For many retailers, the conversation around artificial intelligence still begins and ends with generative AI.

Chatbots, content tools and customer-facing assistants have dominated the discussion since the arrival of large language models in the mainstream. They are highly visible, easy to understand and simple to demo in the boardroom.

But according to Dr Daniel Hulme, CEO and founder of enterprise AI business Satalia and chief AI officer at WPP, that narrow view risks distracting retailers from where AI can make the most meaningful difference.

“Most people think AI is generative AI,” he says. “The reality is there are many different types of algorithms out there that we bundle in terms of what we call AI, from machine learning to optimisation.”

Hulme has spent more than two decades researching, building and implementing AI systems. Satalia, founded in 2008 and later acquired by WPP, has worked with companies including Tesco, DFS, Waitrose and The Coca-Cola Company. Its expertise sits in the less glamorous but commercially critical world of optimisation, operations research, machine learning and decision intelligence.

In Hulme’s view, generative AI is only capable of addressing a relatively small slice of the friction inside a retail business.

“If I’m being totally honest, I think generative AI probably can address about 10 per cent of the frictions across the retailer supply chain,” he says. “You get the biggest bang for your buck if you use optimisation algorithms.”

For retailers under pressure to reduce cost, improve availability, protect margin and serve customers faster, that should be a wake-up call. AI’s greatest value may not be in the front-end experiences everyone can see. It may be in the invisible systems deciding how vans are routed, how stores are staffed, how engineers are allocated, how stock moves and how capacity is unlocked.

Where AI actually creates operational value

One of Hulme’s clearest examples comes from Tesco’s last-mile delivery operation.

Around 12 years ago, Satalia built Tesco’s last-mile delivery solution after the retailer concluded that no existing system could deliver the level of performance it needed. The challenge was not a chatbot problem. It was a series of optimisation problems, each with its own technical complexity.

When a customer enters their address and asks to see delivery slots, the system has to calculate how long it would take to get from that address to existing stops in the schedule. That means producing what Hulme calls a travel matrix in milliseconds.

“If you go and ask Google Maps, give me 1,000 routes, it’s going to take several minutes,” he says. “To be able to create 1,000 or even 3,000 routes that are accurate in 100 milliseconds required us to build bleeding-edge routing algorithms.”

The system then has to decide which slots can be offered, optimise the schedule between one customer booking and the next arriving, then later rebalance routes across drivers so workloads are fair, vans are staggered and capacity is maximised.

The impact was substantial. Hulme says the work helped Tesco save around 20 million miles a year, reducing carbon emissions while unlocking greater delivery capacity.

Satalia has since applied similar thinking to Waitrose, as well as middle-mile challenges such as moving goods from depots to stores. Hulme says a middle-mile project for Tesco took seven years to solve because the problem had never been cracked in that way before. Once solved, however, the underlying innovation could be deployed elsewhere far faster.

“The reason why it took seven years is because that problem had never been solved before,” he says. “But by solving that problem, we can now deploy that new innovation to another company in three months.”

That is a critical point for retailers. Bespoke AI doesn’t always mean starting from zero. Some underlying algorithms can be repurposed across organisations, while others must be tuned to the specific shape of the business.

A grocery drop may have relatively predictable delivery characteristics. A sofa delivery may take anything from 10 minutes to three hours, depending on access, installation and the reality of the customer’s home. The model has to understand the difference.

The hidden value in workforce and store optimisation

The same logic applies beyond vans and warehouses.

Hulme points to work with UK Telco, where machine learning was used to predict the nature of infrastructure faults, the skills of engineers and how long each engineer might take to solve a specific issue. The aim was to stop sending people to jobs they were not best equipped to complete.

“That project had a 200 times return on the investment,” he says.

In retail, a similar approach can be applied to store labour. Hulme cites DFS, where Satalia used machine learning to predict store footfall and customer demographics, then optimisation to allocate the right staff against the shape of demand.

Many retailers still roster teams in ways that are too blunt. A store may be staffed in similar patterns across the week, despite customer behaviour changing sharply by day, hour and demographic. Matching labour to demand is not just an efficiency play. It can directly influence sales, service quality and employee experience.

That wider view is important. Hulme argues AI should not be used to chase a single KPI in isolation. When it is, businesses can end up improving one metric while creating problems elsewhere.

He points to work with a leading accountancy firm, where Satalia built an algorithm to allocate thousands of auditors to jobs. The goal was not simply to increase utilisation. It was also to reduce travel time, improve employee happiness and strengthen client continuity.

“AI can improve all of your KPIs, not just one of them,” Hulme says. “If you focus on just one KPI, it can massively overachieve that goal, and by overachieving that goal, it can then actually cause harm elsewhere in the supply chain.”

For retail leaders, this may be one of the most important lessons. AI cannot be treated as a bolt-on efficiency project. It has to be understood as an operating model issue.

Start with frictions, not technology

If retailers want to move beyond hype, Hulme believes they should begin by listing the frictions across the organisation.

That means identifying where work is slow, repetitive, costly, unpredictable or constrained. Some of those problems may be solved with simple automation. Some may be addressed by buying mature third-party software. Others may require specialist AI expertise and custom-built solutions.

He describes it as a three-part strategy.

First, employees should be given access to tools that help them innovate at the edge of the business. Second, companies need to identify the hard problems where deep specialist expertise can create competitive advantage. Third, they should use partners and existing AI products for back-office tasks they do not need to build themselves.

“Start with listing all your frictions and then start knocking them off one by one by either building them, co-creating them or buying them,” he says.

This is also where the data conversation needs to mature.

For years, retailers have been told to build data lakes, unify everything and wait until their data is ready. Hulme is blunt about that approach.

“Don’t wait for your data lake to be ready. Your data will never be ready,” he says.

Instead, he argues that businesses should start with a clearly defined problem. Once the objective and constraints are understood, the necessary data becomes clearer.

In the accountancy firm example, the problem was how to allocate staff more effectively to jobs. That meant defining the objective function, such as maximising utilisation and minimising travel time, then mapping the constraints, such as availability, skills and client requirements. Only then does the data challenge become practical.

“A problem well defined is half solved,” Hulme says.

That does not mean data quality is unimportant. Poor data can produce poor decisions. But retailers should not confuse imperfect data with unusable data. In many cases, data issues only surface once a system is tested in the real world.

From optimisation to digital twins

The longer-term opportunity is not just solving individual operational problems. It is connecting those solutions together.

Hulme believes retailers should ultimately be working towards digital twins of their organisations, allowing them to model how one decision affects the wider system.

For example, a marketing campaign may increase demand by 10 per cent. But can suppliers cope? Is there enough warehouse space? Are there enough drivers? Can stores fulfil demand? Will the customer promise hold?

“Most retailers, because they are siloed, can’t project those questions across their supply chain,” Hulme says.

The promise of AI, he argues, is to create a simulation layer that allows retailers to test those scenarios before they become operational problems.

DFS offers a clear example of where this can lead. Satalia worked with the retailer on last-mile and middle-mile delivery, then helped build towards a broader digital twin. Hulme says DFS later platformised some of that delivery innovation through The Sofa Delivery Company, turning what began as an internal capability into a revenue-generating opportunity.

“That is the opportunity with AI,” he says. “If you build something that is genuinely differentiated, you can turn it into a revenue generator.”

Why quick wins can be a trap

Retailers are understandably under pressure to show progress. Boards want AI strategies. Shareholders want evidence of adoption. Leadership teams want quick wins.

But Hulme warns that quick wins can be misleading.

Most low-hanging fruit, he says, can be solved by third-party tools at a fraction of the cost. The problems that create true differentiation are usually not quick or easy.

“You need to focus on the problems that are going to differentiate your supply chain,” he says. “And those problems are not quick and they’re not easy.”

That creates a difficult challenge for C-suite leaders. They are being bombarded with AI vendors, consultancies and platforms. Many are being told to build internal teams, launch pilots or adopt the latest agentic tools, often without a clear view of what problem they are actually solving.

Hulme is sympathetic to that pressure but firm on the risk.

“Organisations can’t afford over the next three to five years to place the wrong bets,” he says.

The agentic AI ‘reality check’

No AI discussion in 2026 can avoid agents.

Agentic AI has rapidly become the industry’s latest obsession, promising autonomous systems that can take actions, complete tasks and collaborate with other tools or agents. Hulme believes agents will become hugely important, but he is clear that the market is still immature.

He compares the current agentic moment to the big data boom.

“People think everybody’s doing it, nobody’s doing it, but if they are doing it, they’re doing it badly,” he says.

The difference, he adds, is that agents will eventually drive real value. The danger is that companies deploy them before they know how to verify whether they work.

Large language models, he argues, are still like “intoxicated graduates”. They can be impressive, but giving them agency across a business without proper testing could create serious harm.

The issue is not just security or performance. It is functional verification. If an agent is built to optimise a media plan, for example, can the business prove it will do that well? If it is given a £1m budget, can the business be confident it will not spend it badly?

Hulme believes this will become one of the defining governance questions of the next phase of AI adoption.

The message is becoming increasingly clear. AI agents may be coming, but they need structure, accountability and verification before they are trusted with meaningful business decisions.

The real competitive divide

Hulme’s view of AI in retail is both optimistic and cautionary.

The technology can unlock capacity, reduce waste, improve service, support employees and create new revenue streams. But only when retailers understand the difference between shiny tools and strategic capability.

Understanding where those key frictions are, which problems are worth solving, which capabilities are differentiating, and which experts they need around the table, is fundamental to building AI-centric solutions that have longevity.

But that requires a shift in mindset. AI is a way to rethink how the business allocates resources, predicts demand, responds to complexity and makes decisions at scale.

That work is harder than launching a generative AI pilot, but it’s also where the real value lies.

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The tie-up will see 185 Ryman stores added to Deliveroo, giving customers access to more than 4,000 stationery, office, technology and gifting products for delivery.

The launch comes ahead of exam season, when demand for stationery typically rises. Deliveroo said stationery orders on its platform increased by almost 40 per cent in 2025 compared with the previous year, while demand rose more than 30 per cent during last year’s UK exam season.

Customers will be able to order products including highlighters, office supplies, printer ink, technology accessories and last-minute gifts, with delivery available in as little as 25 minutes.

The partnership will be supported by both Deliveroo Shopping and Deliveroo Express, the company’s white-label delivery service.

Deliveroo Express will allow customers to place orders directly through Ryman’s website or app at checkout, using Deliveroo’s logistics technology and rider network to complete deliveries.

Deliveroo said the move marks another milestone in the expansion of its Shopping division, as it looks to grow beyond food delivery and capture more on-demand retail categories.

Deliveroo vice president of new verticals Suzy McClintock said: “The addition of a household name like Ryman is a testament to the massive momentum behind Deliveroo Shopping.

“We aren’t just adding more choice, we are responding to a fundamental shift in consumer behaviour.

“With stationery orders up 40 per cent in the last year, this partnership ensures we are meeting that demand head-on, proving that Deliveroo is the essential partner for retailers looking to unlock digital growth and omnichannel scale.”

Ryman chairman and owner Theo Paphitis said: “For over 130 years, Ryman has been a staple of the British high street, trusted for our expertly curated range of stationery and office supplies.

“We’re now delighted to partner with Deliveroo to bring over 4,000 stationery, tech, and gifting essentials, plus print services, directly to our customers’ doors in as little as 25 minutes.

“From ink cartridge emergencies to eleventh-hour school projects and saving the day with urgent birthday presents for last-minute Lucy or Larrys, this partnership will help to keep homes and offices running smoothly.”

Ryman joins a growing list of retailers using Deliveroo Shopping, which launched in November 2023.

The platform already includes The Entertainer, The Perfume Shop, HURR and Accessorize, as Deliveroo continues to expand its rapid delivery offer across non-food retail.

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Having taken over the business just five weeks before Christmas 2016, owner Jo McBeth took a leap of faith in what she calls “the biggest impulse purchase of her whole life.” Entering the world of running a business almost entirely alone was a huge undertaking, but with the help of Square’s POS system and loyalty tools, Jo has turned her small shop into a community hub for customers that come back time and time again.  

A gift for all 

From hand-made cards to jewellery ranges, children’s toys, ceramics and candles, customers find themselves stepping through the doors of Chirpy when they want to purchase something special. 

“It’s one-of-a-kind items that are the true selling point of the business,” says Jo. “I tend not to get a lot of the same products. For example, we’ll stock two or three candles, each at different price points. I choose items that are useful and practical – and beautiful at the same time! My motto is that I wouldn’t sell anything that I wouldn’t buy myself.” 

With community at the heart of her business, Jo sources cards, exclusively from independent brands and makers. Selling almost 9,500 cards a year, she describes them as “the bread and butter of the business.”  

Community at the heart

For Jo, running her business solo means she has many roles to play, from manning the till, to stock purchasing, curating the store, and running social media accounts. But it’s the relationship development that’s her favourite role. 

“I take pride in knowing my customers that come into the shop; knowing their names, and what’s going on in their lives. I try to make it a priority to get to know them and understand what they’re looking for,” 

It’s not just Chirpy that builds that community feeling, but the surrounding independent coffee shops, fruit stores, local bookshops and independent toy shops too. “It’s very much the kind of place where people on a Saturday will come and shop along the street to get everything they need in one go.” 

It’s a pattern reflected across the country. According to Square’s recent Local Economy Report, four in five shoppers say their relationship with their favourite local business goes beyond transactional – they value being recognised, feeling a sense of belonging, and knowing the other person behind the counter.   

Loyalty, in store and out

For Jo, meeting those expectations starts with keeping track of who her customers are and what keeps them coming back, and Square’s loyalty programme has helped her do exactly that.  

“We started using the scheme to remember our customers’ details, which was a big factor in building relationships,” says Jo. “Then about two years ago, I discovered there were so many other parts to the feature. We now send our customers vouchers for their birthdays and give them rewards on their anniversary of loyalty to the store via email. We can even send them a reminder that they have a reward to spend, and my customers find it very simple, smooth and quick.” 

Square Loyalty is also able to generate reports to summarise what has been spent each day and from which customer, as well as how many customers have entered the store – which is crucial for revealing the busiest times of the day. 

The best part? It can be customised to fit every business, helping them grow in return. “I’ve witnessed a 25 per cent increase in spend from customers who have enrolled into the loyalty programme. It’s a real gamechanger!”  

Smooth selling every day

Beyond loyalty, Square keeps the day-to-day running of Chirpy effortless. At a shop where 95 per cent of customers pay by card, Jo uses Square Stand – a simple tablet that puts a smarter spin on selling. 

“All my customers need to do is tap the card reader, and it’s as simple as that. Everything is done on an iPad – it’s so easy and user friendly. My customers love that it’s just really unfussy, and the fact they can get receipts sent straight to their phones or emails is a real hit. 

“We also have the Square app, so I could be serving someone at the same time as someone else in the shop, on different devices.” 

Planning for the future

That ease of use pays off in the long run, too. From helping to plan for busy periods like Valentine’s Day, Mother’s Day and Christmas, to tracking customers’ purchasing behaviours, Square helps Jo ensure her business runs smoothly. It allows Jo to look back at sales, discover what’s selling the most, and identify products that need restocking.

“I use Square to help track how many cards I’ve sold in the previous year, and in turn, this helps improve my stock management. It means I’m never overbuying. The best piece of advice I’ve ever received is to not carry too much stock, and Square is essential in keeping me on track.” 

“If I compare the business now to when I first bought it, the growth is unmatched.”  

Want to grow like Chirpy? For more information, visit Square.com.  

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The online fashion retailer has rolled out ASOS Stylist for customers in the UK and US, allowing users to discover ASOS products directly in ChatGPT before completing purchases on ASOS.com.

Built using video commerce platform Bambuser, the app enables shoppers to browse products and complete outfits by category, occasion or trend, while receiving styling advice and recommendations from across ASOS’ own brands and partner labels.

ASOS said the launch marks a move beyond AI shopping tools that are largely limited to text and static imagery, with the new experience designed to bring product video and livestream content directly into the chat.

The retailer said customers could ask for specific edits, such as “pastel floral A-line dresses for spring”, with ASOS Stylist searching across its brand portfolio and returning a curated selection based on the context of the conversation.

Products can be viewed in motion through video, expanded to show details including pricing, and clicked through to ASOS.com to complete the purchase.

The launch builds on ASOS’ existing AI Stylist within its mobile app, which the retailer said had given it a stronger understanding of how customers use conversational AI to discover and shop fashion.

ASOS said it had used those learnings to create a new AI Stylist experience within ChatGPT, as more shoppers begin their product research on agentic platforms.

ASOS head of product Melissa Lim said customers were increasingly turning to AI for shopping and style inspiration, but that the experience could still feel “fragmented and hard to visualise”.

“ASOS Stylist brings fashion discovery, styling advice, and shoppable products together in one seamless, conversational experience,” she said.

“It’s an important step in how we’re using AI to reduce friction and enhance the way customers shop with ASOS.”

Bambuser vice president of product and innovation Kristina Brjazgunova said the partnership combined ASOS’ video content library with Bambuser’s Intelligence Layer and shoppable video player.

“Together, we’re setting the standard for how brands can activate video and AI content analysis inside answer engines like ChatGPT to engage the next generation of shoppers,” she said.

Bambuser’s Intelligence Layer converts ASOS’ product catalogue and video library into structured, machine-readable data that can be processed and returned by large language models in real time as shoppable videos.

ASOS, which was founded in 2000, has 17m active customers across more than 150 markets. Its own-brand portfolio includes ASOS Design, Arrange, Collusion, Topshop and Topman.

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The trade body’s new report, Agentic commerce in UK retail, an unresolved liability question, found that 58 per cent of UK online retail merchants believe AI-initiated transactions have already reached their platforms.

The research, conducted among 100 senior finance, payments and risk leaders at UK online retail businesses during Q1 2026, suggests agentic commerce is moving rapidly from theory to reality.

The Payments Association said the rise of autonomous AI agents, which can search for products, select merchants and complete purchases on behalf of consumers, is outpacing the liability, authentication and operational frameworks needed to support it.

No respondents described agentic commerce as irrelevant to their business, while 72 per cent said they were either preparing or planning their approach to autonomous AI purchasing.

However, the report found significant uncertainty over responsibility when disputes arise. When respondents were presented with a hypothetical disputed £2,000 purchase made by an AI agent, 24 per cent said liability would depend on the specific circumstances, 21 per cent backed a shared liability model, and 18 per cent said the AI vendor should be responsible.

Only 41 per cent of respondents said they felt very confident in current liability frameworks.

The Payments Association chief executive Emma Banymandhub said agentic commerce was “arriving faster than many merchants expected”.

“The concern is that businesses are already processing AI-initiated transactions without the controls, liability clarity or transaction visibility needed to manage the risks properly,” she said.

“Fraud engines built around human behavioural signals are misfiring on legitimate agent traffic, and the question of who carries the loss when an agent transacts incorrectly remains unresolved.”

The report comes as major payment networks, including Visa and Mastercard, have introduced frameworks for agentic commerce, while Google, OpenAI and the FIDO Alliance have also published open protocols for agent-initiated checkout.

However, The Payments Association warned that many identifiers and protocols are not yet widely available in the UK, leaving merchants unable to clearly identify when transactions are being initiated by AI agents.

Banymandhub said the industry was beginning to coalesce around ‘Know Your Agent’ as a complement to existing Know Your Business and Know Your Customer obligations.

She added that UK regulators, including the Financial Conduct Authority, had signalled they were reviewing how existing rules apply to agent-initiated payments.

“Over the next 12 months, the industry will need to converge on common standards for authentication, accountability and consumer protection if trust in these systems is to scale safely,” she said.

The Payments Association’s Merchant Payments Working Group is urging retailers to audit fraud systems, review internal controls and assess payment provider readiness ahead of expected UK regulatory scrutiny in 2026.

The report found merchants with formal policies already in place were nearly four times more likely to feel confident in liability frameworks than those without, suggesting early governance could be a key factor in readiness for AI-led commerce.

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The four-week campaign ran through Runners Need, which is part of Cotswold Outdoor Group, and was powered by Zitcha’s retail media platform.

It combined in-store digital screens across 13 shops with on-site placements, CRM activity and paid media across Meta and Google.

Cotswold Outdoor Group said the campaign drove a 30 per cent increase in HOKA’s share of revenue and a 28 per cent rise in unit share during the period.

Stores included in the in-store screen activity recorded a 13.3 per cent increase in incremental units sold per shop compared with control locations.

The retailer said homepage click-through rates rose 53 per cent, while CRM email activity achieved a 67 per cent open rate.

Product engagement after click-through was 3.9 times higher than the control group, supported by a 19 per cent uplift in overall conversion.

The campaign targeted high-intent running customers, including performance runners and shoppers using Runners Need’s in-store gait analysis service.

Cotswold Outdoor Group chief executive Jamie Kristow said: “This campaign proves that our retail media network is more than just an advertising channel.

“By connecting our in-store expertise with targeted digital touchpoints, we have shown how Cotswold Outdoor Group retailers can deliver measurable growth and immediate demand in a competitive category.”

Zitcha regional director for EMEA Dan Sands said the campaign showed the value of moving beyond on-site ad placements to a full-funnel omnichannel model.

He added: “These results validate the power of an integrated retail media network to convert demand at the point of purchase, both online and in store.”

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The online marketplace said its board and advisers had reviewed the unsolicited offer from the US video games retailer and decided not to engage.

GameStop, led by chief executive Ryan Cohen, tabled the cash-and-stock proposal earlier this month, offering $125 a share for eBay.

However, the retailer’s own market value stood at roughly $12 billion before the bid, far below eBay’s valuation of about $46 billion.

In a letter to Cohen, eBay chair Paul Pressler said the board had considered the “uncertainty” around GameStop’s financing plans, as well as the borrowing, operational risks and leadership structure of a combined business.

GameStop said it would fund the deal using around $9.4 billion in cash and liquid investments, alongside up to $20 billion in debt financing from TD Securities.

The company has also built a five per cent stake in eBay and has said it could take the offer directly to shareholders if the board did not engage.

Cohen has argued eBay could become a stronger rival to Amazon under his leadership, with GameStop’s remaining 1,600 US stores used for authentication, intake, fulfilment and live commerce.

GameStop also claimed it could deliver $2 billion of annual cost savings within 12 months of completion.

However, questions over the funding of the offer have weighed on GameStop’s shares, which have fallen since the proposal was announced.

The retailer, which became one of the best-known “meme stocks” during the 2021 trading frenzy, has been shrinking its store estate and closed 590 shops in 2025.

eBay said it remained confident in its standalone strategy and current management team.

The marketplace is also in the process of buying UK-founded secondhand fashion app Depop from Etsy for around $1.2 billion (£890 million), as it looks to strengthen its appeal with younger shoppers.

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The supermarket told workers that trials of the new locking technology would commence before the end of the year under a broader crackdown on retail crime.

The cabinets will use monitoring systems capable of detecting how long cabinet doors stay open and identifying when items are removed from shelves.

Workers will be alerted if the cabinets detect suspicious activity, such as attempts to clear entire shelves or cabinets of stock.

The supermarket has not confirmed how customers will access items kept inside the locked cabinets once the trials start.

John Lewis told staff that the move demonstrated that the company was not “standing still” in its response to theft impacting stores throughout the country.

A spokesman for John Lewis confirmed that the business was heavily investing in new technology which aimed to deter criminals from targeting shops.

“We are currently investing in a range of advanced technology, including smart technology to deter theft,” the spokesman said.

“As part of this we are planning to pilot lockable smart cabinets for areas such as spirits and champagne soon.

“We already use smart shelf technology in our health, beauty and spirits aisles, which are able to sense unusual customer behaviour, so this would provide an additional layer of security.”

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The company recently began rolling out an in-house product called MeshClaw, which allows staff to create AI agents that can connect with workplace software and complete tasks on their behalf, according to the Financial Times.

However, some employees told the publication that colleagues were using the tool to generate unnecessary AI activity in order to increase their token consumption. Tokens are the units of data processed by AI models.

The reported behaviour comes after Amazon introduced targets for more than 80 per cent of developers to use AI each week. The business has also tracked internal AI usage through leader boards showing token consumption.

While Amazon has told staff that AI usage statistics will not be used in performance evaluations, several employees told the FT they believed managers were monitoring the data.

One employee said there was “so much pressure” to use the tools, while another said tracking usage had created “perverse incentives”.

The term “tokenmaxxing” has emerged in Silicon Valley to describe employees artificially inflating their AI usage metrics, with Meta staff also said to have engaged in similar behaviour.

Amazon’s MeshClaw tool can reportedly initiate code deployments, triage emails and interact with workplace applications such as Slack.

The company said the product enabled “thousands of Amazonians to automate repetitive tasks each day”, describing it as one example of how it is “empowering teams” to experiment with and adopt AI tools.

The group added that it remained committed to the “safe, secure and responsible” development and deployment of generative AI.

However, some Amazon employees reportedly raised concerns about the security implications of giving AI agents permission to act on a user’s behalf, warning that the software could make errors or take unintended actions.

The push comes as major technology companies increase pressure on staff to adopt generative AI tools, as they look to prove the value of heavy investment in AI infrastructure and embed the technology into everyday workflows.

Amazon is expected to spend heavily on capital expenditure this year, with much of that investment directed towards AI and data centre infrastructure.

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But retailers are not restricted to using AI solely in their ecommerce operations. In fact, the technology is being used to bolster the customer experience across physical stores in a multitude of ways.

Retail Gazette looks at six ways retailers have used AI to augment their bricks-and-mortar operations in recent times.

Iceland uses AI to sharpen stock availability and replenishment

Iceland Foods launched new AI-driven inventory and replenishment tools in April as it sought to improve availability, reduce lost sales and bolster store operations across its estate.

The frozen foods specialist said the move was designed to tackle keeping the right goods in stock across a large network of shops and distribution centres, as it responded quickly to shifts in demand. 

Through using AI to analyse sales and supply and demand data in real time, the supermarket said it could make quicker and more accurate replenishment decisions, helping to ensure items are in the right place at the right time.

Designed to go beyond traditional forecasting, its system factors in variables including seasonal peaks,  promotions, new product launches and one-off trading anomalies.

The grocery giant said this should help its operational teams reduce stockouts, limit waste and improve sales opportunities.

M&S launches AI tools for 11,000 staff members

In March, M&S equipped 11,000 of its workers with generative AI and agentic AI tools as it sought to double down on customer service and accelerate its broader digital transformation.

The food and fashion brand purchased 11,000 Microsoft 365 Copilot licences, which were made available to all of its store managers and support centre colleagues, alongside a training programme intended to help staff make full use of the tech.

The retailer said the tools would help workers carry out time-consuming day-to-day tasks more efficiently, such as compiling sales insights, summarising meeting notes, creating shift rotas and preparing handovers.

The move means M&S store managers can access data, analysis and key trading insights faster, as the business positioned the launch as a way to free up additional time for customer-facing activity, improve availability and support shop floor teams.

In its stores support centre, the company said Microsoft 365 Copilot would be used to draft meeting updates and trading summaries, as it helps turn complex reporting into clearer recommendations and actions.

Tesco tests AI shopping assistant with staff before customer rollout

Tesco revealed that it was trialling a new AI-powered assistant in its app last month, as it sought to make meal planning and basket building easier for customers.

The UK’s biggest supermarket rolled out the tool as a beta trial for its staff. Roughly 280,000 of its workers gained early access over the following weeks, ahead of a broader customer launch later in the year.

The AI assistant will initially focus on meal planning, using conversational prompts to suggest recipe ideas based on shoppers’ preferences and dietary needs.

Additionally, it will be able to help customers add ingredients to their basket in the Tesco app by using factors including previous shopping history and product preferences.

The retail giant claimed its long-term goal was to use AI to reduce the time, cost and friction involved in grocery shopping, as well as helping shoppers reduce food waste by suggesting meals around ingredients they already had at home.

Tesco used its staff as an early testing ground, giving them the chance to test the tool in their day-to-day lives and feed back on what worked ahead of its wider rollout. 

Morrisons’ AI tool to help shoppers find products in store

Morrisons introduced Google’s generative AI tool Gemini in July, to help its shoppers locate items in store as part of its ongoing digital transformation.

The tool enables customers to search for the products they are looking for, before the feature provides real-time locations of what aisle they can be found in, according to City AM.

The feature has been trained to handle typos, brand references and vague phrases to find the closest matches based on each shop’s specific layout and inventory.

The system uses Google Cloud’s Vertex AI platform as well as Gemini large language models (LLMs), but was developed by Morrisons’ in-house data science team.

Sainsbury’s launches retail media platform harnessing customer insights and AI

In June, Sainsbury’s unveiled plans to launch a new unified retail media platform later in the year, which it said would revolutionise how brands and agencies ran omnichannel retail media campaigns at scale.

The supermarket’s Nectar360 Pollen platform was built in-house by Sainsbury’s and Nectar360 alongside partner brands and agencies.

Sainsbury’s said it would harness customer insights and AI to deliver audience insights, media planning and activation, optimisation and measurement into one single portal.

In addition, Nectar360 Pollen features a multi-touch attribution model and visualisation tool to enable tracking and analysis of all media touchpoints, demonstrating the impact of each channel on its overall campaign performance.

The platform enables marketers and media professionals to optimise their campaigns quicker, while shoppers benefit from more relevant adverts and an improved customer experience.

LVMH’s data platform for its 75 ‘maisons’

Luxury goods group LVMH launched a data platform in June that could serve the needs of its 75 ‘maisons’ (stores), such as Louis Vuitton and Sephora, while ensuring the autonomy and distinctiveness of each brand.

The business worked with Google Cloud to build the data and AI platform, which has been designed to meet each of its maison’s unique needs.

The platform has been used to deepen the knowledge and capabilities of LVMH’s client advisors at shops including Tiffany & Co, Louis Vuitton and Sephora.

For example, at Tiffany & Co, each advisor has an AI-powered chat interface while talking to clients, which can help to surface each of their clients’ personal tastes. It can also guide advisors to appropriate products that the clients may not have known about. 

The client advisor platform features store procedures and guidelines alongside the latest product information. This enables advisors to quickly query details regarding a new season’s styles or opening and closing procedures.

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