Why Data Maturity Is Becoming the Real Advantage in Agentic Commerce

Elizabeth
Apr 17
3 min read

The shift happening underneath retail

For a long time, retail worked on a fairly predictable logic. The brands that spent the most on advertising won the most visibility. Visibility drove traffic. Traffic drove sales.

That logic is starting to fall apart.

In agentic commerce, visibility is not something you can simply buy anymore. It is determined by whether your product data can be understood and trusted by machines.

When an AI agent receives a shopping request, it does not start with ads or rankings. It starts by querying product data. It checks whether the information is complete, structured, and reliable enough to use.

If the data meets that standard, the product gets surfaced for consideration. If it does not, it is ignored.

There is no partial visibility in this system. A product is either usable by the agent or it is invisible to it.

The real divide is not company size

Jonathan Arena, co-founder of New Generation, describes the emerging divide in agentic commerce clearly. It is not big brands versus small brands. It is data maturity, implementation ability, and speed of execution.

Large retailers may adopt earlier simply because they have more resources to experiment and absorb operational risk. They can run multiple systems in parallel without disrupting the core business.

But mid-market brands are not automatically disadvantaged.

In fact, when their product data is already well structured, they can move faster than larger organisations that are slowed down by legacy systems and fragmented data sources.

The real constraint is no longer budget. It is operational readiness.

The question has shifted from “Can we afford to compete here?” to “Can our data actually participate in this system?”

What data maturity actually looks like

Data maturity is often treated as a technical issue, but in practice it is much simpler.

It comes down to whether an AI system can reliably interpret your product information without needing clarification or correction.

That requires consistency. Missing attributes, conflicting pricing, or outdated stock information all reduce trust in the data. When that happens, an AI agent will not attempt to interpret it. It will exclude it.

Those exclusions are not isolated. They repeat across every query the agent processes, which means weak data leads to consistent invisibility.

For most mid-market brands, the foundation starts with product identity.

A GTIN linked to a UPC creates a verifiable reference point. From there, completing structured product data within systems like the GS1 Data Hub and achieving Verified by GS1 status ensures that the product record can be trusted across systems.

None of this is especially complex. The challenge is consistency and discipline, not technology.

For years, this work was deprioritised because the consequences were not visible in day-to-day sales performance.

That has changed.

An incomplete product record is no longer just a data gap. It is a product that cannot reliably surface in agent-led discovery.

Why timing matters

Traditional ecommerce rewarded brands that could outspend or out-optimise competitors on advertising, SEO, or marketplace placement.

Agentic commerce shifts that advantage toward data quality and system readiness.

This creates a different kind of competition. Brands with cleaner, more structured product data can outperform larger competitors that still rely on fragmented or inconsistent product information.

That advantage is already visible, but it is not permanent.

As adoption increases, more brands will close the data gap. Once that happens, early movers lose their edge and the baseline expectation rises.

In other words, the advantage belongs to those who prepare early, not those who wait for standards to settle.

Mid-market brands that act now are not simply catching up to larger players. They are positioning themselves ahead of the curve before the system fully matures.