The trust numbers behind the backlash
A 2026 Bentley-Gallup survey of 3,270 U.S. adults found that 80 percent believe businesses have raised prices more than necessary specifically to boost profits, and trust that businesses use AI responsibly fell to just 27 percent, down from 31 percent the year before. Those numbers matter enormously for any retailer or restaurant chain rolling out AI pricing tools right now, because they describe an audience already primed to interpret any AI-and-pricing announcement in the least charitable possible light, regardless of the specific technical details underneath it.
That skepticism explains why both affordability and AI have become politically sensitive topics simultaneously, and why public rebuttals from companies using AI in pricing start from behind no matter how carefully those rebuttals are worded. A message that tests well with investors, built around transaction data and operational efficiency, can land completely differently once it reaches a shopper who already assumes price increases are profit-driven rather than cost-driven.
Walmart's pledge and the shelf-label problem
Walmart CEO John Furner published an open letter pledging the company will not use income, shopping history, urgency, or a shopper's perceived willingness to pay to raise prices for that individual. The pledge directly answers critics pointing to a 2023 Walmart patent describing digital shelf labels that can change prices in a dynamic fashion based on items already sitting in a shopper's cart, a capability critics read as a blueprint for exactly the kind of individualized pricing the pledge now explicitly disavows.
Walmart's corporate affairs chief, Dan Bartlett, pushed back directly on that reading, arguing that the ability to update a display more quickly is not evidence that Walmart actually uses that capability to charge individuals differently. Groundwork Collaborative CEO Lindsay Owens offered the sharper counter-framing: Walmart says one thing in its damage-control letters to customers, implying the public pledge and the company's actual internal pricing capability deserve more scrutiny than a single open letter can settle on its own.
McDonald's is fighting the same fight in court instead of in a letter
McDonald's is defending against a proposed federal class action alleging its AI pricing recommendation system for franchisees facilitates price coordination across locations, a claim the company calls filled with inaccuracies while insisting franchisees retain final control over their own prices. At its investor day, McDonald's described its pricing engine as informed by transaction data, with app data specifically cited as useful for training the underlying AI models, language built for investors assessing margin discipline rather than for consumers weighing fairness.
The structural tension here is that transaction-data-informed pricing recommendations and price coordination across a franchise network can look identical from a consumer's vantage point, even when the legal and technical distinction between them is genuinely significant. McDonald's rebuttal strategy mirrors Walmart's almost exactly: draw a firm line that AI does not directly set individual prices, put a senior executive behind that specific claim, and publish explainers that stakeholders, including courts, can cite directly if the dispute escalates further.
Why the same playbook is producing the same exposure
Both companies are running what amounts to the identical communications playbook: a firm technical distinction stated plainly, a senior leader publicly standing behind that distinction, and reference materials built to be cited by analysts, regulators, or courts when the question resurfaces. That approach makes sense on paper, since it gives the company a consistent, defensible position to return to every time a new version of the same criticism surfaces in the press or in litigation.
The problem both companies are discovering in real time is that messages built for one audience do not stay confined to that audience once they reach the public. A technical distinction that satisfies a securities analyst or a judge evaluating a legal standard does not automatically satisfy a shopper who experiences a price change and has no visibility into which specific mechanism produced it. The rebuttal is technically accurate in both cases and is nonetheless failing to resolve the underlying trust problem, because the trust problem was never really about the technical mechanism in the first place.
What other retailers should take from this before their own rollout
Any enterprise currently building or expanding AI-driven pricing capability should treat the communications strategy as a first-class design requirement developed alongside the technical system, not as a reactive explainer written only after criticism surfaces publicly. The specific distinction a company intends to draw, whether that is dynamic versus personalized pricing, or recommendation versus coordination, needs to be testable and demonstrable to a skeptical outside party well before launch, not merely asserted confidently after the fact in a crisis response.
The deeper lesson from both Walmart's and McDonald's experience is that given current trust levels around AI and pricing specifically, the burden of proof has shifted meaningfully onto any company deploying these tools, and a well-worded pledge or explainer is no longer sufficient on its own. Retailers and restaurant chains planning their own AI pricing rollout should expect to need concrete, auditable mechanisms, not just carefully chosen language, if they want their own eventual rebuttal to land any differently than Walmart's or McDonald's has so far.
