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McDonald's is accelerating the deployment of AI-based location-driven dynamic pricing engines in the US, marking a comprehensive deepening of artificial intelligence's application in physical retail pricing strategies. The system processes massive transaction data from over 13,000 company-owned stores and competitors like Wendy's to calculate optimal prices for each store.
This algorithm has led to significant price differences for the same products in the same area. For example, two McDonald's stores only two miles apart in Fresno, California, have a 21% difference in the price of a Big Mac (5.69 vs. 6.89 dollars), and the price difference in New York city stores also reaches 11%. To strengthen strategy implementation, McDonald's has made cooperating with AI pricing advisors a mandatory condition for franchise renewal starting January 2026.
This move reflects that AI dynamic pricing is rapidly spreading from service industries such as aviation and transportation to high-frequency traditional retail. Previously, retail giants had been continuously exploring algorithmic price adjustments based on user profiles. In 2019, Target used AI to track user locations for precise pricing, causing the price of the same Samsung TV to jump by $100 in the parking lot.
Currently, Walmart plans to install electronic price tags in all 4,600 stores across the US by the end of 2026. Its AI patents already have the capability to perform real-time differentiated pricing based on user browsing and purchasing behavior, although its management has recently pledged to limit such applications.
The differentiated pricing mechanism driven by AI algorithms is extending to offline physical commerce, significantly improving the digital monetization efficiency of the retail industry. However, while companies use model computing power to pursue profit maximization, how to balance the algorithm's black box and consumers' right to fair transactions has become an unavoidable compliance and trust challenge in the process of AI commercialization.