AI shopping tools not replacing discovery in retail

Shopfully has warned that AI shopping tools are not replacing consumer discovery in Australian retail, with most local AI investment still focused on back-office advertising functions.

The warning comes as IAB Australia data points to a market where retail media networks are directing AI work towards targeting, measurement, reporting and data analysis, while display and search continue to attract most advertising spend. Only 22% of networks describe their AI work as advanced, according to the industry report cited by Shopfully.

Blake Wright, Sales Director, Brand & Agency at Shopfully, said the gap between AI adoption headlines and actual retail behaviour is wider than some industry claims suggest. Much of the excitement around AI shopping, he argued, has yet to change how Australian households plan and buy their weekly groceries and essentials.

Drawing on Shopfully data from 2.8 million monthly active Australian shoppers, Wright said consumers are still opening retail apps, choosing stores and building shopping lists themselves. In his view, that reflects a slower shift in household routines than recent AI retail coverage implies.

He framed the issue as a gap between technology uptake and spending patterns. Younger consumers may account for a large share of AI adoption metrics, he said, but household spending remains concentrated among established families with larger, more habitual weekly baskets.

That distinction matters for brands trying to judge when AI-led shopping behaviour will begin to affect sales at scale. If adoption moves fastest among lower-spending groups and slowest where the largest baskets sit, the commercial impact may take longer to emerge than user growth figures suggest.

Wright said retail has seen similar patterns before, with new channels arriving faster than consumer habits change. He pointed to earlier shifts in newspapers, printed catalogues and eCommerce as examples of media and shopping formats that took years, or even decades, to become routine.

Discovery risk

Shopfully's sharper concern is not that AI will immediately automate shopping, but that increasingly precise targeting may weaken product discovery. Wright said advertising has spent years moving away from broad, contextual environments towards individual-level targeting, reducing shoppers' chances of encountering unfamiliar brands.

"Programmatic taught us to chase the individual across whatever environment they happened to be standing in, and once you're chasing the person, the room stops mattering. So does the accidental discovery the room used to pay for. We got precision. We paid for it with everything we couldn't measure. And that sets up a question with a familiar shape, because I've written about the promo trap and the performance trap before. If precision targeting is the rational move for every single brand, who funds the discovery a category needs to grow? Chasing in-market audiences is each brand's optimal play. Add it up across the market and nobody is creating new demand; the category quietly shrinks to the already-convinced. Price was the first race to the bottom. Performance was the second. Precision is the third, and it's already running," Wright said.

His argument is that a market built around increasingly narrow targeting may work for brands seeking efficient short-term returns, but do less to attract light buyers and first-time buyers. That could make it harder for categories to expand and for challenger brands to enter established shopping routines.

Wright also drew a distinction between recommendation and discovery in AI-led shopping. Feeds and shopping agents, he said, tend to retrieve products based on past behaviour rather than create the conditions for consumers to find something new.

"When a feed serves you a product, or an agent builds your basket, you haven't discovered anything. You've been fed. The feed is built from your past. It's retrieval dressed up as discovery. Actual discovery starts with the shopper: you open something to see what's out there and you find things you weren't looking for. The catalogue was a discovery engine precisely because everyone got the same forty pages and nobody had sorted them by relevance. The serendipity was the product. This isn't nostalgia, it's arithmetic. Brands grow through light buyers and new buyers, the people whose baskets have never seen you. A feed trained on past behaviour rehearses what you already buy. So here's the question the agentic pitch never answers: how does a challenger brand get into a basket built by an agent that has never seen anyone buy it? Until someone solves that, the places where people still browse on their own terms get more valuable as the feeds narrow, not less. Out-of-home on the drive. Audio in the car. Total TV in the lounge room. The influencer a follower chose. And the pre-shop planning session, the Thursday catalogue's direct descendant, where a shopper goes looking at what's new and what's on offer before they've decided what they want. These are rooms. People walk into them on purpose. A brand can still be found there by someone who wasn't looking for it," Wright said.

Media mix

That view supports continued spending on channels associated with browsing and pre-shop planning, rather than relying solely on AI-mediated targeting. Shopfully pointed to out-of-home, audio, total TV, influencer media and planning moments before a shopping trip as places where consumers still make active choices and can encounter brands they did not intend to seek out.

For retailers and advertisers, the debate goes beyond whether AI tools become common in eCommerce. It also touches on how media budgets are allocated in a market where retail media is growing, but much of the new technology is still being used to refine operations rather than transform consumer behaviour.

Shopfully acknowledged that machine intelligence already plays a role in its own retail planning environment, particularly in surfacing offers, brands and products to shoppers as they prepare for the week ahead. Wright said the distinction is that the shopper initiates that session, rather than receiving a fully constructed basket from an automated agent.

He added that AI infrastructure may still become important as shopping agents develop, especially where current, local and machine-readable offer data is needed. But that stage of adoption remains formative, particularly in the households where most spending still sits, he said.

"The call for brands right now is older and simpler. While the new habit trains itself, keep buying rooms. Because once everything is fed, being findable is the only advantage left. People don't discover what they're fed. They discover what they go looking for. And they still go looking in rooms someone cared enough to curate," Wright said.