Your Price Is a Variable, Not a Number: What McDonald's AI Pricing Engine Teaches Growth Teams About Personalized Pricing

On 2 October 2026, a proposed nationwide class action was filed in federal court in Chicago. It claims a large restaurant chain used an algorithm to coordinate menu prices across company-owned and franchised restaurants. The chain rejects the claims.

The lawsuit cites a Reuters investigation from 29 September 2026. That investigation described a machine-learning pricing system used across the chain's U.S. restaurants. The lesson for every B2C growth team is clear. Price is no longer one fixed number. It is a variable.

What the engine reportedly does

Machine learning is a type of AI that finds patterns in large amounts of data. According to Reuters, this chain has used such a system since at least 2019. It was built and run by Tiger Analytics, according to two former employees.

The system crunches millions of daily transactions across nearly 14,000 restaurants. It produces what the company calls an "optimal price" for each item at each location. Optimal here means the price the model thinks will work best.

One input is price sensitivity. That is a simple idea: how much will demand fall if the price rises? A franchisee-facing screen reviewed by Reuters read, "Your restaurant is showing MEDIUM SENSITIVITY to Price." It cited, in part, the "willingness to pay of customers in your area." The engine also pulls public price information from competitors' online menus.

Here is a concrete example. Reuters checked the chain's app in September. A Big Mac at a company-run store in Fresno, California, sold for $5.69. Another company-run store about two miles away sold it for $6.89. That is a 21% premium. Reuters could NOT confirm the engine caused that gap. The gap is real. The cause is not proven.

Franchisees told Reuters the engine has widened price gaps between stores, including from neighborhood to neighborhood. Five franchisees said the company pressured them to use the AI pricing tools.

The other side of the story

McDonald's strongly disputes the picture above. Its position is clear: "AI does not set the price of a Big Mac or any other menu item. It does not change prices. And it does not determine what an individual customer pays."

The company says franchisees independently set prices. The portal is "a tool, not a mandate." It called the Reuters reporting "speculative and uninformed."

A fair post must carry both sides. Reuters describes a powerful engine, based on screenshots and interviews with nine sources. McDonald's says the engine only advises, and humans decide.

The same Reuters report notes that Yum Brands is also turning to AI for pricing and other operations. Yum owns KFC and Taco Bell. It did not respond to a request for comment. The trend is wider than one chain.

The trust problem: customers notice when they feel sorted

Dynamic pricing means prices that change based on demand, time, place, or other signals. Airlines and hotels have used it for years.

In 2024, another fast-food chain faced backlash after its CEO talked about testing "dynamic pricing." The company said its comments were misconstrued.

In December, a grocery platform ended a limited test of AI tools that showed different grocery prices to different shoppers, after criticism from consumers and lawmakers. The platform said it will never use personal data to determine prices.

The lesson is simple. Customers accept that prices differ by city or store. They push back fast when they feel sorted into groups.

Antitrust law is the other risk. Antitrust means the rules against companies working together to fix prices or limit competition. The new lawsuit claims the tool coordinated prices across company-owned and franchised restaurants using non-public pricing data. The chain rejects that claim.

What B2C growth teams should do now

1. Decide what you will never personalize on. Do not set prices based on one customer's identity, income, or personal data. Use store-level signals instead.

2. Explain the price in plain words. If prices differ by location, say so. Explain what drives the difference, such as rent or local costs. Silence invites suspicion.

3. Track trust, not just revenue. Watch complaints, refunds, app reviews, and social posts after any price change.

4. Keep a human in the loop. Let AI suggest. Let a person decide. Log every change: who approved it and why.

5. Test fairness before launch. Ask: would our best customer still feel treated fairly if they saw this price next to another customer's price? If no, do not ship it.

Price has always shaped growth, loyalty, and retention. AI just makes it faster, more precise, and more visible. The teams that win will treat price as a trust decision, not just a math problem.

Sources: Reuters investigation (via Channel News Asia; via DevDiscourse); class action coverage (Asianet News; SE Daily).