Most ecommerce brands aren't ready for AI to sell on their behalf

Every few weeks we sit down with someone outside Hello Retail for our Conversations series: a Shopify partner, a Klaviyo Community Champion, a growth executive, one of our own data scientists. Different companies, different jobs.

Then four of the last seven guests, with no prompting from us, landed on the same warning.

Most brands’ data isn’t in a shape that lets any AI act for them. Whether that’s the AI a brand builds or the AI a shopper brings along. The advice a buyer gets before they buy is moving to a machine, and most catalogs, inboxes and blog posts aren’t ready to be read by one.

Browse all Hello Retail Conversations episodes →

The shopper’s advisor is no longer a real person

Peter Sommer, CEO of Shopify Plus partner Dtails, framed it around who a shopper asks for advice.

“I found myself actually getting the advice where I simply put up five criteria. It came back with a range of five printers that matched those criteria.” (Peter Sommer)

That’s how he bought his own printer. Five criteria into an LLM, a shortlist back, done. He thinks the disruption lands earlier than checkout. It lands in the advice.

“The larger the customers are, the bigger their product assortment is, and the further up they are in their advancement of being a mature e-commerce merchant, the more you need tools like Hello Retail to fit the needs of their customers.” (Peter Sommer)

Catalog size is what makes this hard. Twenty products fit in one person’s head. Twenty thousand don’t, and an LLM answering “which printer fits these five criteria” can only work with the product data it’s handed.

Watch the full conversation with Peter Sommer →

Most brands aren’t using the data they already have

Anna Sophie Christensen, Head of Email Marketing & Retention at FABO and a three-time Klaviyo Community Champion, found the same gap one step earlier, before any AI shows up.

“People send out to all the subscribers at once, not differentiating the message or the content. And I continuously preach that we need to differentiate, because you have your most loyal customers who have purchased from you 20 times, and you have a lead that signed up a week ago.” (Anna Sophie Christensen)

Most brands skip personalization because the decisions underneath it never get made. Which segment, which product, which moment. So the data an AI layer would need never gets collected in the first place.

“Already then, you are collecting zero-party data that you can use for the overall structure on how you communicate.” (Anna Sophie Christensen)

The readiness problem starts long before AI. It starts with nobody building the structure that anyone, human or machine, would need to act well on a brand’s behalf.

Watch the full conversation with Anna Sophie Christensen →

Personalization without knowing who’s shopping

Sarah Miguel Cournane, a data scientist on Hello Retail’s R&D team, says there’s a structural fix, as long as the product data can carry it.

“If we don’t know who you are, we focus on what you engage with. That’s enough to understand intent.” (Sarah Miguel Cournane)

Identity was never the real constraint. You can act intelligently for a shopper you can’t name, as long as the catalog is mapped well enough to reason about how products relate. Most catalogs are a flat list of SKUs with nothing for an AI to navigate.

“We’ve gone from using AI to support decisions to actually verifying what AI produces for us.” (Sarah Miguel Cournane)

Verification is the part brands underestimate. Pointing a generative model at a product feed is the easy half. Something then has to keep checking that what comes out is correct, on-brand and useful, at a volume no human review process can keep up with.

Watch the full conversation with Sarah Miguel Cournane →

What the AI actually rewards

Jennifer Montague, former Senior Director of GoToMarket at Verdane and now VP of Marketing at Carivo, took the same problem to content.

“UGC is gold for AI discoverability. That’s what people, that’s what the AI wants to see, is other people talking about you. So it kills so many birds with so few stones.” (Jennifer Montague)

AI systems reward what real buyers reward: specificity, evidence, a point of view. They skip past what buyers already skip past.

“To stand out, you have to be more authentic, more human sounding. And to do that, you need to have a position that you can lean into that makes you stand out from the bland sea.” (Jennifer Montague)

Generic content fails twice. Readers scroll by, and the AI systems now sitting between a brand and its next customer never cite it. Same readiness problem, measured in words instead of product attributes.

Watch the full conversation with Jennifer Montague →

The common thread

Taken separately, the four are about different things: an advisory LLM, an email flow, a product catalog, a piece of content. Taken together, they’re one warning said four ways.

AI-mediated shopping doesn’t fail because a brand hasn’t “adopted AI” yet. It fails quietly, upstream, because the product structure, the customer signal and the content were never built well enough for anything to represent that brand accurately. AI just makes the gap show up faster than a slow slide in organic traffic would.

Worth sitting with before you reach for a tool. In all four conversations the fix was the boring structural work: differentiate the audience, map the catalog, write something specific enough to be worth citing. Then whatever you put on top of it has something real to work with.

More conversations

Browse every Hello Retail Conversations episode →