Argentina's official store is built and run by Fanatics. So is England's. englandstore.com looks like the England store, sounds like the England store, and is run start to finish by Fanatics. Not just the shipping. The range, the buying, the stock, the platform, the site, the lot. The federation takes a royalty and stays out of it.

It isn't a national-team quirk either. France, Germany, Italy, Belgium and Argentina all run their federation stores through Fanatics, and so does UEFA for the Euros. Same with PSG. Same with Juventus, Inter and AC Milan. Same with Wembley's retail. Same with a quarter of the clubs in last season's Premier League: Chelsea, Aston Villa, Everton, Leeds and Sunderland. Chelsea have gone further and handed over design and manufacturing too.

That’s a problem for us, because we’ve spent seven articles arguing they’ve all got it wrong.

The awkward bit

If you followed West Park Rovers, you know the argument. Bring it in-house. Own the stack, own the relationship, stop letting a third party sit between you and the people who love your club. We showed the operational reality, the expiring catalogue, the letters and numbers nobody counts, and we finished on the fan graph, which is the whole reason to do any of it.

We stand by all of it. But look at who’s actually renting.

These aren’t clubs that couldn’t afford the alternative. PSG could buy a warehouse before lunch. The FA is not short of a few quid. These are some of the largest, richest, most commercially sophisticated sports organisations on earth, and they have all looked at running their own commerce operation and said no thanks.

Two different things are going on there, and only one of them is defensible.

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You can’t grow into a World Cup

Start with the defensible one.

The barrier to running your own store was never the technology. Shopify solved the website years ago. Anyone can be selling by Friday. The hard part is the operational and merchandising muscle, and the only way to build that is slowly, on small numbers, while nobody’s watching.

Every DTC brand you admire got that runway for free. They were rubbish at forecasting when they were doing forty orders a week, and it didn’t matter, because forty orders a week is a survivable mistake. By the time they were doing forty thousand, they’d made every error once already.

A national team never gets the practice, it’s already fully grown. You cannot put up a website with three shirts on it and say we’re learning, we’ll get there. You need millions of pounds of stock sat in a building before you take a single order, a 3PL that already works, a personalisation line that already works, and a merchandising team that already knows what a good buy looks like, on day one, with the whole world watching.

And a national team's commercial operation matters for six weeks every four years.

The apocalypse, but faster

We wrote a piece called The 12-Month Apocalypse about the thing that makes sports retail structurally horrible: your entire catalogue expires every year. New sponsor, new squad, new competition, and last season’s stock isn’t discounted, it’s dead.

A tournament catalogue doesn't expire in twelve months. It expires the moment you go out. England found that out in the semi-final. Argentina found it out on Sunday. The shirt that was the most wanted garment in the country on Saturday is dead stock by Monday, and no supply chain on earth can turn a run of replicas around fast enough to catch the one week that mattered. Win the whole thing and you get the single exception: Spain's shirt is the only one still worth printing. Everyone else is holding polyester with the wrong names on the back.

Moments, Not Seasons argued that clubs should build the muscle to respond to the unpredictable, because seasonal retail has a calendar and sports retail has a pulse. Fine. But a tournament isn’t a pulse. It’s one beat. No signal you can act on, no second attempt, and the thing you’re forecasting is a penalty shootout.

You can’t build a capability around an event that happens once every four years and lasts a fortnight. You’d be paying to keep a team sharp for 206 weeks of not much so they’re ready for the two that count. Nobody sane does that. You rent it, and you’re right to.

So England gets a pass. Fine.

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Chelsea doesn’t

Chelsea play fifty-odd times a year. So do Everton, Leeds and Sunderland. Every year. Forever. That is about as repeatable as commerce gets, and repeatability is the whole argument. When demand comes back next week, every mistake is tuition. Every spike teaches you something about the next one. The muscle you couldn’t build for a tournament, you can absolutely build across a decade of Saturdays.

So we’re not softening the position. In-housing is better long-term. Own the stack, own the relationship, own the data. Seven articles, no change of mind.

The reason it isn’t happening is that nobody at the club is listening, because nobody at the club has ever been asked to.

A football club is judged on one thing, and it isn’t gross margin on a hoodie. Retail is a royalty line that turns up in a spreadsheet once a quarter and never causes an argument. It isn’t underperforming. It’s just not anyone’s job to notice. And the licensing partner is very good at making sure it never becomes anyone’s job, because the moment it does, the deal’s in trouble.

That’s not a strategy. It’s an absence of one, held in place for twenty years by the fact that nothing ever went visibly wrong.

This isn’t a football problem

Every business has an England store.

Some part of the operation that somebody else runs, not because you sat down and did the maths and outsourcing won, but because it ended up that way early on and nobody has reopened it since. The wholesale portal. Marketplace listings. Returns. The B2B side. The international sites you launched in a hurry in 2021.

It’s rarely the bit that’s broken. Broken gets attention. It’s the bit that works fine, earns less than it should, and is run by somebody whose incentives are not yours.

The tell isn’t in the P&L. It’s that you can’t answer a simple question about it without emailing someone and waiting two days. How many of those customers came back? What drove the spike in March? Nobody in your building knows. Somebody in a building you don’t own does, and they’ll tell you next quarter.

The other tell is the terms. The deal was signed when that channel was the ninth biggest thing you did. It’s now the third. The terms haven’t moved, because nothing’s gone wrong, and nothing going wrong is exactly why nobody’s looked.

Not everything outsourced should come back in-house. England’s genuinely shouldn’t. But you should at least be able to say which category yours is in, and whether you’re renting because the maths says rent or because the question has simply never been raised in a room where anyone could act on it.

Most brands can’t answer that. That’s the same failure as the club’s, just with less bunting.

One thing you should never rent

You can outsource the factory. Sometimes you should.

But the last piece in the series was about the fan graph, and that’s where this stops being sensible.

A World Cup generates the most valuable fan dataset a federation will see in a decade. Every shirt sold over those six weeks is a person telling you something. Who they are. What made them act. Whether they came back after 2022 or turned up new last month. For most of the teams in this tournament, all of it was captured inside somebody else's platform.

The stock will be gone by August either way. The names won’t be worth anything by then. But the graph would have been worth something for twenty years.

Rent the operation if the maths says rent. Just don’t hand over the only bit that appreciates.

What OX is trying to be

We opened with the premise. OX exists to help brand leaders and operators navigate the AI era together, collaboratively, rather than each business figuring it out alone in a tab full of vendor webinars. The event is free for brands, and deliberately so: no cold sponsorship outreach, every partner in the room is someone the founders have worked with personally, in some cases for fifteen years plus.

The framing slide is worth passing on because it’s a useful mirror. Inside every brand there are passengers, waiting for someone else to lead on AI. Lone wolves, independently capable but building alone. Enthusiasts, great at bringing people with them but fuzzy on the tech, often at exec level. And catalysts, the rare combination of high AI clarity and high enablement of others. OX’s stated mission is to turn the people in the room into catalysts and help brands identify and grow their own. It’s a generalisation, and it’s also the most useful two-by-two we’ve seen on this topic.

The other structural note: OX1’s feedback was unanimous that the brand show-and-tells were the point, so OX2 handed the entire morning to three brands showing what they’ve actually built. Not slideware. The real things, running.

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The morning: three brands, three builds

Paul Heath, Adanola

He opened with the most complete data story of the day. Seven months ago he joined as a data leader the day before the only data engineer left, inheriting a two-year-old Power BI and Azure estate and a daily diet of “why don’t the numbers match?”

His first decision is the one worth quoting to every new data hire: don’t rip it out. The Azure stack would do the job. It needed restructuring, not replacing, so he moved it to a medallion architecture, raw, verified, cleansed, modelled, which is twenty-five-year-old thinking and works precisely because it is.

Seven months later: a team of seven, over 250 pipelines running around the clock with Claude-assisted automation on the failure handling, fifteen rebuilt Power BI reports sitting on a single semantic model instead of per-report logic, and, the bit merch actually cares about, a stock rebalancer that takes sales and stock data, works out cover by weeks of stock, and raises the purchase orders back into NetSuite automatically.

Then the showpiece.

When his agentic analytics budget met reality, Microsoft’s MCP endpoints wouldn’t authenticate the way his end users needed, so he asked Claude for an alternative and built a wrapper on the Claude API instead. Total effort, about five days.

The result is ANA, a conversational analytics assistant the merch team named themselves: natural language questions against the Power BI data model, answers with the working shown, suggested drill-downs that change with the question, export to Excel, all behind the normal Microsoft sign-in so nobody sees data they couldn’t already see. It routes simple questions to a cheap model and hard ones to a bigger one, and he estimates $20 per ten to twenty thousand questions. Rollout to the first ten users starts early August.

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His closing point was the one the room wrote down. He’d watched another business demo almost the same screen the week before and admit it hallucinates. The difference, he argued, is four ingredients: good data, a proper data model, context (the business rules a model can’t guess, like “you can’t sum a stock snapshot”), and an ontology for the synonyms, so “gross revenue” means the same thing to the machine as it does to finance. With those four, he claims 99.9%. Without them, you’ve built a confident liar.

Roberta, Technology Solutions Manager at Bluebella,

She showed the build we suspect the most people will copy: a discrepancy dashboard estate that answers one question across the stack, where do our systems disagree? Her diagnosis of the underlying problem was the best articulation of it we’ve heard.

Every team finds cross-platform issues at the worst possible moment for that team: finance at month-end reconciliation, merch just before a launch, ops when a batch of orders has already gone wrong, customer service when somebody complains. Anything that falls between platforms lands on the tech team, late. The dashboard means she finds things the day they happen instead.

The estate covers stock reconciliation across NetSuite and two Shopify instances, orders stuck mid-journey, fulfilment states, despatch speed, and a dedicated ASOS card, because every marketplace has fields and formats that match nothing else. The plumbing runs nightly in n8n: pull from each platform, merge, diff, snapshot, with the previous night’s version kept so every morning there’s a “fixed since last run” view and a briefing pushed to Teams.

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You only open the dashboard when something changed. The same snapshot feeds an MCP into Claude, so a wrong-looking number can be interrogated conversationally: of these orders, pull this information, why do you think this is happening?

Three details to steal.

The diagnosis layer learns by exception: each new failure pattern gets labelled once, and the dashboard names it automatically forever after. The security posture is properly thought through: credentials live in n8n and never touch Claude, access is filtered to exactly the fields needed, everything is read-only, and no customer or payment data exists anywhere in the estate, so sharing a dashboard never grants more access than the recipient already had.

And our favourite: Bluebella’s founder Emily had already built her own ASOS stock tracker, running on her laptop. Rather than replacing it, Roberta absorbed its rules into the shared estate, so the founder’s logic survived and stopped depending on the founder’s laptop being open. That is what healthy internal tooling culture looks like: it collects the intelligence the business already has.

Cost in time, since everyone asked: a few days to land the structure, which partly grew out of conversations at OX1, then half an hour to an afternoon per new dashboard.

Lawrence, managing director of Rough Trade,

He closed the morning, and for a fifty-year-old independent record shop with nine stores across the UK, Berlin and New York, this was the most radical talk of the day, and it was delivered in the calmest voice.

Three months, three problems, all customer service. First the inbox: analysis showed WISMO, where’s my order, was 60 to 65% of all inquiries. Their head of digital built an n8n flow that classifies every inbound conversation into what’s now six paths, and, crucially, once it gained the Shopify MCP connection it stopped merely tagging conversations and started resolving them: checking whether the order genuinely shipped, replying to the customer with the real status, and tagging genuinely late orders in Shopify to create bespoke picking views the warehouse clears daily. Median first response time fell from nearly a day to seconds, and roughly two thirds of conversations are now handled without a human.

Then the honesty that made the room trust everything else he said: CSAT hasn’t followed. Human-response CSAT went up considerably. Bot-response CSAT is low, and their working theory is a plain distrust of labelled automation. They label it anyway, because the alternative is worse, and they’re still working out the nuance. So is everyone.

Then returns, and a reframe worth the ticket price. Digging into the data, they found a huge share of their “returns” were actually cancellations, because two thirds of what Rough Trade sells online is pre-orders. Exclusive pressings, coloured vinyl, order it before it sells out, then minds change or another variant drops. They implemented a best-in-class returns platform and the business rejected it organically, through simple non-use, because it didn’t address the actual problem.

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So they built their own with AI coding tools, shaped around cancellation as the primary event. Customers always get 100% cash back, or can choose 10% extra as store credit, and the finance team’s raised eyebrow lasted until the numbers landed: three to four grand a week retained, an estimated £200,000 to £250,000 a year that would otherwise have walked out as refunds. Hosting costs about ten quid a month against the couple of hundred a SaaS tool wanted.

The app also standardises damage claims (image upload required, because vinyl buyers want pristine and the product is inherently fragile), gates non-delivery claims behind a configurable number of days so the inbox doesn’t drown, and resolves everything in one click into Shopify. Next up: using carrier data to systematically claim back lost deliveries, money most brands write off because the claims process is designed to exhaust you.

And the discipline underneath, which is the transferable bit. After a day of what Lawrence cheerfully called vibe coding, he concluded he was “being so dumb here” and the team adopted structure: Matt Pocock’s Grill with Docs skill, which interrogates a feature request, around a hundred questions in one case, until it fully understands it, maintains the context documentation as it goes, and outputs a PRD the coding agent can execute against for hours unaided.

He’d been back to a four-month-old codebase built without this and had to rebuild the context before any work was possible. Their head of ecommerce, Paddy, then used the same connected setup to build an agent that reviewed every out-of-date help centre article against current policies in Intercom and Shopify and rewrote the lot, with a plan to run it on a schedule. The job at the bottom of every to-do list, now just happening.

Two closing thoughts from Lawrence that summarise where every brand in the room is heading. First, with 85 to 90% of their tools now connected to their AI setup, the constraint has moved: it’s no longer capability or data access, it’s training and empowering non-technical teams, and navigating the culture. Rough Trade sells music as art and won’t be publicly pro AI music, while internally a small-margin business with a DIY ethos has a responsibility to use the tools.

He called the polarised debate what it is, tech-utopian on one side and doom on the other, and planted the business in the middle: it’s a tool, and the DIY tradition now scales. Second, the confession everyone recognised: a period of what he called AI mania around March, everything suddenly buildable, like walking into a bakery where everything’s free, and the resulting discipline that the question is no longer can we build it. It’s should we, and in what order.

The afternoon: three rooms

After lunch the room split into three rotating groups. In one breakout, Henrik from Dema ran a build-along: not a demo watched from chairs but the room building on the platform together, which is the difference between seeing conversational analytics and having made some.

In the other, our own Luke ran a session on transcription, everything he’s learned about going from a meeting recorder being switched on to transcripts becoming an actual working asset in the business, the pipeline from captured conversation to searchable, usable operational knowledge. If you’ve ever recorded a meeting and then done nothing with it, which is everyone, this was the session.

The main room ran the partner roundtables: Dema, Loop, Torque, Emfas, High Cohesion, Centra, ProCarrier, Torque, Resourced, Whanau and us, ten minutes a table, a whistle, and everybody moves. We won’t reproduce table conversations, that’s the covenant of the format, recorders on every table and flip it over if you want off the record. But we can report what recurred across tables independently, because that’s data: returns remains a data blind spot at every size of brand, and the wholesale side of it is a decade behind the consumer side.

The 3PL-versus-own-warehouse question is being asked everywhere, and the useful conversations were about what quotes hide, not about rates. Automation debt is arriving on schedule at the sophisticated brands. And the morning’s theme repeated all afternoon in different clothes: the constraint has moved from capability to judgement, priorities and people.

The rooftop opened at three. What was said up there stays up there.

The ask

Luke and Lauren closed the formal part with a request we’ll amplify, because it’s the right one. OX grew out of the back office, finance, ops, merchandising, customer service, systems, the functions this industry’s events chronically underserve, and the goal is cross-functional: if the day was valuable, bring colleagues from the other functions next time, because the catalyst thing only works if it spreads. We’ll share OX3 details as soon as they exist. If your job is running an ecommerce operation rather than talking about one, be in the room.

Find out more about OX

This one is the fullest version of that yet.

Three brand-side builds

The morning is Show and Tell. Bluebella, Adanola, and Rough Trade. Three operators, three real builds and they’re all in production. The through-line across all three: one workflow, one owner, one data source and one measurable outcome. None of it is a roadmap or a pitch. It all runs on the day.

Three afternoon tracks

The afternoon splits into rotating sessions across three rooms. Dema run a live build, tuned to the room each time, so you see the decisions get made rather than a finished result. Commerce Thinking on what actually happens after you transcribe a meeting, and the work of turning a transcript into something a team acts on. And the clinic, brand-side operators, partners who build software for brands, and dedicated time to work through real problems rather than sit through pitch decks. Bring a live blocker and you’ll get somewhere on it.

After the day

Rooftop drinks from 3pm. Then the Mulberry Bush from 5pm for anyone still standing.

OX2 is Thursday 16 July at Coin Street Conference Centre, London.

We are pretty much at capacity but if you want to be in the room, apply at operatorexperience.ai and we will make space.

Most brands miss that window. Not because they weren’t paying attention. Because they were too busy growing to notice it closing.

The mistakes that don't look like mistakes

We speak to brand leaders regularly who are worried about rolling out AI because of the mistakes they have already seen. We prefer to call them failed experiments. But they are worth understanding, because they follow a pattern.

The most common version is AI being used to automate tasks by people who have never done them manually first. Without that grounding, there is no frame of reference for when the output is wrong. Work gets produced, looks plausible, and passes without challenge because nobody in the chain has done it the old way and knows what to look for.

The more serious version is AI being used to generate outputs by people who do not understand the inputs and therefore cannot verify them. A report that looks credible but is not. An analysis that reads as thorough but has not been checked. The risk is not that AI was used. It is that it was used invisibly, without accountability, by people who did not fully understand what they were looking at.

Neither of these happens because teams are careless. They happen because AI adoption runs ahead of process. And the fix is not to slow down the adoption. It is to build the foundation underneath it.

The foundation nobody builds first

The single biggest unlock for effective AI use is process documentation. Not a corporate manual. A clear, end-to-end record of who owns what, how processes feed each other, and where handoffs create inefficiency.

Fast-growing brands rarely have this. Not because they do not know it matters, but because the person who would write it has always been too busy keeping the business moving to do so. The critical path exists. It lives in someone’s head. It has just never been on paper.
AI does not fix an undocumented process. It accelerates it. Point a capable AI tool at a clean, well-documented operation and you get real leverage. Point it at an undocumented one, and you get the mess moving faster, produced by more people, with a convincing veneer on top.
Process mapping is the exercise that changes this. Not over-engineered. Not a six-month project. A lean, usable record of how the business actually runs, built before the AI is pointed at it.

The window

Startup to scaleup brands are in the best position to embed AI properly before they scale headcount. The org chart is still fluid. Cross-functional conversations can still cut across the whole business without becoming a change management project. And the decisions made now about how AI sits inside the operation will either compound well or compound badly for years.

The model worth building toward is cross-functional squads: human and agent working together, with tasks mapped clearly across the workflow. Some kept with the human. Some delegated to the agent. Some dropped entirely because they were never worth doing in the first place.

At this stage, that is a conversation. At 40 or 50 people, it is a restructure. The brands that get this right are the ones that use the window while it is still open, embedding agents into the places where monotonous, repeatable work is currently absorbing a person’s entire week, rather than hiring someone to do that work full time and then trying to unpick it later.

The question is what shape the org chart turns into.

The room where this gets worked out

The types of people we see in every business relative to AI, the enthusiasts with fuzzy deployment, the passengers waiting to be told, the lone wolves, and the rare catalysts, are not evenly distributed, and they are more likely built than hired. That is not a hiring problem. It is a leadership and process problem.

These are exactly the conversations that happen at OX. Not theory. Operational reality, from brand-side leaders working through the same things in their own businesses.

OX2 is on 16 July at Coin Street Conference Centre, London. We are pretty much at capacity, but if you want to be in the room, apply at operatorexperience.ai, and we will make space.

Most of that played out, but some of it was more complicated than it looked at the time.

What actually happened

The August 29, 2025 deadline landed roughly as predicted in terms of direction. Complications came from the execution.

Courier networks took the hit first, with processing times stretched. Duty invoices were inconsistent for weeks. Shipments were cleared without issue one day and held without warning the next. The infrastructure simply was not built for the volume of declarations that suddenly needed filing. Teams that had been operating on predictable lead times lost that predictability overnight, and it took most of the autumn to settle.

The duty arbitrage played out. Brands that had already moved inventory into US 3PLs found themselves at a structural cost advantage over brands still shipping direct. The gap between paying duty on retail value versus landed cost was real, and in categories like fashion and wellness it was often the difference between a viable US channel and an unviable one.

What the original piece underestimated was the documentation burden. Getting HS codes right turned out to be harder and more expensive than most brands anticipated. Sloppy classification created cost surprises across thousands of orders. Several brands absorbed unexpected penalties before they got their data in order. The compliance piece was not a one-week job. For some, it was a quarter.

What AI changed about this

Here is the part that did not exist in the same form when we wrote the original piece.

The documentation problem, the one that caught brands out, is now largely a solved problem if you are willing to use the tools. HS code classification, duty modelling and invoice reconciliation against what you were actually charged. These are exactly the kinds of jobs AI handles well: repetitive, rule-based and requiring patience rather than judgement.

We wrote earlier this year about the work that was never worth doing. The fifty-quid invoice error nobody caught because catching it cost more than it was worth. Cross-border duty reconciliation was the same problem at a larger scale. A misclassification across a month of US orders was worth chasing. Nobody had the bandwidth to chase it, but now they do.

That changes the calculus on whether a US channel is viable, because it lowers the cost of managing it correctly.

What it tells us about what comes next

The US experience is the closest thing we have to a preview of how the EU and UK changes will land. The pattern was consistent: the tariff itself was not the worst part, the shock to the system was. Courier networks were not built for the declaration volume. Duty invoices were inconsistent. Clearance times became unpredictable. The brands that came through it cleanest were the ones that had already moved stock into the US before August 29, not because they were lucky but because the duty arbitrage calculation had already made the case.

The same arbitrage exists for any market where you are currently shipping direct. Duties assessed on retail value versus duties assessed on landed cost is not a small difference. For the brands that ran the numbers and moved, that gap funded the 3PL setup.

What to actually do about it now

Start with the model. Work out how many of your EU and UK orders would sit under the relevant thresholds, because those are the ones that get more expensive when the exemptions close. Establish the actual landed cost impact if duty applies to every one of them. Most teams are surprised at the size of the number.

Get your HS codes in order. The US experience showed that sloppy classification creates cost surprises across thousands of orders and penalties that take a quarter to unwind. That is work to do before the pressure arrives, not after.

Review your minimum order thresholds. Orders under a certain value may no longer be commercially viable once duty and handling stack up. Know where your line is.

And seriously model the regional fulfilment case. The brands that moved inventory into the US before the deadline are operating at a structural cost advantage now. The window to do the same for the EU exists. It will not stay open.

The party ended a year ago in the US, with the EU and UK versions next. The pattern is the same, and the question is whether you use the interval to get ready, or wait until the deadline is already live.

None of it arrived as one big headline. It came in pieces. A customs tweak here, an accessibility deadline there, a product safety rule that most brands only noticed when their listings got pulled. Individually, each one is a footnote. Stacked up, they add up to the biggest change in what it takes to sell into the EU since GDPR.

And that GDPR comparison is the thing to hold onto, because it explains all of it. Every piece of this applies based on where your customer is, not where you are. A brand in Manchester or Austin does not get a pass because it has no office in Frankfurt. If a German shopper can buy from you, the EU considers you in scope. Your postcode is irrelevant. Your customer’s postcode is everything.

Which is a problem, because most brands are already far more international than they ever decided to be. The revenue went global. The operations, mostly, did not. All of the rules below land on exactly that gap.

Here is what has already gone live, what is landing next, and what any UK or US brand actually needs to do about it.

The €3 that eats your margin (live from 1 July 2026)

Start with the one people are actually talking about, because it hits the number that matters.

For years, any parcel worth €150 or less could enter the EU free of customs duty. VAT still applied, but duty did not. That exemption is gone from 1 July 2026. In its place, the EU is charging a flat €3 customs duty on those low-value parcels while it builds the proper system underneath.

Sounds small. It is not, and here is the sting. The €3 is charged per product category, not per parcel. The technical bit is that it lands on each distinct item type in the order. So a parcel with a blouse, a belt and a pair of socks is three different headings, which is €9, not €3. The more varied the basket, the more it stacks.

One thing worth knowing for later: country of origin will also affect the calculation, but not yet. During the interim phase through to July 2028, the €3 flat rate applies regardless of where the product was made. From July 2028, when the EU Customs Data Hub goes live, real tariffs based on HS code and country of origin replace the flat fee. That is when trade agreements and anti-dumping duties come back into play.

For anyone shipping low value, multi item orders direct from outside the EU, that is a real dent.

Then it gets worse before it settles. A separate handling fee of around €2 per parcel is expected from November 2026, which takes the combined charge to roughly €5. The €3 rate itself is temporary. It runs until the EU’s new customs data hub comes online around mid 2028, at which point normal product by product tariffs replace it. So the flat fee is not the destination. It is the holding pattern.

This is not the EU acting alone, either. The US killed its own $800 de minimis threshold in 2025. The UK confirmed in the Autumn Budget 2025 that it is scrapping its £135 threshold too, with reforms landing around 2029. The whole world is closing the small-parcel loophole at once.

What to actually do: work out how many of your EU orders sit under €150 today, because those are the ones that just got more expensive. Get your HS codes right, because sloppy classification now costs you money on every order. And seriously look at holding stock inside the EU. Bulk import once, clear duty once at the border, fulfil locally, and the whole per-parcel problem disappears. This is the point at which international complexity stops being a future problem and starts showing up in this quarter’s margin. The brands that move stock in before July will be selling at a structural discount to the ones absorbing €5 a parcel in August.

The product safety rule that's already pulling listings (live since December 2024)

If the customs change is the one everyone is talking about, the General Product Safety Regulation is the one everyone missed. It has been live since 13 December 2024, and plenty of brands only found out when Amazon or Etsy hid their listings.

The GPSR replaced an old directive and tightened the rules for basically every non-food consumer product sold to EU shoppers. The headline requirement is the one that catches out non-EU brands. You now need an EU-based “responsible person” before your product can be sold. That is a named economic operator inside the EU (or Northern Ireland) who holds your technical documentation and answers to the regulators. It can be an EU importer, a fulfilment provider, or an authorised representative you appoint. But it has to be someone, and it has to be someone inside the bloc.

No responsible person, no market access. Your product listings also have to display the manufacturer’s contact details, the responsible person if the manufacturer is outside the EU, product identifiers, an image, and any safety warnings. The marketplaces are already enforcing this. Non-compliant listings get delisted, and you lose the sales before you understand why.

What to actually do: if you sell physical goods into the EU and you have not appointed a responsible person, that is the single most urgent line on this whole list, because you may already be non-compliant. Fix the responsible person, then fix your listing data.

The accessibility deadline that went live in June 2025

The European Accessibility Act switched on for enforcement on 28 June 2025, and it is aimed squarely at your website.

If you run an ecommerce service for EU consumers, your site and app have to be accessible to people with disabilities. In practice, that means meeting the WCAG 2.1 AA standard across the whole shopping journey, and it is all of the criteria, not the convenient ones. Screen readers have to announce your variant pickers. Keyboard users have to get through your checkout. Price changes have to be read out when someone changes an option. The places sites usually break are the fancy bits, the carousels, the multi-step checkouts and the dynamic carts.

There is a carve out for genuine microenterprises, under ten staff and under €2m turnover. Almost nobody reading this qualifies. Existing systems get some breathing room until June 2030, but new work is expected to be accessible now, and you are supposed to publish an accessibility statement either way.

This is not theoretical. French disability groups filed formal notices against major retailers within days of the deadline and took several to court by late 2025. Regulators in the Netherlands, Germany, Sweden and others are actively surveilling. Penalties vary by country and run into the millions in the worst cases.

What to actually do: get someone to run your full purchase flow with a screen reader and see where they get stuck. That one exercise tells you 80% of what you need to fix. Then publish an accessibility statement. It is also, for what it is worth, just good commerce. There are tens of millions of EU shoppers with disabilities, and an inaccessible checkout is an abandoned cart with a legal risk attached.

The AI rules, and the part that actually applies to you (landing August 2026)

The EU AI Act has been phasing in since 2024, and most of the coverage misses the point for ecommerce brands, because most of the scary stuff is about high-risk systems like credit scoring and biometrics that you probably do not run.

The bit that lands on marketing and ecommerce teams is the transparency rule, and it applies from 2 August 2026. In plain terms, when a customer is talking to a bot, you have to make it obvious it is a bot. When content is AI-generated, including synthetic images and deepfakes, it has to be labelled. If you have leaned into AI chat support, AI product recommenders, or AI-generated creative and imagery, those are the touchpoints in scope. Worth knowing, given how many operators are figuring out AI on their own, that “we’ll sort the compliance bit later” is a common and risky default here.

One caveat worth knowing. The EU has a simplification package in flight (the so called Digital Omnibus) that reached political agreement in May 2026 and may shift some of the high-risk timelines. The transparency obligations are the part that matters for most brands, and those are the ones to plan around. The Act also reaches beyond the EU’s borders, same as everything else here, and the fines are the largest of any of these rules, up to €35m or 7% of global turnover at the top end.

What to actually do: audit where AI touches your customer. Label your bots. Label your synthetic content. It is not a heavy lift, but it is the kind of thing that is embarrassing to be caught not doing.

Packaging, and the death of the oversized box (from August 2026)

Last one. The Packaging and Packaging Waste Regulation applies from 12 August 2026, and it changes how you can ship.

The rule ecommerce teams will feel first is the empty-space limit. Void in a parcel cannot exceed 40%. The era of a lipstick in a shoebox full of air pillows is over, because that air now counts against you. There are also recycled-content requirements, restrictions on nasty substances in packaging, and, for non-EU sellers, a requirement to appoint an authorised representative to handle it. Digital labelling on packaging follows from 2027.

What to actually do: right-size your parcels and talk to your packaging supplier about recycled content and documentation. The unboxing flex has a compliance cost now.

Digital product passports, coming for your labels (from 2027)

This one is further out but worth flagging now, because the lead time to comply is long.

The EU is building towards a system where products carry a Digital Product Passport, a QR code or similar that links to verified information about materials, carbon footprint, repairability and where the thing came from. The first mandatory version is the battery passport, live from 18 February 2027 for larger batteries. Textiles and apparel are next in the queue, with the rules expected to be adopted around 2027 and compliance landing roughly eighteen months after that, so realistically late 2028 into 2029. Furniture, electronics and others follow.

There is no single deadline. It rolls out category by category. But if you sell textiles, footwear, electronics or furniture into the EU, the passport is coming for your product data, and gathering that data across a messy supply chain is exactly the kind of job you cannot start the week before it is due.

The pattern, and the point

Read all of that back, and you notice it is the same move, over and over. Your parcel, your website, your product, your packaging, your bot. Each one is now something the EU will regulate regardless of where you sit, on the single condition that a European can buy from you.

That is the mental shift. Selling into Europe used to be a shipping question. It is now a compliance question, and the truth is that the barrier to entry just went up. Which, if you look at it from the right angle, is not entirely bad news for the brands that get organised. Every one of these rules is a hurdle. Every hurdle thins the field. The cheap, non-compliant, drop-shipped competition that used to undercut you on price is exactly what most of this is designed to squeeze out.

So the brands that treat this as a checklist and get it done are not just avoiding fines. They are slowly pulling away from everyone who is still hoping none of it applies to them. That is usually how era changes actually play out: the shift looks like admin right up until it looks like a gap nobody can close.

It applies. Get the map, and start walking.

OX runs the other way round. The room is the product. The number on the door isn’t a capacity we’re straining against, it’s a decision. And right now that decision leaves 17 places.

That’s not a countdown timer engineered to make you panic-click. No fake “only 2 left!” that resets every time you reload. When the 17 are gone, they’re gone, because the entire value of OX is the specific people sitting in it. Add 40 more to hit a revenue target and you don’t get a bigger OX. You get a worse one.

What it is

Operator Experience is an invite-only community for brand-side leaders. No agencies. No platforms. A closed room of people in the same seat, under the same pressure, trying to solve the same things, without anyone in the corner waiting to pitch them.

OX2 is the second one. It runs from 10:00 on Thursday 16 July at Coin Street Conference Centre in London.

The morning is show and tell. Operators walk the room through workflows they’ve actually built for real brands. Not slides about what’s theoretically possible. The thing they shipped, why they shipped it, and what broke on the way.

Lunch at 12:00.

The afternoon is three rotating 40-minute practical sessions, and everyone does all three. Groups are mixed on purpose, so you’re sat next to someone from a different function rather than nodding along with your own kind. The day ends on the rooftop from 15:00, while it’s still light. Pub after for anyone who wants it. No obligation if you don’t.

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Why it’s capped

OX1 happened in Manchester. Nearly 90 brand-side operators, and the feedback came back saying the same thing over and over. The best part wasn’t the agenda. It was the person next to them.

So OX2 is built to manufacture more of that and less of everything else. More operators describing what they’ve actually made. More tables deliberately mixed so the conversation doesn’t stay in its lane. A finish time that respects the fact you have a train to catch and a life to get back to. (Or stick around in the pub afterwards, no pressure).

You cannot engineer that quality of conversation in a room of 300. You can in a room this size. That’s the whole reason for the cap, and the whole reason there are only 17 seats left.

Who it’s for

Senior people working brand-side: ecommerce, ops, supply chain, logistics, merchandising, buying, finance, product data, tech or CX, at a fast-growing fashion, lifestyle or consumer brand.

Or, if you’re in-between roles and want to share your knowledge and/or learn from other operators, please come along.

If you’re an agency or a platform hoping to slip in, this isn’t the one. That’s not snobbery, it’s the deal. OX is free to attend because partners fund it on one condition: they play by the same rule as everyone else. No pitching, no posturing, treat operators as peers. Headline partner Dema, event partners Loop, Torque and Emfas, and community partners including Vervaunt, HighCohesion, Resourced, Centra, Whanau, Pro Carrier, Commerce Thinking and Waye.

The 17

The room is small because small is the point. If you’re the right person in the right seat, this is the rare event built entirely around you, rather than around the people trying to sell to you.

There are 17 places left. When they’re claimed, OX2 is full.

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The variable that consistently predicted trust across both returns and cross-border, was support. The cohort: 24 brands, fashion-led, UK-headquartered, £10m to £100m. The data is operator data, not vendor data.

Not support in the ticket-response-time sense. Operators aren’t measuring their vendors by how quickly an automated acknowledgement lands in their inbox. What they’re measuring is something harder to systematise: genuine urgency when something breaks, commercial understanding that goes beyond technical resolution, and a willingness to adapt alongside the business rather than deflect to a product roadmap.

Our research found that 71% of operators cited support as the top factor in vendor sentiment. 68% said they would pay more for better support. And 45% had left a vendor specifically because of poor support. Those numbers are striking on their own. But the qualitative feedback is what gives them context.

The vendors rated most highly weren’t the ones with the most features. They were the ones that showed up.

That framing has limits worth naming. Not every operator arrives at vendor evaluation on equal footing.

"A lot of the big cross-border and returns platforms are positioning themselves as a benefit when in fact, legacy brands have very strong relationships with carriers, and so there is less of a benefit." That's from Katie at Strathberry.

The point isn't that support doesn't matter. It's that the baseline value proposition varies by operator, and sentiment follows from that.

Reveni scores 9.5 out of 10 in our research, and the feedback points directly to this: “Reveni have genuinely gone above and beyond for us. They’ve built custom setups, developed new integrations, and worked closely with our team throughout, without cutting corners or over-promising at any stage.” That’s not a feature review. It’s a description of a working relationship.

Swap draws similar feedback, particularly around responsiveness during high-stakes periods: “We launched Swap exchanges on Black Friday week and there were no issues whatsoever. Plain sailing during BF week, unheard of.” The value isn’t just that it worked. It’s that it worked when the stakes were highest and the margin for error was zero.

Loop sits in a different position: respected for stability, trusted at scale, with an opportunity to lean further into operator-led insight and commercial clarity. The platform works. The relationship has room to deepen.

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At the other end, Global-e, Narvar, and ZigZag are more frequently described as slow, rigid, or difficult to evolve. Not universally negative, but the friction is higher and the patience is lower. One operator described their cross-border provider as "the market leader, thus regarded as best in class, but not a pragmatic partner."

A managing director at a luxury footwear brand was more specific about what that looks like in practice: their cross-border provider's portal was built around returns, not exchanges. "I said, but guys, this is a refund portal. This is not an exchange portal. This is not fit for purpose."

The cost equation follows the same logic

Brands will pay premium pricing when value is visible and the relationship feels like a genuine partnership. They’ll reject lower-cost options that feel opaque or hard to measure. The vendors winning right now aren’t the cheapest. They’re the ones that make it easy to see what you’re getting.

For operators evaluating vendors, or reconsidering existing relationships, the practical implication is to weight support quality more heavily than most RFP processes currently do. A platform with a slightly shorter feature list and a genuinely responsive support model will outperform a more capable platform with a generic helpdesk almost every time.

Our full research report covers vendor perception in detail, including how support shapes sentiment across the full returns and cross-border landscape.

Read the full report

Then Shopify Editions Spring 26 landed this week, and a good chunk of it reads like the same argument with a product roadmap attached.

What they actually shipped

The headline of the agentic section is not “your brand in AI.” It is “your products optimised for AI.” That distinction matters. Shopify has decided, at a platform level, that the unit that matters in agentic commerce is the product, not the brand.

The clearest piece is Shopify Catalog. It automatically standardises and enriches product data so AI models can reason about it, and Shopify claims data it syndicates drives twice the conversion in AI chats. Around it sits the rest: checkout inside Copilot via the Universal Commerce Protocol, an agentic plan that lets non-Shopify brands sync products into Catalog, and a Catalog API that lets agents pull real-time pricing, availability and variants for products at request.

The product catalog is now the interface between your business and every AI agent that might recommend you.

Why this is good news, and a trap

It is good news because it confirms the thing we have been saying. Product-level visibility depends on structured, enriched, machine-readable product data. Shopify has now built tooling for exactly that and put a conversion number next to it. The 30% to 40% of the problem that Glara put in the “enrich the catalog” bucket at Pulse now has a button.

The trap is assuming the button is the job.

Catalog enriches what you feed it. It does not fix what you feed it with. If your product data is inconsistent across your PIM, your storefront, your marketplaces and your 3PL, then automatic enrichment makes that inconsistency travel further and faster. You do not get clean data in AI channels. You get the same mess, syndicated, with a higher conversion rate on the wrong information.

That is the 60% to 70% nobody is selling a tool for. It is not catalog enrichment. It is whether the brand can describe its own products the same way everywhere they live, and whether the operating model can keep them that way as the business changes.

The bit Editions does not solve

Editions makes the front of the stack easier. It does almost nothing for the back.

It does not tell you which system is the source of truth for a product attribute when three systems disagree. It does not resolve who owns product data when ecommerce, merchandising and operations all touch it, and none of them is accountable for it. It does not align your head of ecommerce and your IT director on a shared priority list, which in our experience is the single thing that determines whether any of this moves.

A platform can make your products legible to agents. It cannot make your organisation coherent enough to keep them legible. That work is internal, slow, and has no vendor category. It is also the work that decides whether the tooling pays off.

What we would actually do about it

Before you spend a quarter on agentic strategy, here are three unglamorous checks to carry out.

One. Pick your source of truth for product data and write down which system wins when systems disagree. If you cannot name it, that is the project.

Two. Name who is accountable for product data quality across the business, not who manages the PIM. These are different people more often than they should be.

Three. Get ecommerce and IT into one priority list for this work. Not aligned in principle. The same list, in the same order.

Do those three and Catalog enrichment becomes worth doing, because you are enriching something coherent. Skip them and you are buying reach for data you have not agreed on.

The brands that win the agentic shift will not be the ones who adopted Catalog first. They will be the ones who had something worth syndicating when they did.

The theme was Back to Basics: checkout flows, product feeds, returns, the operational work that actually moves the needle.

It wasn’t a trade show. It wasn’t a networking event dressed up as a conference. It was a room full of people who wanted to get better at the work.

Luke’s talk drew on what’s already played out in UK and US markets, both further along in their evolution, and the observation that the challenges aren’t that different from what brands in Norway are navigating now. The operating decisions that separate the brands that scale cleanly from the ones that stall look the same whether you’re in London or Oslo.

A scene that wants to build properly

What struck Luke most wasn’t the content on stage. It was the energy underneath it. Something is building in the Nordic brand scene, a real appetite to establish it as a serious player over the next decade, not just a satellite of what’s happening elsewhere. That ambition was in the conversations, not just implied by the talks.

The Back to Basics framing fits that. The unglamorous operational work is where the compounding happens before anyone else notices. The brands in that room understand it.

CT works in that gap, the tech and ops decisions that decide who scales cleanly and who stalls. If that's the work your business is in the middle of, you know where we are.

Most events you’ve been invited to this year were built around agencies, platforms or vendors. The brand-side operator, the person running ops, owning the back-office, holding the supply chain together, is either an afterthought or isn’t there at all.

OX2 London is the next one.

What it actually is

Operator Experience is an invite-only community for brand-side leaders. No agencies. No platforms. A closed room of people in the same seat, dealing with the same pressures, trying to figure out the same things.

OX2 is the second event. It runs from 10:00 on Thursday 16 July at Coin Street Conference Centre in London. The morning is show and tell, with operators walking the room through workflows they’ve actually built for real brands. Lunch at 12:00. The afternoon consists of three rotating 40-minute practical sessions, and everyone hits all three. Groups are mixed deliberately, so you’re sitting next to people from other functions, not just your own.

The day ends on the rooftop from 15:00 to 17:00, while it’s still light. Pub after for those that want it.

The format is tighter than most events you’ll attend. You’ll know exactly who you’re with and what you’re there to do from the moment you walk in.

What’s different about OX2

The feedback from Manchester was consistent. The peer conversations were the best part of the day. OX2 is built to create more of them.

Operators telling the room what they’ve actually built and how it works. Rotating sessions that mix functions so the conversation doesn’t stay siloed. Rooftop drinks that end at a sensible hour, so people can stay or head off without the evening turning into an obligation.

OX isn’t static. Each event is shaped by what the previous one taught us.

Who's behind it

OX2 is free to attend for brand-side operators. That's made possible by partners who've committed to the same rules as everyone else: no pitching, no posturing, treat operators as peers. Headline partner Dema, event partners Loop, Torque, and Emfas, and community partners including Vervaunt, HighCohesion, Resourced, Centra, Whanau, Pro Carrier, Commerce Thinking and Waye.

If you're the right person

Senior people working brand-side in ecommerce, ops, supply chain, logistics, merchandising, buying, finance, product data, tech or CX at a fast-growing fashion, lifestyle or consumer brand.

The room is small and curated on purpose. Places are limited, so if you want one, move quickly.

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I keep seeing the same moment in Shopify agency decks. The slide where they tell the room “we’re experts in ERP”.

It’s the throwaway line in every deck, and so common that many brands don’t always stop to question or scrutinise it.

Then the same brands are 3-6 months into a new site build or migration project. And the flow of data in and out of the ERP is being rejected by Finance because the numbers don’t line up.

Or worse, the gaps in data or the errors in setup aren’t detected until post go-live.

Then it goes wrong and month-end takes longer, reconciliation throws up surprises, and confidence in the system evaporates across finance, ops and merch.

That’s the moment that should make you sweat. Not because ERP is scary, but because a lot of agencies aren’t actually selling ERP expertise.

They’re selling familiarity with connecting to ERPs. They just don’t truly realise it (or do, and are pulling the wool).

ERP integration isn’t ERP expertise

No one’s the bad guy here (unless you’re knowingly pulling the wool, if so, get f****d). The market’s packed with teams who can build slick storefronts, amazing site back ends, and chase conversion, and it’s still harder to find people who can talk about Shopify like an ops system that has to reconcile cleanly into finance.

The problem is what happens next. That shallow exposure gets mistaken for knowing what good looks like, and then somebody confidently starts making decisions that belong in finance, ops, and systems.

If you’re a brand-side operator reading this, you know the situation. The people least qualified to decide how orders should land in the ERP are often the people talking the most in the meeting.

If you’re an agency reading this, you’ve probably felt it too. Your team can build the storefront, then the conversation drifts into transaction types in ERP, allocation logic, partial shipments, and returns. And suddenly you’re out of your depth, live, in front of the client.

Quick test. Can your ERP operator walk a real order end to end without sweating under questioning from finance? How an order type shows up in the chart of accounts? How to update payments? How to map out all the related records? How to configure ERP connections to 3PL to handle inventory adjustments linked to returns?

If not, you’ve got a connector. Not an ERP specialist.

I know because I used to think I was an ERP specialist, until I worked with Dom and Maxime on the Commerce Thinking team and realised “oh shit, that’s what being an expert in this really means”.

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What shallow ERP integration looks like

A lot of agency-led ERP work looks like this. Orders go from Shopify into the ERP, inventory comes back, and someone declares it job done.

That’s a start. But it’s also not the hard part.

The hard part is everything that sits around those syncs. Exceptions, data ownership, and who fixes what are the boring bits that decide whether the system behaves at scale.

The ugly truth is the integration often doesn’t fail loudly. It just quietly does the wrong thing, or never does the thing you assumed it was doing.

We’ve seen this play out. An integration between multiple Shopify expansion stores into a multi-subsidiary NetSuite instance that had been hobbling along for 12 months with gaps in data, low trust in the accuracy of sales and refunds data in NetSuite, all after a stressful 9 month migration project led by the Shopify agency. And Commerce Thinking are able to square the issues away in <12 weeks.

Who pays when you over-claim

First, your client pays.

When the ERP and webstore don’t work together cleanly for departments like Finance, Ops, Merch, then the cost shows up everywhere. Orders get retyped and inventory drifts, and finance ends up having to reconcile the long way and never really trust their numbers. Merch start to rely on data exports from other systems and portals because they don’t trust the numbers in ERP either.

Second, you pay.

These projects stop being profitable for a generalist agency the moment they go sideways. You either eat the hours, or you go back for change requests and watch trust drain away.

Then your reputation takes a quiet hit. The client stops treating you like the grown-up in the room.

They might not fire you immediately. They just stop inviting your ERP lead to the important calls, because they’ve clocked you’re not helping.

If you’ve ever wondered why you got dropped from the integration workstream but kept on the site tweaks, that’s what happened.

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Why this keeps happening

Part of this is structural. Shopify’s partner ecosystem is brilliant at front end, design, dev, and growth, and it’s thin on back-office operators.

So agencies stretch. Clients assume it’s normal. Everyone walks into the room with the wrong expectations.

The agencies also want to control as many dependencies inside a project and capture as much of the clients wallet as possible. Adding integration to their service lines makes sense through that prism. But ignores the fact they’re not qualified.

The other part is risk and complexity. Integration projects like this have a habit of running late, overrunning, and breaking in ways that only show up once you’re live.

If you’re treating that risk as a bolt-on service line, you’re gambling with your client’s operation and your own credibility. So you better be fucking good.

The better move for agencies

Don’t pretend you’ve got ERP covered. Be the agency that knows exactly where your expertise ends, and brings in the right people before the client loses confidence.

Drop the act. Not because you’re bad at your job, but because you’re playing a game you can’t win.

If you want a practical way to run this, keep it boring.

• Own the scope. Lead Shopify, UX, and front end delivery, but don’t let yourself get cast as the person designing finance and ops workflows.

• Bring the specialist in early. Discovery, data mapping, and end-to-end testing are where projects are won or lost, not when everyone’s stressed and trying to save face.

• Price and plan for the initial period after go-live. That’s where the ugly stuff shows up, and where most teams get found out.

None of that makes you smaller. It means you do less, better, more profitably.

It also makes you look better to the client, because you’re not forcing them to put up with you learning on the job.

Where Commerce Thinking fits in

This is the bit where we stop dancing around it.

This is why Commerce Thinking exists. Our work sits in the unsexy middle where Shopify touches finance, ops, and ERP, and we tend to get pulled in when someone wants it owned properly.

If you’re an agency owner and you want to keep the client relationship and still get these projects delivered cleanly, bring us in early. Not to take your client, but to make sure the bits that can make you look naive don’t become the lasting impression.

If you’re a brand-side operator and your agency is leading ERP conversations, push a bit. Ask who owns end-to-end testing, who owns reconciliation, and who owns the initial period after go-live.

If you’re not convinced by the answers, or want some help qualifying the answers just get in touch. Our ERP study programme is designed for brands in your position. Let us take a look under the lid and help you to do a migration properly from the start, rather than a rescue job later.

If this hit a nerve, get in touch. We’d rather have the awkward conversation now than watch you have it with your client later.

So you pay it. You pay it every month. You have always paid it.

This is not a finance problem. It is a maths problem. Every business runs an unspoken calculation on every task: is the time worth the result? Most of the time the honest answer is no. The juice is not worth the squeeze. So a whole category of work sits just below the line. Worth doing in theory. Never done in practice.

That line just moved.

Take carrier invoices. Give Claude your order data as a CSV and the rate card you actually agreed with DHL. Ask it whether the invoice matches. It goes line by line, the way nobody has the patience to, and tells you where the numbers don’t add up.

Here is the part that makes it useful rather than just clever. When the invoice comes back too high, it is often not the carrier’s fault. Someone shipped a week of standard orders as express. The warehouse used the wrong labels. Claude flags the gap, you find the cause. Sometimes it’s DHL. Often it’s you.

The same trick works on 3PL invoices. They charge per unit in, per unit out, plus a list of extras nobody reads. Give Claude the contract and the invoice and let it tell you if they line up.

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Global-e payout files are the best example, because they are genuinely horrible. Cross-border charges, odd payment terms, surcharges that move with the price of fuel, every brand on a slightly different deal. Give Claude the rules you signed up to and it will tell you whether the payout is what you were owed, instead of you taking it on faith because the file is too long to argue with.

Review contracts before the need for legal

Then the contracts. The eight grand deal that lands in your inbox with twelve pages of terms. Too small to bother the legal team with. Too boring to read yourself. So you sign it. Claude reads it in a minute and pulls out the bits that matter.

None of this is new work. You always knew you should check the invoices. You always knew the contract had a clause in it somewhere. The job has not changed. The cost of doing it has.

That is the bit worth sitting with. AI has not handed you a superpower. It has lowered the price of attention. And when the price of attention drops, a whole pile of work that was never worth your time becomes worth it. Twenty minutes to save fifty quid used to be a bad trade. Now it is twenty minutes you did not spend, because Claude did.

So go and look at the jobs you have been ignoring because they are too small. That is where the easy wins are. They were always there. You just could not afford to look.

Panini sells basketball cards. Sealed boxes, the kind grown men queue for. When a hot product drops it sells out in seconds. An hour later it’s on eBay and StockX at double. You know the pattern. Sneakers do it. Consoles do it. Gig tickets do it. Anything with hype and a fixed price ends up here.

So Panini tried something. On their First Off The Line boxes they run a Dutch auction. The price opens stupidly high, say a thousand dollars a box, and drops every couple of minutes until the thing sells out. You pay whatever the screen says when you click. Wait for it to fall further and you risk it selling out from under you. The first time they did this, in 2019, boxes opened at $1,000 and sold out before the price dropped to $900. People paid near the top. Willingly.

Panini didn’t invent this. Google floated its shares the same way in 2004. Topps, who now hold the big sports licences, have started running the format on their own drops. So it isn’t a one-off. It’s spreading.

Reddit hates it. Of course Reddit hates it. The complaint is simple and fair. This just lets Panini charge more. The margin is already baked into the retail price. So why are loyal collectors being asked to pay over the odds to the people who made the thing?

Here’s the bit worth sitting with.

The markup was always there. A box that sells for £80 and instantly flips for £200 has a £120 premium sitting on top of it. That money is real. The only question has ever been who pockets it. Under the normal system the answer is a bloke with a bot and a stack of cards. The loyal collector rarely gets the box at retail at all. They get a sold-out page and a StockX listing. The cheap price was a fiction. It was never really for them.

All the Dutch auction does is move that £120 from the scalper to Panini. Same premium. Same buyer paying it. Different pocket.

Put like that, it’s almost admirable. It cuts out the middleman. The manufacturer captures the value of its own hype instead of gifting it to resellers. If you ran any other business and found a stranger skimming the entire upside off your best product, you’d want it back too.

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So why does it feel wrong?

Because a fixed price is a promise and an auction is a negotiation. When a brand posts a price and you pay it, you feel fairly treated, even after queuing for hours. When a brand makes you bid against everyone else, you can win and still feel mugged. Nothing about the money changed. The feeling did.

This is the thing brands forget. People will happily pay a scalper double, because that happens off to one side, in the shadows, nothing to do with the brand they love. The same person feels betrayed paying the brand directly. We don’t mind the markup. We mind seeing the markup. The Dutch auction’s only real crime is switching the lights on.

Which is also its risk. Scalping damages nobody’s brand. Panini scalping, in public, with its own name on it, damages Panini. You can be economically right and still lose the room.

So, is there a better way to get product into the hands of loyal fans? Yes. But every option comes with a bill.

Raise the price and say why, plainly. Honest, unpopular, keeps the relationship clean.

Run a ballot. Everyone enters, winners drawn at random, price fixed. Nike do this. It feels fair, because luck feels fair in a way money never does.

Gate it to verified fans. Loyalty points, order history, account age. Hard to game, costly to build, and you’ll still be accused of picking favourites.

Or do nothing. Let the resellers keep the premium and treat the secondary market as free advertising for how badly people want your stuff.

There’s no clean answer. There never is. Scarcity always creates a premium, and that premium always lands in somebody’s pocket. Pretending otherwise is the actual mistake.

Panini have decided the pocket should be theirs. It’s the rational call. It might also be the one that costs them the thing that made the cards worth fighting over in the first place.

Worth watching whether that top price sells out. If it does, they were right about the money. Whether they were right about the customers takes a lot longer to show up.

A year ago we published a piece called Say No to Grow. The argument was not complicated. The brands that win are rarely the ones doing the most. They are the ones doing fewer things, properly. Fewer SKUs, managed well. One marketplace, run hard. A range the merch team can actually forecast. We pointed at Liquid Death turning down the deals that didn’t fit, and we made the case that strategic refusal is a competence, not a weakness.

I still believe every word of that. But a year is a long time, and this one has put the theory under real pressure. Not because the theory is wrong. Because saying no has got harder.

What’s changed isn’t the cost

People will tell you AI has made everything cheaper. I would be careful with that. Some jobs are quicker, the savings are real but smaller, and someone still has to check the work. Cost is not really the thing that has changed.

What has changed is access. AI is more available than it has ever been, and the barrier to entry has dropped through the floor. A year ago, taking on something new meant a business case, a budget, maybe a hire, a project with a start date. Today, anyone with a browser and twenty minutes can have a go. That sounds like progress, and in places it is. But it has changed the conversation inside your business, and not in your favour.

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The boss, the colleague, the competitor

Here is how it shows up.

Your CEO has noticed. The next time you push back on taking something on, the next time you say we do not have the capacity for that, you will get the same question back. “Can’t AI just do it?” On the surface it is a fair question. It is also very hard to say no to in a meeting, because the honest answer has caveats, and caveats lose to confidence every time.

Someone on your team has noticed too. There is at least one person in merch, in retail, in ecom, who has been experimenting. They have built something. And they will show you. “Look what I made.” And it will look good. It will look like proof that this is easy now, that the thing you called a quarter of work is really an afternoon.

And your competitors have noticed. They are using AI as well, or at least saying loudly that they are. So now there is a third voice, the one that says if we do not do this we fall behind. The temptation to stay ahead by saying yes to everything is strong, and it is exactly the temptation Say No to Grow was written to warn you about.

Three pressures. The boss, the colleague, the competitor. None of them existed in this form a year ago, and all of them push the same way. Say yes. Do more. AI makes it possible.

Accessible is not the same as good

So here is the thing I most want you to take from this piece.

A low barrier to entry is not the same as a high standard of output. They are two completely different things, and the last year has blurred them on purpose.

Accessible does not mean good. The fact that anyone can now generate a forecast, a report, a product feed, a campaign, does not make any of those things right. The colleague who showed you what they built showed you a demo. A demo is not an operation. The version that looks impressive on a Friday afternoon, on one tidy example, is not the version that holds up across four thousand SKUs, a messy catalogue, a real trade meeting and a peak you cannot afford to get wrong.

AI has made it easy to start things. It has done almost nothing to make it easy to finish them well. And “well” is the entire game. It always was.

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This matters more now, not less

This is where the original argument gets sharper, not weaker.

Because AI is now so accessible, everyone in your business can point it at the operation. That cuts both ways, hard. Point a capable AI system at a focused operation, one with a tight range and product data that is clean and consistent, and you get real leverage. A small team punches well above its size. Point that same system at a sprawling one and you do not get relief. You get the mess, faster, and now produced by more people, because the barrier to producing it has gone.

AI is a multiplier. It does not care what you point it at. Accessibility just means more hands on the multiplier. If your operation is in good shape, that is a genuinely brilliant thing. If it is not, you have handed everyone in the building a faster way to make it worse, and a cover story that they were being productive while they did.

Picture two brands in the same category.

Similar revenue, similar headcount.

The first spent the last two years saying no. They held their range tight, killed the SKUs that never earned their shelf space, ran one marketplace properly instead of three badly, and kept their product data clean because they had less of it to keep clean. Nobody writes case studies about the SKUs you didn’t launch.

The second said yes to everything. Every channel, every territory, every range extension a supplier waved at them. More ambitious on paper. More going on.

Now AI becomes accessible to both of them, on the same day, to every person in both teams.

The first brand gets leverage. The second gets a faster, more democratic way to be confused. Same technology, available to everyone, completely different outcome. And the outcome was decided years before either of them typed a single prompt. It was decided every time one of them said no and the other said yes.

We have written before about product data being the real differentiator for agentic commerce, and that argument has only sharpened. When an AI agent is doing the shopping, your product data is your shop floor, your sales assistant and your window display all at once. Brands with clean, focused data are about to look very good. Brands with sprawl are about to find out exactly how much of it they were carrying, because the AI will surface every contradiction in it, at speed, in front of a customer who is really an algorithm.

Saying no is now quality control

So no, Say No to Grow has not aged out. The reason behind it has changed, and the new reason is the one that matters.

A year ago, saying no protected your team’s time. Today it protects something harder to see. It protects you from mistaking activity for progress. The CEO’s question, the colleague’s demo, the competitor’s noise, none of those is evidence that you should do the thing. They are evidence that the thing is now easy to start. Easy to start has never been the bar. Easy to start is how brands end up with four systems and a trade meeting nobody trusts.

Saying no is now an act of quality control. It is you, the operator, holding the line on the difference between what is possible and what is good, at the exact moment everyone around you has stopped seeing the difference.

The question for this quarter

So here is the question I would put to your leadership team this quarter, the next time someone asks why you are not doing more with AI.

Not “can AI do this.” Almost certainly it can, in some form, badly, on a good day. The better question is whether it will hold up across your whole operation or only in the demo. And the harder one: if we pointed the best AI available straight at our business tomorrow, would it make us faster, or just make our mess move quicker?

If you are not sure of the answer, that is not an AI problem. It is a focus problem. And the fix is the same as it was a year ago. Decide what you are going to be genuinely excellent at, and say no to the rest. No matter how easy the rest has become to start.

Grow by doing less. It was true last year. AI has only made it harder, and more important, to actually do.

This is what OX is for

Everything in this piece, the pressure to say yes, the demo that does not survive a real catalogue, the question of what good actually looks like, is exactly the sort of thing we get into at OX.

OX is the community and event series we built for operators. Finance, merch, commercial, ops. The people who carry the weight of every yes, and who have spent years underserved by an events scene built for everyone but them. It came out of more than sixty conversations with brand-side leaders, and it is deliberately practical. Real operators, real problems, no slide decks pretending it is all simple.

If you are the one being asked “can’t AI just do it”, come and have that conversation with people facing exactly the same thing.

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These are real questions. They’re just not the first ones.

The brands likely to do this well aren’t the ones with the boldest AI roadmaps. They’re the ones whose operating models can actually move when a decision is made. Most can’t.

Brand visibility vs product visibility

A panel at Pulse recently, with Ian from Axel Arigato, Chris from Glara.ai (a GEO platform tracking AI visibility for ecommerce down to the SKU) and Lauren from Pion33rs & Operator Experience (ex-Oh Polly, now in advisory), made the point clearly.

Brand-level visibility scores are mostly useless for ecommerce. It’s nice your brand gets mentioned. It doesn’t move revenue. What moves revenue is your products getting pulled into the carousels and recommendations AI agents generate inside ChatGPT, Gemini and the rest. That’s product-level visibility, and it’s a different problem.

There was a number attached. Enriching the product catalog, the descriptions, attributes, structured facts AI models can actually reason about, gets you 30 to 40 percent of the way there.

The interesting part is the other 60 to 70 percent.

That sits in the operational layer. Whether product data is consistent across every system it lives in. Whether the brand can describe its own products the same way in its PIM, its storefront, its marketplaces, and its 3PLs. Whether the operating model can prioritise this work alongside everything else competing for attention.

Unglamorous questions. Also the ones that determine whether the rest works.

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Where accountability sits

Axel Arigato came up as an example of doing this well. The brand had appointed one person internally as a “shepherd”. A specialist to guide the AI work across the business.

What mattered was not the title. It was where the accountability sat.

The shepherd is not the owner of AI at Axel Arigato. AI is a standing agenda point at the exec committee every two weeks. There are open monthly forums from junior to senior. The shepherd guides. The business owns it.

Most brands have inverted this. They hire or appoint someone senior, and quietly let the rest of the business consider AI to be that person’s problem. It becomes a silo. When the silo conflicts with anything else competing for resource, the silo usually loses.

Axel Arigato had structured it the other way round. The work moves because the whole exec is accountable. The specialist is there to make sure the conversation has the right inputs.

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The misalignment underneath

A simpler version of the same point. The head of ecommerce and the IT director have to share a vision and a priority list.

If they don’t, they end up in conflict over priorities. The whole thing stalls.

The brands that struggle with operational and data transformation aren’t usually struggling because the technology is wrong. They’re struggling because ecommerce wants one thing, IT wants another, finance has a third priority, and the founder is making whichever call is loudest that week. Technology doesn’t fix that. It makes the cost of it more obvious.

A high-functioning AI strategy sits downstream of a high-functioning operating model. If ecom and IT are not aligned now, the agentic conversation will not align them. It will expose that they were never aligned to begin with.

The part that does not get talked about

The agentic commerce conversation, as it currently exists in most rooms, is heavily weighted toward strategy and tooling. Which makes sense. Strategy and tooling are visible. They have vendors attached.

The operating model work has none of that. It is internal. It is slow. It does not have a clear vendor category. It is rarely the most exciting part of any quarterly plan.

It is also the work that determines whether everything else lands.

The brands that get there first are unlikely to be the ones with the loudest AI strategy.

That was the panel title at Pulse, and the brief was straightforward. Get three operators who’ve expanded channels in the room and let them be honest about what broke.

On stage: Lawrence Montgomery (MD, Rough Trade), Tom Clements (CTO, Mint Velvet) and Chris Perrins (Platform Director, Gymshark) and me moderating.

Three businesses with different starting points.

Rough Trade selling records and around 1,500 events a year across three markets, with email, Discord and ticketing on Dice in the mix.

Mint Velvet running 110 retail locations alongside its web business and partners like John Lewis and Next.

Gymshark, born online, now operating a Regent Street flagship, seven owned stores across the UK, Netherlands and US, MENA franchises, and wholesale partners including Selfridges.

Channels look identical. They aren’t.

The temptation to treat a new channel as a slightly different version of the existing one is universal. It’s also wrong.

Tom put it cleanly. Every channel is different. Every requirement for adding it is different. Clicking the add app button in Shopify Marketplace is trivial. What it means for data, inventory, ops and finance is not.

That asymmetry is where most of the unhappiness in multichannel expansion lives. The front end of adding a channel is usually quite easy. The back office consequences are not. Teams underestimate that gap and then spend the next two years closing it.

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Don’t bundle your problems

Lawrence’s biggest lesson from Rough Trade’s Shopify migration was about scope.

Six hundred thousand SKUs. Three regional product catalogues being merged into one global one. A headless front end being rebuilt at the same time. The migration succeeded in headline terms but the product data issues that surfaced ten days after launch were not headline material. They were boring, granular, and degrading customer trust in ways that were hard to see at first and harder to reverse.

There was a second-order point in there too. He’d booked a work trip to New York the week after go-live. The realisation that something was wrong didn’t land for ten days, because in big migrations the problems sprinkle in rather than landing in one neat bundle. That cost him the headroom to react.

Rough Trade’s next big move is an ERP migration. The interesting thing isn’t that they’re doing it. It’s that they’re explicitly slowing it down. Shopify POS first. Other prerequisites second. ERP six months later than they’d like. The discipline is in the sequencing, not the ambition.

Wholesale runs on a different clock

Chris was direct on this. Online forgives missed dates. Wholesale doesn’t.

If a freight delay or a production slip hits a DTC drop, you can usually absorb it quietly. If you’re shipping to a wholesale partner who’s planned their revenue around your stock landing on a date, you can’t. The calendar isn’t yours anymore.

A lot of brand-side teams interrogate the commercial side of a wholesale relationship and never quite get to the production calendar implications. That’s usually the bit that hurts later.

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Retail will eat your DC playbook

The Gymshark Regent Street story deserves its own headline.

They opened with what was effectively a mini DC inside the store. Roughly thirty thousand units, four to five thousand SKUs, bins, locations, the lot. Scannable, sophisticated, traceable. Any unit findable in seconds. A pair of black shorts that looks very similar to another pair of black shorts? Not a problem. Location A12. Done.

Six months later, everything was everywhere.

The system hadn’t failed. The conditions around it had. Retail has high staff turnover. Training has to keep pace. Sophisticated operations need monitoring and reinforcement. None of that happens easily in a flagship store running on retail rhythms.

The instinct to over-engineer is what catches DTC-native teams in stores. They assume retail can run on warehouse logic. It can’t, because the team composition, the pace, and the incentives are all different.

Get finance, ops and merch in the room early

This kept coming up.

New channels look like front-end decisions. They aren’t. They land hardest on the teams who don’t get invited to the early conversations. Tom’s advice was to shift left. Find the problems before launch, not a month after, when finance walks in with a question nobody can answer.

It’s a familiar pattern. Most channel launches we see at Commerce Thinking that go sideways don’t fail in marketing or fit-out. They fail in the back office, because finance, ops and merch were brought in too late to flag the things that were always going to surface. By the time the data inconsistencies show up in a P&L, the trust is gone. By the time ops realises the warehouse can’t handle the wholesale pick profile, the partner is already unhappy.

The fix is unglamorous. Involve those teams at the planning stage, not the go-live stage.

What it actually adds up to

The thread running through all of this. A new channel is a new business inside your business. Treat it like one.

Different calendar. Different operating model. Different data shape. Different people. Different problems.

The brands that scale across channels successfully aren’t the ones with the most ambition. They’re the ones who slow down enough to sequence the work properly, who let the back office in early, and who don’t assume the next channel will behave like the last one.

It almost never does.

That’s an honest description of where our returns and cross-border research landed. We spoke to operators at 24 fast-growing ecommerce brands. We heard consistent patterns, clear frustrations, and genuine insight about what’s working and what isn’t. We also heard the same questions surfacing repeatedly, across different businesses, different team functions, and different levels of operational maturity. Questions that don’t have clean answers yet.

What does good look like for exchange conversion?

Nobody we spoke to felt confident they knew. Most brands are capturing some value through exchanges and store credit. Very few can benchmark their performance against anything meaningful. They know the mechanic works. They don’t know if they’re running it well.

Cross-border margin by market

This one came up constantly and almost always in the same form: finance wants to know, operations can’t tell them, and the data to bridge the gap lives across systems that don’t reconcile cleanly. Brands with 40%, 50%, 60% international revenue are making market-level decisions without market-level visibility.

When to bring it in-house

There’s no universal answer here, and the research doesn’t pretend there is. The right call depends on volume, team capability, market complexity, and a dozen other variables that look different for every business. But the question is being asked more frequently and more seriously than it was two years ago, particularly as platform costs attract more scrutiny.

The diffuse pain problem

How do you build a business case for re-platforming when the pain is diffuse? This might be the hardest one. The operational drag from a returns or cross-border setup that’s no longer fit for purpose is real, but it doesn’t always show up in a single line on a P&L. It shows up in manual workarounds, in margin that leaks before anyone measures it, in finance questions that ops teams can’t answer, in customer service overrides that create inconsistency nobody is tracking. Quantifying diffuse pain well enough to justify a migration is genuinely difficult, and most teams don’t have a good framework for doing it.

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Why the unhappy paths matter

Our consultant Dom Macgowan captured the underlying complexity well: “From a data perspective, returns are inherently messy. You’re relying on the customer doing exactly what they said they would to stay on the happy path. With returns, like many processes, it’s the unhappy paths that take up 80% of the complexity. Customers register what they want to return, but what actually arrives at the DC can be completely different. Processes can quickly fall over and create lots of manual work if these unhappy paths aren’t thought through from a systematic point of view.”

That’s true of returns. It’s equally true of cross-border operations, and of the broader challenge of building operational infrastructure that can handle international complexity at scale.

What this research is, and isn’t

We’re not going to pretend the research closes these questions. What it does is make them visible, give them a shared vocabulary, and surface enough pattern across enough businesses to make them feel less like individual operational failures and more like structural challenges that the industry is working through together.

This report is the first in an ongoing series. Over the coming months we’ll be expanding the research, going deeper on benchmarking, cross-border cost transparency, and integration architecture. The goal isn’t a definitive guide. It’s an ongoing body of work that reflects how mid-market brands are actually operating and surfaces the insights that help operators make better decisions.

If any of this reflects your reality, or raises more questions than it answers, the full report is the right place to start.

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In this series, we’ve been following West Park Rovers, the fictional club from Kieran Maguire’s The Price of Football, as they bring retail in-house.

This is the final piece in this series as we wrap up what it means to sports clubs when the own and understand their fan, ticketing and commerce data.

Catch up on this series:

• Part I - West Park Rovers

• Part II - The Business Case

• Part III - Your Club Shop is a Factory

• Part IV - The 12 Month Apocalypse

• PartV - Moments not Seasons

• Part VI - The Alphabet Business

When sports clubs talk about data, they usually mean performance data.

Player metrics.
Injury risk.
Training load.
Opposition analysis.

Entire teams exist to capture, analyse, and act on these signals. Marginal gains are found in fractions of seconds and percentages of possession. No serious club would outsource this thinking to a third party and accept a summary once a quarter.

And yet, when it comes to fan data, many clubs still do exactly that.

Retail, ticketing, and engagement data are often fragmented, delayed, or partially invisible because the systems that generate them sit outside the club’s control. The result is a strange imbalance. Clubs that are obsessive about understanding what happens on the pitch often have only a partial view of what happens off it.

This is where bringing commerce in-house starts to matter in ways that go far beyond selling products.

Commerce data is behaviour data

Retail data is often framed as financial reporting. Revenue, margin, sell-through. Important, but limited.

In reality, commerce data is some of the richest behavioural data a club has access to.

It shows what fans choose to buy, not just what they say they like. It shows timing, context, repetition, and response to moments. It reveals how engagement translates into action.

A fan who buys a personalised shirt after a big win is behaving differently to a fan who buys a generic item six months later. A supporter who returns every season behaves differently to one who only appears around major events. These are not abstract personas. They are observable patterns.

When commerce is outsourced, much of this data is either delayed, summarised, or never fully shared. The club sees revenue, but not the underlying behaviour.

Owning the commerce stack changes that.

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From transactions to relationships

Once retail is brought in-house, every transaction becomes a data point the club can actually interrogate.

Not just what was bought, but when, why, and in what context.

Over time, this builds something more valuable than a sales report. It builds a fan graph.

A map of how individuals interact with the club across moments, seasons, and life stages. What triggers action. What fades. What compounds.

This is fundamentally different from traditional segmentation. It is not about demographics. It is about behaviour.

And because it is rooted in real transactions, it is far harder to fake.

Why licensing models limit this view

Licensing and third-party retail models are not designed to surface insight. They are designed to deliver efficiency and predictable returns.

Data is usually shared at a high level. Aggregated. Sanitised. Useful for accounting, less useful for understanding.

This creates blind spots.

Clubs know what sold, but not necessarily who bought it, how often they return, or how behaviour changes in response to on-pitch events. They know revenue increased, but not whether that came from deeper engagement or broader reach.

Without ownership of the underlying data, the club is always working with a lagging indicator.

In-house retail turns commerce into a live signal.

What clubs start to see when they look properly

As West Park Rovers begin to analyse their own commerce data, patterns emerge that were previously invisible.

They can see which moments actually drive purchasing, not just traffic. They can distinguish between interest and intent. They can identify supporters who respond to emotional moments versus those who engage consistently regardless of performance.

They notice how different product types correlate with different behaviours. Personalisation patterns. Repeat purchasing. Gaps between visits.

This is not about building a more sophisticated shop. It is about understanding the fan base as a dynamic system rather than a static audience.

Once that understanding exists, it starts to influence decisions well beyond retail.

Commerce as an early warning system

One of the most overlooked benefits of owning commerce data is its ability to act as an early signal.

Retail behaviour often shifts before other metrics do.

Changes in purchasing patterns can reflect sentiment. Fatigue. Excitement. Frustration. Optimism. These signals appear in buying behaviour long before they show up in surveys or social media analysis.

A drop in repeat purchases may indicate disengagement even when match attendance remains stable. A surge in personalisation around certain players may reveal emerging fan favourites faster than media coverage does.

Clubs that pay attention to these signals gain a different kind of awareness. Not predictive in a mystical sense, but grounded in observed behaviour.

Why this matters outside the shop

Once commerce data is treated as behavioural insight, its relevance expands.

Marketing becomes more precise, because messaging can align with demonstrated preferences rather than assumed interests.

Sponsorship conversations become more grounded, because the club can articulate not just reach, but depth of engagement and purchasing behaviour around moments and narratives.

Even broader strategic decisions benefit. Understanding which stories resonate, which identities fans buy into, and how engagement evolves over time helps leadership think more clearly about brand, positioning, and long-term growth.

This is where retail stops being a side business and starts becoming an intelligence function.

The danger of partial visibility

It is important to be clear about one thing. Partial data can be worse than no data at all.

Clubs that only see finished-goods sales, or only look at revenue, often draw the wrong conclusions. They optimise for what is easy to measure and miss the constraints and behaviours underneath.

This is why earlier parts of this series focused on components, moments, and expiry. Without that context, commerce data tells an incomplete story.

Owning the fan graph requires owning the underlying systems. Inventory logic. Personalisation rules. Availability constraints. Without those, insights are distorted.

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From hindsight to advantage

Most clubs are good at explaining what happened.

They are less good at understanding why it happened, or what it implies for the future.

Commerce data, when owned and interpreted properly, starts to bridge that gap. It does not replace intuition or experience, but it grounds them in evidence.

The advantage does not come from dashboards alone. It comes from asking better questions.

Why did this moment convert and another didn’t?
Why do some supporters deepen their relationship while others plateau?
Why does engagement spike around certain narratives and not others?

These are strategic questions, not retail ones.

Why this is hard to outsource

Just as clubs would never outsource tactical analysis and accept a summary report, they cannot outsource behavioural understanding and expect to retain advantage.

Third parties can process transactions. They can ship orders. They can even provide reports.

What they cannot do is embed insight into the fabric of the organisation.

That only happens when data flows freely between systems and teams, and when commerce is seen as part of the club’s operating model rather than a revenue line.

The long-term payoff

The clubs that commit to this way of thinking do not see immediate transformation. The first year is still operationally heavy. Systems need bedding in. Data needs cleaning. Habits need changing.

But over time, something compounds.

The club becomes more responsive without being reactive. Decisions feel less speculative. The gap between what fans feel and how the club acts starts to narrow.

Retail is no longer something that happens alongside the club. It becomes something the club learns from.

The short version

Bringing retail in-house is not just about margin, control, or speed. It is about ownership of insight.

Commerce data is one of the clearest signals of fan behaviour a club has. When that data is fragmented or filtered, opportunity is lost quietly. When it is owned, connected, and interpreted, it becomes an advantage.

Clubs already invest heavily in understanding performance on the pitch. Owning the fan graph is the equivalent investment off it.

That is what turns commerce from a transactional function into a strategic one.

Not creative. Not customer service. Not some glamorous corner of the AI conversation. Trade reporting. The most thankless, finicky, week-eating job in commerce. The thing nobody puts on the OKR doc but everyone does on a Sunday night.

That number is the reason Dema is our headline sponsor.

Trade reporting is where AI actually lands

You can tell a lot about an industry by what it pretends to care about versus what people actually do all day.

Operators publicly care about omnichannel strategy, brand world, customer obsession, the future of retail media. Privately they spend three hours every Monday morning stitching together a sales pack from six different tools, fighting over whether “revenue” means gross or net, and arguing about which definition of margin is the right one.

The trade meeting is the cathedral. The prep is the panic.

Henrik Hoffman, Dema’s co-founder, put it well in a post a few weeks ago. The problem was never the meeting. It was the prep that destroyed the meeting. Different reports. Different logic. Different definitions. Different levels of analysis. Five people walk in with five versions of the truth, and the first forty minutes get burned reconciling them before anyone makes a decision.

This is the part of the job AI is genuinely good at. Not because it’s clever. Because it’s tireless and consistent. The same query, the same definitions, every Monday, forever. No “I think Sarah pulled it differently last week.” No fighting over whether the returns are in or out.

Dema’s whole thing is plugging into the connected commercial picture, the bit that’s actually a mess, and turning it into something a team can argue about productively rather than argue about pointlessly. Marketing, inventory, products, margins, customers, all in the same place. Same definitions. Same logic. So the meeting is about what to do, not what happened.

If your weekly trade meeting starts with someone saying “wait, why is your number different to mine,” this is the category we’re talking about.

The AI conversation people aren’t having

There’s a version of the AI conversation in commerce that’s all vibes. Will it replace creative? Will the agents take our jobs? Should we let it write our product descriptions?

Then there’s the conversation operators are actually having, which is much less sexy and much more useful. Where in my week is the highest-leverage thing AI can take off my plate. The answer is almost always something boring. Reporting. Pulling. Reconciling. Pasting into a deck.

Henrik’s been beating this drum for a while. His take is essentially that the brands getting in late aren’t behind on the headline AI stuff. They’re behind on the unglamorous foundation. Clean, connected, AI-ready data. Without that, AI just helps you make bad decisions faster.

We agree. And it’s why we wanted a headline sponsor whose product points squarely at the work, not at the hype.

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Brands you’ll already know

Dema isn’t a name being parachuted in for the event. They already work with a chunk of brands you’d recognise from the OX cohort and the wider community. Tatti Lashes. Represent. The Couture Club. The kind of operators who don’t have time for theory and need the report to land before the meeting starts.

That’s the bit we cared about. We didn’t want a sponsor whose pitch was a slide. We wanted one with customers in the room.

What this means for OX

Headline sponsorship for us isn’t a logo placement. It’s a content commitment.

Dema will be running sessions and conversations through the year that go after the trade reporting problem properly. What does the weekly trade meeting look like when prep is fifteen minutes instead of three hours. What changes when everyone walks in with the same numbers. Where AI helps and where it absolutely doesn’t, like brand, strategy, the human moments, the bits that don’t survive automation and shouldn’t.

We picked them because they’re operators talking to operators. Henrik scaled Babyshop before he built Dema. He talks like someone who’s done the job, because he has. There’s a noticeable absence of the AI-flavoured corporate stage voice that’s started to creep into the category, which we appreciated.

If 52% of you want to automate trade reporting

Then 52% of you should probably come and meet the people building exactly that.

OX is built around the idea that the operator job is harder than it looks, and most of the conversation around it is louder than it is useful. Dema fits that brief. They’re shipping product against the actual problem, not the conference-stage version of it.

We’re glad they’re headlining.

Want to chat to the Dema team at OX? They’ll be everywhere. You won’t miss them.

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