The convergence of AI and IoT presents the opportunity to significantly impact the dynamics of operational costs. For food retailers, energy is not a peripheral expense. It is foundational to how stores operate and serve communities. Lighting, HVAC systems, and refrigeration units are essential to trading safely and profitably. So, when energy prices increase, the impact is both immediate and impossible to ignore.

The energy price rises add growing pressure on already razor-thin margins for retailers, and The British Retail Consortium (BRC) has identified rising energy costs as a leading cause behind increasing food prices. Fresh food products – those reliant on refrigeration, such as meat, fish, and produce – have experienced the highest inflation, with prices rising 3.4% year-on-year. This highlights how vulnerable fresh food supply chains are to energy volatility. 

Passing costs directly to consumers is not a sustainable solution, particularly amid continued cost-of-living pressures. Retailers have both a commercial imperative and a social responsibility to protect value for customers while safeguarding their own profitability. Therefore, the real opportunity lies not in incremental cost control, but in fundamentally rethinking how energy and operational performance are managed.
This is where AI and IoT move from optional innovation to strategic necessity. 

By harnessing connected assets and intelligent analytics, retailers can move beyond static energy management and reactive maintenance towards continuous, real-time optimisation. IoT-enabled systems generate vast streams of operational insight. When combined with advanced AI, that data becomes a powerful decision-making engine, enabling retailers to anticipate risk, eliminate inefficiency and actively optimise performance. 

The strategic question is no longer whether retailers collect data, but how effectively they use it. Unlocking the full computational power of technology is what turns operational data into measurable commercial impact. 

Harnessing the power of AI 
The computational potential of AI to process vastamounts of data and consistently diagnose performance creates significant opportunities for retailers to transform operational effectiveness, particularly in energy-intensive environments such as food retail. 

By gaining an overarching view of IoT-connected equipment performance, potential degradation and exposure to changing environmental pressures, AI-driven models can be applied to predict and prevent equipment problems and failures. Crucially, performance degradation does not only increase the risk of failure. It also leads to excess energy consumption as struggling machines work harder to maintain required outputs. Identifying these inefficiencies early ensures that equipment operates at optimal performance, preventing unnecessary energy waste before it compounds into higher operating costs. 

Predictive insight therefore reduces not only the likelihood of breakdowns, but also the hidden energy drain caused by underperforming assets. Engineers can be deployed with a complete picture of a machines condition and history, improving first-time fix rates and minimising the period during which equipment operates inefficiently.
AI’s power also enables more dynamic energy management across IoT assets. Rather than operating on static schedules or manual oversight, AI can evaluate equipment performance within the broader operational context in real-time. By continuously monitoring energy usage patterns and operational requirements, systems can, for example, intelligently optimise refrigeration performance – aligning energy consumption with actual demand while maintaining food safety and compliance. 

Managing operational costs for profitability and resilience  

As energy pressures are likely to persist, the ability to optimise operations through technology will differentiate those retailers who thrive from those who struggle this year. Leveraging AI and IoT will enable food retailers to mitigate the impact of rising energy costs, unlock operational efficiencies and protect margin profits.
Adopting AI-enabled, data-driven approaches isn’t just a competitive advantage, it’s essential for long-term profitability and resilience.  

The message for retailers is clear. The partnership between AI and IoT holds the key to safeguarding, and even improving, profit margins and increasing operational performance this year. Smart, data-driven solutions are now a foundational part of a modern food retail strategy. 

Energy volatility is not going away. Retailers who act now will be best positioned to protect profitability and build resilience.

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Since then, we have been cited in trade press, referenced at industry conferences, and held up as an example of what specialist retail can do when it stops thinking of itself merely as a place to buy things and starts thinking of itself as a high-value media environment.

The recent HOKA Mach 7 campaign delivered a 30 per cent increase in share of revenue, a 13.3 per cent lift in incremental units sold in activated stores, and product engagement nearly four times higher than the control group. Those numbers are real and we are proud of them.

But if I am being honest about what the past year has actually looked like from the inside, the story is more complicated, more demanding, and ultimately more instructive than any campaign result communicates on its own.

We Built the Network Whilst Rebuilding the Foundation

Launching a retail media network is not a marketing decision. It is an infrastructure decision. And we made it at the same moment we were undertaking a significant business and group-level digital transformation; a new customer experience platform and a data integrity programme that touched virtually every part of how we operate.

That combination created complexity that was, in retrospect, underrepresented in our change management planning. When you are simultaneously reengineering your CXP, cleaning and unifying your data estate, and launching a commercial media proposition to brand partners, you are not just running three workstreams in parallel. You are running them in interdependency.

We navigated that. And what is emerging on the other side of it is genuinely exciting. The capability we are building into the platform right now will unlock value in our retail media network that was simply not possible twelve months ago.

The commercial opportunity ahead of us is significantly larger than what we have already demonstrated. But I want to be direct: getting here required investment, tolerance for complexity, and organisational patience that should not be underestimated by any retailer considering this path.

The Change Management No One Talks About

The technology is the visible part of a retail media network, and we’ve got great partners in Zitcha to support us there. The organisational change is not visible, but it is where the real work happens, and where the real risk lives.

Building our network required us to transform how marketing, data, measurement, retail operations, merchandising, and trade teams work together. These are functions that have operated – successfully, I should say – with a degree of structural separation for years. We have people in this business who carry institutional knowledge that no system can replicate. The challenge was not to replace that knowledge. It was to connect it.

That meant upskilling teams across the organisation, not just in technical capability, but in a new way of thinking. We needed people to become genuinely curious about data they had not previously had access to, to be comfortable with faster feedback loops, and to trust cross-functional conversations that previously would not have happened. We had to break down silos that were not broken through lack of intelligence or effort, but through decades of structural separation that simply had not needed to change until now.

That cultural shift is ongoing. It is not something you solve in a quarter. But the progress is visible. The quality of our campaign planning, how quickly we can respond to performance signals, and the conversations we are now able to have with brand partners about what the data is actually telling us are all reflections of this progress.

Marketing Integrity Is Not Optional

One of the less comfortable truths about launching a retail media network is that it forces you to hold a mirror up to your own marketing accountability. When you are selling media to brand partners based on the quality of your data, your attribution, and your ability to demonstrate incrementality, you have to be able to meet that same standard yourself.

Our level of sophistication from a marketing integrity perspective had to increase materially over this past year. We invested heavily in our omnichannel data capability, in media attribution frameworks, and in understanding the richness of the signals available to us across our customer estate. That work made us significantly better at understanding the true value of our own marketing investment, not just the investment our partners make through the network.

That accountability is now a genuine competitive strength. Brands like HOKA, Asics, Montane, and Nike are not partnering with us because we can offer them screens. They are partnering with us because we can offer them proof. Proof of incrementality. Proof of audience quality. Proof of commercial outcomes. The integrity of that proof starts with how seriously we take our own data.

What the Next Chapter Requires

One of the less obvious shifts of this past year has been in how we think about the role of our buying function. Historically, a buyer’s accountability ended at the purchase order – select the right product, negotiate the right terms, get it onto the floor. That discipline remains essential. But in a retail media environment it is no longer sufficient. Our buyers now own the full product lifecycle from purchasing through to the end customer.

That means understanding how retail media investment can accelerate rate of sale and reduce markdown risk, and it means sitting with brand partners not just to negotiate ranging and margin, but to co-design go-to-market activation that genuinely connects product to consumer.

The buyers who have leaned into this are becoming some of the most commercially rounded operators in our business — because they are now offering brand partners something more valuable than a range decision: a route to the customer, with data behind it.

That commercial evolution only works, however, when every go-to-market tactic is pulling in the same direction. The campaign that moved the needle for HOKA was not built on a single channel. It worked because in-store digital screens, onsite placements, CRM, and paid media were designed as a unified system rather than a collection of independent activations.

That level of integration requires planning disciplines and briefing processes that align marketing, digital, retail, and trade around a single view of the customer journey from the outset. When it works, the effect is compounding – each tactic amplifies the others, and the whole becomes significantly more powerful than the sum of its parts.

Underpinning all of it is a commitment to keeping the customer at the centre of every decision we make. In specialist retail, customer trust is the most valuable asset we hold – and it is also the thing that makes our retail media proposition worth something to brand partners in the first place.

What we are building with brands like HOKA, Asics, Montane and Nike is not an advertising arrangement. It is a storytelling partnership.

Their product expertise, combined with our customer knowledge and the depth of our service environments, creates something neither party could deliver alone: a rich, relevant and genuinely useful experience for the customer at the exact moment it matters most.

A customer mid-gait-analysis, or deep in conversation about expedition layering, is not a passive audience. They are at the most consequential point in their purchase decision. Connecting them with the right brand story at that moment is what this network was always built to do.

I am looking forward to an honest conversation at Shoptalk, not just about where we’re going, but where we’ve been.

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Earlier this year, I spotted a comprehensive covering payment orchestration, cross-border infrastructure, merchant of record models, stablecoin rails, and the operational demands of scaling into growth markets. Several contributors address agentic commerce specifically, and with genuine rigour.

McKinsey projects the model could generate up to USD 3 to USD 5 trillion in global revenue by 2030. Visa, Mastercard, and the Agent Payments Protocol (AP2) initiative are already laying the technical groundwork for agent-led transactions at scale.

It is an impressive picture. And there is one conspicuous gap in it. Across many pages of analysis of what agentic commerce will require from the payments ecosystem, dispute management is not addressed, it simply does not appear.

The report describes, in considerable detail, how an AI agent will initiate a transaction, select a payment credential, route through an acquirer, and settle cross-border. It does not describe what happens when that transaction is later challenged.

Why that gap matters now

The payments industry has correctly identified that agentic commerce represents a structural shift. The IXOPAY contribution to the report captures it precisely: we are moving from card-present and ecommerce to a third channel altogether, in which the user interface disappears and the agent emerges. That framing is right. The operational consequences of that shift extend further than the current infrastructure conversation acknowledges.

In a human-initiated transaction, the evidence of intent is inherent in the act itself. A consumer opens a browser, navigates to a product, enters payment details, and confirms a purchase. Each of those steps generates a signal. When a dispute arises, that signal trail is what allows a merchant, processor, or card scheme to assess whether the transaction was authorised. The process is imperfect, but the framework exists.

In an agentic transaction, that trail looks different. The consumer granted permission to an agent, potentially days or weeks before the purchase occurred. The agent selected a product, compared alternatives, applied a stored payment credential, and completed a transaction, all without a human present at the moment of execution. The question of what was authorised is no longer answered by what the consumer did at checkout. It is answered by what permissions were in place beforehand, and whether the agent acted within them.

The report itself notes that the critical question of liability in agentic commerce remains unresolved, with existing rules failing to clearly assign responsibility when something goes wrong. That is precisely the environment in which disputes escalate, costs multiply, and trust in the model erodes.

The missing layer

Payment orchestration, fraud detection, tokenisation, and settlement rails are rightly described as the foundations of agentic commerce. But they are not sufficient on their own. Each of those components addresses what happens before and during a transaction. None of them addresses what happens after, when a consumer does not recognise a charge, when an agent exceeded its authorised scope, or when a merchant’s fraud system has declined a legitimate purchase because it could not distinguish an AI agent from a malicious bot.

The dispute layer is where intent is verified after the fact. In an agentic commerce environment, that layer needs to capture something the existing infrastructure was not designed to record: the consent framework that preceded the transaction. What was the agent authorised to purchase? Within what price range? With what constraints? Was the transaction it executed consistent with those parameters?

Without that record, disputes in agentic commerce become almost impossible to arbitrate fairly. The merchant cannot demonstrate authorisation. The consumer cannot demonstrate that the agent exceeded its remit. The processor has no evidence to assess. The outcome is either an automatic chargeback, a blocked transaction, or a dispute that takes far longer and costs far more than it should.

This is the infrastructure gap that the payments industry needs to close. The evidence architecture for agentic transactions, capturing what an agent was authorised to do, the limits in place, and a timestamped record of each action taken. This is as foundational to the model’s success as payment orchestration or fraud detection. It simply has not been treated that way yet.

It is work that we’ve built into Unified Dispute Management System (UDMS) and ResolveLab, which use AI and machine learning to construct and analyse the consent and permission architecture that agentic transactions require. Rather than relying on point-of-transaction signals alone, UDMS captures what an agent was authorised to do, the limits in place, and a timestamped record of each action taken, giving merchants and financial institutions the evidence base to classify transactions accurately, manage disputes efficiently, and recover revenue that would otherwise be lost to false declines or unwinnable chargebacks.

Building for the third channel

The industry is right to treat agentic commerce as a new channel requiring new infrastructure. The payment initiation layer is receiving the attention it deserves. The dispute and evidence layer is not, and the cost of that oversight will become clear at scale.

Merchants who invest now in consent frameworks, permission trails, and evidence capture infrastructure will not simply be protecting themselves from disputes. They will be building the foundations of trust that the agentic commerce model needs in order to scale. In a channel where AI agents select merchants partly on the basis of transaction reliability and dispute performance, that is not merely a competitive consideration but a commercial necessity.

The payments stack for agentic commerce is taking shape and the dispute layer NEEDS to be part of it.

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In retail logistics, many people think that modernization just means bringing in more machines. However, it’s becoming clear that this isn’t enough. A lot of retailers have poured resources into things like autonomous sorters and high-density storage systems, but they often realize that just adding automation doesn’t necessarily make their operations more resilient. In fact, some of the most automated operations have lost flexibility, leaving them with costly systems that struggle to adapt when the retail market shifts.

Retailers should prioritize facilities designed for flexibility and adaptability, seeing optionality as their main strategic advantage. Rather than focusing solely on full automation, organizations must ensure their operations can adjust to shifting demands and disruptions.

Optionality in the supply chain is not just about technology. It depends on leadership, culture and a readiness to adapt. Leading retailers are using cross-training, flexible shift models and temporary micro-fulfillment centers to handle demand spikes and supply disruptions. These operational strategies are just as critical as hardware investments for building resilience.

The Feasibility Filter: Avoiding Preconceived Solutions

Too often, organizations approach supply chain challenges by starting with a vendor or a specific technology, assuming it will solve every problem. This leads to costly mistakes when solutions are applied without a clear understanding of the retailer’s actual needs.

To avoid pitfalls, supply chains should be evaluated using agnostic feasibility, ensuring every design meets four essential criteria before considering technology vendors or robotics. This process starts by defining inventory physicality, such as handling pallets, cases or units, and evaluating the footprint to balance density with local land costs. Designers must also calculate the required throughput during peak events and identify where operational complexity occurs, whether in case, layer or unit-pick stages. Addressing these foundational questions ensures technology remains a tool that supports the primary business objective.

Optionality should also extend to supplier relationships. Retailers can reduce risk by diversifying suppliers, developing contingency plans for last-mile delivery and establishing data-sharing partnerships with upstream vendors. Taking a broader view of the supply chain ecosystem helps organizations manage risk and identify new opportunities for growth.

From Displacement to the “Decision Co-Pilot”

It is a common misconception that technology is intended to replace people. The most advanced warehouses foster collaboration, combining human intuition with robotic capabilities. Robots are well-suited for repetitive and physically demanding tasks, but have traditionally struggled with tasks that require fine motor skills. Advances in AI-driven machine vision and robotics are beginning to address this gap. The focus should be on augmenting the workforce, not eliminating roles.

Change management is critical as technology evolves. Retailers who invest in upskilling and clear communication help their teams adopt new tools effectively. Fostering a culture that supports experimentation and digital transformation will position organizations for long-term success. Positioning technology as a decision-making co-pilot allows supervisors to oversee both human and robotic teams. This approach enables employees to focus on tasks that require intuition and innovation, while automation manages repetitive work.

The Digital Safety Net: Engineering a Predictable Result

Retail boardrooms now require data-driven decisions rather than relying on intuition. Digital twin technology enables organizations to move from estimated ROI to measurable, evidence-based outcomes.

Simulation enables organizations to create a digital replica of the entire network and test a year’s worth of volume in a matter of hours. This approach identifies issues before capital is committed. Simulation is not limited to testing new automation. It can identify the optimal location for facilities and stress-test the network against changes in manufacturing, demand spikes and cost fluctuations. This ensures that investments deliver predictable results.

The next step for simulation is real-time scenario planning. As supply chains become more connected and data-driven, retailers will be able to model disruptions as they occur and respond in real time, rather than relying on static plans. The leaders will treat digital twins as ongoing tools, not one-time projects.

Resilience in retail is about adopting a mindset that embraces strategic options and adaptability. The leaders who will thrive in uncertain times are the ones who prioritize practical solutions, encourage teamwork and make use of digital tools to create flexible supply chains that can handle whatever comes their way. To truly stand out in the retail marketplace, it’s important to break away from industry standards and design a supply chain that is built to adapt and evolve over time.

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And yet, for all the excitement, we are starting to hear sceptical voices argue that the revolution has been overhyped. Yes, they say, it’s growing quickly. Yes, they say, it’s generating meaningful revenue for some retailers. And yes, they say, it can deliver powerful outcomes for brands, but it won’t fundamentally reshape the world of advertising.

I think it will.

Common criticisms focus on surface-level issues: pricing, scale, measurement, and whether retail media can truly build brands, or whether it’s simply another performance channel.

These concerns have validity of course, but to some extent they miss the point.

Retail Media growth has slowed slightly, and temporarily, not because Retail Media doesn’t work, but because the systems that allocate advertising budgets were built for a different era. They have not yet caught up. But that will change.

The constraints are institutional, not technological. History shows us that the revolution is merely delayed. And history also shows us that if you want to reap the rewards, you need to prepare. Now.

Once you start looking at the ecosystem through this lens, the current moment in retail media starts to make a lot more sense. Let me explain why.

The passé debate: can retail media build brands?

According to the Ehrenberg Bass Institute, brands grow if you increase customer penetration by:

• Growing mental availability
• Building physical availability

Retail Media offers a wide array of formats with which to do this. In fact, it is fairly unique in this regard.

And when Retail Media Networks move beyond acting as publishers (owners of space/inventory) into the world of audience provision the toolkit broadens to an even wider range of formats. In fact, most media formats can be bought as retail media.

If a brand runs a CTV campaign using retailer audiences, that is retail media. If a retailer sells audience enriched TikTok videos or Instagram reels, then that too is retail media. Even DOOH can be sold as retail media.

The line between retail media and traditional formats becomes blurred. A lot of retail media is traditional media.

So retail media can build brands. But that’s not the right question.

It’s whether the systems that allocate the largest advertising budgets have been designed to buy it. And today, in many cases, they haven’t. Let’s look separately at agencies, retailers and brands.

Agencies: planning systems and the cost of execution

Most large advertising budgets are planned using sophisticated tools that model things like:

• Reach Curves
• Frequency
• Cost per reach point
• Media Effectiveness Benchmarks
• Attention-adjusted reach

These systems are designed to help planners allocate very large budgets efficiently across major channels with minimal complexity. Too often retail media doesn’t sit inside the agency planning tools which makes it much harder for planners to recommend inclusion at any kind of scale.

And to compound the issue, instead of one large buy, planners may need to coordinate across multiple retailers, each with their own formats, reporting structures, measurement methodologies and retail media terms.

Even if the media works well, it introduces operational overhead. And agencies are low-margin businesses.

For a planner running a £10m national campaign, a £250k retail media activation across a few retailers can feel like a disproportionate amount of work for a small slice of the budget.

So, Retail Media doesn’t struggle here because it lacks effectiveness. It struggles because it introduces complexity into systems that have been built for scale and efficiency.

Large agency groups are now deploying AI-driven planning platforms designed to optimise billions of dollars of advertising investment.

These platforms work by modelling large datasets to determine where budgets should go. They favour channels that have clean cost curves, high reach, standardised measurement and consistent formats.

As standards are rolled out (largely with the help of the IAB) and more retailers become integrated into agency platforms, the key increasingly becomes for retail media to become properly integrated into agency planning suites so that the AI does the heavy lifting, rather than a media planner. This is already starting to happen.

Retailers: the legacy of trade income

Many RMNs still behave like retailers monetising supplier relationships rather than media owners building a channel. Traditional media owners learned long ago that if they wanted budget at scale, they had to sell upstream to planners and strategists, educate the market, provide insight freely, and position their media within broader campaign objectives.

Too many retail media businesses are still playing a shorter-term game: monetising access, monetising data, and relying on existing supplier relationships rather than building the kind of sales maturity that would secure a more permanent role in media plans.

At the same time there are internal dynamics to manage. Commercial teams may be wary of retail media teams moving revenue into a different P&L. Customer experience teams may resist new ad formats because they believe advertising could harm the shopper journey.

Launching new propositions, and planning campaigns more holistically, often requires investment in people, technology, data infrastructure and measurement. In the short term, this can dilute margin.

So, retailers are trying to transition from something that resembled trade income extraction to something that looks more like a genuine media business.

This transition needs time, education, and senior sponsorship to create the necessary alignment.

Brands: budget silos and measurement bias

The third constraint sits inside brand organisations.

Most large companies don’t manage marketing budgets as a single pot. Instead, budgets are typically split across different functions:

• Brand marketing (TV, video, reach media)
• Shopper marketing and trade promotion
• Ecommerce and retail media

Each of those areas usually has different leadership, different KPIs and different planning frameworks.

Moving money between them can be politically difficult. And none of the teams has full ownership of retail media. It sits awkwardly between them all.

Brand teams are often focused on reach, salience and long-term brand growth.

Retail media is frequently perceived – rightly or wrongly – as lower-funnel activity designed to convert demand rather than create it.

There’s also a measurement paradox. Traditional brand channels are often evaluated using long-term modelling approaches such as marketing mix modelling or brand tracking. Retail media, by contrast, is frequently measured using immediate sales attribution. That creates an asymmetry.

Smart brands are evolving their approach. Slowly but surely.

Historical parallels

There’s a well-recognised pattern here.

History suggests that transformative technologies rarely fail because the technology is wrong. They lose momentum because the institutions around them were designed for an earlier era.

The internet was expected to transform commerce in the late 1990s. When the dot-com crash happened, many concluded the whole thing had been overhyped. In reality, the infrastructure – broadband, logistics networks, digital payments and smartphones – simply didn’t exist or hadn’t caught up yet.

Railways followed a similar pattern in the nineteenth century. Early enthusiasm was followed by financial crashes and scepticism before rail networks eventually consolidated and transformed economies.

Even within advertising, programmatic media went through the same cycle: early hype, messy execution, industry backlash and then gradual integration into the core planning infrastructure.

Retail Media is simply following the same trajectory. Institutional adaptation is slower than technological adoption.

How to win in Retail Media

Retail media will become more central to the structure of advertising. AI will accelerate this. Change rarely happens overnight, but nevertheless:

Agencies will produce planning systems and workflows that properly integrate retail media.
Retailers will transition from legacy trade income models to credible advertising propositions.
Brands will find ways to think about how they structure and integrate retail media into broader marketing planning and measurement frameworks.

As several agency leaders have pointed out, these are the same structural debates the industry had around digital, mobile, social and programmatic. The pattern is familiar: hype arrives first, then organisational resistance, then eventually a workable model emerges.

These changes are already happening. The winners will be those who adapt efficiently and professionalise the fastest. Some will rightly focus on measurement standardisation, others on full-funnel planning, and both will play a role, but having considered various historical examples my personal prediction is that the key success factor will be organisational design.

All this raises a practical question. If retail media does become the defining growth channel in advertising over the coming years, do you really want to be scrambling to play catch up while competitors move ahead?

Or do you want to have your house in order so that you can reap the rewards?

History suggests that when technology collides with institutional inertia, the technology usually wins – eventually.

The tracks have been laid, the trains have been built, so the real question is simply are you putting yourself in a position to start loading the goods when the boom arrives?

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Instead it was met with carnage and chaos-scenes from Saturday showed huge crowds of people gathering at stores worldwide.

So what went so wrong? Or right depending on you ask?

Swatch marketed the collaboration like most brands do, with a couple of social media teasers hinting at what was coming and the official announcement a few days later.

The teasers did their job well, almost too well, and sparked a lot of hype and conversation around the release, with consumers trying to guess the what the posts were hinting at.

So it was no surprise that engagement on the retailer’s official announcement post was high, receiving over 410,000 likes on Instagram and 21,000 on TikTok.

In comparison, sportswear brand On’s Instagram post about the release of its LightSpray Cloudmaster trainer, designed in collaboration with fashion house Loewe, received over 18,000 likes and H&M’s Instagram post on its collaboration with Stella McCartney has under 5000.

The announcement itself also sparked more conversation as rather than releasing a classic wristwatch, the pair unveiled a pocket watch. According to Swatch, they were inspired by Swatch’s pop watches from the 1980’s and Audemars Piguet’s Royal Oak collection.

The collection aims to “change the way [consumers] wear watches” and it certainly sparked their imagination with the announcement post having over 17,000 comments. Whether they loved or hated it, people were talking.

And that conversation helped to drive up demand, as shoppers wanted to see what all the fuss was about.

It could also be suggested that one of the reasons demand was high was the fact the collab made Audemars Piguet watches accessible (well, more accessible than they are usually). The luxury brand’s products are usually price on request, which is unattainable to the vast majority of people.

However, one watch from the Royal Pop collection will set consumers back £335.00, which while still pricey, is a price more consumers can afford.

Reselling is another major reason demand may have soared. Audemars Pigue watches are extremely expensive and price combined with a perceived scarcity could have caused many to assume (probably correctly) that resale value would be higher than RRP.

Pieces in the collection are currently being listed on second hand marketplace Ebay from £450 upwards (as of writing this article).

All of this combined then led to the crazy scenes we saw Saturday.

Swatch did address the issues via a statement on its site and social media.

It read: “To ensure the safety of both our customers and our staff in Swatch stores, we kindly ask you not to rush to our stores in large numbers to acquire this product. The Royal Pop Collection will remain available for several months.

“In some countries/regions, queues of more than 50 people cannot be accepted, and sales may need to be paused.”

However many consumers felt this wasn’t enough and suggested the watch retailer should have anticipated demand and done more to prevent it.

So what could have Swatch done to avoid the chaos?

Well, as many users suggested on the brand’s Instagram account- they could have followed in Apple’s footsteps and made the initial release online only before rolling it out to stores nationwide.

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Fragmented, frequent purchasing

Variety and value continue to be key motivators for online shoppers. Consumer card spending data (ONS, published 6 February 2026) shows notable shifts in spend across merchant categories, including ‘department stores’ and ‘discount stores’. The former accounted for a total of 8% of online spend in 2024, while online spending with discount stores rose from 2.3% in 2019 to 6.2% in 2024.

The data comes as little surprise. You only have to look at the popularity of marketplaces and the evolution of retailers, such as Next and M&S, which have added more third-party brands to their ranges, to know that proliferation of choice is a big draw for shoppers. John Lewis has become the latest department store to expand its portfolio. It added 100 new clothing brands late last year and has since launched Topshop and Topman. It’s clear that variety is vital for attracting shoppers.

Volatile consumer confidence, economic turbulence and soaring living costs are fuelling an even stronger push towards value. Shoppers want their money to go further and will invest time in hunting down the best deals.

The challenge for retailers is that variety and value are making shoppers more and more transient. Average spend per transaction, per cardholder is shrinking, while overall spend is increasing. The ONS data shows average spend per online shopper increased by 16.7% in September 2025 when compared with 2019, while their spend per transaction decreased by 13.8%.

Purchasing is becoming more fragmented and frequent, with the average cardholder now making 35% more transactions online. Retailers have to work harder than ever to win and keep customers. Personalised packaging offers a powerful way to standout and engage shoppers.

Data-driven personalisation

Personalised packaging can deepen emotional resonance with shoppers. It can create a connection with customers that delivers moments of delight, boosting levels of satisfaction and perceived brand value. When done well, it transforms a functional transaction into an engaging experience that drives repeat purchasing and advocacy. To achieve this, personalisation must be meaningful, useful, regularly refreshed and underpinned by strong data and insight.

Reliable data and analytics enable retailers and brands to shift from mass-market packaging to truly customised in-pack and on-pack content. Customers can be segmented by shopping habits, buying motivations, purchase history, seasonality, demographics, their engagement with promotions, and a wide range of other attributes. Messages, images and promotions can then be tailored, so that packaging continually appeals to customers.

Being meaningful and useful

When packaging is personalised according to shopper behaviour and motivations, it becomes meaningful. It becomes a point of interest that’s worth engaging with. Customised promotions and discounts can be included inside e-commerce orders, which customers will find useful. Bespoke on-pack messaging can deliver information that matters, making shoppers feel valued by a brand and more inclined to share their experiences.

Meaningfulness and usefulness can be enhanced through smart, connected packaging. Linking to digital experiences by including QR codes on pack, for example, can extend the brand relationship. Customers can be easily directed to content that helps them to make the most of their purchase or they can access exclusive online content tied to personal goals and values. This could be short videos about the sustainability impact of their purchase or practical lifestyle hacks.

Personalised loyalty rewards are another way of increasing meaningfulness and usefulness. Including rewards linked to a customer’s behaviours and preferences can turn packaging into an incentive system. Hidden codes, special offers, invites for early access and exclusive drops can create a strong sense of customer recognition and reward, which strengthens brand loyalty.

Keeping it fresh

Packaging personalisation is most effective when it’s regularly refreshed. Frequently updating messages, visuals and promotions prevents on-pack and in-pack content from becoming stagnant. It avoids the ‘same old’ thinking among customers, replacing this with anticipation and expectation. Shoppers actively start looking for what’s new and different when they receive an e-commerce order, boosting engagement levels and loyalty.

Seasonal themed designs, limited-edition promotions and tapping into cultural moments, such as major sporting events, as well as life milestones including birthdays and anniversaries, are powerful ways to keep packaging personalisation fresh and engaging. This all requires customer insight and sophisticated fulfilment processes, which enable variable data printing and customised inserts.

Brand storytelling and customer collaboration are also effective methods of keeping packaging personalisation fresh. Weaving narratives into pack designs and inviting customers to co-create artwork or share experiences can turn packaging into an interactive touchpoint. This gives customers more reason to connect and reconnect with the pack and the brand behind it. Again, customer data and insight are important. Shoppers have to be at the heart of storytelling, and they have to find collaborations meaningful and useful.

As online shopping habits become more fragmented and frequent, retailers and brands face even fewer touchpoints to build emotional connections. Personalised packaging can prove a powerful point of difference, which creates standout, strengthens brand loyalty and creates opportunity to bring back emotional richness during increasingly digital-only transactions.

Hayley Simon is Head of Staci Create, which is part of bnode – a global business with services in more than 200 countries. Staci Create operates within the group’s 3PL business unit, Paxon, and specialises in the design, sourcing and delivery of branded packaging and marketing materials.

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The GCA & Defra

Moving the Groceries Code Adjudicator under Defra will not make the supply chain “more fair” and risks misunderstanding the watchdog’s true role, writes GSCOP expert and Retail Mind director Ged Futter.

Be careful what you wish for. On 1^st July, the GCA’s office will move from the Department for Business and Trade (DBT) to the Department for Environment, Food & Rural Affairs (Defra). This means it will fall under the same boss as the Agricultural Supply Chain Adjudicator, so is this a good thing?

When MPs rabble-rouse with comments such as saying the GCA is ‘an apologist or PR machine’ for the supermarkets or that he is ‘riding with the horses and riding with the hounds’, do they actually understand what the GCA’s role is?

The Grocery Code Adjudicator was established in June 2013, three years after GSCOP became law. We are the only country in the world to have someone overseeing the Code of Practice. The role of the GCA is not to advise Suppliers if they believe a Retailer is behaving poorly, it is to act as an Adjudicator if the Supplier is unable to reach an agreement & it ends up at an arbitration!

If the GCA advises a Supplier on a Retailer, how is he then able to act independently in any arbitration? GSCOP is very clear on how Retailers need to behave with their Suppliers, that is why the market does not operate like the Wild West it used to be!

Since GSCOP became law in 2010 the grocery landscape has changed substantially, as has retailers’ behaviour. This is because we have GSCOP AND the GCA. The GCA can keep retailers on the straight & narrow. This is done by working closely with Retailer’s Code Compliance Officers & nipping issues in the bud before they get out of hand.

It is often cited that the lack of a fine by the GCA is evidence that the role is toothless. Invariably this is stated by those with limited understanding of how to use GSCOP proactively with a retailer, not as a stick, but to keep relationships balanced.

I have a fear that there are those who think that moving the GCA’s office under Defra will bring more fairness to the supply chain. The problem is, what does fair mean, who defines what is fair? That is not actually the role of the Agricultural Supply Chain Adjudicator who has stated that they are not there to intervene on price.

The GCA’s office is independent. It is funded by the retailers. It is not a role that is there to decide what price a supplier or grower should be paid. It is also not there to decide if a retailer has treated a supplier fairly, unless a part of GSCOP has been breached.

I fear that there is an expectation that the GCA will work closer with the ASCA to create a fair supply chain. The ASCA has already worked in the Pig Sector. Fairness is not defined & never will be. Anyone who believes fairness is the future may also be seeing pigs flying in the sky!

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In a cost-of-living crisis, simple, tangible value really matters, and a paper voucher at the till is visible proof that the brand is on your side.

At the same time, Tesco and Boots are supporting something far more radical – using loyalty data to help flag early signs of cancer and prompt people to seek medical advice sooner. That is a very different kind of loyalty benefit – still about using data to drive behaviour change, but the outcome is not just a bigger basket or an extra visit. It could be a longer life.

Put those two stories together and we find ourselves at a fascinating crossroads for retail loyalty in the UK. Turn left for a rollback to highly visible, old-school discount mechanics. Turn right for a glimpse of loyalty as AI-powered infrastructure for real societal impact.

From points to personalisation – and beyond

For years, UK loyalty was largely transactional. Clubcard, Nectar and Advantage Card taught shoppers to collect points and vouchers and enjoy being rewarded for their regular shop. Morrisons bringing back physical coupons plays directly into that tradition.

Then came the personalisation wave, as data science turned loyalty schemes into engines for targeted promotions. From Clubcard and Nectar member-only prices to app-only offers and gamified bonuses, personalised value has become a core battleground for relevance, margin and share of the weekly basket.

Now a third phase is emerging, where that same data is being used to play a deeper role in customers’ lives. An early example is the recent announcement that Imperial College London is launching a major study, exploring how Tesco Clubcard and Boots Advantage Card data could help to flag the early signs of disease. It’s built on the hypothesis that repeat purchases of certain medications or remedies over time could be a signal that something is not quite right.

Crucially, the proposed outcome is not diagnosis; it’s a nudge to speak to a pharmacist or GP. If handled sensitively and transparently in partnership with health professionals, this use of loyalty data has the potential to drive earlier medical interventions for a variety of cancers and reduce pressure on the NHS.

What responsible use really looks like

All of this only works if the use of loyalty data is responsible, proportionate and clearly in the shopper’s interests, because the line between helpful and creepily invasive is wafer thin. So, here are a few principles that should guide UK retailers as we move into this brave new world.

1. Transparency and consent

People need to know, in plain language, what is happening with their data and why. Health-related uses in particular should be explicitly opt-in and easy to opt out of.

2. Direct benefit to the individual

Societal impact is important, but for loyalty it has to be rooted in tangible personal benefit – better value, better health, less stress, more convenience. If data is being used to support public services, retailers need to explain how that ultimately helps the customer too.

3. Strong internal guardrails

Just because you can, doesn’t mean you should. Retailers need clear policies on what’s acceptable, who can access sensitive insights and how interventions are delivered. An internal ethics lens is as important as a legal one.

4. Partnerships with trusted institutions

Where loyalty data can contribute to health or social outcomes, retailers should not go it alone. Working closely with the NHS, charities and local authorities helps ensure that interventions are appropriate, proportionate and credible.

How else could this approach be deployed?

For retailers and agencies alike, this shift opens up a tantalising question: how else could deeper loyalty data analysis deliver societal impact, whilst also creating feelings of support and empowerment for customers? Two areas feel like immediate opportunities:

1. Supporting everyday value

The cost-of-living squeeze has forced millions of UK households into constant trade-offs. Loyalty data can reveal when shoppers are switching into cheaper brands, dropping out of categories or chasing promotions in a way that suggests real financial strain.

Used well, that insight can power more humane responses: tailored discounts on essentials or price freezes on core baskets, to help customers feel more in control of their budget and less anxious about the weekly shop.

2. Nudging healthier and more sustainable choices

Loyalty data is uniquely good at tracking behaviour over time. That makes it a powerful tool for nudging small and sustainable shifts in diet and lifestyle, and for encouraging lower-impact choices that feel achievable and meaningful.

Retailers could use it to reward healthier baskets in a non-judgy way, making better choices more prominent, or incentivising eco options with meaningful emotional value.

Of course, none of this replaces the basics – in a value-obsessed market, if your prices are missing the mark, no amount of life or planet-saving purpose will save you. That is why moves like Morrisons’ coupons still resonate: they hit a very real need.

But retailers who choose the more purposeful path – and do so carefully, transparently and with consent – will not just have more detailed customer profiles. They will have something far harder to copy: genuine, emotional loyalty that transforms data from a sales tool into a social asset.

In a world where anyone can launch an app, print a coupon or personalise an offer, the real competitive edge is no longer just smarter promotions. It is proving that when customers hand over their data, they get something meaningful in return – not just points, but a retailer that is actively on their side.

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Writing, art, film and music should be a purely human process, one that doesn’t require the aid of Artificial Intelligence.

But the line might not be so easily drawn when it comes to advertising. Advertising can be artistic and it’s certainly creative, but it’s also a multi-billion dollar industry. And creatives have constraints they need to work to, whether those be time or budget.

And AI can help them, there’s no doubt about that, whether that’s by helping to draft an email, synchronising processes, assisting with research or more controversially assisting with the actual creative process.

But should it?

A report from the from the UK Government Digital Sustainability Alliance (GDSA) predicts that AI will increase the worlds water usage from 1.1bn to 6.6bn cubic metres in 2027.

The World Economic Forum Report on Global Risks believes that “adverse impacts of AI technologies” and “biodiversity loss and ecosystem collapse” will pose significant risks to the environment in the next 10 years.

And in my opinion that risk just isn’t worth it.

However, it would remiss of me not to acknowledge that the GDSA has laid out measures that AI firms can use to become more sustainable, which include placing data centres in areas not at risk from water stress and investing in new cooling methods such as air cooling, which do not utilise as much water (whether the big tech firm’s pay attention to this is another matter).

And aside from the environmental impact, AI also takes away individuality. When you use AI in the creative process, work becomes technically perfect but often loses the human “voice”.

However, many brands and agencies would disagree with me.

Just recently peanut butter brand Pip and Nut used AI to bring to life their new mascot. The brand disclosed that the squirrel used in its marketing drive was originally photographed in Sweden by a wildlife photographer and then animated using AI.

The advert was devised by director Ali Dickinson alongside Pip and Nut’s in-house creative team. It was  produced by creative agency and AI studio Scary Robots.

The AI mascot has garnered plenty of attention on LinkedIn, much of it positive with many loving the little squirrel and the accompanying advert.

The ad received a 5.9 “Exceptional” Star Rating, from System1, which puts it in the top one per cent of all TV adverts.

This is a vastly different response to that of Coca Cola’s AI generated 2025 Christmas ad, ‘Holidays Are Coming’. Many viewers called out the ad, believing it to be “creepy” and “uncanny”. And the reaction to its 2024 AI generated Christmas ad wasn’t much different.

So why was the reaction to Pip and Nut so different?

Well, for one thing, Pip and Nut used real imagery which it then used AI to animate, whereas the Coca Cola ad was almost entirely AI generated. Another reason audiences could have reacted so strongly was that you could tell the Coca Cola ad was AI. Something just seemed a little off with it.

And audiences hated it.

Though one might argue that the Pip and Nut ad might reflect a change in opinions. But as someone who is chronically online, I don’t believe that is the case.

The ad had positive comments in one silo – a post by the CEO. That doesn’t reflect the opinions of everyone in the country, just one select group of the public, whose opinions seem to align with the brands.

And there are plenty of people online who still view anything AI as ‘slop’. A YouGov study showed that while 34 per cent of UK adults use AI frequently, 42  per cent of respondents did not trust it. And 75 per cent believe it could “pose a threat” to humanity.

This distrust can leave marketers in a tricky position, how do they get consumers to trust their marketing campaigns, if shoppers distrust any AI use in campaigns?

Well the obvious answer is to not use AI at all. But in today’s changing world that may not be possible. One solution may be to take the Pip and Nut approach, use AI when designing the creative but only partially. Another is to ignore the consumer entirely and use AI anyway.

Or they may choose to use leave AI out of the advertising creation but use it to send hyper personalised ads to consumers instead, which they may trust more. A market research report from Worldmetrics found 74 per cent of consumers preferred ads tailored by AI to their behaviour.

But whatever method marketers choose, one thing is for sure – they can’t keep everyone happy.

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