Australian consumer protection laws are struggling to keep pace with the rise of AI-powered dynamic pricing tools that adjust pricing based on individual shopping behavior, a retail technology expert has warned. Dr Aayushi Badhwar, a lecturer at RMIT University, highlighted that these algorithms are already influencing what Australians pay in real time, raising concerns about the legal framework governing such practices.
“Consumer laws need to actively start considering technology and data, not just pricing in isolation,” Dr Badhwar said. “We can’t have loose regulations that don’t account for the ethics of technology.” Unlike traditional dynamic pricing, where prices fluctuate based on overall demand, personalised pricing allows for different individuals to see varying prices for the same product at the same time, a phenomenon Dr Badhwar described as a legal “grey zone” that regulators have yet to address.
The prevalence of this practice is significant, particularly in the fashion industry, according to Dr Badhwar. Retailers can monitor how long a shopper lingers on a product, what items are added to their cart, and what gets abandoned, using this data to tailor prices or discounts in real time. Shoppers who leave items in their carts may receive discount emails shortly after, while others might find the prices of the same items increased.
“The system is trying to determine one thing: what is the most you are willing to pay?” Dr Badhwar explained. For instance, lower-income or price-sensitive shoppers might see more discounts, albeit just enough to encourage a purchase, or they might be shown cheaper alternatives while premium options are withheld. This targeting could disadvantage these shoppers, as they might not receive better deals and may be nudged towards spending the maximum they can afford. “It’s less about providing the best possible price, and more about discovering the threshold where that person will convert,” she added.
Dr Badhwar noted that AI’s capabilities in understanding consumer behavior have significantly advanced. AI can now accurately assess whether a customer is merely browsing or is likely to make a purchase, how often they need to see an item before buying, and whether they prefer to wait for sales. While these insights may seem minor on an individual level, applied across millions of shoppers, subtle improvements in targeting can lead to substantial revenue increases for brands, which raises ethical concerns about the potential for consumers to be quietly “squeezed.” “If the system knows your comfort zone, it can keep prices just below that point, meaning you’re consistently paying the maximum you’re willing to pay,” she said.
The relationship between consumers and pricing algorithms is also evolving. With the introduction of advanced AI shopping tools, consumers might express price preferences, such as only wishing to purchase items when they fall within a specific price range. “That feels empowering, but at the same time, you’re giving brands very clear signals about your limits,” Dr Badhwar cautioned. As retailers already incorporate sales and discounts into their pricing strategies, this does not guarantee a genuine bargain but rather an offer within the buyer’s comfort zone.
Despite Australia lagging behind the United States in the deployment of fully autonomous shopping AI, Dr Badhwar indicated that the gap is closing rapidly. AI-generated product summaries are already appearing prominently in Australian search results, and advertising platforms are now tailoring campaigns based on user behavior data. She predicts that within five years, consumers may routinely delegate their purchases to AI agents that monitor prices and negotiate with retailers on their behalf, while retailers operate their own AI systems in response.
The Australian Competition and Consumer Commission (ACCC) has initiated inquiries into some aspects of this landscape, but Dr Badhwar argues that mere disclosure requirements are insufficient. She cited the inquiry’s findings indicating that many Australians do not grasp the implications of data collection agreements encountered while browsing. “When you read ‘by browsing this site, you agree to data collection,’ what does that really mean? The language used in these policies is not easy to understand unless you’ve spent time learning about it,” she stated.
She also highlighted a regulatory loophole where conduct that would be deemed illegal between businesses may not be subject to the same scrutiny when executed through algorithmic means. “That gap is exactly what needs attention,” she said. Regarding whether AI pricing tools could be designed to curtail consumption instead of promoting it, Dr Badhwar affirmed that while the technology to do so exists, the incentive does not. “Most AI pricing systems are designed to do one thing really well: maximise revenue. Any positive impact on overconsumption is usually a side effect, not the intention,” she explained. “The reality is we’re operating in a system where consumption drives revenue. So, most tools naturally lean toward profit over reducing demand.”
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