Artificial intelligence is present in digital marketing communication. It is part of how many brands plan, produce, and distribute digital content. Marketers use AI to help them create text, images, videos, ideas, and other campaign materials more efficiently (Bormane & Blaus 2024). This creates a huge opportunity for marketing teams to save time and produce content flexibly. However, an important question arises: if consumers know that a marketing image was created with AI, will they still trust what they see?
Marketing images do more than just attract attention. They shape how consumers perceive the brand, the product, and the credibility of the message. In digital environments, consumers often make evaluations with very limited information and rely on the available cues to decide whether a source appears to be reliable, competent and honest (McKnight et al. 2002).
Visual quality is not the same as authenticity
A marketing image can be technically remarkable and still feel unconvincing. Authenticity is something consumers interpret through cues that make an object or message feel genuine, believable, or aligned with what they expect from the brand (Beverland & Farrelly 2010).
AI-generated images can be visually impressive and use composed details, but this does not automatically make them trustworthy. If the visuals appear too perfect, artificial, or unrealistic, realism becomes part of how consumers judge credibility.
What young consumers noticed
A recent bachelor’s thesis at LAB University of Applied Sciences studied how 72 consumers aged 18 to 30 evaluated AI-generated and human-made visual marketing content from sportswear, outdoor apparel, performance and lifestyle brands (Gómez Antunes 2026). The study focused on authenticity, trust and brand attitude.
Human-made visuals were evaluated more positively than AI-generated visuals across all three dimensions. Participants associated authenticity and trust with realistic lighting, human presence, natural imperfections, believable product use and visual coherence. While AI-generated content was often associated with artificiality, exaggerated details, unrealistic situations and doubts about product reliability (Gómez Antunes 2026).
Still, the findings do not suggest that young consumers reject AI completely. Participants recognized that AI can be useful in marketing communication and the issue was whether the final content felt realistic, coherent, and credible.
What marketers should consider before using AI visuals
AI-generated content should not be evaluated only by speed, cost, or visual appeal. Brands should ask whether the image supports authenticity and trust.
First, the visuals need to feel realistic. This is important in sportswear, outdoor apparel and performance products, where consumers expect the products to work in real-life conditions. If the body, movement, environment, or product presentation appears unrealistic, the image may weaken credibility.
Second, AI-generated content needs to fit the brand identity. An experimental style may work for some brands, but not for brands that depend on tradition, technical reliability or real product performance. Consumers not only evaluate the image, but also what it communicates about the brand.
Third, human creative direction is essential. AI can support idea generation and content production, but it should not replace strategic decisions. Marketers still need to evaluate whether a visual is coherent, ethical, realistic and meaningful. AI-generated content is not flawless and often requires human supervision (Bormane & Blaus 2024).
Transparency matters, but disclosure alone may not be enough. Some participants became more critical when they knew an image was AI-generated, especially when they noticed errors or artificial details (Gómez Antunes 2026).
AI can create marketing images but trust still depends on something more human: how carefully brands use realism, brand alignment and human creative direction.
Authors
Juan Andrés Gómez Antunes is a double degree student in Business Administration and International Business at LAB University of Applied Sciences and Universitat Politècnica de València.
Sanna Kokkonen works as a Senior Lecturer at LAB University of Applied Sciences, Faculty of Business and Hospitality Management.
References
Beverland, M. B. & Farrelly, F. J. 2010. The quest for authenticity in consumption: Consumers’ purposive choice of authentic cues to shape experienced outcomes. Journal of Consumer Research, Vol. 36(5), 838–856. Cited 8 May 2026. Available at https://doi.org/10.1086/615047
Borba, J. 2022. Un hombre sentado en un escritorio con una computadora. Unsplash. Cited 8 May 2026. Available at https://unsplash.com/es/fotos/un-hombre-sentado-en-un-escritorio-con-una-computadora-YFZwUBTnHrs
Bormane, S. & Blaus, E. 2024. Artificial intelligence in the context of digital marketing communication. Frontiers in Communication, Vol. 9, 1411226. Cited 8 May 2026. Available at https://doi.org/10.3389/fcomm.2024.1411226
Gómez Antunes, J. A. 2026. Customer perception of AI-generated content in marketing communication. Bachelor’s thesis. LAB University of Applied Sciences. Cited 12 May 2026. Available at https://urn.fi/URN:NBN:fi:amk-2026051110988
McKnight, D. H., Choudhury, V. & Kacmar, C. 2002. Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, Vol. 13(3), 334–359. Cited 8 May 2026. Available at https://doi.org/10.1287/isre.13.3.334.81
