The platform, which forms part of the easy family of brands, marks a major expansion for the business into everyday shopping and will be built around its long-standing focus on simplicity and value.

easyShop.com will be operated by ecommerce and marketplace technology company OnBuy.com and powered by its proprietary OnCommerce platform, which has been developed to support cross-border trade at scale.

OnBuy said the technology has already been proven through its own marketplace, which has grown more than 86 per cent over the past three years and now supports more than 100m products across Europe.

Several of its markets are also delivering quarterly growth of more than 150 per cent.

The partnership includes significant long-term investment, as both businesses look to establish easyShop as a major new online marketplace.

easyGroup said the model allows it to move into online retail through an asset-light structure, while OnBuy provides the marketplace expertise, regulatory compliance and operational capability needed to support international growth.

For shoppers, easyShop will offer a value-led online shopping destination aligned with the easy brand’s wider presence across travel, transport and consumer services.

For retailers, the platform will offer access to a new marketplace launching across multiple countries at once, without the complexity of managing separate local marketplaces or building infrastructure country by country.

Sir Stelios Haji-Ioannou, creator and owner of the easy family of brands, said: “The easy family of brands has expanded into many new sectors by focusing on simplicity and value for money. I believe an online retail marketplace using a great domain like easyShop is a natural next step.

“I am delighted to welcome Cas Paton of OnBuy into the easy family, and I am looking forward to working with him to start recruiting sellers for our marketplace from now in 21 different countries.

“We are aiming for a consumer-facing launch for consumers to be able to buy online from Q4 in 2026. Exciting times for the easy family.”

OnBuy founder and chief executive Cas Paton said: “easyShop removes many of the barriers that typically slow retail expansion by operating as a pure marketplace without inventory, logistics, or local infrastructure.

“Working with the easy family of brands brings together one of Europe’s most recognisable consumer names with proven marketplace expertise, accelerating awareness and enabling retailers to reach customers across multiple countries much more quickly.”

easyShop will operate as a pure marketplace, meaning it will not hold inventory or compete directly with sellers on the platform.

Seller onboarding will begin ahead of the consumer launch, giving retailers the chance to establish an early presence before trading begins.

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The kitchen specialist has struck a deal to buy the Pontefract-based parent company of Ultima Furniture Systems, which trades as DIY Kitchens.

DIY Kitchens sells exclusively online and targets customers who want to self-manage the purchase, planning and ordering of their kitchen.

Howdens said the business has a “highly profitable and growing” model that is distinct from its own trade-only, full-service approach, giving the group direct access to non-trade consumers in the UK.

DIY Kitchens’ online platform offers self-service planning, design and ordering tools, while its model combines low selling overheads with made-to-order manufacturing and customer prepayment.

Following completion, the retailer will remain an online-only business focused on non-trade shoppers and will operate separately from Howdens’ larger trade-only business.

Howdens chief executive Andrew Livingston said: “Howdens’ highly successful trade-only model is built around supporting solely trade customers with outstanding in-stock availability, expert local depot teams, and an end-to-end service from design through to delivery.

“The acquisition of DIY Kitchens, which will be operated on a standalone basis, adds a complementary very profitable business to the group, providing access to non-trade end customers through its direct online channel with self-service planning, design and ordering tools.

“DIY Kitchens shares many of the characteristics that underpin Howdens’ success including well-invested manufacturing, strong vertical integration, scalable capabilities and a deep, well-embedded entrepreneurial culture.”

Livingston added: “We are excited to welcome the DIY Kitchens team to Howdens. We have great respect for the innovative business model they have built, and we look forward to supporting the business’s continued growth and investing behind its next phase of development.”

Founded by the Ellis family, DIY Kitchens traces its roots back to 1982 and now employs more than 550 people in the Wakefield and Leeds postcode area.

Howdens operates two main UK factories in Runcorn, Cheshire, and Howden, East Yorkshire.

The deal remains subject to customary regulatory approvals.

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The retail group, which is listed on AIM as Boohoo, reported a 0.5 per cent rise in gross merchandise value in the three months to 31 May, while gross margin expanded to 53.5 per cent from 52.1 per cent a year earlier.

Trading strengthened significantly in May, with gross merchandise value up around eight per cent year on year.

Debenhams and PrettyLittleThing were the group’s strongest performing brands during the period, while improvements were also recorded across Boohoo, BoohooMan and Karen Millen.

Debenhams Group chief executive Dan Finley said: “This is the result of the heavy lifting of our multi-year turnaround: the move to an asset light marketplace model, the warehouse consolidation, the cost reset, and the rebuild of every brand on a single proprietary platform.

“That work is now translating into materially improved profitability, with adjusted EBITDA margin expanding and a substantial increase in adjusted EBITDA in the period, alongside significantly improved cashflows.”

The group said the momentum seen in the first quarter had strengthened its confidence in delivering double-digit percentage growth in full-year adjusted EBITDA, ahead of the £53m guidance set out for FY26 in March.

Shares in the business jumped 19 per cent following the trading update, rising to more than 22p, although the stock remains down around three per cent so far this year.

The retailer said its cost-cutting programme was also gaining traction, with exceptional costs falling 72 per cent in the quarter and capital expenditure dropping 54 per cent year on year.

PrettyLittleThing, which was revamped last year, has helped drive the recent improvement, alongside lower volumes of returns applications.

Analysts said the business was benefiting from its shift away from a pure fast fashion model towards an asset-light marketplace approach, supported by third-party sellers across its platforms.

Wealth Club chief investment strategist Sarah Streeter said: “Its turnaround strategy has been rooted in rebuilding the group around the Debenhams marketplace platform to try and counter several bruising years of sliding sales.”

AJ Bell investment director Russ Mould said the recovery had been a “hard slog” following the pandemic, but added that progress against a difficult consumer backdrop was “a feather in the cap for management”.

Panmure Liberum analyst Wayne Brown described the update as a “major inflection point” for the company’s turnaround.

“The turnaround plan to simplify the operations, cut major costs, integrate all the brands into one ecosystem and then reinvigorate the brands seems to be coming together,” he said.

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New data from global digital trust and fraud prevention platform Forter found that AI-generated damage claims are now the fastest-growing form of return abuse.

The company said increasingly flexible retail policies were being exploited by both consumers and organised fraud networks.

With generative AI tools now widely accessible, fraudsters can quickly doctor receipts or create realistic images of damaged goods, including apparently torn clothing, to support false refund claims.

Forter warned that retailers are facing coordinated groups using AI to fabricate convincing “evidence”, from fake damage photos to sophisticated “item not received” narratives.

The company said some fraud rings have also scaled their operations by offering “returns-as-a-service”, taking a percentage of the proceeds in exchange for helping shoppers secure fraudulent refunds.

The attacks are evolving faster than legacy fraud systems can respond, with returns becoming a particular weak point for retailers.

Fashion retailers are especially exposed, as high return rates already place pressure on margins and operational costs.

Forter’s data also found that 53 per cent of merchants are reporting issues with “wardrobing”, where shoppers wear clothing before returning it.

Meanwhile, 30 per cent of consumers admit to buying additional items purely to qualify for free shipping before later returning them.

The pressure on retailers is mounting, with 44 per cent of UK businesses saying they are being affected by returns and refund abuse.

Almost half of retail leaders said the issue had become so severe that they had considered scaling back operations or shutting down entirely this year.

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As part of the app experience, customers can browse products and receive styling advise and recommendations.

Head of Digital Product at ASOS Melissa Lim explains that the retailer decided to implement an AI stylist feature on ChatGPT because it had found that consumers were increasingly using agentic platforms as part of their shopping journey.

She says: “For us, this is about being present where customers are already discovering fashion and making sure ASOS is part of that inspiration journey, rather than sitting on the side-lines.

“The ASOS Stylist app helps us show up in that moment of inspiration, reach new potential customers and drive discovery back to ASOS.com.”

According to Lim, the ASOS Stylist app in ChatGPT is part of its strategy to reduce friction in the shopping experience. It’s part of a plan to meet consumers where they are.

She adds: “It is also a natural extension of our role as The Stylist at ASOS: helping customers discover, combine and wear fashion with confidence.

“By bringing that styling support into new platforms, we can show up earlier in the inspiration journey and make it easier for customers to turn ideas into outfits and outfits into purchases.

Lim highlights that the stylist tool recommends products that are “genuinely” right for the consumer.

She says: “At ASOS, we see our role as helping people style themselves with confidence, not steering them towards products that serve a short-term commercial objective.

“The value of this experience lies in giving customers recommendations they can trust, because building that trust over time matters far more than any immediate gain. That principle was fundamental to how we designed the stylist.”

She explains that up until now, ChatGPT has primarily been a text-focused experience. However, fashion is a visual experience and can oftentimes be an emotional purchase. Movement, draping and fabric details all come into consideration when buying clothes.

She says: “Our experience is designed to be highly visual, bringing immersive product imagery and video, including livestream content, directly into the chat.

“We’ve applied learnings from years refining the e-commerce journey on ASOS to create familiar user interface (UI) patterns so shopping in ChatGPT feels more familiar. This shows in the way ASOS Stylist presents an edit or recommends similar items as alternatives.”

She highlights that the retailer’s Stylist app enables its customers to shop more “confidently” as it offers advice and guided conversation on how to shop for a particular body shape or style.

She says: “User research and A/B test insights from our product pages also show that videos provide more nuanced information about fit, so bringing this more prominently within the chat experience allows customers to better visualise how an item might look like on them.”

Lim adds that the retailer made the decision not to allow consumers to log in to their ASOS accounts through the ChatGPT for the initial release, so consumers are referred back to ASOS.

However, if customers have chosen to preserve its chat history within ChatGPT itself, the stylist will have access to previous conversations.

Lim says: “It’s still early days, but so far we have seen promising early engagement, with traffic from the ChatGPT channel increasing since launch, especially from new users.

“Once this new audience lands on ASOS, we see them actively browsing and saving products rather than leaving without any further interaction.”

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The rise came after Asda revealed plans to roll out Ocado’s ecommerce technology across Asda stores and warehouses from early 2027, as it looks to improve its online grocery offer and close the gap with rivals including Tesco and Sainsbury’s.

The agreement will see Asda use Ocado’s software to support store picking, fulfilment and home delivery as part of a wider push to strengthen its digital operations.

The deal marks a positive moment for Ocado, whose shares were hit last year after US supermarket giant Kroger said it would close three underperforming warehouses built using the UK company’s technology.

Kroger later agreed to pay Ocado $350m in compensation, but the closures raised investor concerns over the long-term growth prospects of Ocado’s technology solutions arm.

The Asda partnership has therefore been seen as an important vote of confidence in Ocado’s platform, particularly as the supermarket works to rebuild momentum online following a challenging IT transition.

Asda has spent more than £1bn moving away from systems operated by former owner Walmart, a project that caused disruption to stock availability and weighed on sales.

The disruption also slowed efforts by executive chair Allan Leighton to revive the grocer’s performance after years of market share losses under private equity owner TDR Capital.

Ocado chief executive Tim Steiner told the Financial Times that the first priority would be moving Asda customers onto Ocado’s platform, which will support the retailer’s existing online grocery operation of more than 700,000 orders per week.

However, he said the partnership could eventually expand beyond software, with Asda potentially using Ocado’s automated warehouse technology in the future.

“They’re going to run the in-store fulfilment, the delivery and the front end of the platform,” Steiner said. “We hope in the future they can start to think about adding some automation.”

Analysts said the partnership could provide a meaningful uplift for Ocado.

Peel Hunt analyst James Lockyer estimated the deal could add around £30m to Ocado’s annual revenue and approximately £20m in earnings before interest, tax, depreciation and amortisation, based on Asda generating £3bn in ecommerce sales a year.

Ocado reported adjusted earnings before interest, tax, depreciation and amortisation of £178m last year on revenue of £1.36bn.

The Asda deal also adds another major UK grocery name to Ocado’s technology client base, with Morrisons already using its software to run online operations.

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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 grocer, which is the UK’s third-largest supermarket, will roll out Ocado’s Smart Platform technology across its website, mobile app, in-store picking and home delivery service from 2027.

The partnership will begin with an upgrade to Asda’s website and online shopping experience, designed to make the platform more stable and easier to use for the millions of customers who shop with the supermarket online each year.

The revamp will include improved search, more relevant product recommendations and a simpler checkout process.

Asda said further upgrades to in-store picking and last-mile delivery will follow, helping stores manage orders more efficiently, improve on-time deliveries and increase the number of available delivery slots by making better use of its existing fleet.

The supermarket currently fulfils more than 700,000 online grocery orders a week and trades from around 1,100 stores across the UK.

Under the agreement, Asda will retain full control of its customer offer, pricing and online operations. Scheduled home delivery and click-and-collect orders will continue to be fulfilled from Asda stores and existing fulfilment hubs.

Orders placed through rapid delivery partners including Deliveroo, Just Eat and Uber Eats will be picked by Asda using Ocado’s technology before being delivered by the third-party provider.

The partnership comes as Asda looks to reverse a recent loss of market share to larger rivals Tesco and Sainsbury’s.

Asda executive chairman Allan Leighton said: “Asda is the cheapest full-range supermarket, as consistently shown by independent price comparisons from Which? and The Grocer, and has a large, well-established online offer.

“We know that continued success in this highly competitive market is dependent on providing a positive experience for customers every time they shop.

“Partnering with Ocado will strengthen our online offer and provide a consistent and high-quality experience for millions of shoppers, from order through to delivery, while supporting our Formula for Growth.”

Ocado’s in-store fulfilment solutions are already used in more than 1,000 grocery stores worldwide, while the wider Ocado Smart Platform supports around 70 million orders each year.

The deal marks a significant new technology partnership for Asda as it seeks to sharpen its digital grocery offer in an increasingly competitive UK food retail market.

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