The Search > Browse > Compare > Buy funnel that shaped two decades of retail strategy is being dismantled faster than most brands have noticed.
AI shopping agents are increasingly handling the entire journey on the consumer’s behalf, from preference-matching to completing the final transaction.
According to McKinsey’s ConsumerWise research, 68% of U.S. consumers used at least one AI tool in the past three months, with adoption highest among younger shoppers – 85% of Gen Z and millennials report using AI regularly. Among those using AI-powered search, 44% now say it is their most preferred source of product information, ahead of traditional search engines at 31%, and well ahead of retailer and brand sites.
Indeed, this is more than a behavioural trend. The systems through which products get discovered, evaluated and purchased are being rebuilt from the ground up, and that has serious implications for any brand looking to compete in the U.S. market.
How agentic systems make decisions
AI agents don’t browse. They don’t respond to beautifully shot campaigns, brand heritage pages or editorial storytelling. Instead, they execute tasks based on the probability of a successful outcome, querying structured data and APIs, and optimising for fulfilment certainty, delivery speed and returns friction.
When a consumer delegates a purchase to an AI agent, the agent needs to make a high-confidence recommendation at speed. It will therefore favour products that are machine-readable, geo-available, well-stocked and supported by a reliable fulfilment track record. Products that cannot be cleanly indexed, or that introduce operational uncertainty, will be ranked lower or might not even be considered at all.
This is why U.S. inventory is becoming the new SEO. In the traditional funnel, visibility was won through search optimisation, paid media and editorial coverage. In agentic commerce, visibility is determined by data structure, operational reliability and trust within the ecosystem.
Why EMEA brands get filtered out
All of this creates an exposure that many EMEA fashion and retail brands are unprepared for.
Shipping from London to New York introduces variables that AI systems are increasingly trained to treat as risk – think customs delays, cross-border return complexity, delivery uncertainty and, in some frameworks, sustainability penalties for long-haul logistics.
Even if a product is competitively priced and a strong match for what a consumer is looking for, an AI agent may deprioritise or discard it in favour of a domestically stocked alternative that guarantees a more frictionless fulfilment outcome.
The brands most at risk are those investing heavily in U.S. acquisition and visibility while underinvesting in the operational infrastructure that agentic systems use to evaluate them. They are winning attention but losing the sale at the point where the algorithm makes its decision.
This, ultimately, is what algorithmic exclusion looks like in practice.
Other signals that matter beyond the inventory
Localising U.S. inventory is the most direct way to remove the logistics risk signal, but it is only one part of a broader picture. Indeed, there are several other areas where EMEA brands can strengthen or weaken their position in agentic commerce systems.
Product data quality is one of the most underestimated factors. AI agents and the retail platforms they operate within will prioritise items that are clean, standardised and easy to index. That means proper UPCs and GTINs, rich structured attributes covering category, fabric, fit, colour and price, and clear geo-availability flags showing what can be fulfilled in the U.S. and on what timeline. Products lacking this level of data structure are harder for agentic systems to process confidently, and lower confidence means lower recommendation priority.
Proven scale and ecosystem trust matter more than many brands expect. Major U.S. retailers are already testing agentic selling tools, and when they pilot new programmes, they do so with partners they have an established track record with. A brand with no U.S. retail history is competing for algorithmic attention it has not yet earned.
Delivery and returns performance is becoming a reputational trait in its own right. Some U.S. marketplaces already require a 48-hour return SLA as a condition of listing. As AI agents begin drawing on historical fulfilment data to inform recommendations, brands with poor or inconsistent records on delivery times and return handling will find that history working against them.
Brand authenticity will also carry weight as these systems mature. As agentic systems grow more sophisticated, they will increasingly factor in whether a brand’s identity and values align with the consumer’s own preferences and tastes, and not just whether a product matches a search query. A brand that communicates consistently across channels gives AI systems more to work with when making that judgement call. One that doesn’t will struggle to surface, even if its product is exactly what the shopper is looking for.
Building for agentic visibility
For EMEA brands with ambitions for the U.S. market, navigating the path forward requires treating this as an operational and data infrastructure challenge instead of a marketing one.
For instance, stateside fulfilment removes the logistics risk signal entirely and dramatically improves a brand’s standing with AI-led discovery systems. Paired with localised returns, it addresses the consumer trust dimension too. Adyen’s data shows that 45% of U.S. shoppers cite confidence in best-value optimisation as their primary condition for trusting AI with a purchase, and that calculation includes total landed cost after duties and delivery fees. Brands shipping cross-border are already at a disadvantage on that measure before the algorithm has made its call.
Alongside this, clean, machine-readable product data is now non-negotiable. Inventory that cannot be reliably indexed will not be reliably recommended. A full audit of data standards against U.S. retail requirements should be a prerequisite for market entry.
Integration within the U.S. retail ecosystem provides a layer of credibility that no brand can generate independently. Relationships with established retailers, and the operational partners embedded within those environments, expose brands to new retail media capabilities and agentic selling tools as they are rolled out, often before they are available to brands approaching the market alone.
Meanwhile, brands that only ship shallow runs of seasonal styles give AI systems very little to work with. Retailers won’t put marketing spend behind products they can’t be confident will stay in stock, and agentic systems will reflect that same logic when deciding what to surface. The brands seeing the strongest results are often those that build a reliable set of proven styles alongside their seasonal offer, creating the kind of consistent demand signal that both algorithms and retail buyers respond to.
The U.S. market hasn’t slowed down – it’s been reprogrammed
Selling into the U.S. has always been complex. What has changed is the pace at which the rules are being rewritten, and the number of systems changing at the same time.
AI is accelerating this, but it sits alongside shifting cross-border regulations, evolving fulfilment economics and retailer-controlled discovery platforms, which are all moving in parallel. EMEA brands that approach the U.S. market the way they would have two years ago will struggle to make sense of why their visibility and conversion numbers don’t add up.
The answer, increasingly, is that the systems doing the deciding have moved on. Building for agentic commerce means getting the operational infrastructure right, the data right and the ecosystem relationships right. That is what earns discoverability in the market that now exists.
