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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That sounds brilliant. And in the right hands, for the right tasks, it is.

The problem is most businesses won’t use it in the right hands, for the right tasks. They’ll do what businesses always do with a shiny new capability: hand it to everyone and call it a transformation.

So before that happens, it’s worth understanding exactly how this goes wrong, because the failure mode isn’t subtle, and it’s one most finance and ops teams have already lived through once before.

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You’ve already solved this problem. Don’t un-solve it.

Before proper ERP systems, most businesses ran on spreadsheets. Not one spreadsheet, dozens of them. The warehouse had their version of stock levels. Finance had theirs. The trading team had another. Nobody’s numbers matched, nobody agreed on whose version was right, and half the working week went on reconciling data that should never have diverged in the first place.

The whole point of an ERP, NetSuite included, was to fix that. One system, one version of the truth, everyone working from the same data.

The MCP, used badly, is a direct step backwards.

If everyone in your business starts pulling reports through a prompt, each prompt becomes its own spreadsheet. Different filters, different assumptions, different outputs. The FD is looking at one number, the ops director is looking at another, and neither of them knows why they don’t match. You’ve rebuilt the exact problem NetSuite was supposed to solve. You’ve just replaced Excel with a chat interface.

If you’re still running your business from actual spreadsheets and considering NetSuite, understand that this is the problem you’re trying to solve. Adding an AI layer on top of fragmented data doesn’t fix fragmented data. It just makes the fragmentation faster.

What the MCP is actually good for

None of this means the MCP isn’t useful. It is, in specific, bounded situations.

The sweet spot is one-off queries. A question you need answered today that you don’t have a saved search for. A quick check on something specific. Surfacing an anomaly you weren’t expecting. The kind of thing where you’d otherwise wait two days for a developer to build a report you’ll only ever need once.

That’s the job. It’s a fast-lookup tool, not a reporting strategy.

The moment it becomes your default way of getting data out of NetSuite, you’ve got a problem.

The script-writing use case is the underrated one

Less talked about, more genuinely valuable: using Claude to help write SuiteScripts, Restlets, and other NetSuite scripts.

This is useful whether you’re a developer who wants to move faster, or a functional person who understands what they need but can’t build it themselves. The iteration loop is quicker, and you can get to a working draft faster than going through a traditional development cycle.

The rules here are non-negotiable though. Keep scripts narrow, one script, one specific thing. Updating a field from a CSV. Syncing a value between two records. Don’t try to build something complex and push it straight out. Always build and test in sandbox first, because NetSuite has no undo for deleted records, and AI-written code is not production-ready by default. It needs reviewing by someone who knows what they’re looking at before it goes anywhere near live data.

The data question nobody’s answering properly

There’s a concern sitting underneath all of this that isn’t being addressed well by anyone in the NetSuite ecosystem right now.

When you use the MCP, your data leaves NetSuite and goes into Claude. What happens to it at that point? Who’s responsible for it? How is it handled?

A NetSuite rep was recently asked at SuiteConnect exactly this in front of a room of potential users. The answer wasn’t good enough. “Once it’s out of NetSuite we have zero control or responsibility of that data” (paraphrased).

That lack of clarity is making brands nervous, and it should, particularly for businesses in competitive categories where their operational data is genuinely valuable. In reality, that’s a question for another article or one to ask our OX Community.

The subtler risk is less obvious but worth thinking about: AI models learn from context. If you’re a consultant working across multiple brands in the same space, there’s a real question about whether the AI keeps client data properly separated, or whether patterns from one client’s operation start bleeding into thinking about another’s. Not maliciously. Not obviously. But the risk is there.

This isn’t a reason to avoid the MCP. It’s a reason to be deliberate about what you use it for and what data it touches.

The thing most people miss about AI and ERPs

There’s a distinction that gets lost in almost every conversation about AI in business systems, and it matters here.

There’s a difference between using AI to build automation and making AI the automation. The first is smart. You use Claude to write a script, test it, refine it, and then deploy something that runs reliably without needing AI involved every time. The second is expensive, inconsistent, and fragile, because you’re paying for a prompt every single time the process runs, and you’re dependent on the AI producing the same answer each time it does.

The goal should always be to use as little AI as possible in the final running solution. Use it heavily in the build. Use it lightly, or not at all, in the operation.

The MCP is a build tool and a one-off tool. The moment you start treating it as infrastructure, you’ve misunderstood what it’s for.

So what should you actually do?

Use the MCP for ad hoc queries where speed matters and repeatability doesn’t. Use Claude to help write and iterate on scripts. Keep anything that needs to be consistent, scheduled, or shared sitting on a proper process, saved searches, dashboards, reports that everyone pulls from the same place.

And if you’re still on spreadsheets: an AI that can query your data faster isn’t the answer to your data problem. Getting the data into one place first is.

The ERP exists to give everyone the same version of the truth. Don’t let a well-marketed AI feature talk you into giving everyone their own version again.

Three things came up over and over. They’re the reason Operator Experience exists.

Problem 1: Nobody knows where to go for AI help

This came up in almost every conversation. Not “what tool should I use?”, but the bigger question. How should I be thinking about this? What should we be doing as an organisation? Who do I trust to give me a straight answer?

The advice landscape is a mess. LinkedIn grifters, vendor pitches, self-appointed AI influencers, half of whom have shipped nothing inside an actual brand. For operators trying to do their jobs while also working out where AI fits in the next 12 months, that’s not useful. It’s noise.

What people kept asking for was time with peers in similar brands, fulfilling similar roles who were genuinely trying things, prepared to share what worked and what didn’t, and not flogging something at the end of it.

Problem 2: Operators are underserved in the event space

This is the one I think the industry has known for a long time and not done much about.

The established communities and events that are hosted are mostly pointed at the sexier stuff. Digital, paid media, marketing. There’s no shortage of well-lit rooms for performance marketers, and no shortage of glossy stages for creatives to talk about brand.

But finance, merchandising, commercial, ops, CS, the people running the actual business, don’t have the same spaces. When I asked operators where they go for community, the honest answer was usually a shrug. A WhatsApp group here and there. A Slack they were lurking in but not contributing to. Nothing really built for them.

These are the people making the decisions that determine whether a brand survives the next two years. They deserve a community that takes them seriously.

Problem 3: Events are slide decks and sales pitches, not practical action

Even where operator-relevant content does exist, the format usually doesn’t help anyone.

The standard ecom event is a stage, a panel, a few keynotes, a Q&A that runs out of time. People nod, take photos of slides, network at the bar, go home. There’s nothing wrong with that format in moderation, but it’s passive. They’ll tend to focus on inspiration and what people are up to, but very rarely focuses on how to actually impact your brand operations or help you solve the thing you’re actually wrestling with on Monday morning.

Operators don’t need more keynotes. They need rooms where they can compare notes with peers who are dealing with the same problems, with enough structure to make the conversation useful and enough space to make it honest.

The solution: Operator Experience

OX (Operator Experience) is a community and event series built specifically for the people running brands. Not the people marketing them.

The format is intentional. Invite-only, peer-led, practical. We’re not trying to build the biggest ecom event in Europe. We’re trying to build the most useful one for operators.

There are two parts to it.

1 - The events. OX1 was the first. Smaller groups, fewer slides, more conversation. Practical sessions on AI integration, organisational design, the operational work most brands are figuring out without much help. The mix in the room matters: founders, ops leads, finance, merch, supply chain, CS, all in the same conversation, because the problems they’re solving don’t sit neatly inside any one function.

2 - The Slack community. Between events, the conversation continues. Members share what they’re working on, ask each other for input, post things they wouldn’t say publicly on LinkedIn. It’s the bit that turns a single event into a network that compounds.

Why this matters now

In my latest article, I spelled out how the next five years will be defined by which brands can adapt their operating models to the AI era. The ones that can’t restructure around process teams, that can’t move people from doing the work to directing it, will struggle. The ones that can will scale further with leaner teams will win.

That transition is happening inside the operator function. It’s the merch teams figuring out how to integrate forecasting agents. The finance teams rebuilding planning workflows. The CS teams deciding which decisions to keep human and which to hand off. None of this is being well-served by an event circuit still mostly pointed at how to spend a Q4 marketing budget.

Operators need their own space. Not because marketers don’t deserve theirs, but because the questions are different and the answers don’t transfer.

Credit where it’s due

I should be honest about who’s actually built this. The concept is one thing. Turning it into a real community with members, a Slack that’s active and an event that actually happens, that’s Lauren. She’s driven things, brought in the brands and the partners, and shaped the format into what it is. Without her, this would still be an idea being bounced around (likely, on repeat).

What’s next

OX1 was a starting point. OX2 in London is next. Date, venue and focus is all being announced very soon. So stay tuned.

The plan is four events over 12 months. We want to achieve a regular event cadence, host in cities with high concentrations of brands, a growing Slack community, and a format that stays small enough to be useful.

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If you’re running a brand and you’ve ever sat through a marketing conference wondering why nobody’s talking about the things that actually make the business run, OX is for you.

We figured if we didn’t do it, no one would.

This is what I shared.

Eras Change. Always Have.

The retail and fashion industry has been here before, twice in recent memory.

Between 2005 and 2015, the shift was from on-premise infrastructure to cloud. Between 2015 and 2025, it was from single-tenant systems to SaaS. Both transitions created a new cohort of breakout brands, and ended the relevance of those who didn’t adapt. The brands on the wrong side of both those eras didn’t disappear because they made bad products. They disappeared because their operating models couldn’t move.

We’re now entering the third era: 2025 to 2030. The question isn’t whether AI will reshape how brands operate. It’s which brands will be on the right side of it.

My position is direct: AI will allow brands to scale further, faster, and with leaner teams than most have previously thought possible. The biggest shift won’t be technological. It’ll be organisational. And established brands are the most exposed, precisely because they have the most established org models to unpick.

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The Lesson Isn’t What You Think

To make the point concrete, I drew on two brands I worked with simultaneously, starting in 2016.

The first was Ivy Park within the Arcadia Group; huge, hierarchical, and fundamentally risk averse. The dominant mentality: too big to fail, cynical about what smaller brands were doing, prone to groupthink. By 2020, Arcadia was bust.

The second was Gymshark; fast-growing, nimble, wired to learn from failure. The mentality there was different: hire good people, trust them, and move quickly enough to extract lessons from mistakes. By 2020, Gymshark was a unicorn.

Same era. Same market conditions. Opposite outcomes. The lesson isn’t that size is a liability, it’s that mindset and organisational adaptability are the real variables. Which makes the current moment more urgent for any brand that has grown into complexity.

The Organisational Challenge: From Silos to Process Teams

The structural problem most brands are running into is one they built themselves. Traditional vertical departments; each team fixing its own function, each head of department accountable to their own metrics, made sense in an era where specialisation was a competitive advantage. In an AI-fluent organisation, that same structure becomes a liability.

Siloed functions preserve handoff inefficiencies, create metric misalignments, and generate duplication that compounds over time. When agentic workflows enter that structure, they don’t resolve it, they expose it faster.

The alternative is process teams: squads organised around end-to-end outcomes rather than functions. New product launch. Margin optimisation. Stock-turn. Product quality. Teams built this way surface root causes rather than symptoms, align on shared metrics, and learn iteratively. In a hybrid model, humans and agents working together, human resource shifts from doing the work to directing, analysing, and improving it. That’s a fundamentally different job description, and most org charts aren’t designed for it.

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The Individual Challenge: Which Profile Are You?

The organisational problem has an individual equivalent. The framework I use maps people on two axes: AI clarity (how well someone understands where AI fits and what it can actually do) and enablement ability (how effectively they bring others along). Four profiles emerge.

The Enthusiast is high on enablement, fuzzy on AI, great with people, working from instinct rather than understanding.

The Passenger is low on both, waiting for someone else to lead.

The Lone Wolf has genuine AI capability but operates in isolation, building things that don’t scale because nobody else can run with them.

And then there’s the Catalyst: high clarity, high enablement. Sees where AI fits, builds something useful, and brings the rest of the team with them.

Every brand will have a mix of all four right now. That’s expected. What matters is direction of travel.

My prediction for where most brands get this wrong: hiring for build capability while ignoring enablement. A lone wolf who can’t bring anyone with them isn’t a multiplier, they’re a bottleneck. The Catalyst is the profile every brand will want more of. And importantly, it’s a learnable combination of skills, not a fixed personality type.

What This Means in Practice

Five implications worth naming plainly:

1. Technology drives era shifts, creating breakout brands while making incumbents that don’t adapt irrelevant.

2. Resistance to change isn’t caution; it’s risk, and inertia massively increases the probability of becoming non-competitive.

3. The brands that adopt early and well will scale faster with higher margins.

4. Vertical silos won’t survive the transition, cross-functional process teams are how brands adapt at pace.

5. And Catalysts will be the scarce resource every brand is competing for.

AI is both the biggest threat and the biggest opportunity facing established businesses today. The organisations that become adaptive, and the individuals who push themselves toward becoming Catalysts, are the ones history suggests will win.

History has a funny way of repeating itself.

On paper, a PLM is a workflow tool. It’s where tech packs get built, where sampling gets tracked, where approvals get chased, where the product moves from concept through to production with everyone involved able to see where it is and what it needs. That’s what it was designed to do, and that’s how most vendors still sell it.

Talk to the ops leaders actually running PLM implementations right now, though, and a different picture starts to emerge. The workflows are still happening. But they’re not necessarily happening inside the PLM.

What is happening inside the PLM is something more foundational. It’s becoming the product data master. The single source of truth for every attribute, every measurement, every material, every supplier reference, every sustainability data point, every piece of structured information that needs to exist on a product. And from there, it’s feeding everything else.

Shopify pulls from it. NetSuite pulls from it. Production trackers, sampling systems, allocation tools, reporting dashboards. All downstream. All dependent on the PLM having clean, structured, accurate data at the centre.

“We don’t really do the workflows in it,” one ops lead told us recently. “But if the PLM is wrong, everything else is wrong.”

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That’s the shift worth paying attention to.

For a long time, the case for investing in a PLM was built around efficiency. Faster sampling cycles, fewer email chains, better visibility across the product calendar. All real, all valuable, but all essentially productivity arguments.

The newer case is structural. A brand that can’t describe its own products in structured data can’t do any of the things that now matter most. It can’t populate a digital product passport. It can’t feed an AI model that’s trying to help with demand planning. It can’t integrate cleanly with a new channel partner. It can’t answer sustainability questions from a regulator or a retailer. It can’t scale without the data layer underneath it becoming the bottleneck.

The brands that are getting this right are the ones who’ve stopped thinking about their PLM as a project management tool and started thinking about it as infrastructure.

The distinction matters because it changes what you’re optimising for. If the PLM is a workflow tool, you care most about user experience, approval flows, notification logic. If it’s a data master, you care most about the structure of the data model, the quality of the integrations, the discipline around tagging, the governance of who can change what. Different priorities. Different implementation. Different definition of success.

One head of IT at a contemporary fashion brand put it simply when we spoke: “The PLM is where truth lives. Everything else is a view.”

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That framing is useful. It means the PLM doesn’t have to win the UX battle with a designer’s preferred sampling spreadsheet, or a production manager’s WhatsApp group, or a sustainability lead’s bespoke tracker. Those tools can all keep existing. What the PLM has to do is be the definitive, structured, trustworthy version of what the product actually is. Everything else orbits around that.

It’s a quieter role than the one the category was originally sold on. But it’s arguably a more important one.

For brands thinking about implementing a PLM, or re-implementing one that stalled, the question to start with isn’t “how do we move our workflows into this system.” It’s “what data needs to live here, and what needs to happen to make sure it stays accurate.” Those are very different projects. The first is about change management. The second is about architecture.

And the second is what the operators actually getting value from their PLMs are really doing.

Wednesday. Hallé St Peters in Manchester. 50+ brand-side leaders in one room. CTOs, Heads of Ecom, COOs, Digital Directors, CFOs, Merchandisers, Ops Leads, IT managers. People who have actual jobs at actual brands and actually had to take a day out of their week to be there.

We didn’t sell tickets to vendors. We didn’t have a sponsor parade. We had brands.

And it worked.

Chris Perrins on AI and safety

The session that removed (perceived) barriers to getting started with AI and building the confidence through your org to make the impact last. Chris Perrins from Gymshark on AI and safety. The kind of talk you’d expect to be either preachy or a bit over-whelming. It was neither.

Honest. Practical. Grounded in what’s actually happening inside of brands at scale. Where the risk really sits. What the team are actually doing about it. Where the speed-versus-safety conversations land. The bits that look easy but aren’t, and the bits that look hard but are.

Half the room had their notebooks out. The other half had their phones out. Nobody was on email.

That’s the bar for a keynote. He cleared it.

6 Roundtables, 6 honest conversations

After the keynote, the room split into six tables. Here’s what landed at each.

Ecom and merch. The Inverted Trade Pack. AI surfaces the trade headlines Monday first thing. The team spends the day doing the actual work and building the actions. The Monday meeting stops being a recap and starts being a decision. The line that stuck: “Get AI to mark the homework before we walk in the room.” Not write the trade report. Check it.

Tech. Internal knowledge bases. Documentation that lives somewhere queryable, without a human in the loop of every interaction. Updates that share themselves. The easiest AI project most businesses have not built. Funniest line of the day came out of this table: “Copilot is what your nan uses. ChatGPT is what your teenagers use. Claude is what the workers use.”

Ops. Cross-functional landed cost and approval intelligence. The tool that turns a dozen Slack threads into one answer.

Commercial. AI-generated product mockups for customisations, driven off user input. Visualisation at the point of want, not six weeks later.

Ecom and digital product. Product creation and launch readiness. The bit nobody posts about. Everybody needs it.

Data integrity, governance and workflow. The table where someone said out loud, “AI is a dirty word in our office,” and the rest of the table immediately recognised the pattern.

Each table did the same exercise. Spot the opportunity. Make it viable. Handle the objections. Action plan. Share back. By the end of the day, every table had something concrete to take home.

The bit that surprised us

How honest people were.

“We’re a bit of a shitshow on this.” “We’re racing without thinking about the foundation.” “I don’t know if anyone in this room has it figured out.”

You don’t get that at a normal industry event. You get it at OX1 because the room was curated, the format was right, and the people who turned up trusted each other enough to say it out loud.

That trust is the thing we’re building.

The next one

OX2 is being planned. This time, London.

If you want to be in it: https://operatorexperience.ai/

Thank you, Manchester.

Join OX2: LDN

Are we profitable internationally, by market? What are the real return reasons by country? What’s the duties and tax impact on our margin? How much revenue are we actually retaining through exchanges?

The data to answer these questions exists. It’s sitting somewhere in your stack right now. The problem is that it’s sitting in four different systems that don’t talk to each other, reconciled by nobody, and interpreted differently depending on which team you ask.

That’s the analytics gap. And it’s one of the more quietly damaging findings in our latest research.

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It comes up consistently across both returns and cross-border operations, and it matters because it’s not just a reporting inconvenience. It shapes decisions, or more accurately, it prevents them. When you can’t see international profitability by market, you can’t make intelligent decisions about where to invest, where to pull back, or where margin is leaking fastest. When you can’t reconcile exchange revenue cleanly, you can’t build a business case for improving the exchange flow. When duties and tax treatment lives outside the core systems, finance teams are always a step behind.

Our consultant Amy Washington put the buying and merchandising perspective clearly: “What you need as a buyer or merch is the qualitative feedback. To know your returns rate is high, that’s a KPI, that’s a marker of performance, but what you really need to know is why. What a buying and merch team would be looking for from a returns platform is: can we get the analytics out of what is being returned and why? That’s what’s important.”

The returns rate number is available. The why is buried in a system that doesn’t surface it cleanly, if it surfaces it at all.

The same pattern shows up on the cross-border side. Brands are running international operations with meaningful revenue in multiple markets, but their view of margin by market is either non-existent or assembled manually from sources that may or may not be consistent with each other. They know they’re profitable overall. They can’t tell you whether Germany is worth it or whether the US operation is subsidising returns costs that nobody has fully accounted for.

What operators want, when you ask them directly, isn’t more dashboards. It’s one trusted operational view. A single place where the numbers reconcile, where finance and operations are looking at the same data, and where decisions can be made with confidence rather than approximation.

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That’s harder to build than it sounds. It requires integration work that most implementations don’t prioritise, reporting architecture that cuts across vendor boundaries, and a level of operational discipline that’s difficult to maintain when teams are already stretched. But it’s also the gap that matters most, because almost everything else in the operation, routing decisions, exchange strategy, market prioritisation, platform evaluation, depends on having reliable numbers underneath it.

The brands that are investing in this now, building the connective tissue between their systems and establishing a single source of truth for operational data, are building a genuine competitive advantage. Not because the technology is sophisticated, but because most of their competitors are still working from four spreadsheets and a Slack thread.

Our full research report covers the analytics gap in detail as part of a broader look at where returns and cross-border operations are failing, and where the biggest opportunities for improvement sit.

Read the full report

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.

So far, we’ve covered the business case for making the move, the operational reality of running what is effectively a small factory, and in the last piece, The 12-Month Apocalypse, the brutal economics of a catalogue that expires every year.

That piece was about pressure with a fixed horizon. Time you can see running out.

This one is about the opposite.

Moments that arrive without warning. A goal. A farewell. A signing that shifts the mood of a whole city overnight. Demand that exists right now and will be gone by Thursday.

Seasonal retail has a calendar. Sports retail has a pulse.

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

When clubs review retail performance, the conversation almost always starts with finished products. Shirts, training wear, special editions, limited runs. Which items sold through, which sizes caused problems, which launches exceeded expectations.

That focus is understandable. Finished goods are visible. They are what fans recognise and what revenue reports are built around. They are easy to explain to boards and easy to benchmark season to season.

But when clubs bring retail and e-commerce in-house and begin to operate it as a real business rather than a licensed afterthought, something changes. The further you move into the operational detail, the clearer it becomes that the most important products in the system are not always the ones fans see.

They are the components that sit underneath personalisation. Letters, numbers, badges, and patches. The alphabet.

These items rarely feature in planning decks. They are cheap, unglamorous, and operationally inconvenient. Yet they quietly determine whether large parts of the retail operation function at all.

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The product fans buy is not the product you manage

From a supporter’s point of view, a personalised shirt is a single product. You choose a size, add a name and number, and check out. The complexity is invisinble.

From the club’s point of view, that same order is not a product. It is an assembly process.

A base garment must be available in the right size. The correct letters must exist in the correct font and size. Numbers must match both the shirt size and competition rules. Patches may be required or optional depending on context. Placement rules must be respected, and not every combination is valid.

Every personalised order therefore consumes multiple SKUs at once. If even one of those components is missing, the product is no longer fulfilable in the way the fan expects.

What makes this difficult is that the system often does not fail immediately. The shirt still appears available. The checkout still accepts the order. The problem emerges later, when fulfilment is delayed, substitutions are required, or customer service has to intervene.

These failures are subtle, which is why they persist.

Why personalisation only works if components are treated as critical inventory

Personalisation is often positioned as a feature. Something that can be added on once the basics are in place.

In practice, it is closer to a manufacturing capability.

It increases average order value. It strengthens emotional attachment. It turns moments on the pitch into commercial outcomes. But it also introduces a dependency on component-level inventory that many clubs are not set up to manage properly.

When letters, numbers, and badges are treated as background supplies rather than first-class inventory, the system becomes fragile. Stock may exist, but not in the combinations required to meet demand. Availability becomes theoretical rather than real.

This is not a question of better picking or more careful packing. It is a question of whether the business actually understands what it is capable of selling at any given moment.

Clubs that scale personalisation successfully are not the ones that offer the most options. They are the ones that model availability accurately and constrain it intelligently.

The smallest stockouts cause the most damage

Running out of a popular shirt size is obvious. It is visible on the site, visible in reports, and usually prompts action.

Running out of a specific letter, number, or patch is different. The site often still looks healthy. Orders continue to place. The issue only becomes visible once fulfilment begins.

At that point, the cost is already incurred. Delays lead to service work. Service work leads to refunds or goodwill gestures. Goodwill gestures erode margin. Repeated experiences erode trust.

The irony is that these components are usually inexpensive. They are easy to replenish in theory. Yet because they are shared across many potential products, a single stockout can block an entire class of sales.

This is why letters and numbers matter far more than their unit cost suggests. They are not revenue drivers in isolation. They are revenue enablers. When they fail, revenue does not disappear dramatically. It leaks quietly.

Why this is not a warehouse problem

It is tempting to frame these issues as operational execution problems. Counting errors, picking mistakes, or warehouse discipline.

That framing misses the point.

The root issue is almost always upstream, in how availability is modelled and understood. If systems only track finished goods, they provide an incomplete picture of reality. They tell you how many shirts exist, not how many sellable configurations are possible.

Those two numbers are rarely the same.

Clubs that operate retail in-house for long enough begin to plan from components upwards. They understand that the true constraint in the system is not demand or even garment supply, but the weakest link in the component chain.

This shift is uncomfortable because it forces the organisation to confront complexity rather than abstract it away. But it is also where operational resilience comes from.

How the alphabet becomes a strategic bottleneck

The importance of components becomes even clearer when demand concentrates around specific moments.

Sport does not create evenly distributed demand. It creates spikes. One player, one performance, one narrative can dominate attention overnight.

When that happens, demand does not rise across the entire catalogue. It focuses sharply on a particular name and number combination. If the components required for that combination are unavailable, the moment is only partially captured.

Nothing crashes. There is no obvious failure. The club may even feel that it handled the moment well. But in reality, part of the opportunity was lost without being measured.

This is why many clubs underestimate how much revenue they fail to capture during peak emotional moments. The limitation is rarely visibility or traffic. It is fulfilability.

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Why this rarely reaches board-level discussion

Most retail reporting is designed to be legible rather than complete. Revenue by category, margin by range, sell-through by product. These metrics are useful, but they flatten reality.

They do not show how often demand was blocked by missing components. They do not surface how many orders were delayed or modified due to stock constraints at the component level. They do not capture the cost of quiet failure.

Without that data, leadership assumes the system performed as intended. From a distance, it looks like it did.

In practice, the system may have been operating below its true potential for months.

Treating the alphabet as infrastructure

Clubs that move beyond this stage stop thinking about letters, numbers, and badges as accessories. They start treating them as infrastructure.

They track them with the same seriousness as finished goods. They plan replenishment deliberately. They model availability at the configuration level. They design storefront logic to reflect what can actually be fulfilled.

Once that happens, personalisation stops being a source of operational risk and becomes a durable advantage. Moments are captured more reliably. Customer trust improves. Margins stabilise rather than erode quietly.

This is not about adding complexity for its own sake. It is about acknowledging where complexity already exists and managing it properly.

The short version

In sports retail, growth is not constrained by what you sell. It is constrained by what enables the sale.

Letters, numbers, and badges rarely get attention because they are not exciting. But they define the limits of the system more than any shirt ever will.

Clubs that recognise this build personalisation that scales. Clubs that don’t continue to lose revenue in ways that never quite show up on a spreadsheet.

In the final part of this series, we’ll look at what happens when this level of operational understanding is fed back into the wider organisation, and how retail data starts to influence decisions far beyond the shop.

Average AI confidence score across the group: 5.3 out of 10.

60% have no formal AI policy in place, including at businesses where AI use is actively encouraged. The most common blocker isn’t cost or capability or someone at the top saying no. It’s just not knowing where to start.

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The things people actually want to use AI for aren’t ambitious. Automating trade meeting notes. Cleaner forecasting. Product descriptions that don’t take half a day. Weekly reports that don’t eat a morning. Operational problems that have existed for years and now look like they might finally be solvable. Nobody is trying to reinvent anything. They just want Monday back.

The other thing that came through clearly: everyone is experimenting, and nobody knows what anyone else is doing. Someone in ecommerce has built something quietly. Someone in ops has a workflow running in the background. One respondent described their current situation as “fragmented and manual, with valuable knowledge spread across teams.” The shared learning that would make all of it faster and less risky isn’t happening because there’s nowhere for it to happen.

That’s what OX1 is for. But we should be straight about what it is and isn’t.

Everyone involved in building this aren’t standing at the front of the room with the answers. Nobody has AI solved right now. The technology is moving too fast for that to be true of anyone, regardless of what their LinkedIn bio says. What the people in the room have is practical experience, honest questions, and no commercial reason to pretend otherwise.

The question sitting underneath a lot of the survey responses was this: “is this replacing me or my team?” That question doesn’t get asked out loud very often. It should. It’s the room OX1 is trying to create.

If your confidence is sitting around a five, you’re in good company. If you’ve got something running in the background that nobody else in the business knows about, same. If you’re not sure whether your business is ahead or behind, you’re probably right not to be sure, because the reference points don’t really exist yet.

That’s not a reason to wait. It’s a reason to be in the room.

16th April. Hallé St Peters, Manchester. Brand-side operators only.

operatorexperience.ai

RSVP for OX1: Manchester

So many conferences, roundtables, and events you've been invited to this year was built around agencies, platforms, or vendors. The brand-side operator - the person running ops, owning the tech stack, holding the P&L together - is either an afterthought.

OX1 Manchester on 16th April is being built for a different kind of room.

What it actually is

Operator Experience is an invite-only community where brand-side leaders learn and build AI-era capabilities together. No agencies pitching. No platforms grandstanding. A closed network of people in the same seat, dealing with the same pressures, trying to figure out the same things.

OX1 is the first event. It runs from 12:30 at Hallé St Peters in Manchester, and the afternoon is structured around one practical challenge: what if your brand committed to automating 30% of all operational tasks by 2030?

Working in small squads, attendees will map end-to-end processes, identify automation opportunities, and leave with a concrete initiative - not a slide deck, not a framework to file away. Something you can actually take back on Monday.

The speakers worth knowing about

Chris Perrins is one of the confirmed speakers for the AI Fundamentals session. His take is worth paying attention to. The argument isn't that AI is coming - it's that it's already there, whether your business has formalised it or not. Data loss tools at Gymshark showed AI being used across the business long before any policy existed to govern it.

His framework is pragmatic rather than theoretical. Prioritise confidence over governance. Separate experimentation from production (vibe coding is not production-ready, and the "next Excel problem" - important tools nobody properly owns - is a real risk). And create a structure where ideas can spread safely, through champions in each function rather than controllers at the top.

The goal, as he puts it, is confident and responsible use, not perfect governance. That's a more honest framing than most AI content you'll read this year.

He's joined by Luke Hodgson and Lauren Muir as hosts, with the full agenda shaped by community members who've sat in the same roles as the people in the room.

Tables are filling. The event is running six ten-person tables and is already operating a waiting list for the afternoon session. Fashion photographer Nathan McDowell will be there to capture the day, so attendees leave with professional shots as well as a concrete plan.

After the workshop, the evening continues at The Castle on Oldham Street. A proper pub. A proper crowd. Strictly brand-side. No pitching.

That last detail matters more than it sounds. The conversations that happen when nobody in the room has a commercial agenda are a different category of conversation entirely.

Who's behind it

OX1 is free to attend for brand-side leaders. That's made possible by partners who've committed to a no-promotion, no-posturing environment. Headline sponsor Dema, event sponsors Loop, Torque, and Emfas, and community partners including Vervaunt, High Cohesion, Whanau, Centra, Resourced, and Commerce Thinking.

OX2 is already confirmed for July. This is not a one-off.

If you're the right person

Directors, heads of function, and senior leaders working brand-side in operations, finance, ecommerce, merchandising, buying, or product data at a fast-growing fashion, apparel, or consumer brand.

Places are limited and curated deliberately. If you want one, move quickly.

operatorexperience.ai

Open your inbox. Scroll through the vendor pitches that have landed in the last six months.

Count how many have a .ai domain. Count how many claim to be “the first AI-native [something] platform” or position themselves as the “AI-first” solution in their niche.

This is the current state of B2B software positioning, and it’s working.

Deals are getting done. They’re genuinely impressive in a demo, they move fast, and they’re being funded aggressively.

Fast growing businesses are adopting these new-breed tools and attempting to run critical operations on them.

But there’s a big, unspoken risk attached to such a move…

There are going to be far more venture backed businesses with .ai domains that fail in the next five years than succeed. That’s just how venture maths works at scale. And right now, fast-growing retail brands are considering building their operational backbone around these tools as if that reality doesn’t apply to them.

But it does.

The numbers behind the narrative

BLS (Bureau of Labor Statistics) data shows that 20% of newly created businesses don’t survive their first year. 50% are gone within five. Only 34.7% of businesses born in 2013 were still operating a decade later.

That’s not software-specific. But before you sign a multi-year contract with a company that was incorporated eighteen months ago and has a founder who’s never shipped an enterprise product before, it’s the right place to start.

The instinct is to dismiss this. “We’re not buying from some seed-stage startup. Our vendors have raised serious money.” That’s where the thinking tends to go wrong.

Venture economics are built on extreme skew. Only 1 in 140 VC-backed companies ever becomes a unicorn. The model assumes a small minority of portfolio companies become massive winners and a large majority don’t. That maths works fine for a fund managing across dozens of bets. It’s a terrible basis for choosing the system that runs your inventory, your finance, or your order orchestration.

Fashionable and well-funded is not the same as durable. The more capital floods into a single narrative, the more companies that narrative attracts - including a lot that won’t survive the consolidation that always follows a hype cycle. Funding is not a durability signal. It’s a signal that someone made a calculated bet on upside. Those are different things, and the retail industry keeps confusing them.

The question missing in the evaluation room

Being first to adopt an emergent tool in your marketing stack is a calculated risk worth taking. Being first to run your inventory, your finance, and your fulfilment on one is a different kind of bet entirely.

If it goes wrong, you’re not dealing with a campaign that underperformed. You’re dealing with an operational crisis, a migration under pressure, and a vendor that may not have the resources to support you through either.

The vendor evaluation process at most brands goes the same way. Features. Pricing. Integration complexity. Maybe a reference call with a brand two sizes bigger than yours. Nobody asks whether the company will still exist in three years. Nobody models what a forced migration would cost. Nobody asks what happens to their data if the Series B doesn’t close.

Those are the questions that should be leading the conversation. Especially now.

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Private equity isn’t necessarily the safe alternative you think it is

The counter-argument usually goes: “We don’t buy from scrappy startups. Our vendors are PE-backed.”

Moody’s data shows PE-backed companies defaulted at roughly double the rate of non-PE-backed peers between 2022 and 2024. Even among the largest, most established sponsors, default rates sat around 14%. S&P counted over 80 US PE portfolio company bankruptcies in 2024 alone.

What retail operators should actually be doing

The problem isn’t that brands are buying software from young or PE-backed companies. Some of the best tooling available right now comes from exactly those places. The problem is that the evaluation process doesn’t match the stakes.

If a vendor sits in the control layer of your business - finance, inventory, order management, replenishment - the bar for selection and ongoing management should be different from a tool running your email campaigns or your content.

For the control layer, the questions that actually matter:

On operational continuity. If this vendor ceased trading tomorrow, what’s your data extraction plan? How long would a migration take, and what would it cost? Have you modelled that scenario, or is it just uncomfortable to think about?

On financial durability. When did they last raise, and what does their runway look like? What’s their path to profitability?

Is it really worth the risk. When compared to established vendors in the space, do the feature and speed benefits really justify the inherent risks?

Most brands can’t answer those questions for their current vendors. That’s the real exposure.

The contrarian position

The standard advice is to avoid young vendors and stick to established platforms. That’s not quite right either.

The right frame is: match vendor maturity to workflow criticality. New, AI-native tooling can earn a place in your stack. Just maybe don’t put it at the centre of your entire operation just yet. Run your experimentation, your personalisation, your content tooling on the exciting new thing. Run your finance and inventory on something with a 15-year track record, a deep implementation ecosystem, and a balance sheet that’s survived at least one economic downturn.

The brands getting this wrong aren’t naive or foolish. They’re running an evaluation process that assumes software companies to be more durable than the data suggests they actually are.

Reliability is about to get expensive again. The vendors that survive the next round of consolidation will charge more for it, and buyers will pay, because they’ll have learned the hard way what the alternative looks like.

The uncomfortable truth

Somewhere right now, a founder is in a board meeting celebrating a new ERP go-live. The vendor they chose has eighteen months of runway and a Series B that hasn't closed. Nobody in that room knows. That's the problem.

But when they break, they break in exactly the same places.

That’s one of the more useful findings from our latest research, and it’s worth unpacking because it changes how you diagnose operational problems and where you look for the fix.

Most ecommerce stacks are layered. And the layers don’t fail evenly.

At the surface is what customers see: checkout flows, returns portals, localised pricing, branded experience. These layers tend to work well. They’re visible, well-supported, and heavily optimised for conversion and customer satisfaction. When brands invest in new tools, this is usually where the improvement shows up.

Beneath that sits the logic layer: rules, policies, routing decisions, market configurations, exchange logic, exceptions. This is where complexity starts to compound. Edge cases become day-to-day reality. And the assumptions baked into the platform start to show their age.

Underneath both is the operational core: inventory accuracy, landed cost, duties and tax treatment, margin attribution, financial reconciliation. This layer is the hardest to see, the hardest to change, and the least forgiving when something goes wrong.

When brands add new tools, they usually improve the top layer. When they scale internationally, the strain shows up in the layers below.

The friction points our research surfaced were identical across both returns and cross-border: ERP connections requiring manual reconciliation, WMS integrations that don’t reflect real-time inventory, OMS workflows creating duplicate orders or missing data, Shopify Markets configurations that don’t align with operational reality. It doesn’t matter which part of the operation you’re talking about. The failure mode is the same.

And then there are the workarounds.

Every brand we spoke to has at least one spreadsheet, one Slack channel, or one person who just knows how to fix the thing that should work automatically.

As our co-founder Andy Slater put it: “Every brand has temporary fixes that eventually became permanent. You’ll find that last Black Friday something broke, so a plaster got applied to make it work, and then that plaster just becomes part of the business. No one questions it, no one documents it, and it only surfaces again when something else breaks or the one person who built it leaves.”

That’s not a technology failure. It’s an organisational one. And it’s almost impossible to see from the outside, or even from the inside, until something forces it into view.

The reason this framing matters is that it shifts where you look when something isn’t working. If returns feels broken, the instinct is to evaluate returns platforms. If cross-border margin is leaking, the instinct is to interrogate the cross-border provider. Sometimes that’s the right call. But more often, the problem is in the connective tissue: the integrations, the routing logic, the manual processes that have hardened into infrastructure.

Fixing a layer-one problem when the failure is in layer three doesn’t solve anything. It just gives you a better-looking surface on top of a fragile foundation.

Our full research report maps this in detail across both returns and cross-border operations, including where the shared failure points show up most consistently.

Read the full report

That’s the dangerous bit. ERP can fail without a big bang and an error message. It fails quietly, then becomes the reason teams keep a separate spreadsheet and route workflows around the system instead of through it.

If you’re in that limbo, don’t default to blaming the software. In practice, the problems can be self-inflicted. You can have a great system and a great SI, and still end up with something that technically works but nobody trusts because it doesn’t reflect how the business actually runs.

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The quiet failure pattern

The first sign you’re off track is when the project starts optimising for sign-offs instead of outcomes. Milestones get ticked off, demos look slick, but nobody can walk a real order through the full flow without wincing.

The internal tell is confidence. If every test reconciliation is throwing up surprises, and confidence in the system is evaporating, that’s not teething issues. That’s a project that might not recover.

This is why brands without an in-house ERP operator get caught out. Each partner can genuinely deliver their slice, but if you haven’t got enough internal expertise you don’t know how to challenge it.

And the ugly truth is the in-between bits are where things break. Orders moving from your ecommerce platform into ERP, being picked in the warehouse, and landing back into finance is the chain where issues hide. A missing field, a misaligned mapping, a forgotten checkbox, and suddenly your operation breaks in a place nobody owns.

So if your project status looks green but your team’s behaviour is screaming red, listen to the behaviour. Workarounds aren’t coping mechanisms; they’re your warning system.

The stuff that actually derails go-live

If you’ve effectively outsourced decision-making to your vendor and your SI, you’re already late. Training isn’t the same as confidence, and confidence doesn’t appear by magic. It shows up when someone inside the business has the authority and experience to push the system further, and to keep pushing when it gets uncomfortable.

The pitfall here is thinking you can buy capability. You can’t. You either bring in an in-house operator early, or you choose partners who transfer knowledge rather than hoard it, and you set that expectation from day one.

The next sign is capacity theatre. Everyone says they don’t have time, but the real issue is you haven’t made room for the work.

You can’t implement something like ERP by squeezing it in around the edges. That’s not a time problem. That’s a capacity problem.

If your finance lead is meant to redesign the close, run UAT, and still hit month-end with a skeleton team, you’re not running an implementation. You’re setting yourself up to cut corners, miss issues, and then pay for it later.

Then there’s data. Not data in the abstract. Your product data, your SKUs, your attributes, your categorisation, the stuff merch and product dev live and die on.

If data is incomplete, outdated, or inaccurate, the outputs are useless. And if you’ve got no clarity on who owns that data across the business, you’ll end up duplicating entry, arguing about definitions, and migrating a mess with a nicer UI.

The pitfall is leaving product data clean-up as an IT workstream you’ll get to later. You won’t. You’ll hit cutover, discover items don’t behave properly, and you’ll be trying to fix it at the exact moment the business needs stability.

Timeline pressure is the other classic. Vendors will happily sell you speed, and the business will happily attach go-live to a milestone because it feels decisive. The cost of that decisiveness lands on you when quality gets traded away to hit a date.

Missing a vendor’s deadline is frustrating. Missing your own readiness is catastrophic.

If you’re hearing “can we go live in 90 days,” you’re asking the wrong question. The right question is what needs to be true for you to go live in 90 days.

That shift matters because it forces the awkward conversation. If sponsor attention, capacity, integrations, and data readiness aren’t there, the answer isn’t cross your fingers. The answer is to move the date.

Testing is where all of this gets exposed. If UAT is mostly happy-path clicking, you’re setting yourself up for the sort of failure that doesn’t even throw an alert.

Integrations can fail quietly. Orders can stop flowing, postings can be incomplete, and you won’t know until someone reconciles properly and realises things don’t tie out. That’s when the panic starts, because you’re now debugging while customers are waiting and finance is trying to close.

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That’s why end-to-end testing beats vendor sign-offs when you’re testing whether your business can trade. You’re not testing modules, you’re testing whether your business can trade.

And don’t kid yourself that some functions can join later. If finance isn’t engaged during implementation, then relies on the ERP for reporting, you can end up with the data wrong because the system didn’t meet their needs and nobody caught it in testing.

By the time you realise, it’s not a quick fix. With a large volume of inaccurate records, you’re into a long slog that could’ve been avoided by involving the right teams from the start.

Why talking to someone who’s done this matters

The difference between a successful project and a rescue mission usually comes down to whether someone in the room knows where the skeletons are buried.

Not theoretical skeletons. The actual ones. The integration that looked fine in the demo but breaks under volume. The data migration that passes validation but lands finance with a reconciliation nightmare. The cutover plan that works on paper but collapses when your warehouse team is still picking orders.

This is what Commerce Thinking’s ERP practice exists for. We’re former brand-side operators from companies like Gymshark, Liverpool FC, and Burberry who’ve run these projects, lived through the mistakes, and know what good looks like when the pressure’s on.

We’re not here to sell you more config or extend timelines. We’re here to tell you what’s actually going to break before it does, challenge the bits that don’t make sense, and make sure your team can own the system when we leave.

Because the goal isn’t dependency. It’s capability. If you need us six months after go-live to run a routine process, we’ve failed.

If you’re mid-project and starting to feel that unease, or you’re about to kick off and want to avoid the traps, talk to us. We know what a good project looks like because we’ve built them. And we know what a struggling project looks like because we’ve rescued them.

If you’ve got that sinking feeling

ERP doesn’t solve problems. People do.

If you’re mid-project and you’ve got that feeling, treat it as a signal. You’ve still got time to fix the fundamentals, make room, clean the data, and force end-to-end ownership before the system becomes a very expensive spreadsheet generator.

You don’t need deeper config. You need broader context, the kind that understands how your teams actually work, not how the process map says they should. If you don’t bake that context into design and testing, you’ll go live and still be arguing about why merchandising missed PO lock dates or why a landed cost update has just blown up reporting deadlines.

None of this is about perfection. It’s about reality.