AI-driven pricing changes affect shoppers as retailers adopt AI in retail operations.

A federal antitrust lawsuit filed this week alleges that McDonald's uses an artificial intelligence system to adjust menu prices across its U.S. locations. The case highlights a broader shift in retail, where supermarkets and fast-food chains are integrating AI into daily operations, raising concerns about how these tools could change what shoppers pay.

Grocery chains are already testing data-driven pricing models. Kroger uses a platform called FlashFood to discount items nearing expiration and promote them through a mobile app. Electronic shelf labels are also expanding at major retailers including Walmart, Amazon Fresh, and Whole Foods, while U.K. chains like Tesco and Asda follow suit. Some companies are experimenting with facial recognition payments to speed up checkout. These systems gather detailed records of shopping habits, browsing patterns, and location data, giving retailers a clearer view of customer behavior.

Experts caution that this technology enables rapid price adjustments tied to market conditions or individual buying habits. The line between market-driven adjustments and individualized pricing is narrowing. When stores combine broad market data with personal shopping history, charges can shift to match what a specific buyer might accept. Dynamic pricing means changing prices in response to changing market conditions, such as demand, timing, capacity or competitors' prices, said Miroslava Marinova, a commercial law lecturer at the University of East London. The convergence of these systems complicates how economists track inflation. The consumer price index relies on tracking a standard set of prices, but rapidly changing individual rates blur that baseline. That works well when prices mostly move slowly and uniformly. But when prices shift continually – and differently for each shopper – the idea of a 'representative' price becomes strained, Bank of England economists Clare Lombardelli and Rupal Patel noted.

State governments are moving to regulate data-driven pricing. New York requires companies using personal information to set prices to disclose their methods clearly. Maryland has banned personalized pricing for food and delivery services. New Jersey and Connecticut have passed laws targeting surveillance pricing practices. Retailers maintain they use these tools only to streamline operations, not to charge individual shoppers different amounts.

The transparency gap remains a central concern for buyers. When prices change based on hidden data points, shoppers struggle to compare costs or find better deals. If customers cannot see how prices are set or switch suppliers easily, normal market competition weakens. Households may also face different inflation rates, leaving aggregate economic figures less reflective of everyday spending.