A lawsuit challenging the use of artificial intelligence in McDonald’s franchise pricing has brought a fundamental competition issue into focus: when does software designed to improve commercial decisions begin to undermine independent pricing? The case alleges that an artificial intelligence-powered tool used within the restaurant chain allows franchisees to benefit from commercially sensitive information that could weaken competition and contribute to higher prices. McDonald’s disputes the characterisation of its system, maintaining that franchise operators retain responsibility for their own prices. The dispute highlights the difficulty of applying established competition principles to pricing systems that can analyse extensive commercial data and influence decisions across thousands of outlets.
Pricing Algorithms Change How Businesses Compete
Businesses have long used sales figures, local demand, inventory information and competitor prices to decide how much to charge customers. Artificial intelligence can process these variables at scale, identify patterns and produce recommendations that would be difficult to generate manually. For restaurant operators, such tools may help assess the relationship between menu prices, customer demand, product costs and local trading conditions.
The commercial attraction is clear. A restaurant that prices its products too high may lose customers, while one that charges too little may struggle to cover labour, ingredients, rent and other operating expenses. Analytical software can help operators identify pricing opportunities and evaluate how changes might affect sales and profitability. For a large franchise network, a common system can also make analysis more consistent across locations with different customer profiles and cost structures.
The competition concern emerges when a pricing tool does more than analyse information available to an individual business. If a system uses nonpublic information from multiple outlets to generate recommendations, it may influence how competing businesses respond to market conditions. Depending on the design and use of the system, access to shared information could reduce the uncertainty that normally encourages independent pricing decisions.
However, the use of a common algorithm does not automatically establish unlawful coordination. A tool that recommends prices using general market trends or an individual outlet’s own information may operate differently from one that uses competitors’ confidential data to coordinate pricing behaviour. The relevant questions concern the information collected, the recommendations produced, the extent of participation and the degree of independence retained by each business.
Franchise Structures Complicate Questions of Independence
McDonald’s operates through a large network of restaurants, many of which are owned by independent franchisees. The relationship between a franchisor and its franchisees combines separate business ownership with contractual obligations governing branding, operations, products and other aspects of the customer experience. This structure creates efficiencies, but it also raises questions about how much commercial independence individual operators retain.
Standardisation is central to the franchise model. Customers expect familiar products and service standards across locations, while the franchisor seeks to protect the brand and maintain operational consistency. Shared technology can support those objectives by providing common analytical tools and helping operators respond to changes in costs and demand.
Pricing, however, has direct implications for competition between nearby outlets. Where franchisees operate in overlapping markets, their decisions about menu prices can influence whether customers choose one location over another. Independent pricing gives each operator an opportunity to compete on price, even when products and branding are similar. If a shared system materially reduces that independence, the competitive consequences may extend beyond the relationship between the franchisor and its franchisees.
The lawsuit alleges that the pricing system uses nonpublic sales and other commercial information in ways that raise antitrust concerns. The company, by contrast, has maintained that its tool provides recommendations and does not directly determine prices for individual restaurants. These positions illustrate the distinction between formal authority and practical influence. An operator may technically retain the final decision while still relying heavily on recommendations generated by a system that shapes the available choices.
Determining the significance of that influence requires evidence. The court would need to consider how the system works in practice, whether franchisees can reject recommendations without consequences and whether the information used allows competing operators to anticipate or align their pricing decisions.
Shared Data Can Improve Efficiency or Reduce Competition
The economic value of data depends partly on who can access it and how it is used. An individual restaurant can analyse its own sales to determine which products sell best at different prices. It can also use publicly available information to understand local competition. These activities generally help businesses make decisions based on their own commercial circumstances.
A shared platform can provide additional benefits by identifying patterns across a wider network. Operators may learn how demand varies by location, how promotions affect sales or how menu changes influence purchasing behaviour. Such insights can help businesses manage costs and avoid decisions based on incomplete information.
Yet access to competitors’ nonpublic information may change the competitive environment. If businesses can observe detailed sales or pricing information that would otherwise remain confidential, they may gain an ability to anticipate competitors’ behaviour. An algorithm that aggregates information and recommends similar prices could potentially reduce independent decision-making, depending on its design and the surrounding market conditions.
The risk is not limited to traditional agreements between competitors. Automated systems can influence commercial behaviour without requiring every participant to communicate directly with another. That makes the technical details particularly important. Regulators and courts must distinguish between software that helps businesses respond independently to common market conditions and systems that facilitate coordination through shared sensitive information.
The same analytical distinction applies to other industries. Airlines, hotels, online retailers and property businesses use pricing systems to adjust charges in response to demand and available supply. Such practices can improve efficiency, but the use of shared data or common software may create competition concerns when it substantially reduces independent pricing decisions. The legality of a particular system depends on the applicable law and the facts, rather than on the mere presence of artificial intelligence.
Consumer Prices Require Careful Economic Analysis
The lawsuit also raises the question of whether algorithmic pricing has contributed to higher menu prices. Establishing that connection requires more than showing that prices increased while a pricing tool was in use. Restaurant prices can rise because of changes in food costs, wages, rent, energy expenses, financing and other operating conditions. The effect of a particular algorithm must be assessed against these competing explanations.
A robust analysis would examine pricing patterns across locations, the timing of recommendations, the extent to which franchisees followed them and the differences between restaurants using the tool and comparable businesses operating under other conditions. Evidence about margins, customer demand and the availability of alternatives could help clarify whether the system changed competitive behaviour or merely reflected common cost pressures.
The distinction matters for consumers because higher prices can have different causes and require different policy responses. If prices rise because operating costs increase, improving supply conditions or reducing cost pressures may be more relevant than restricting pricing software. If a system facilitates unlawful coordination, competition enforcement may be necessary to restore independent decision-making and protect customers.
The outcome could also influence how companies design commercial analytics. Businesses may need to assess whether their systems rely on confidential information from competing operators, whether recommendations are sufficiently transparent and whether customers or franchisees retain meaningful choices. Clear safeguards can help preserve the benefits of data analysis while reducing the risk that a shared platform becomes a mechanism for weakening competition.
The central issue in the McDonald’s dispute is therefore not whether artificial intelligence should be used in pricing. It is whether the way a system gathers information and influences commercial decisions preserves the competitive independence required by law. As automated pricing becomes more sophisticated, courts and regulators will need to evaluate not only who formally sets a price but also how the tools behind that decision shape the market in which businesses and consumers operate.
(Adapted from Reuters.com)
