MediaScience Finds AI Labels Do Not Reduce Ad Performance

According to Marketing Dive and TV Technology reporting on a MediaScience study, researchers ran an online experiment with 900 U.S. respondents to measure the impact of AI-disclosure labels on video ad effectiveness. The study tested four labeling conditions versus a no-label control: a text label in the first three seconds, a delayed text label shown in seconds four through six, a full-duration text label, and a full-duration icon. MediaScience found no statistically significant decline across measured outcomes, including brand choice, ad memory, brand recognition, brand attitude, ad liking, and perceived production quality. All four labeling conditions increased viewers' awareness that the ad was AI-generated. Dr. Duane Varan, CEO of MediaScience, was quoted: "There has been a lot of anxiety in the industry about what happens when you tell people an ad was made with AI. The data gives us a clear answer: if the creative is good, disclosure does not hurt it. Advertisers do not need to be afraid of the label."

TV Technology reports specific awareness and ad memory scores from the study. Displaying a disclaimer during the first three seconds increased viewers' awareness that the content was AI-generated by 28%; running the label continuously raised awareness by 36%. On ad memory, text labels outperformed the no-label control score of 36 across all conditions: 46 for the 3-second label, 40 for the delayed label, and 49 for full-duration. The icon scored 38, close to the control. While 42% of respondents preferred the visual icon, it was the least effective format for increasing AI awareness. Audiences reported the strongest need for AI labeling when content generates humans (60%), followed by animals (46%), product placement (45%), and voices (45%).

TV Technology reports the study was conducted by MediaScience in collaboration with the Ehrenberg-Bass Institute at Adelaide University, a marketing science academic center, and MediaPET.ai, an AI video content platform developed by MediaScience. The experimental design contrasts early, delayed, persistent text, and persistent icon disclosures against a no-label control, and reports null effects on standard advertising KPIs across the tested U.S. sample.

The research was published amid tightening regulation: TV Technology notes New York's AI Transparency in Advertising law takes effect in June 2026 and the EU AI Act introduces binding disclosure obligations in August 2026. Industry reporting frames the study as directly relevant to advertisers and platforms preparing for those disclosure regimes. The study's experimental design was described as reflecting frameworks under active consideration by U.S. and EU legislators.

Whether results replicate across broader demographics, ad formats (short social vs. long-form video), and real-world programmatic placements. Regulators, platforms, and measurement vendors may publish guidance specifying wording, timing, or iconography for disclosures; such specifications could alter user perception relative to the formats tested. Reported differences in subgroups (age, prior AI familiarity) and longer-term brand metrics, which the study summary does not detail, would further inform how practitioners interpret this single-study result.