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Start with the work your team actually does
Before writing policy, list the marketing tasks that use AI and map how information and outputs move through them. The inventory should be short enough to maintain, but specific enough to show who owns each use and where risk can enter.
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For each use, record:
- Purpose and owner: what the team is trying to do and who is accountable for the workflow.
- Tool and vendor: which system is used, including relevant outside software providers.
- Inputs: what data, source material, or customer information is entered.
- Destination: where the output goes, such as an internal planning document, an ad, or a customer-facing chat.
- Audience and reviewer: who may be affected and who checks the work when review is needed.
- Stop or escalation condition: what would make the user pause, seek help, or avoid the workflow.
Examples include campaign ideation, audience research, segmentation, copy and image generation, translation, personalization, customer-facing chat, and performance optimization. This is a practical marketing inventory, not a prescribed NIST list. The National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) supports mapping systems, context, and ownership across an AI system’s lifecycle.
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Set review depth by risk, not by whether AI was used
A useful first screen asks what could happen if the output is wrong, whether it reaches the public, how sensitive the inputs are, how personalized or targeted the work is, what claims or rights must be substantiated, which jurisdictions apply, and how easily the result can be corrected. Use those factors to assign a review route rather than sending every prompt or draft through the same queue.
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| Tier | Typical use | Default route |
|---|---|---|
| Routine | Internal brainstorming in an approved tool using non-sensitive inputs. | Proceed under standard data and brand rules. The marketer remains responsible for editorial decisions. |
| Review | External-facing creative, factual product claims, personalization, translations, endorsements, or synthetic imagery. | A designated campaign reviewer checks accuracy, audience fit, rights, and any required disclosures before publication. |
| Escalate or block | Sensitive personal data, high-impact targeting, unsupported regulated claims, potentially misleading realistic synthetic depictions, or unclear ownership or data rights. | Ask the relevant specialist to resolve the issue, or do not use the workflow until it is resolved. |
These tiers are an implementation pattern, not a legal classification published by NIST or the Federal Trade Commission (FTC). NIST says organizations should determine the level of risk management they need in light of their risk tolerance and context.
Write approved-use rules people can apply mid-task
Make the approved-use policy concrete enough to answer questions at the moment someone is choosing a tool or preparing an asset. Match it to the systems the team actually uses, and name a policy owner and a fast escalation route.
- Which tools and use cases are approved, and which require prior review?
- What information must not be entered, including sensitive personal or confidential data?
- Can prompts and outputs be retained or used for model training under the tool’s settings or terms?
- Which assets need provenance records, and what evidence must be kept with a claim?
- Which channels, audiences, or types of generated content trigger a disclosure check?
- Who can approve an exception, and where should staff report a concern?
Include external models, software, and data in the governance perimeter. NIST governance guidance addresses third-party software and data, rights, and contingency planning for high-risk vendor failures. A tool being familiar or widely used does not, by itself, resolve those questions.
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A reviewer needs context, enough time, authority to reject or revise the work, and a clear standard for making that decision. Give them the source material and the relevant campaign brief rather than asking them to judge a generated asset in isolation.
For review-tier work, check the points that matter to the output:
- Verify factual statements against reliable source material.
- Match product claims to their substantiation before publication.
- Check that generated images depict the product accurately and that the team has the necessary rights.
- Review personalization and targeting for audience expectations and potential harm.
- Confirm that required disclosures are present and understandable in the relevant context.
NIST’s AI RMF Core calls for differentiated human-AI roles, documentation that supports review, testing, and incident identification. Human oversight is useful when the person reviewing can act on what they find.
Check advertising claims on the final asset
AI assistance does not lower the standard for an advertisement. The FTC’s U.S. federal guidance says advertising claims must be truthful, not deceptive or unfair, and supported by evidence. The campaign owner should keep the substantiation with the claim and verify the final published asset, not just the prompt or an earlier draft.
Pay particular attention to generated testimonials, reviews, endorsements, health-related product claims, and environmental claims. The FTC flags additional substantiation concerns for health-related products and says environmental claims require competent and reliable scientific evidence. This general guidance does not replace specialized legal review, and obligations can differ by product, audience, and jurisdiction.
Check disclosure rules for the market and the output
Do not assume that every sentence drafted with AI needs a public label—or that no label is needed because a human edited it. For campaigns in the European Union, the European Commission’s Article 50 transparency guidance, published 20 July 2026, says the relevant obligations apply from 2 August 2026. The requirements depend on roles and output types.
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For a specific EU use, determine whether the organization acts as a provider or deployer, whether a person interacts directly with an AI system, whether generated content must carry machine-readable marking, and whether a deployer’s content falls into a specified disclosure category such as a deepfake or public-interest text. Article 50 includes an exception for public-interest text after human review or editorial control where a natural or legal person holds editorial responsibility. Check the current rules and obtain local legal advice for a real campaign; the EU framework should not be generalized to other markets.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep routine work moving with a simple service model
Speed comes from making ordinary decisions self-serve and keeping the escalation route narrow—not from skipping checks that matter. Build a small working library of approved tools, prompts, brand assets, claim evidence, and disclosure patterns so teams can reuse material that has already been checked.
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- Let routine work proceed: users follow the approved-tool and data rules without seeking case-by-case permission.
- Route review-tier work to one named reviewer: define who owns the queue and set a clear response expectation appropriate to your team.
- Reserve specialist escalation for defined triggers: route legal, privacy, or security questions when a use crosses a stated boundary, rather than copying specialists on every draft.
- Review the rules on a schedule: check whether tools, campaigns, audiences, and obligations have changed.
- Update after a material change or incident: revise the relevant rule, tier, or approved workflow when vendor behavior or risk changes.
These are operational recommendations based on documented governance practices, not a measured promise of a particular time saving.
Use incidents to improve the rules
Give staff a short, known route for reporting incorrect claims, privacy exposure, rights complaints, misleading synthetic media, vendor changes, or unexpected model behavior. Record what happened, who decided the response, and what corrective action followed. Then update the affected workflow or escalation trigger so the same issue is easier to catch next time.
NIST describes governance as ongoing across an AI system’s lifespan and the organization’s hierarchy. Its AI RMF is voluntary guidance, released on 26 January 2023—not a statutory marketing checklist or legal certification. NIST also reports a Generative AI Profile released on 26 July 2024 and says AI RMF 1.0 is being revised. For marketers, the practical implication is to assign owners, document controls, monitor how systems are used, and periodically review whether the rules still fit.
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