when scanned items don’t match what’s in the basket. Sainsbury’s, for example, has deployed AI-powered video analytics at self-checkouts across hundreds of stores, detecting when items may not have been scanned and prompting customers to correct errors — reducing theft while minimising friction for honest shoppers. In transaction data, algorithms detect suspicious patterns that might indicate employee theft or payment fraud.

  6. AI-enabled stock management

    Keeping shelves stocked sounds simple; doing it efficiently across hundreds or thousands of SKUs is anything but. AI-enabled stock management uses cameras, sensors and predictive algorithms to monitor inventory levels in real time, with AI-powered ERP and inventory management systems automatically triggering replenishment when stocks run low.

    Morrisons became the first UK supermarket to deploy cameras and AI analysis across all its stores for monitoring shelf availability and identifying gaps that need filling. The system doesn’t just count products — it can spot misplaced items and detect produce approaching spoilage. For store colleagues, this shifts time away from manual stock walks and towards activities that directly help customers.

  7. Call routing

    When customers do need to speak with someone, AI-powered call routing can help them reach the right person quickly. Rather than navigating frustrating phone trees, customers are directed based on their query type, sentiment, language and even predicted query complexity. Such intelligent routing means agents receive enquiries they’re equipped to handle, improving first-contact resolution rates and reducing the back-and-forth that frustrates everyone involved. For retailers managing high call volumes with constrained teams, getting routing right can meaningfully improve both efficiency and satisfaction scores.

  8. Price optimisation

    Pricing in retail has always involved balancing competitiveness against margin. AI adds speed and precision to these calculations, analysing competitor prices, demand signals, inventory levels and customer sensitivity to adjust pricing dynamically.

    In a cost-of-living environment where shoppers are acutely price-conscious, getting this balance wrong carries real risk — price too high and customers walk away; price too low and margins erode unnecessarily. AI-powered pricing optimisation helps retailers find the sweet spot, adjusting in response to market conditions. BCG research has found that retailers using AI-optimised pricing improved gross profit by 5% to 10%.

  9. Virtual fitting rooms

    Returns are expensive — logistically, environmentally and in terms of customer goodwill. Virtual fitting rooms use AI and augmented reality to help shoppers visualise how products will look on them before purchasing, reducing the likelihood of disappointment. Goddiva, an occasionwear brand, recently launched an AI virtual dressing room that lets online shoppers provide measurements and upload an optional photo to see how garments look on them before purchasing. For fashion and beauty retailers especially, helping customers get it right the first time addresses one of ecommerce’s most persistent friction points.

Benefits of AI in Retail

AI can deliver measurable advantages throughout retail operations. Here are four AI benefits where UK retailers are seeing the greatest returns:

  • Increased efficiency: AI systems go beyond what simple rules-based systems can handle to respond to common customer enquiries in natural language, categorise and route support tickets, process invoices and match them to orders, and generate product descriptions at scale. Retailers report meaningful productivity gains — without proportional increases in headcount — as routine work shifts from people to systems. The Retail Economics/Eversheds Sutherland research projects UK retailers could see 4.9% annual growth in sales per employee through 2030, rising to 6.4% annually as AI matures further.
  • Deeper insights: AI analytics can identify patterns in sales data, customer behaviour and operational performance that would be difficult or impossible to spot manually. These insights support better decisions on everything from inventory allocation to marketing spend.
  • Less shrinkage: AI-powered loss prevention has become a priority investment. Visual recognition at self-checkouts, anomaly detection in transaction data and predictive analytics that flag high-risk situations are helping retailers protect margins and reduce theft-related losses.
  • Automation and process optimisation: Beyond better automation of individual tasks, AI enables end-to-end process improvement. Automated replenishment systems respond to real-time demand signals. Dynamic pricing adjusts to market conditions without manual intervention. Warehouse operations optimise picking routes continuously. The cumulative effect is a more responsive, cost-efficient operation.

An AI-Powered Retail Solution with NetSuite

Implementing AI often means integrating multiple point solutions, consolidating data from disparate systems, and building technical capabilities that smaller organisations customarily lack. NetSuite takes a different approach. AI capabilities are embedded into NetSuite ERP for Retail, a cloud-based platform that connects finance, inventory, order management and customer data in a single system. Rather than bolting AI onto an already fragmented infrastructure, retailers can access intelligent forecasting, automated workflows and actionable analytics from within the tools they already use.

This matters because AI is only as good as the data it draws from. NetSuite’s unified data foundation means AI-driven insights reflect the full picture — inventory levels, sales trends, supplier performance and customer behaviour — without the integration headaches that derail many AI initiatives. For UK retailers navigating labour cost pressures, supply chain complexity and rising customer expectations, NetSuite offers a path to AI-powered operations without the need for extensive custom development or dedicated data science teams.

The retailers gaining ground today aren’t necessarily the largest or best-funded — they’re the ones embedding AI into their operations while competitors hesitate. From demand forecasting that helps limit waste to personalisation that builds loyalty, AI is reshaping what’s possible. For retailers, the opportunity is clear: Build AI capabilities now and the advantages will compound over time.

AI in Retail FAQs

What are the main challenges retailers face when implementing AI?

Retailers most commonly cite cost, employee resistance to change and integration with existing systems as primary challenges. Many organisations also struggle with data quality — AI systems require clean, consistent data to yield accurate results and retailers relying on legacy systems often find data preparation particularly challenging. Building internal expertise is another hurdle; smaller retailers may lack dedicated teams with AI and data science skills.

How can retailers get started with AI?

Start with a unified data foundation. Unintegrated systems creating proprietary data undermine AI effectiveness, so consolidating operational data is often the essential first step. From there, prioritise high-ROI use cases where AI can deliver quick wins. Demand forecasting, loss prevention and customer service automation are common starting points. Many retailers find that ERP solutions with embedded AI capabilities offer a faster path to value than assembling separate point solutions.

How is AI disrupting the retail industry?

AI is affecting all facets of the retail value chain. Forecasting has become more accurate, reducing waste and improving availability. Personalisation has moved from basic segmentation to individualised recommendations at scale. Loss prevention has shifted from reactive to predictive. And routine tasks are increasingly handled by AI systems, freeing staff for higher-value work. Industry research suggests that by 2035, nearly 60% of retail tasks could be automated or augmented by AI, fundamentally changing how the sector operates.