People pick up their menu at the McDonald’s restaurant in Times Square, Manhattan on September 29, 2026, in New York.
KHemaz | GVN | Getty Images
Fast food giants and supermarkets are rolling out a range of AI tools that could affect the prices shoppers pay, but experts warn that the spread of data-driven tools could make it easier to roll out personalized pricing.
Just this week, a federal antitrust lawsuit was filed against McDonald’s has alleged that the fast food giant uses an AI-powered “pricing engine” to set menu prices at all U.S. locations and overcharge customers for Big Macs and fries.
McDonald’s denied using AI to determine what each customer is willing to pay and said it provides its franchisees with “tools, resources, research and recommendations to help them make informed decisions.”
Despite this, food companies around the world are increasingly digitizing their operations using AI. Earlier this year, an American grocery chain Kroger said it uses an AI platform called FlashFood to mark perishable products nearing the end of their shelf life and market them to shoppers through an app.
Meanwhile, electronic shelf labels (ESLs), which display the prices of in-store items on digital screens, are becoming increasingly popular at supermarkets like Kroger, Amazon Costs, Walmartand whole foods.
The technology is also gaining ground among UK supermarkets such as TESCOMorrisons and Asda. More recently, global financial platform Revolut trialled facial recognition payment in some cafes, allowing customers to pay with just a glance.
CNBC contacted Amazon Fresh, Whole Foods, Tesco, Morrisons, Asda and Revolut for comment on the use of AI, but did not immediately receive a response.
As the use of AI becomes more normalized among retailers, experts warn that it could lead to more dynamic pricing — that is, frequent and rapid price changes in real time that could significantly affect shoppers’ experience.
“Dynamic pricing means changing prices in response to changing market conditions, such as demand, timing, capacity or competitor prices,” Miroslava Marinova, lecturer in commercial law at the University of East London, told CNBC. “It’s not new. Airlines, hotels and ride-sharing services have been using it for years.”
Clare Lombardelli and Rupal Patel, economists at the Bank of England, said in April that more sophisticated technology led to more frequent price changes and greater individualization, which could lead more companies to charge “as close to the maximum price a consumer is willing to pay for a good or service,” which they defined as “perfect price discrimination.”
These conditions could make it more difficult for statisticians to “measure and interpret” monthly inflation data, as the consumer price index is based on a representative sample of consumer prices.
“This works well when prices generally move slowly and evenly. But when prices change continually – and differently for each buyer – the idea of a ‘representative’ price becomes strained,” the BOE economists added.
AI collects more consumer data
Although dynamic pricing has long been in play, BOE economists and Marinova noted that AI tools such as ESLs and facial recognition are changing the amount of information companies can collect about consumers, from transaction history to browsing behavior, location and purchasing habits.
On Wednesday, British supermarket chain Sainsbury’s released “SmartLists,” an AI feature that helps customers create shopping lists and find products simply by uploading photos of what they need or typing in meal ideas.
“This is also the reason why the traditional distinction between dynamic pricing and personalized pricing becomes less clear in practice,” explained Marinova. “Dynamic pricing primarily responds to market conditions, while personalized pricing uses information about the consumer to estimate their willingness to pay.”
Since businesses use both pricing systems, it is questionable whether customer information is used to determine the prices consumers see.
“As retailers combine market-level insights with increasingly detailed consumer data, the line between dynamic and personalized pricing becomes finer,” Marinova added.
Walmart and Kroger have publicly insisted in recent years that they do not use dynamic or surge pricing to set individualized prices for customers, but rather use tools to streamline operations.
Several US states are moving to reduce data-driven pricing. New York requires most companies that use customer personal data to set prices to clearly disclose it. Maryland blocked food retailers and delivery services from using personalized, data-driven pricing to charge higher prices for certain foods, while New Jersey and Connecticut adopted measures targeting “watchdog pricing.”
Consumer choice compromised
Dynamic, personalized pricing isn’t automatically bad for shoppers, Marinova said, explaining that it can discount items for some consumers, making certain products and services more accessible.
However, the risk of individualized pricing is that consumers become uncertain whether the price they receive reflects general market conditions or whether it has been influenced by information about their own behavior.
“This makes it much more difficult to compare prices and know if another consumer is being offered a different price for the same product,” she said. “If consumers cannot understand why they received a particular price, cannot compare it to prices offered to others, and cannot effectively switch to another supplier, the normal disciplinary effect of consumer choice weakens.”
BOE economists added that an additional challenge is that personalized pricing “splits the consumer experience,” meaning households will face increasingly different inflation rates.
“And when prices differ for the same thing, inflation becomes even more personalized – and aggregate measures may no longer reflect household experience,” they said.
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