McDonald's Is Using AI to Recommend Menu Prices. What Could Go Wrong?

The price of a Big Mac is becoming a technology story.

For decades, the Big Mac has been one of the most recognisable products in global consumer culture. Same golden arches. Same familiar burger. Same promise of a predictable McDonald's experience wherever you happen to be.

But the price has never been quite as universal as the product itself.

Now artificial intelligence is entering that equation. A Reuters investigation published on September 29, 2026, found that McDonald's uses a machine-learning pricing system to provide restaurant-specific recommendations across its nearly 14,000 U.S. restaurants and some international markets. The engine analyses large volumes of transaction data and other market information to generate what McDonald's internally describes as an "optimal price" for individual menu items at individual restaurants.

And that has turned an ordinary business function, setting the price of a burger, into a much larger conversation about AI, consumer trust, algorithms and how much a brand should know about what customers are willing to pay.

Visual 01, hero image: the product everyone recognises. Suggested caption: "The Big Mac has become one of McDonald's most recognisable global products, and now one of the symbols at the centre of the AI-pricing debate." Source via McDonald's official Media Assets Library.

So, is AI actually setting the price of your Big Mac?

Not exactly. That distinction is important.

Reuters reported that McDonald's pricing engine uses machine-learning algorithms to analyse millions of daily transactions. Its interface can incorporate assessments of local price sensitivity and information including nearby competitors' publicly available menu prices. Reuters reported that the system can generate a recommended price for individual products at restaurant level.

But on October 1, McDonald's issued a public response disputing some of the way the story had been characterised. The company says, "AI does not set the price of a Big Mac." McDonald's global chief impact officer Jon Banner went further on social media, stating "Reuters got it wrong."

McDonald's says its technology provides recommendations, rather than automatically changing prices. Franchisees remain responsible for deciding the final menu price at their restaurants. The company also explicitly says it does not use dynamic pricing that raises and lowers prices in real time based on the time of day or an individual customer.

So the more accurate description is, AI informs pricing, humans still approve the price. That may sound like a subtle difference. From a brand perspective, it is anything but.

Two miles. Same burger. Different price.

One of the most striking examples uncovered by Reuters came from Fresno, California.

In September, Reuters found one company-operated McDonald's selling a Big Mac for $5.69. Another company-operated McDonald's around two miles away offered the same burger for $6.89. That is roughly a 21% difference.

Reuters explicitly noted that it could not determine whether the AI pricing engine caused that particular difference, different restaurant costs and local market conditions can also explain price differences. McDonald's itself argues that restaurants only a few miles apart can effectively operate in different markets, facing different costs, competitors and consumer conditions.

And technically, that makes sense. But branding isn't experienced technically. It is experienced emotionally.

A customer sees, Big Mac, McDonald's, price. They rarely think through the chain of independently operated franchise, local labour cost, geographic market conditions, algorithmic recommendation, franchisee decision.

That gap between how a business operates internally and how consumers perceive it externally is where the branding problem begins.

Visual 02, digital menu boards. A photograph of a digital menu board works well here, connecting the pricing algorithm to the moment customers actually experience the price. Source via McDonald's official Media Assets Library.

What does the algorithm actually look at?

The interesting part isn't simply that McDonald's uses AI. Large companies have been using analytics to optimise pricing for years. What changes the conversation is the scale and sophistication of those recommendations.

Reuters reported that the system evaluates large amounts of restaurant transaction data and can incorporate factors such as local demand, previous pricing behaviour and publicly available competitor pricing. Screenshots reviewed by Reuters showed the platform assessing whether a restaurant's customers demonstrated low, medium or high price sensitivity.

According to Reuters, McDonald's works with analytics company Tiger Analytics on the platform. Two former Tiger Analytics employees told Reuters that corporate rules can also influence recommendations, for example, favouring increases on products that haven't been raised recently in roughly two years, or avoiding certain increases on drinks and ice cream during summer. Tiger Analytics declined to comment to Reuters on its work for McDonald's.

This means the algorithm isn't simply asking what a Big Mac should cost. It is closer to asking, given everything known about this restaurant, this location and previous customer behaviour, what price makes sense here. That is far more powerful, and potentially far more sensitive.

The phrase causing the biggest problem: "willingness to pay"

Reuters reported that McDonald's pricing interface can display assessments partly based on what it labels customer "willingness to pay" in an area, with screenshots showing messages such as a restaurant registering medium sensitivity to price.

Those three words change the way consumers interpret the system. There is an enormous psychological difference between "our costs are higher here" and "our data suggests people here may pay more."

McDonald's strongly rejects the interpretation that the system determines what any individual customer is willing to pay. The company says its tool operates at the restaurant and local-market level rather than personalising prices for specific people.

That distinction matters legally and technically. But from a communication perspective, the controversy demonstrates something every company using AI should understand. What your algorithm can do is only half the story. What customers think your algorithm is doing can matter just as much.

This isn't Uber-style surge pricing

This is another distinction worth making clear.

When consumers hear AI plus pricing, many immediately imagine surge pricing, demand goes up, the algorithm sees demand, the price instantly rises. McDonald's says that isn't happening. Its October response says the pricing recommendation tool does not automatically alter prices at different times of day, does not determine individual customer prices and does not engage in real-time dynamic pricing.

That makes this fundamentally different from a system where a burger costs $6 at 2pm and suddenly becomes $8 during the dinner rush.

Nevertheless, the backlash illustrates how little tolerance consumers may have for ambiguity around algorithmic pricing. Wendy's discovered something similar in 2024 when discussion of potential "dynamic pricing" triggered a major public reaction; Wendy's later said those comments had been misconstrued and that it had not implemented surge pricing. Once the idea enters the public imagination, explaining the technical distinction becomes much harder.

Video insert: Reuters also produced a short video demonstrating the issue by purchasing Big Macs from different New York restaurants and comparing prices. The reporter paid $7.17 for a Big Mac at one location and $8.05 at another restaurant roughly two miles away. As with the Fresno comparison, different prices alone do not establish that the AI engine caused the difference. Link to the Reuters video as an external source rather than downloading and re-hosting the footage, per Reuters' licensing terms.

There is another tension: McDonald's vs its franchisees

This story isn't only about customers. It is also about the relationship between McDonald's corporate headquarters and the independent franchisees operating most restaurants.

McDonald's says its recommendations are optional and franchisees independently determine their prices. Its own online terms similarly state that individual restaurants set their own prices.

Reuters, however, interviewed franchisees who described pressure to follow the recommendations. Five store owners told Reuters that McDonald's had pressured them in various ways to use its pricing guidance. Company documents reviewed by Reuters also referenced franchisees "constructively engaging" with approved pricing consultants and tools as part of business standards, while CEO Chris Kempczinski has discussed "pricing non-compliance" in some franchisee reviews.

McDonald's maintains that recommendations remain recommendations, not mandates. That creates a fascinating business-design problem, how much central control can a global brand exert while its restaurants remain independently operated businesses?

Consistency has always been one of McDonald's greatest strengths. Yet pricing is one area where complete consistency is practically impossible. AI may make local optimisation better. But it could also make those differences more visible.

The antitrust question

There is also a regulatory dimension.

Reuters reported that McDonald's own pricing-platform terms warn franchisees that individual restaurant owners may be considered competitors and therefore need to comply with antitrust and competition laws.

Former U.S. Federal Trade Commission commissioner William Kovacic told Reuters that the language indicated awareness of the potential issue, although other legal experts interviewed by Reuters argued that the actual regulatory risk could be limited because courts have historically allowed franchisors substantial influence over franchise systems. McDonald's says it takes antitrust compliance seriously and rejects the idea that those warnings demonstrate anti-competitive conduct.

So this isn't evidence that McDonald's has violated competition law. It is evidence of a broader issue companies are going to encounter increasingly. Once algorithms influence commercial decisions across huge networks, regulators will want to understand what information those systems share and how recommendations are produced.

The bigger issue isn't AI. It's trust.

This is where the story becomes particularly relevant from a branding perspective.

The actual technology is easy to understand. Businesses have always tried to estimate what they can charge, what competitors are charging, how sensitive customers are to price, which products can absorb an increase, and where to discount. AI simply allows those questions to be answered using vastly more information.

The difficult question is whether consumers believe the company is using that intelligence for them or against them.

McDonald's argues that its pricing systems increasingly support affordability and that recent recommendations can include price reductions. Reuters similarly reported that the engine has recently pushed more conservative pricing in some circumstances, creating friction with franchisees facing higher operating costs.

That makes the situation more complicated than the viral version of the story, that AI figures out how much money it can squeeze from customers. That interpretation is catchy. It isn't a complete representation of what the evidence currently shows.

Visual 03, McDonald's as a global system. A restaurant or drive-thru photograph works well here, since the story is ultimately about a system operating across thousands of physical locations rather than one piece of software. Source via McDonald's official Media Assets Library.

AI is becoming invisible infrastructure

McDonald's isn't treating AI as a flashy chatbot sitting on its homepage. It is increasingly appearing deeper inside the business.

The company's broader McDonald's NEXT strategy describes a future involving greater automation, digital systems and AI-supported operations as it attempts to improve restaurant productivity and the customer experience.

That may be the more important long-term story. The first phase of consumer AI was visible, typing something into a chatbot, generating a picture, talking to a bot. The next phase will often be invisible. AI may influence the price you see, the product you're recommended, the advertisement you're shown, the inventory a store carries, the offer appearing in your app, and eventually the design of the experience itself.

You may never know an algorithm was involved. And that changes the role of branding.

When optimisation collides with brand consistency

There is something almost contradictory about McDonald's using hyper-local intelligence.

McDonald's became one of the world's largest brands partly by creating consistency. A Big Mac in Los Angeles is recognisably related to a Big Mac in London, Dubai or Tokyo. The architecture may change. The menu may change. The cultural context certainly changes. But the brand feels familiar.

Algorithmic optimisation moves businesses in the opposite direction. It asks why every customer should see the same thing, why every market should receive the same recommendation, why every restaurant should use the same price, why every experience shouldn't adapt.

This creates one of the defining challenges for brands entering the AI era, how personalised can a brand become before it stops feeling consistent? There isn't a simple answer. But McDonald's may become one of the most interesting companies through which to watch that question play out.

The Darwin perspective

McDonald's AI pricing controversy isn't really a story about hamburgers. It is a preview of a much larger shift.

Businesses now have tools capable of making increasingly sophisticated decisions about customers, markets and behaviour. The competitive advantage is obvious. The brand risk is equally obvious.

Companies will need more than technically correct algorithms. They will need explainable experiences. If consumers understand why something changes, they may accept it. If an invisible system appears to be making decisions about how much money can be extracted from them, trust can disappear remarkably quickly.

The smartest AI strategy therefore may not be the one that optimises every possible metric. It may be the one that knows which things a brand should optimise, and which things customers expect to remain human.

AI can calculate the optimal price. It can't calculate the price of losing trust.