The Impact of Visual Generative AI on Advertising Effectiveness

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Abstract

The advertising industry stands at a pivotal moment as visual generative AI (genAI) can transform creative content production. Despite growing enthusiasm, empirical evidence on when and how to integrate visual genAI into advertising remains limited. This study investigates three approaches: (1) human expert-created ads, (2) genAI-modified ads, in which genAI enhances expert designs, and (3) genAI-created ads, generated entirely by visual genAI. Using a mixed-methods design that combines latent diffusion models, a laboratory experiment, and a field study, we evaluate the relative effectiveness of these approaches. Across studies, we find that genAI-created ads consistently outperform both human-and genAI-modified ads, increasing click-through rates by up to 19% in field settings. In contrast, genAI-modified ads show no significant improvement over human-created benchmarks. These results reveal an asymmetry: visual genAI delivers greater value when used for holistic ad creation rather than for modification, where creative constraints may limit its effectiveness. Effectiveness increases even more when genAI also designs product packaging, representing the lowest degree of output constraints. Mechanism analysis reveals that genAI-created ads elicit stronger emotional engagement and achieve higher visual processing fluency, while genAI-modified ads fail to preserve ecological validity. Finally, we find that disclosing AI involvement in ad generation significantly reduces advertising effectiveness by up to 31.5%, underscoring trade-offs relevant to evolving AI disclosure policies. Overall, this research provides systematic empirical evidence on the impact of visual genAI in advertising and offers practical guidance on deploying visual genAI for creation and modification tasks, contributing to a deeper understanding of how generative technologies shape marketing outcomes.