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AI pricing tools can either recommend a price for a person to approve or change prices automatically. The label “AI” does not tell you which workflow a vendor sells—or what data it uses. Franchise owners evaluating one should focus on its inputs, authority to act, customer-level targeting, auditability, data sharing, and full cost.
How AI pricing tools set or recommend prices
Pricing software processes data and applies rules or models to produce a price or recommendation. The Competition Bureau of Canada describes algorithmic pricing as algorithms that set or recommend prices based on data inputs. The system may optimize for an objective chosen by the business, so ask the vendor what it is trying to achieve and what trade-offs that objective creates. Competition Bureau of Canada
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Rules-based systems
A person defines conditions and thresholds, and the software responds when a condition is met. For example, a rule might trigger a recommendation when demand or inventory crosses a preset threshold. The rules may be automated, but that does not necessarily make the system machine learning.
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Machine-learning models identify patterns in historical or incoming data and may update recommendations as new information arrives. Their behavior can be harder to interpret than a fixed rule, so owners should request explanations of individual recommendations and access to audit records.
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Recommendations versus automatic changes
These are separate questions from whether a tool uses AI. A recommendation workflow presents a proposed price for a person to review; an automated workflow can apply a change without individual approval. Some products combine rules, machine learning, human approval, and automation. Ask the vendor to demonstrate the actual workflow, including default permissions and who can override or reverse a change.
What data can influence a price?
Depending on the system, inputs can include store sales and transaction history, demand, costs, inventory, competitor prices, time, weather, local events, location, and customer information. Some systems may also use demographics, browsing behavior, loyalty data, or purchase history. Inputs can come from the franchisee, the franchisor, the vendor, or third parties; a vendor should identify each source and explain whether it is required, optional, inferred, or shared.
Dynamic pricing is not the same as personalized pricing
Dynamic pricing
Dynamic pricing responds to changing market or operating conditions, such as demand, inventory, competitor prices, time, or local events. A change based on store-level conditions is not automatically a price tailored to a particular customer.
Personalized pricing
Personalized pricing uses information about an individual or group—such as demographics, browsing, location, or purchase history—to tailor a price or offer. The two approaches can overlap: a system may consider both local demand and information about a customer’s behavior. In January 2025, the FTC said intermediaries it reviewed used personal information, including location, demographics, and webpage interactions, to tailor prices or offers. Its initial staff perspective covered intermediaries working with at least 250 clients; that figure is not a count of all pricing vendors. FTC, January 2025
In an August 2026 announcement, the FTC described a draft enforcement policy statement on personalized pricing. The agency said undisclosed collection or use of personal data to set a price may violate the FTC Act, while stating that it lacks authority to ban personalized pricing in all circumstances. The announcement listed September 18, 2026, as the comment deadline; the announcement alone does not establish what happened after that date. FTC, August 2026
Questions to ask before buying or enabling a tool
1. What data feeds the model?
- Request a field-level inventory covering sales, costs, demand, competitor information, inventory, location, time, customer profiles, browsing, loyalty, and transaction history.
- Ask which fields are mandatory, optional, inferred, or provided by third parties, and how often they are updated.
- Clarify whether customer-level data is necessary for the product’s stated function.
2. Can the tool change prices without approval?
- Ask which actions require owner or franchisor approval and whether approval can be configured by store, item, or change size.
- Find out whether changes can be scheduled or deployed automatically, who can override them, and how to restore the prior price.
- Request a demonstration of the live workflow, not just a description of the model.
3. Does it price by store, market, segment, or individual?
Ask whether two customers could receive different prices or offers because of personal data. Clarify how the product handles identity, loyalty records, device identifiers, and browsing signals, and what customers are told about their use.
4. Can you explain and audit a recommendation?
Ask the vendor to show an example with the inputs considered, the recommended change, the objective being optimized, and the record of who approved or applied it. Confirm that logs are available to your business and specify how long they are retained. The Competition Bureau notes that machine-learning systems may be difficult to understand, making usable explanations and audit trails important controls.
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Ask whether the vendor or franchisor pools nonpublic sales, cost, or pricing data across franchisees; who can access it; how it is aggregated; and whether recommendations for one location draw on information from nearby locations or competitors. A vendor’s access and data-use terms matter as much as the model’s output.
This is also a competition-law diligence question, not just a privacy question. An Associated Press report published October 6, 2026, described a lawsuit alleging that McDonald’s uses nonpublic franchise-level data in a way that coordinates prices. McDonald’s denied the allegations and said its optional tools do not automate, coordinate, or fix prices. The case is a live dispute; the allegations are not a court finding. Associated Press, October 6, 2026
6. What is the complete cost?
Get implementation, integration, platform, data, support, and renewal charges in writing. Ask which fees are one-time or recurring, what triggers additional charges, and what happens to access and data if you leave. FTC franchise materials report concerns about undisclosed technology and payment-processing fees; they do not determine whether a particular software contract or fee is lawful. FTC franchise-fee materials
7. What safeguards protect customers and the brand?
- Ask how the product limits unusually large or rapid changes and handles stale or incorrect competitor data.
- Clarify how promotions, advertised prices, and prices shown at checkout remain consistent.
- Ask how errors are detected, who gets alerted, and what emergency pause or rollback is available.
The FTC says dynamic pricing is permitted under the specific rule it discusses when pricing information is not misleading. Its upfront total-price provisions apply to live-event tickets and short-term lodging, so franchisees should confirm which rules apply to their sales channels and jurisdiction rather than assuming those provisions cover every business. FTC rule FAQ
What the McDonald’s example does—and does not—show
In an October 1, 2026 statement, McDonald’s said its tool provides restaurant-specific recommendations but does not set or change prices; franchisees retain independent menu-price decisions. The company also said the tool is not dynamic and does not price according to an individual customer’s willingness to pay. These are the company’s descriptions of its own system, not an independent product audit. McDonald’s cited a global network of more than 46,000 restaurants as context for local market variation. McDonald’s, October 1, 2026
The AP reported McDonald’s statement that franchisees own and operate 95% of its 14,000 U.S. stores. That company-reported figure helps explain why authority over local pricing is central to the dispute, but it does not establish how another franchisor or vendor operates. The practical lesson is to verify the specific product’s permissions, data flows, and contract rather than infer its behavior from the “AI pricing” label or another company’s description.
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