How AI Could Reshape Grocery and Fast Food Prices for Consumers

Artificial intelligence is increasingly influencing the pricing strategies of major grocery chains and fast-food companies, potentially altering what consumers pay for everyday items. This shift toward data-driven pricing methods has sparked debate about transparency and fairness in pricing across the retail sector.

Recently, a federal antitrust lawsuit accused McDonald's of using an AI-powered "pricing engine" to set prices for menu items such as Big Macs and fries at its U.S. locations, allegedly causing customers to pay inflated prices. McDonald's has denied claims that it uses AI to tailor prices for individual customers, stating instead that it provides franchise operators with tools, research, and recommendations to help set prices.

The use of AI in food retail is not limited to fast food. Kroger, a leading American supermarket chain, has introduced an AI platform named FlashFood, which helps mark down prices on perishables nearing expiration. Customers are then notified of these discounts through a mobile app, potentially reducing food waste while offering savings. Additionally, the adoption of electronic shelf labels (ESLs) that digitally display prices is becoming more common at stores like Kroger, Walmart, and Amazon Fresh. While Whole Foods Market has confirmed the use of ESLs, the company clarified that it does not employ dynamic pricing and views digital labels primarily as a way to ease price updates and cut down on paper waste.

Across the Atlantic, U.K. supermarkets including Tesco, Morrisons, and Asda are also experimenting with digital pricing technologies. The British Retail Consortium’s director of food and sustainability, Andrew Opie, has emphasized that these supermarkets are not pursuing dynamic or surge pricing. Instead, their focus lies on improving efficiency and ensuring accurate pricing information through faster updates.

In a related development, Revolut, a global financial platform, recently trialed facial recognition payment technology in select coffee shops. This system allows customers to pay by simply presenting their face, matched to a previously submitted selfie ID used for security purposes. No extra personal data is collected during the transaction.

Experts warn that as AI tools become widespread, dynamic pricing—where prices fluctuate rapidly based on factors such as demand or competitor pricing—may become more prevalent. Miroslava Marinova, a senior commercial law lecturer at the University of East London, noted that dynamic pricing is not new, citing its long-standing use in industries like airlines, hotels, and ride-hailing services.

Bank of England economists Clare Lombardelli and Rupal Patel have highlighted that technological advances are enabling prices to change more frequently and even become personalized to individual consumers. This practice, sometimes called perfect price discrimination, involves charging customers as close as possible to the maximum amount they are willing to pay. Such pricing patterns could complicate the measurement of inflation, as traditional consumer price indices rely on representative samples of prices that assume uniform changes. When prices vary constantly and differ by shopper, the concept of a "representative" price becomes less reliable.

AI’s ability to collect detailed consumer data further blurs the lines between dynamic and personalized pricing. Transaction histories, browsing behavior, location data, and purchasing patterns can all inform pricing decisions. For example, Sainsbury’s in the U.K. recently launched "SmartLists," an AI-powered feature that helps shoppers create grocery lists by uploading photos or typing meal ideas. This tool enhances convenience but also signals how AI integrates deeper into consumer shopping habits.

Marinova explained that dynamic pricing primarily reacts to market conditions, while personalized pricing uses individual consumer data to estimate willingness to pay. As retailers combine market analytics with detailed customer profiles, the distinction between these approaches grows less clear. Although some retailers, such as Walmart and Kroger, insist they do not use AI to set personalized prices, concerns remain about potential undisclosed price variations.

In response, some U.S. states are implementing laws to regulate data-driven pricing. New York mandates disclosure when businesses use personal data to set prices. Maryland restricts personalized pricing for food retailers and delivery services, while New Jersey and Connecticut have introduced measures against what is sometimes called "surveillance pricing."

While dynamic and personalized pricing can offer benefits, such as discounts that improve access to certain products, there are important drawbacks. Consumers may lose transparency regarding whether the prices they see reflect market forces or are influenced by their own data. This opacity makes it difficult to compare prices and determine if others are paying different amounts for the same items. Without the ability to understand and compare pricing, consumer choice loses some of its power to regulate the market.

The Bank of England economists also pointed out that personalized pricing could fragment consumer experiences, leading households to face distinct inflation rates. As prices for identical goods diverge between shoppers, aggregate inflation figures may no longer capture the economic realities faced by individuals.

As AI technology continues to evolve and integrate into retail pricing strategies, consumers and regulators will need to closely monitor its impact on fairness, transparency, and market competition.