Impact of Generative AI on Freelance and Professional Artists: An Empirical Study

  • Coraline Zoi Visco Kunskapsskolan, India
Keywords: Generative AI, Evaluation, Impact analysis, Professionals, Artist

Abstract

Generative artificial intelligence (GenAI) is changing the way digital art is created and shared. It provides new opportunities for faster content creation but it also creates concerns about artists careers and creative ownership. This study examined how freelance and professional artists perceive the growing use of GenAI in the creative industry. A descriptive quantitative research design was used. Data were collected through an online questionnaire, and 80 valid responses were analysed using descriptive statistics. The findings show that most participants were familiar with GenAI and understood how these tools work. While many respondents reported that AI had not yet affected their work, nearly one-third experienced a decline in client opportunities. Most artists were concerned that AI models are trained on copyrighted artwork without permission. They also believed that artists should be compensated when their work is used for AI training. Respondents expected freelance artists to face greater challenges in the coming years because of increasing competition from AI-generated content. At the same time, they felt that human creativity, originality, and emotional expression remain difficult to replace. The study concludes that AI should be used as a supporting tool rather than a substitute for artists. Clear copyright policies and fair practices are needed to protect creative professionals while encouraging responsible use of AI.

References

Akhtar, Z. Bin. (2025). Generative visions: AI, human imagination, and the future of art. Contemporary Visual Culture and Art, 1(1).
Al-Busaidi, A. S., Raman, R., Hughes, L., Albashrawi, M. A., Malik, T., Dwivedi, Y. K., Al-Alawi, T., AlRizeiqi, M., Davies, G., & Fenwick, M. (2024). Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights. Journal of Innovation & Knowledge, 9(4), 100630.
Bojić, L., Žikić, S., Matthes, J., & Trilling, D. (2024). Navigating the Digital Age. An In-Depth Exploration into the Intersection of Modern Technologies and Societal Transformation.
Brewer, P. R., Cuddy, L., Dawson, W., & Stise, R. (2025). Artists or art thieves? media use, media messages, and public opinion about artificial intelligence image generators. Ai & Society, 40(1), 77–87.
Chakraborty, R. (2024). Create vs generate: A study on the impact of AI on the anatomy of animation industry and animators.
Copper, C. (2025). Research guides: artificial intelligence for image research: how generative AI models work. University of Toronto Libraries. https://guides. library. utoronto. ca/c. php.
Emma, O., & Peace, P. (2022). The Impact of Generative AI On Traditional Artistic Practices.
Erickson, K. (2024). AI and work in the creative industries: digital continuity or discontinuity? Creative Industries Journal, 1–21.
Jia, H. (2024). The application and impact of artificial intelligence in the field of animation as well as the existing disadvantage. Transactions on Computer Science and Intelligent Systems Research, 5, 660–671.
Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., Hanna, A., Flowers, J., & Gebru, T. (2023). AI Art and its Impact on Artists. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 363–374.
Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., Hanna, A., Flowers, J., Gebru, T., & Artist, A. (2023). AI Art and its Impact on Artists. AIES 2023 - Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 363–374. https://doi.org/10.1145/3600211.3604681
Jiang, H. H., Taylor, J., & Agnew, W. (2026). How Professional Visual Artists are Negotiating Generative AI in the Workplace. Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems, 1–6.
Kawakami, R., & Venkatagiri, S. (2024). The impact of generative ai on artists. Proceedings of the 16th Conference on Creativity & Cognition, 79–82.
Li, L., Cheah, N. L. Y., & Kim, S. H. (2025). AI art in the gig economy: Investigating the effects of non-copyrightability in online labor markets. Decision Support Systems, 114545.
Navigating the Digital Age. (n.d.).
Octavia, A. (n.d.). Diffusion Models: From Theory to Practice in Generative AI A Comprehensive Survey of Probabilistic Generative Frameworks.
Otmar, R., Michael, R., Mullins, S., & Day, K. (2025). Ethics and the use of generative AI in professional editing. AI and Ethics, 5(2), 1719–1731.
Pardeshi, A. (2024). Animating intelligence: Impact of AI & machine learning revolution in animation. IJCRT.
Rapp, A., Di Lodovico, C., & Torrielli, F. (2025). How do people experience the images created by generative artificial intelligence? An exploration of people’s perceptions, appraisals, and emotions related to a Gen-AI text-to-image model and its creations. International Journal of Human-Computer Studies, 193, 1–16.
Roca, T., Roman, A. C., Vega, J. T., Duarte, M., Wang, P., White, K., Misra, A., & Ferres, J. L. (2025). How good are humans at detecting AI-generated images? Learnings from an experiment. ArXiv Preprint ArXiv:2507.18640.
Tsao, J., Liang, C. X., Nogues, C., & Wong, A. (2026). Perceptions and integration of generative artificial intelligence in creative practices and industries: a scoping review and conceptual model. Ai & Society, 41(3), 2259–2278.
Zhang, L., Wilson, K., & Amos, C. (2025). The rise of AI art: A look through digital artists’ eyes. First Monday.
Zhou, E., Lee, D., & Harding, M. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3(3), pgae052.
Published
2026-09-04
How to Cite
Visco, C. (2026). Impact of Generative AI on Freelance and Professional Artists: An Empirical Study. International Journal of Social Science Research and Review, 9(9), 204-214. https://doi.org/10.47814/ijssrr.v9i9.3536