Generative AI in Marketing: From Content Scale to Trust Crisis

Generative artificial intelligence is transforming digital marketing. It allows brands to create content faster, cheaper, and at a much larger scale than before. Product descriptions, social media posts, visuals, campaign ideas, and advertising messages can now be produced in minutes rather than hours or days.

However, the growing scale of AI-generated content also creates a new challenge: AI slop.

AI slop can be understood as mass-produced, low-quality AI-generated content shared across digital platforms. It is content that may be visually attractive, algorithmically optimized, and easy to produce, but not necessarily useful, credible, or authentic from the user’s perspective (Stanusch et al., 2025).

The scale of the phenomenon is already measurable. In a recent analysis of TikTok and Instagram search results, Stanusch et al. (2025) examined synthetic AI imagery across 13 hashtags in three European countries: Spain, Germany, and Poland. The study focused on topics such as politics, health, history, and current affairs.

The results are striking. On TikTok, roughly 25% of top search results for selected hashtags contained synthetic AI imagery. More than 80% of AI content identified in the study was photorealistic. This matters because photorealistic content can look plausible and may blur the boundary between authentic and synthetic communication (Stanusch et al., 2025).

The authors also point to the rise of “Agentic AI Accounts” — accounts specializing in automated or semi-automated production and distribution of AI-generated content. In their dataset, over 80% of synthetic AI content on TikTok was posted by such accounts. This suggests that AI slop is not only about individual users experimenting with generative AI. It is increasingly connected with systematic content production designed to gain visibility, engagement, and possibly monetization (Stanusch et al., 2025).

Another important issue is transparency. The study shows that platform labelling remains inconsistent. Around 50% of synthetic AI content on TikTok was not properly labelled as AI-generated. On Instagram, only 3 out of 13 posts containing synthetic AI imagery identified in top search results were labelled as AI content (Stanusch et al., 2025).

For marketing, these findings are particularly important. Brands operate in the same digital environment in which synthetic, low-quality, and algorithmically amplified content is becoming more visible. As a result, audiences may become more skeptical not only toward anonymous AI-generated content, but also toward brand communication that appears generic, automated, or inauthentic.

This connects directly with findings from the systematic literature review by Baryshkov et al. (2026), which analyzed 35 studies published between 2020 and 2026 on consumer trust in AI-generated marketing content. The review shows that consumer responses to AI-generated marketing communication are strongly linked to perceived authenticity. When consumers perceive content as lacking human intention, effort, sincerity, or emotional investment, trust-related outcomes may decline (Baryshkov et al., 2026).

The review also highlights a transparency dilemma. Consumers often want AI-generated content to be disclosed. At the same time, AI disclosure can activate persuasion knowledge and skepticism. In other words, people may prefer transparency in principle, but once they see that a message was generated by AI, they may evaluate it as less authentic or less trustworthy — especially when the content is emotional, creative, or closely connected to brand identity (Baryshkov et al., 2026).

A third important perspective comes from the experimental study by Møller et al. (2026), conducted with 680 U.S. participants in a realistic social media environment. Participants were assigned to discussion groups and used different AI tools, including an AI chat assistant, conversation starters, feedback on draft comments, and reply suggestions.

The study shows a clear duality. AI tools increased participation and content volume. For example, the average comment length increased from 19 words in the control group to 29 words when participants used an AI chat assistant, and to 28 words when they used AI-generated suggestions. The AI chat assistant was used by 94% of participants in that condition, generating 960 prompts in total (Møller et al., 2026).

However, more content did not automatically mean better communication. From the audience perspective, AI-assisted content was often perceived as less informative, lower quality, more generic, impersonal, or robotic. Participants valued AI support for idea generation, clarification, fact checking, and overcoming “writer’s block”, but they also noticed a lack of authenticity and personalization (Møller et al., 2026).

This is highly relevant for marketing practice. Generative AI can help brands produce more content and communicate more frequently. It can support brainstorming, personalization, content adaptation, and customer engagement. But if AI is used mainly to increase content volume, brands may contribute to the very problem that users are beginning to notice: feeds filled with repetitive, synthetic, and low-value communication.

The key challenge is therefore not whether marketers should use AI. The more important question is how they should use it.

AI can strengthen marketing communication when it supports human creativity, improves clarity, enables better personalization, and remains embedded in responsible editorial processes. But when it is used as a shortcut for mass-producing generic content, it may weaken one of the most important assets of any brand: trust.

In the age of AI slop, authenticity may become a competitive advantage. The brands that succeed will not necessarily be those that generate the most content, but those that use AI to create communication that remains useful, transparent, contextual, and meaningful to people.

Bibliography:
Stanusch, N., Degeling, M., Romano, S., Schüler, M., & Semenzin, S. (2025). AI-generated algorithmic virality. arXiv preprint arXiv:2508.01042.

Baryshkov, K., Kuzina, Y., Smuk, I., & Tkachuk, M. (2026). Consumer Trust in AI-Generated Marketing Content: A Systematic Literature Review and Research Agenda. American Impact Review, 1(1), e2026024-e2026024.

Møller, A. G., Romero, D. M., Jurgens, D., & Aiello, L. M. (2026). The impact of generative AI on social media: An experimental study. Scientific Reports.

This post is part of the project “People and Algorithms in Organisations: Competences to Work in the Digital Environment” (DIGIT_People and algorithms), funded by the NAWA – Narodowa Agencja Wymiany Akademickiej.

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