Clean Feeds, Not New Tools: What AI-Ready Retailers Have in Common

The retailers surfacing in artificial intelligence-generated results today rarely run dedicated "AI commerce" projects. Instead, they have product data that has been clean, consistent, and channel-ready for years.

When a shopper asks an AI assistant for "a waterproof hiking jacket under $200" or "cushioned running shoes for marathon training in size 9," the brands that surface don’t have the loudest marketing. These retailers have feeds that contain "waterproof rating," "activity type," "cushioning level" and "size" as discrete, accurate fields. They aren’t investing in a new infrastructure layer; rather, they're protecting the one they already have.

The Cost of Not Preparing for Agentic Commerce is Already High

Agentic commerce isn’t a future readiness problem. It is already here. The data weaknesses that will make a product invisible to a shopping agent are already making it harder to surface on Google Shopping.

When attribute fields are missing, inconsistent across channels, or out of sync with the live product page, the immediate consequence isn't yet an AI agent ignoring the listing, but it is an existing media budget burning faster. Disapprovals climb and impressions drop. Performance Max algorithms have less to work with so cost-per-click rises to compensate. This leads to retailers assessing the return on spend contract and concluding the answer is more spend, when the real issue is upstream of the ad.

AI agents only amplify this. They lean even more heavily on structured data because it's all they have. A product page headlining "the ultimate weekend sneaker" with lifestyle imagery delivers the message to a human shopper instantly. An AI agent comparing options needs that same information as machine-readable fields. If "activity type: running," "cushioning: high," "use case: marathon" and "size: 9" aren't all in the feed, the product doesn't get surfaced as marketing copy doesn't compensate for these gaps.

What Goes Wrong … and Why it Keeps Going Wrong

The pattern repeats across thousands of retailer feeds. Product data lives in silos — one version in the ad platform, another on the marketplace listing, and a third on the live site. Each was probably accurate at the moment it was set up but very few sync reliably to each other now.

The mismatches are usually small — a price that lags by 20 minutes, a stock status that updates daily instead of hourly, an attribute that exists in one feed but not another. None feel like a crisis alone. But they cascade, meaning a product drops out of a marketplace listing, falls out of a Performance Max campaign, and stops appearing in AI overviews, often on the same day, often without anyone noticing.

The deeper organizational problem is that this data sits with different teams. E-commerce, paid media, marketplaces, and merchandising all draw from the same source but rarely coordinate on it.

The Discipline of the Well-Prepared

The retailers showing up well in AI-generated results are not doing anything extreme. There's clear ownership of the product field and automated checks flag any drift between the feed and what's live on the site. Attribute completeness is measured as a percentage, broken out by channel and reviewed often enough to catch a missing field before the channel does, not a catalog tidy that gets booked in when the quarter is quiet. Product fields look identical regardless of what the retailer sells.

This is the same discipline that worked on Google Shopping five years ago. Brands that did the work back then already have a head start in AI search. Catching up in agentic commerce will be harder than it sounds. The reassuring part is that catching up doesn't require a new team, a new budget line or an "AI commerce" project. It requires making the discipline automatic, starting with one source of truth for product data, so that every channel draws from the same place instead of three different ones. Brands should automate the repetitive work, the field mapping, the error fixing, the attribute enrichment so that gaps get caught the day they appear rather than the next time someone opens the feed. And they should treat attribute completeness in real time rather than only look at it during the annual clean-up. This is easy enough to do now, but waiting until "agentic commerce is here" will make it much harder.

Rob van Nuenen is co-founder and CEO of Channable, the multichannel e-commerce platform.