These notes follow on from the panel AI and the future of retail at The Retail Bulletin’s Retail Technology Summit in London on 4 November, and from the Q&A after it.
Start backstage
How do retailers make sure AI solves real customer problems rather than adding more technology? My answer is that the real problem usually isn’t where the AI is pointed.
Much of the visible conversation about AI sits in front of the customer: the chat assistant, the personalised feed, the agent that will one day shop on someone’s behalf. But a customer-facing AI inherits everything behind it. If a return depends on a handover between two teams that don’t share a case history, the assistant will explain the process fluently and the customer will still repeat themselves. If the stock data is wrong, the agent will confidently recommend a product that isn’t there. In service-blueprint terms, what happens in front of the line of visibility depends on work behind it that nobody on the front stage can see.
So my instinct is to start backstage, with the repetitive, rule-bound work of reconciling, routing, tagging and checking, where AI can be genuinely useful and where failures are easier to contain. Backstage is also where an organisation keeps what it has learned: in its processes, its exceptions and its workarounds. That matters later.
What the model forgot
The argument of this piece started at my own desk.
I was using an AI to triangulate several studies inside one long conversation: usability tests, tree tests and card sorts. The synthesis that came back was convincing. It had structure, it had themes, it cited findings across the studies. At some point, though, the model had quietly stopped drawing on the earlier tests. Nothing in the output said so.
I noticed because I had read the raw reports before feeding them in, and because I already had doubts about the quality of some participants. That doubt existed outside the synthesis. Nothing in the synthesis could tell me it was missing.
My role in that session wasn’t to check the AI. It was to carry into the conversation something the conversation could no longer tell me it had lost.
It changed how I use these tools. I now spend more time asking them to falsify what they have synthesised. Synthesis asks what the data says; falsification asks what it forbids. A model can be asked to challenge its own conclusion, and it will. What it can’t do is restore evidence that has already fallen outside its frame. And when you ask it to critique its synthesis, the critique arrives in the same fluent, confident register as the synthesis did. The frame defends itself.
Using these tools has another cost I didn’t expect. To get them working, I have to write out things I used to do without thinking. I become the documentation of my own expertise.
Many AIs, no overview
Now scale that conversation up to an organisation.
Every team now has tools with AI built in, and not only the digital ones: the warehouse system, master data, store operations, finance, analytics, session replay, the CRM, site search, customer service, the research repository. Each produces its own summary inside its own frame, and those frames rarely line up. The customer exists whole only in the space between them: the behavioural pattern in one tool, the hesitation in a recording in another, the complaint in a third, the stock that never reached the shelf in a fourth.
What’s missing is the triangulation across those summaries. The risk is that nobody owns it: each team trusts its own tool’s summary, and the overview falls between them. The tempting answer is another AI to summarise the summaries. But each additional layer can hide what fell outside the frame before it, so a summary of summaries risks compounding the loss. The work belongs to a person, assisted by AI: using it to retrieve and line up the raw material behind each summary, not to merge them. Deciding what conflicts and what is missing stays human.
There is a second problem, about pace rather than knowledge. AI doesn’t arrive evenly. One team adopts it early and becomes much faster: more campaigns, more product pages, more analysis. If the teams further along the flow haven’t moved at the same speed, that gain doesn’t reach the customer. It piles up at the next handover, in product data waiting to be enriched, or in a warehouse receiving demand it can’t fulfil. A local improvement becomes a bottleneck for the whole service. Seen one team at a time, every AI project looks like progress. Only a view across the whole flow, a service blueprint rather than a team roadmap, shows where the speed is actually going.
The more AI each team has, the more valuable the person who reads across all of it becomes, and the less likely the organisation is to have named that role.
The colleague who told you months ago
I’ve caught myself trusting a tool over a person. A colleague raises a problem. Months later I put the same question to an AI, and I take its answer more readily than I took theirs. Only when I dig into the output do I find what’s wrong with it, and realise it’s exactly what the colleague had pointed out. Then they say: I told you that months ago.
The knowledge was in the room. It just carried less authority than a fluent paragraph. There is a name for leaning too hard on automated output, automation bias, but nobody needs the literature to recognise that moment. The cost isn’t only the months lost. It’s a colleague learning that their warning counts for less than an answer from a tool.
So a team needs at least one route to a conclusion that didn’t start from the same model, prompt or data: pen and paper, raw data read directly, customers observed, an expert view taken before anyone sees the synthesis. Someone has to be allowed to argue the contrary. The usual condition for dissent is that it brings concrete logic, because a contrarian by habit gets tuned out. The uncomfortable part is that the colleague who told you months ago usually had the logic. They lost to the answer that sounded finished.
Human judgement belongs on both sides of the model: context, data and doubt before it; validation against real customers after it.
That second side is where the difference between a transcript and a person shows. A transcript preserves what was said. Someone in the room also has access to the pause before the answer, the hand that goes back to the product the customer just said they didn’t like, the gap between what people say and what they do. Much of what research is for lives in that gap, and it never reaches the transcript.
The time AI gives back
The store is where the time AI saves becomes concrete, and the rota is the clearest example.
Scheduling is an obvious optimisation problem for AI: matching staff to predicted demand while respecting constraints around hours, availability and cover. The hard question is what it optimises against, and who decides what a store’s performance actually is.
Here is a hypothesis rather than a case. Take an experienced sales associate who sells more than anyone else. The optimiser, reasonably, schedules them against every peak. The newer colleague gets fewer quiet hours working alongside them. Productivity goes up. What the newer colleague would have learned by standing next to them goes down. Six months later the experienced associate leaves, and the store’s numbers fall for reasons nothing in the rota explains.
The optimiser improved the metric while weakening the thing that produces future performance. It would just as happily do the opposite, moving the best people off the peaks so others can learn from them, if the business told it that was what performance meant. The tool doesn’t make that choice. Whatever the business feeds back to it as success does. That is the line I’m trying to find: where an AI judging people’s work should stop, and where a person has to decide what counts.
Some knowledge moves as instructions. Other knowledge moves through contact: watching someone handle an awkward customer, noticing the moment they stop explaining and start listening, seeing how they recover when the process doesn’t fit. None of that is in the manual. It passes between people who work side by side.
AI can document the procedure. The harder question is whether we keep the conditions in which judgement develops.
The abstraction debt
Junior roles are not cheaper versions of senior ones. The years between junior and senior are the formation of judgement: mistakes made near someone who reads the situation differently. Much of the work AI now absorbs is the work through which that judgement used to form: first-pass synthesis, tagging, drafting, the checking before and after a model runs.
There is a tension here with where I started. The backstage work I’d hand to AI first is exactly the work through which people used to learn how an organisation really runs: where the exceptions are, which workaround holds the process together. Automating it can still be the right decision. But removing work and removing learning are not the same decision. If the work disappears, the learning has to be designed back in somewhere else.
If it isn’t, nothing fails dramatically. The procedures are still there, the outputs keep arriving, the slide decks look like research. What goes is the capacity to read what those outputs can’t show. That is what I mean by abstraction debt: the organisation keeps the appearance of a capability after the experience that produced it has started to disappear.
It also changes what an organisation has to know about its own knowledge. There used to be two places to look: what people know, and what is written down. Now there is a third: what the AI can retrieve and act on. Learning teams now write for employees and for the AI systems, agents and skills those employees use or build. The question is no longer only whether knowledge is written down. It is whether a machine can retrieve it, whether the machine’s version is faithful, and whether people still know enough to notice when it isn’t.
And some knowledge was never written down at all. Every company has unwritten rules: how far you go for a customer when the policy says no, what the brand would never say, which exception is fine and which one isn’t. Nobody can hand those to an AI yet, because nobody has put them into words. People learn them by working inside the company, from the people who already know them. That shared experience is where a brand actually lives, and it is the first thing an abstraction debt erodes.
What leaves with the employee
What happens when an employee leaves? More precisely: what happens to the way they used AI?
I don’t think anyone has a good answer yet. The obvious one is to keep their prompts, agents and documentation. We can keep the prompt. We can keep the agent, the repository, the process, the training material. None of it necessarily keeps the judgement that made them useful: knowing why they were right, when they were wrong, and when the situation needed something else. The employee leaves the documentation behind and takes with them the judgement that made sense of it.
That is the debt coming due.
AI does give time back. The question is what we do with it. Spend all of it on more output and the debt grows. Spend some of it letting people learn from customers, from each other and from the colleague who disagrees, and the time saved becomes something the organisation can keep.