Generative AI Applications in Advertising
DOI:
https://doi.org/10.54097/4tggde31Keywords:
Generative Artificial Intelligence, Artificial Intelligence Generated Content, Advertising, MarketingAbstract
This paper aims to investigate the application of generative artificial intelligence (GenAI) models in advertising, particularly how their use affects the quality of advertising content and consumers' perceptions of AI-generated advertisements across different industries. In this paper, a curated AI-Generated Advertisement Dataset is used to conduct a content-level analysis, aiming to examine existing AI-generated advertisements based on their contents. Statistics and correlation analysis are applied to assess the quality AI-generated advertisements in four evaluation categories: Visual Quality, Style Consistency, Semantic Accuracy, and Creativity. The other, Xiaohongshu dataset, is used for user engagement and sentiment analysis. Two regression models are applied to the dataset to understand how people react and interact with AI-generated contents. This paper suggests a necessary improvement for the current development of GenAI models. That is, paying more attention to improving users’ attitudes/perceptions toward AI-generated advertisements, leading to strengthened authenticity and reliability of advertisements. This suggestion is especially critical for industries where consumers value these qualities more, to enhance the overall effectiveness of AI applications in advertising. This paper addresses the real-world impact of GenAI model’s application in advertising, offering new marketing lenses on how AI-generated ads can gain more consumers’ attention and trust.
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[1] Li, H. (2019). Special section introduction: Artificial intelligence and advertising. Journal of Advertising, 48(4), 333–337. https://doi.org/10.1080/00913367.2019.1654947.
[2] Qin, X., & Jiang, Z. (2019). The impact of AI on the advertising process: The Chinese experience. Journal of Advertising, 48(4), 338–346. https://doi.org/10.1080/00913367.2019.1652122.
[3] Gao, B., Wang, Y., Xie, H., & Hu, Y. (2023). Artificial intelligence in advertising: Advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. SAGE Open, 13(4). https://doi. org/10. 1177/21582440231210759.
[4] Beyari, H., & Hashem, T. (2025). The role of artificial intelligence in personalizing social media marketing strategies for enhanced customer experience. Behavioral Sciences, 15(5), 700. https://doi.org/10.3390/bs15050700.
[5] Sharakhina, L. V., Ilyina, I., Kaplun, D., Teor, T. R., & Kulibanova, V. (2023). AI technologies in the analysis of visual advertising messages. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00255-1.
[6] Xiang, Y., Wu, Y., Song, J., Gong, Y., & Liang, P. (2024). Generative AI in industrial revolution: A comprehensive research on transformations, challenges, and future directions. Journal of Knowledge Learning and Science Technology, 3(2), 11–20. https://doi.org/10.60087/jklst.vol.3n2.p20.
[7] Raudeliūnienė, J., Davidavičienė, V., Tvaronavičienė, M., & Jonuška, L. (2018). Evaluation of advertising campaigns on social media networks. Sustainability, 10(4), 973. https://doi. org/ 10.3390/su10040973.
[8] Achar, C., So, J., Agrawal, N., & Duhachek, A. (2016). What we feel and why we buy. Current Opinion in Psychology, 10, 166–170. https://doi.org/10.1016/j.copsyc.2016.01.009.
[9] Sheng, X., Li, L., Chen, P., Wu, J., Xu, L., Yang, Y., & Li, Y. (2023). Technical quality-assisted image aesthetics quality assessment. In Lecture Notes in Computer Science (pp. 50–62). https://doi.org/10.1007/978-981-99-8552-4_5.
[10] Zheng, J. (2025). Generative AI-driven visual advertising creativity generation and style consistency modeling. Advances in Computer and Communication, 6(5), 262–267. https://doi. org/ 10.26855/acc.2025.12.001.
[11] Ganmote, D. P., & Bapuso, S. S. (2025). Artificial intelligence and the future of English semantics. In Global Dimensions of Multidisciplinary Research (p. 47). https://doi.org/ 10. 25215/ 9371837764.07.
[12] Yang, Y., Guo, C., Liu, F., Sun, L., Liu, C., & Sun, Q. (2022). Semantic communications with artificial intelligence tasks: Reducing bandwidth requirements and improving artificial intelligence task performance. IEEE Industrial Electronics Magazine, 17(3), 4–13. https://doi. org/10. 1109/ mie. 2022. 317433.
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