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Set approval rules by the risk of the finished marketing asset, not simply by whether AI was used. Assign a content owner to every asset, add claim, subject-matter, legal, privacy, or compliance reviewers when the content warrants them, and make each approval specific to the final version and its evidence. AI assistance does not remove an advertiser’s responsibility for truthful, non-deceptive, evidence-based claims.
What should an AI marketing approval policy cover?
Define “AI-assisted” broadly enough to include material drafted, generated, edited, or transformed with AI. Decide whether the policy applies to text, images, audio, video, synthetic voices or likenesses, testimonials, reviews, chat assistants, and consumer-facing interactions. Specify whether internal-only material follows the same review gates as published campaigns.
The IAB Framework V2 covers multiple media forms and consumer-facing interactions, but it is industry guidance—not a general legal rule. Your policy should also name the markets and channels it covers, who owns each review, and how teams handle exceptions. A simple internal checklist can implement the policy; workflow software is optional, not a substitute for clear decision rights.
Who should approve AI-generated ad copy?
Give every asset one accountable content owner who is responsible for getting it through review and publishing only the approved version. Then add reviewers based on what the asset says or depicts. The following tiers are a practical implementation of risk-management principles, not a matrix prescribed verbatim by the FTC or NIST.
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| Tier | Examples | Minimum review | Escalation |
|---|---|---|---|
| Routine | Formatting, spelling, low-stakes ideation, or ordinary copy without material factual claims | A trained content owner checks brand fit, audience, obvious errors, and that the asset is the intended version. | Escalate if review finds an objective claim, a potentially misleading depiction, or a sensitive audience or subject. |
| Claims-bearing | Product benefits, performance, comparisons, price or availability, environmental claims, or measurable outcomes | The claim owner checks the supporting evidence and approves only wording, qualifiers, and comparisons that the evidence supports. The content owner checks the final asset against that approved wording. | Route claims involving specialized categories or material uncertainty to the relevant subject-matter, legal, or compliance reviewer. |
| Elevated risk | Health or financial claims; endorsements, testimonials, or reviews; audience-sensitive material; synthetic depictions or voices that could mislead; or content aimed at children | Obtain review from the responsible subject-matter, legal, privacy, or compliance reviewer, as relevant, before publication. Record the decision and any conditions. | Pause publication if evidence, authenticity, audience impact, or applicable requirements remain unresolved. |
For claims-bearing assets, the FTC’s Advertising and Marketing guidance says: “Under the law, claims in advertisements must be truthful, cannot be deceptive or unfair, and must be evidence-based.” The FTC also notes that specialized product categories can carry additional rules. Its guidance identifies endorsements, reviews, children’s marketing, and health claims as areas needing attention.
What should reviewers check before publishing?
Keep claim substantiation, brand review, and disclosure review as distinct decisions. “Approved” should say what was checked; a brand-fit sign-off alone does not establish that a factual claim is supported.
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- Claims and evidence: Is each objective claim true and supported by appropriate evidence? Does the wording stay within that evidence, including its qualifiers, context, and comparisons? Check extra requirements that may apply to health, environmental, origin, performance, or specialized-category claims.
- Representations and authenticity: Could an image, voice, quote, testimonial, review, or chatbot interaction mislead people about who said or did something, or whether an endorsement or experience is genuine?
- Brand and audience: Does the finished asset fit the intended audience and brand, and has the appropriate owner checked obvious errors introduced during generation or editing?
- Final-version match: Does the asset that will actually run match the approved copy or media and the evidence reviewers considered? Changes to a claim, qualifier, depiction, offer, or target market may require a new decision.
- Disclosure: Has the team checked whether the market, channel, media form, or relationship requires disclosure, and whether any disclosure is clear in context?
How should teams handle endorsements, testimonials, and reviews?
Apply heightened scrutiny when AI creates or alters an endorsement, testimonial, or consumer review. Do not use synthetic content to create a false impression that a real consumer had an experience or endorsed a product. FTC guidance covers endorsement relationships, influencer marketing, native advertising, and the Consumer Reviews and Testimonials Rule.
When a material connection requires disclosure, make it clear and conspicuous where the audience encounters the recommendation. FTC guidance says affiliate disclosures should be close to the recommendation and link; burying a disclosure in comments is avoidable and may not be clear and conspicuous.
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Do AI-generated ads need a consumer-facing disclosure?
Not automatically just because AI was used. Decide by target jurisdiction, channel, content type, and the role of the provider or deployer, then check the applicable law and platform terms. Ask whether the output or interaction could cause someone to mistake a synthetic person, voice, image, video, or chatbot for a real person or event. Separate that consumer-facing question from whether AI helped with routine internal production.
The IAB’s August 18, 2026 Framework V2 recommends a risk-based, materiality-driven approach. Its announcement identifies generated images and videos, some synthetic voices and avatars, certain digital twins, and chatbots that might be mistaken for humans as examples for disclosure consideration. It says routine production uses and text or copy do not automatically require labels under the IAB framework. The framework is an industry resource, not a replacement for statutes, regulations, or platform rules. IAB Vice President of AI Caroline Giegerich said, “That said, not every use of AI needs a label — labeling everything teaches consumers to ignore labels and could negatively impact advertisers.”
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IAB reported that more than half of respondents in research with Sonata Insights in January 2026 wanted brands to disclose when an ad was fully AI-generated or used AI imagery or video. It also reported that 73% of Gen Z and Millennial respondents said clear disclosure would increase or have no impact on their likelihood to purchase. The August announcement excerpt does not provide sample size or full methodology, so these figures should not be treated as representative of every consumer or as creating a legal obligation.
For campaigns aimed at the European Union
Check whether the relevant activity and output fall within Article 50 of the cited consolidated EU AI Act text. It distinguishes provider marking of covered synthetic outputs from deployer disclosure for deepfakes and certain public-interest text, and includes conditions and exceptions. For public-interest text, the text includes an exception where human review or editorial control occurs and a natural or legal person holds editorial responsibility. The cited consolidated text states that the Act applies from August 2, 2026. Do not turn these scoped provisions into a blanket claim that every AI-assisted marketing asset needs a label.
What should the approval record contain?
Keep a traceable record that ties the decision to the exact asset, evidence, and market. A practical record can include:
- Campaign and asset identifier, intended channel, and target market.
- Final approved copy or media version, plus revision history.
- Claims made and the evidence supporting them, including approved qualifiers or conditions.
- AI tool and material AI use, if your policy tracks them.
- Disclosure decision and rationale for the relevant jurisdiction and channel.
- Reviewer names and roles, decision date, approval conditions, and the accountable content owner.
Store the approval record with the approved asset and its substantiation so the published version can be traced back to the decision. Reopen review when a claim, offer, output medium, target market, or applicable requirement changes. This record design is a recommended internal control, not a schema prescribed by the sources. NIST’s AI Risk Management Framework is voluntary and can help organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation; it does not provide a ready-made marketing sign-off chart.
How to put the policy into practice
- Scope it: List covered asset types, consumer-facing interactions, internal-only uses, markets, and channels.
- Assign ownership: Name a content owner for every asset and identify which roles can approve claims, disclosures, and elevated-risk material.
- Route by risk: Use the tier table to send routine work through a lighter check and add specialist review for claims or higher-risk content.
- Approve distinct questions: Record the claim-evidence decision, brand decision, and disclosure decision separately when each applies.
- Lock the approved version: Preserve the final asset, its supporting evidence, reviewers, conditions, and disclosure rationale together.
- Reassess on change: Send materially revised claims, offers, depictions, channels, or markets back through the relevant review gates.
The NIST AI RMF is voluntary, and neither it nor the cited FTC and IAB materials prescribe a universal approval matrix or service-level target. Teams should tailor routing to their products, audiences, and markets rather than treating this example as a legal safe harbor.
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