McDonald’s Is Letting AI Set the Price of Your Big Mac

The system uses machine-learning algorithms to analyze millions of daily transactions across McDonald’s nearly 14,000 U.S. restaurants. It then recommends what the company considers an optimal price for individual menu items at each location, from Big Macs to discounted coffee. The technology assesses factors such as local demand and how sensitive customers appear to be to price increases. It also uses publicly available prices from nearby competitors, such as Wendy’s and Burger King, as a benchmark to see how McDonald’s prices compare with the local market.

The result can be noticeable price differences between restaurants located only a few miles apart. In Fresno, California, one McDonald’s location reportedly charged $5.69 for a Big Mac, while another just two miles away charged $6.89, a difference of roughly 21%. It is unclear whether the pricing system caused that particular gap, since local costs and other market factors can also influence prices.

McDonald’s says restaurant operators still have final control over what they charge, describing the AI pricing system as “a tool, not a mandate.” But the technology does directly recommend prices for individual menu items at specific restaurants, and franchisees have reportedly faced pressure to follow those recommendations. McDonald’s also tracks when franchisees deviate from suggested prices, and pricing compliance can come up when the company considers franchise renewals and requests for additional locations. Since January, franchisees have also reportedly been required to “constructively engage” with approved pricing consultants and tools as part of McDonald’s business standards.

The technology itself has been around for years. McDonald’s has used some form of AI-assisted pricing since at least 2019, and CEO Chris Kempczinski said in 2023 that the company had developed proprietary tools capable of evaluating pricing at individual restaurants. Some franchisees said the system recommended larger increases during and after the pandemic, while more recent recommendations have included price reductions.

Despite the focus on what customers may be willing to pay, the system does not currently appear to set prices for individual diners. Instead, recommendations are made at the restaurant level using broader transaction and market data. Still, as companies collect more customer data and AI plays a larger role in pricing decisions, the technology raises questions about how easily restaurant-level pricing could eventually become more personalized. That possibility is already drawing regulatory attention, with the Federal Trade Commission examining personalized pricing practices that use consumer data to estimate what an individual may be willing to pay.

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