McDonald’s is increasingly relying on artificial intelligence (AI) to determine pricing at its restaurant locations, marking a shift toward more dynamic and data-driven price-setting in the fast-food industry. According to recent reports, the burger chain is encouraging franchise owners to use AI-powered algorithms that analyze customer data across its approximately 14,000 U.S. outlets. This technology assesses local factors, including consumer willingness to pay, to set prices individually for each restaurant.
The impact of this approach is evident in price discrepancies within the same metropolitan area. For example, in Fresno, California, a Big Mac may be priced at $5.69 on the McDonald’s app at one location, while a nearby outlet just two miles away charges $6.89 for the identical item. Such differences highlight a broader trend of location-based, personalized pricing strategies that can contribute to rising food costs for consumers.
This method of "surveillance pricing" involves companies tracking customer behavior and demographic data to adjust prices according to perceived purchasing power. Experts warn that shoppers are effectively being monitored, with prices tailored not only by geography but also by factors such as device type, browsing habits, and repeat visits. Lindsay Owens, author of “Gouged: The End of a Fair Price — and What That Means for Your Wallet,” describes the practice as a combination of consumer surveillance and price inflation, a tactic likely to generate consumer frustration.
Retail sectors beyond fast food are also exploring similar tactics. Supermarkets are increasingly shifting toward electronic shelf labels that allow for real-time price adjustments. These systems could facilitate dynamic pricing models, where costs fluctuate based on variables such as time of day, weather, and demand, resembling pricing strategies long used in airline ticketing. While major grocery chains like Kroger and Amazon have denied immediate plans to adopt this method, some analysts remain skeptical.
The rise of dynamic and AI-driven pricing coincides with changing buyer behaviors, including increased reliance on delivery and online shopping. The convenience of ordering with minimal effort has revealed consumer tolerance for higher prices, often without much resistance, underscoring broader economic factors impacting affordability. Critics caution that while such technologies provide companies with powerful tools to optimize revenue, they also risk alienating customers by eroding price transparency and fairness.
As AI becomes more embedded in retail pricing, consumers may face growing challenges in comparing costs and identifying value. Awareness of these evolving pricing practices remains limited, suggesting a need for greater public understanding and potential regulatory scrutiny to ensure equitable market conditions.