The Federal Trade Commission has opened a public comment period on a draft enforcement policy statement addressing personalized pricing — the practice of charging different customers different prices for the same product or service based on data collected about them, frequently through algorithmic or AI-driven systems.
The statement itself is not a new law or rule. It is a policy document meant to clarify how the FTC intends to apply existing consumer protection and antitrust authority to personalized and algorithmic pricing practices. The comment period gives businesses, trade groups, and consumer advocates a chance to weigh in before the agency finalizes its position.
Personalized pricing has become common not just in consumer retail but increasingly in B2B contexts, where AI-assisted CPQ (configure-price-quote) systems, sales enablement platforms, and dynamic pricing engines adjust quotes based on a prospect's firmographic data, deal history, competitive signals, or even browsing behavior on a pricing page. These systems are typically built to maximize win rates or margin, not to comply with consumer protection law — but the FTC's move suggests that distinction may matter less than the underlying data practices.
For a 10-200 person B2B company, the immediate legal exposure is limited: no rule currently exists, and enforcement policy statements are guidance, not binding regulation. But they typically foreshadow the theories the FTC will rely on in future investigations or complaints, and they often get cited by state attorneys general and private plaintiffs' attorneys pursuing similar claims under state consumer protection statutes.
Companies that have deployed AI or rules-based pricing logic in their sales stack — whether through a dedicated pricing engine, a CRM add-on, or a vendor's built-in dynamic quoting feature — should treat this as a prompt to audit what data drives those decisions. Key questions: What inputs determine a given customer's price? Are any of those inputs proxies for characteristics the FTC or state law would flag as discriminatory? Is there a documented, defensible business rationale for each pricing variable? Is the logic auditable, or is it a black box supplied by a third-party vendor with no visibility into its scoring inputs?
This is unconfirmed as a binding requirement, but the direction is clear enough that waiting for a final rule before reviewing pricing automation would be the riskier bet. Sales operations leaders who rely on AI-assisted quoting should treat the comment period as a deadline to get their own documentation in order, not as a reason to wait.