A practical framework for AI disclosure in marketing
There's a lot of noise around AI labels right now. Some want disclosures on everything. Others want none. Both miss the point.
If we want trust, we need a simple filter: context, consequence and audience impact. Use that to decide when disclosure matters - and when it's just clutter.
The continuum that actually works
Context: where and how AI is used
Is AI behind the scenes (grammar, outline, segmentation) or directly shaping what customers see (copy, images, chat)? Internal use rarely needs a label. Public-facing content demands more scrutiny.
Consequence: could it mislead or distort perception?
Apply a materiality test. If AI's role would change how people interpret the message - trust, credibility, expertise - disclosure moves from "nice" to "necessary."
Audience impact: what do people expect here?
Academic paper? Full transparency. Political ad? Immediate, unmissable. Marketing email? Readers expect curated content, not a footnote on every subject line. Set the bar by audience expectations.
Apply the continuum: common marketing scenarios
Internal productivity or planning tasks
- AI to segment an email list (RFM or similar)
Sample prompt: "I'll upload a spreadsheet with recency, frequency and monetary (RFM) data. Segment into groups."
Context: Internal, behind the scenes.
Consequence: None for the recipient.
Audience impact: Zero.
Guidance: No disclosure needed - this is analytics acceleration.
Caveat: Automated processing of PII can trigger disclosure/rights under privacy laws like GDPR. See guidance on automated decision-making from the ICO here. - AI to draft an internal creative brief
Sample prompt: "Here's campaign info. Turn this into a creative brief."
Context: Internal doc.
Consequence: Minimal; humans review and edit.
Audience impact: Zero.
Guidance: No disclosure needed.
Written content creation and transformation
- AI to brainstorm headlines or subject lines
Sample prompt: "Here's the body copy; give me 10 subject lines."
Context: Creative assist; human selects/edits.
Consequence: Low.
Audience impact: Low.
Guidance: No disclosure needed. - AI to organize a human brain dump into a draft
Sample prompt: "Here are my notes. Turn them into a rough draft."
Context: Human ideas; AI structures and phrases.
Consequence: Moderate - depends on how much AI adds beyond your input.
Audience impact: Variable.
Guidance: If AI is clarifying your ideas, no disclosure. If it's adding new ideas or claims you didn't originate, you're in co-authorship territory - disclose. - AI to fully generate written content (published under a person's byline)
Sample prompt: "Write a 600-word post on marketing automation trends."
Context: Generative, end-to-end.
Consequence: High - machine-authored work presented as human expertise.
Audience impact: Significant.
Guidance: Disclosure required - and frankly, rethink the approach. If you proceed, state how AI was used and include the core prompts. Better: start from your own notes, analysis or sourced research. - AI to summarize or paraphrase third-party content
Sample prompt: "Summarize this article for our newsletter."
Context: Efficiency, not original thought.
Consequence: None, if the summary is faithful.
Audience impact: Low.
Guidance: No AI disclosure needed. Do attribute the source - that's citation ethics, not an AI issue.
Visual content generation
- AI to create a background image
Context: Supporting visual, interchangeable with stock art.
Consequence: None for meaning.
Audience impact: None.
Guidance: No disclosure needed. - AI to create a visual metaphor or concept image
Sample prompt: "Illustrate 'work burnout' with flames."
Context: Symbolic, clearly not documentary.
Consequence: Low to moderate.
Audience impact: Low.
Guidance: Disclosure usually not necessary if it reads as illustration. - AI to generate images of people presented as real
Sample prompt: "Generate a picture of a customer for this testimonial."
Context: Looks like a real human and implies authenticity.
Consequence: High - risks misleading.
Audience impact: High - directly affects trust.
Guidance: Disclosure required - better yet, don't do it. Use real people or clearly marked illustrations.
Caveat: Synthetic likenesses of public figures create deepfake risk and potential legal exposure. The FTC has warned against AI-enabled deception here.
Quick decision checklist
- Does AI materially change meaning, credibility or perceived expertise? If yes, disclose.
- Would your audience feel misled if they knew how the asset was produced? If yes, disclose.
- Is the AI use internal or invisible to the recipient? If yes, disclosure is usually unnecessary.
- Are you processing personal data with automated methods? Check your privacy obligations.
- Are you fabricating people, facts or endorsements? Don't do it. If you must, label clearly and reconsider the tactic.
Practical next steps for marketing teams
- Define a simple policy: when to disclose, where to place the label, and example language.
- Audit high-risk touchpoints: expertise-led content, testimonials, ads with realism, chatbots, personalization.
- Create standard microcopy for disclosures (short, plain language) and keep it consistent.
- Log prompts and edits for sensitive assets - helpful for QA and compliance.
- Train your team on the continuum and run spot checks. Consistency builds trust.
If you want structured training for marketers, see our AI Certification for Marketing Specialists or browse courses by job.
Use AI responsibly, disclose when it matters
Blanket labels dilute trust. Thoughtful labels protect it. Treat AI like any other tool: disclose when it changes the message, the meaning or the audience's perception. Leave it behind the curtain when it doesn't.
That's respect for your audience - and for your brand.
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