Ecommerce Doesn't Need More AI Agents. It Needs Someone to Coordinate Them.

I have to admit, yes, I'm a Lord of the Rings fan. But as a millennial, the visual reference that actually comes to mind is that scene from Fantasia where Mickey Mouse enchants the broom to do chores for him, and, with remarkable precision, it sows chaos instead.

In the same spirit, as a representative of the upper end of the demographic curve, alongside metaphors, I think it's worth backing up the argument with numbers.

Only 11% of consumers in the United States are willing to let AI decide a purchase for them, even in low-risk categories like personal care or household supplies, according to a Gartner survey published in May 2026. That figure demolishes the narrative that's taken hold in most leadership committees: that the consumer is already ready for autonomous purchasing. They're not. And yet, 58% of customers prefer a generative AI system to recommend products to them, according to Capgemini.

The contradiction isn't a measurement error — it's a clue to how each party involved sees the future, and that's an insight in itself. Shoppers aren't rejecting AI, they're rejecting losing control while using it.

54% of people who've used AI to shop have had to double-check the information the system gave them, and 62% consider that information a waste of time, according to the same Gartner study. This isn't an adoption problem, it's a design problem being attacked from different angles. Platforms are tackling it from availability and accuracy, while managing the actual experience falls on those of us who want to build differentiated value with it.

The symptom: scattered agents, broken experience

Over the last three years, most ecommerce businesses have followed the same script: a chatbot in customer service, a recommendation engine on the catalog, a dynamic pricing system at checkout, maybe a personalization agent in email.

Each piece works fine on its own. Each vendor can show off its own success story. The problem shows up when the customer moves between those pieces and discovers that none of them knows what the other just did — and this doesn't just break the experience, it can also be a source of problems at multiple levels whenever actions get automated across the whole martech stack.

To put it simply with an example: as a customer, I might have just closed a car insurance claim successfully with the tow truck, while at the same time considering canceling my policy for other reasons — and that combination could make me furious if I then get hit with the wrong upsell campaign. Response-time indicators, handling costs, or NPS can all look great in isolation and still completely miss why the problem exists.

Back to my number obsession: only 14% of the more than 20,000 consumers surveyed across 26 countries say they're satisfied with their online shopping experience, according to the IBM Institute for Business Value. Given how much technology has been rolled out over the last decade, that number should embarrass anyone of us sitting on this side of the equation (understanding, designing, implementing).

There's also a structural threat that few leadership committees are prioritizing, that we SEOs haven't managed to communicate properly, and that Gartner has been warning about for a while: back in 2024 it forecast a 25% drop in traditional search volume by 2026, displaced by AI chatbots. If the traffic that reaches you today from Google starts arriving (or not arriving) through a conversational assistant answering on the user's behalf, the question is no longer how to rank a page. It's how to get an external agent to understand, cite, and recommend your catalog. That fight is won with structured data, context, and cross-channel consistency. Not with one more chatbot.

The marketing name for this is zero-click search; the precise business term for it is "I'm losing visibility into my customer's context" (to put it politely).

The thesis: what's missing is a layer that perceives, interprets, and learns

The mistake isn't having too few AI agents — it's having them disconnected. Each agent perceives one slice of the customer (what they buy, what they ask, what they abandon in the cart), but none of them builds a combined picture. And without that combined picture, no real interpretation is possible: recommendations get generated off a fragment of behavior, not the full journey.

The cost of not having it is already measured, and not just by consulting firms: 70.22% is the average cart-abandonment rate in ecommerce, according to the Baymard Institute, which aggregates 50 independent studies published between 2006 and 2025. It's not a new problem, nor one exclusive to AI, but it's exactly the symptom a well-designed cognitive layer should reduce: the customer doesn't abandon because technology is missing, they abandon because nobody connects what every piece of that technology already knows about them.

What's missing is shared context — not one more agent, but the logic that connects the ones that already exist. Picture that customer from the car-claim example earlier: first, someone perceives that they just closed an incident with the tow truck, and, in parallel, that they've spent weeks looking at the cancellation policy. Then, someone interprets those two signals together, not separately, and understands this isn't the moment for an upsell. The system feeds that decision back: if the customer responds well to a different message, it remembers that for next time. And all of that turns into augmented experience at the one moment the customer actually sees: a message that, finally, makes sense given what's happening to them.

That's what, in the physical world, we'd call "breaking out of silos to work toward a shared goal." Here, it's four functions feeding into each other: perception, smart interpretation, feedback, and augmented experience. Without this layer, every agent keeps optimizing its own metric in isolation. With it, agents stop competing for the customer's attention and start building on what the previous one already learned.

This augmented experience no longer lives inside a fixed interface. For thirty years, ecommerce was a screen the product team designed once, and the customer always navigated the same way. That's dissolving: when the purchase happens inside ChatGPT, inside a Copilot assistant, or inside whatever interface an agent controls (what ECDB calls the Interface-First model), the brand no longer has a page to design — it has a conversation to win, different every time, assembled in real time based on whatever that agent decides to show. It's what design circles are starting to call liquid interfaces: there's no fixed layout to memorize anymore, there's a surface that reassembles itself with every interaction based on context, the agent, and the customer's moment.

And this is where Fantasia's broom comes back. If the interface is no longer fixed, and the logic governing it doesn't exist either, every agent not only acts on its own, it also presents itself on its own, with its own judgment about what's relevant to show the customer in that instant. The cognitive layer is optional in a world of fixed interfaces, where the error shows up on a dashboard. It's essential in a world of liquid interfaces, where the error shows up (or doesn't) inside a conversation nobody else is watching.

The point most people skip: governance, not total autonomy

This is where a lot of AI projects in ecommerce aim at the wrong target. The default ambition tends to be "more autonomous," as if autonomy itself were the sign of maturity. The same survey cited at the start disproves that: 72% of users say generative AI shows up in their experience without them having asked for it. That doesn't build trust, it builds rejection.

Kate Muhl's quote sums it up better than any internal report could: consumers aren't looking to hand purchase decisions over to AI, they're looking for AI to help them find better information while keeping final control themselves. That has a direct translation for any ecommerce leadership team: the goal isn't to automate more, it's to decide precisely which parts of the process can be coordinated between systems, which ones need explicit governance (rules, limits, human approval), and which ones can run with real autonomy because the risk of error is low and the cost of review is high.

That distinction (coordinated, governed, autonomous) isn't a technical nuance — it's the difference between a system a director can defend in front of a risk committee and one they can't. It's also why most AI pilots in ecommerce stall out: they're designed to maximize autonomy instead of maximizing trust, and the consumer (and the internal team itself) ends up switching off whatever they don't understand or control.

The market is already moving

Agentic commerce is set to move $8 billion in 2026 and is projected to reach $3.5 trillion by 2031 — a 43.24% growth rate over five years, according to ECDB/Juniper Research. This isn't an abstract forecast: traffic that already arrives from ChatGPT converts at 11.4%, 54% above the average for every other source, direct traffic included. And the infrastructure to capture that value is being built around an aggressive pricing strategy: ChatGPT's referral fees sit well below the take rates Amazon (15.8%), Walmart (15.0%), or eBay (10.9%) charge today — the same "come in cheaper, grab share" play Amazon used to eat traditional retail alive twenty years ago, now aimed at who controls the transaction, not just the catalog.

And there's no irony to spare here: the same firm telling you only 15% of retailers use AI to recommend products is the one rating the 19 vendors already selling it to them.

Gartner's Magic Quadrant for Digital Commerce, published on November 3, 2025, evaluates 19 vendors (Adobe, Salesforce, SAP, Shopify, commercetools, among others) and confirms that investment in AI capabilities and agentic commerce is already part of the roadmap for every major platform in the category. This isn't an early-adopter bet. It's the direction the entire category is moving in.

And the business case for moving now isn't new, it's just gotten more urgent: companies that get personalization right generate 40% more revenue from those activities than the industry average, according to McKinsey — a 2021 figure none of the platforms Gartner evaluated has managed to overturn in four years, precisely because the underlying problem (connecting signals, not stacking up tools) hasn't changed.

Even so, the gap between what the customer expects and what the retailer delivers remains enormous: only 15% of retailers use AI for product recommendations, and only 10% have automated inventory management with AI, according to Quid's State of AI in Ecommerce Report (2025), against 58% of customers who already prefer that kind of recommendation. That gap between demand and delivery capability is, right now, the cheapest competitive advantage in ecommerce. You don't need to convince the customer of anything — you need to build what they're already asking for.

What this means for whoever's in charge, not whoever's implementing

No ecommerce director needs to understand the technical architecture of a conversational agent. They do need to ask themselves three questions before signing off on the next AI project:

• Does this agent share what it perceives with the rest of the systems, or does it just add another silo with an "intelligent" label on it?
• Do we know, precisely, which decisions the system can act on alone and which ones need human governance — or are we figuring that out as we go?
• Does the customer notice a more coherent experience, or do they just notice there are now more chat windows?

If the answer to any of the three is "we don't know," the project isn't ready to scale, no matter how good the demo looks.

The cognitive layer isn't a fancier AI layer, it's the one that decides how the ones you already have coexist, and the one that turns every new agent into an improvement to the whole system instead of one more tool someone has to check by hand.

To wrap up, because you already know I'm a sucker for numbers: this article draws on twelve different sources, and more than a few years of personal bias, which is the polite way of saying experience.

I split them into two camps: the consultancies (Gartner, McKinsey, IBM) see moderate growth and a challenge that's mostly emotional — trust — while the product platforms see explosive growth and a challenge that's mostly technical. Neither side talks much about the conflict the other one clearly sees. And both agree, without ever having compared notes, on the same vanishing point: control, trust, and verified data as the lever that actually matters.

Two ways of seeing the world, one point of connection we believe in.

At Pleg, we're designing exactly this layer for several ecommerce teams who already have agents deployed but no logic coordinating them. If you want to see how we're building it, or just compare notes on where your organization stands on this axis of perception, governance, and autonomy, let's talk.