The Interactive Advertising Bureau has released an updated framework for AI disclosure in advertising, giving marketers a risk-based approach to labeling AI-generated content as regulators in the US, EU, and Asia move toward formal requirements.
The IAB's AI Transparency and Disclosure Framework Version 2 advises brands to prioritize disclosure for AI uses that could affect authenticity or mislead consumers - such as synthetic videos, avatars, digital twins, and AI-powered chatbots that might be mistaken for humans. Routine applications like copy generation, audio enhancements, and standard post-production generally don't require disclosure under the guidance.
"Not all uses of AI need a label," said Caroline Giegerich, Vice President of AI at IAB. "Labeling everything teaches consumers to ignore labels, and any negative impact on advertisers."
Consumer attitudes toward AI in advertising
IAB's initial framework, released in January 2026, found that 58% of consumers viewed AI use in creative contexts negatively, and more than half said they wanted to be told when an ad uses AI. However, 73% of Gen Z and Millennials said clear disclosure would have little to no impact on whether they'd buy a product. Only 27% said they'd be less likely to purchase based on AI use.
In the updated framework, 34% of Gen Z and Millennials described brands using AI in advertising as "creative," while 30% called it "inauthentic." The report says this tension highlights why consistent disclosure matters - not to discourage AI use, but to build the trust needed for responsible adoption.
Why blanket labeling falls short
Advertisers largely support industry standards but resist mandatory labeling for every AI use. IAB found that 72% believe there should be industry standards for disclosing AI use in creative development, yet only 17% think AI should be disclosed every time it's used.
The framework recommends a "materiality-based approach rather than blanket requirements," arguing that over-labeling can lead to consumer indifference. Disclosure should focus on "the uses that matter most to consumers: those where AI involvement creates a material risk of misleading audiences about authenticity, identity, or representation."
There's a commercial trade-off to consider. An NYU Stern study found click-through rates fell by 31.5% when consumers were told an ad was created using generative AI. That drop may be justified when AI has been used to mislead, but labeling low-risk uses could create costs without clear consumer benefit.
"Under-labeling leaves consumers at risk of being misled. Over-saturation could risk negative effects for advertisers," Giegerich said.
Regulatory landscape remains fragmented
Since IAB's original framework, regulators have moved AI disclosure from voluntary guidance toward formal requirements - but approaches differ by market. New York's synthetic performer law took effect in June 2026, the EU's Article 50 of the AI Act was enforced in August 2026, and South Korea's revised AI Basic Act took effect in January 2026.
In the US, advertisers can meet disclosure requirements with either a standard sparkle icon or a clear text label. The EU's AI Act requires disclosure for AI-generated content and deepfakes, but no icon is currently mandated, and the accompanying Code of Practice remains voluntary. That means design and placement requirements for AI labels aren't yet standardized across markets.
For marketing teams, this creates a practical challenge: a single global AI workflow must accommodate multiple disclosure systems. IAB's framework offers a baseline, but marketers still need to comply with jurisdiction-specific rules.
David Cohen, CEO of IAB, said the guidance matters for industry credibility. "Putting the right transparency and disclosure standards is increasing in significance to the growth of AI. This helps improve consumer confidence in our industry."
Why this matters for marketing professionals
Marketers face a narrow window to establish disclosure practices that satisfy regulators without alienating audiences. The IAB framework suggests a practical starting point: audit your AI use cases, identify where AI involvement could mislead consumers about authenticity or identity, and apply labels there - not across every piece of content.
For those building AI workflows, the fragmented regulatory landscape means disclosure decisions should be built into campaign planning rather than handled as an afterthought. AI for Marketing training can help teams understand where AI disclosure requirements intersect with creative development. Marketing managers overseeing campaign production may also benefit from AI for Marketing Managers courses that cover AI implementation in advertising contexts.
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