The Association of National Advertisers says retailers chasing ambitious advertising-revenue targets are running into four consistent roadblocks — from inconsistent measurement to fragmented data access — that make retail media networks harder to scale than brands originally expected. According to Adweek, the ANA's findings land at a moment when nearly every major retailer, from grocery chains to pharmacy operators, has built part of its profit strategy around selling ad space inside its own digital storefront.

Retail media has gone from a side hustle to one of the fastest-growing categories in digital advertising over the past few years, with networks like Amazon Ads, Walmart Connect, Target Roundel, Kroger Precision Marketing and Instacart Ads all competing for the same brand budgets. But launching an ad network and running a mature one are different problems, and the ANA's obstacle list reads like what happens when dozens of retailers try to become media companies at the same time — without the shared infrastructure that legacy ad platforms spent two decades building.

A gold rush that outran the plumbing

Brands have poured money into retail media because it sits closer to the point of purchase than almost any other channel — a search ad on a retailer's own site can be tied directly to a cart add or a completed sale. That promise is what pushed retail media past traditional linear TV and toward parity with social advertising in a matter of years. The problem, as the ANA's research suggests, is that the infrastructure retailers built to sell that inventory hasn't kept pace with the size of the checks brands are now willing to write.

The four sticking points

The obstacles the ANA highlights, as described in Adweek's reporting, cluster around a familiar set of growing pains for an industry still assembling its own rulebook:

• Inconsistent measurement. Each retail media network reports performance on its own terms, using its own definitions of reach, viewability and attribution — making it difficult for a brand to compare a campaign on one retailer's site against a campaign on another's.
• Fragmented data access. Clean rooms and first-party data-sharing arrangements differ from retailer to retailer, forcing brands and their agencies to rebuild integration work for every new network they want to test.
• Network sprawl. With dozens of retailers now selling ad inventory, media buyers are managing separate self-serve platforms, creative specs and billing systems for each one, which adds operational overhead disproportionate to the budget any single network receives.
• Internal talent gaps. Retailers are being asked to run sophisticated media businesses — media planning, measurement science, account management — with teams that were built to run merchandising and e-commerce, not advertising sales.

What it means for ad-tech and AI builders

None of these four problems is exotic, and none of them is easily solved by adding more headcount — which is exactly why they're a live opportunity for tooling. Measurement inconsistency is a data-normalization problem: someone has to build the translation layer that maps each retail media network's reporting schema into a common currency a brand can actually act on. Clean-room fragmentation is a matching and privacy problem that machine learning is already suited to, since most clean-room workflows exist specifically to let two parties compute a joint result without either side seeing the other's raw data. Network sprawl is a workflow-automation problem: campaign setup, creative trafficking and reporting across a dozen disconnected platforms is precisely the kind of repetitive, structured task that agentic tools are being built to handle. In our estimation, that combination — a large, fast-growing ad category with no shared standards yet — is likely to draw a wave of AI-driven measurement and campaign-management startups over the next year, the same pattern that played out around programmatic display a decade earlier.

AiiN's takeaway

The ANA's four-challenge list is less a warning about retail media's growth than a map of where the category still runs on manual effort. Brands aren't pulling back from retail media budgets; they're running into the limits of managing that spend by hand across an increasingly crowded field of networks. For teams building ad-tech or AI tooling, the opening isn't in convincing retailers that retail media works — the ad dollars already answer that. It's in building the measurement, data-matching and workflow layers that let a brand run one retail media strategy instead of a dozen disconnected ones.