Answer capsule
Its 2026 framework gives advertisers a starting point for consistent, context-sensitive disclosure decisions.
What the source establishes
- IAB released its first AI Transparency and Disclosure Framework in January 2026.
- The framework emphasizes transparency, proportionality, consistency, and clarity.
- It is industry guidance, not a legal determination.
CMO ownership
Legal can interpret obligations, but marketing owns the repeatable workflow that tells teams and partners when a disclosure question must be raised.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Consistency needs metadata
Campaign systems should record tool, asset type, degree of generation, likeness, material claim, audience, market, and approved disclosure treatment.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Proportionality is not convenience
A disclosure decision should reflect likely consumer understanding and potential deception, not only how much space a channel makes available.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Test comprehension
Where disclosure matters, evaluate whether the intended audience notices and understands it rather than treating the presence of a label as the outcome.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Decision test
Ask whether the source changes the decision itself, the evidence required, the implementation sequence, or only the language used to describe an existing capability. Record which claims are directly supported, which are provider statements, which require an independent test, and which remain unknown. A source-linked review should make uncertainty easier to see, not bury it inside a blended score.
Questions to take into review
- Which people and channels are represented?
- Can each insight be traced to evidence?
- What training, input, and output rights apply?
- Which review gates cover claims and brand expression?
- Which repository owns approved content?
- How are market and channel variations controlled?
- What is the optimization target?
- Which placements and audiences can be excluded?
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