In the last two years, there has been a boom in the use of artificial intelligence for everything, and generative AI has taken over almost every part of our lives. This even includes the use of generative AI in marketing campaigns. As of mid-2024, 86% of US marketers were already using AI tools in their work (Kirk & Givi, 2025). Brands now use it for everything, going from writing advertising copy, product descriptions, social media posts, visuals, to personalized emails, mostly because it saves time and helps teams create more variations of content.
But this raises a new dilemma.
Two years ago, when you saw an ad, you did not question who made it. Today, you might have to ask yourself whether it was created by a human or by artificial intelligence. And the answer is not easy, since consumers “struggle to distinguish between human and GenAI content based on perception alone” (Brüns & Meibner, 2024, p.9).
So, does AI-generated marketing content affect consumer trust, and how can brands use generative AI without losing it? Where do we draw the line? This article looks at both sides.
What is AI-generated marketing content?
Let us take a step back and start from the top.
Generative AI is a type of artificial intelligence that creates new content, such as text, images, audio and video, based on patterns learned from the data it was trained on. Tools like ChatGPT rely on large language models (LLMs), which are trained on huge amounts of text to produce human-like writing and images.
In the marketing field, it is important to understand the difference between AI-generated and AI-assisted content.
AI-generated content is produced mostly by the AI system, from scratch. Brüns and Meibner (2024) call this the automation scenario, where brands “automate the entire content creation process, thus replacing human input” (p. 2). AI-assisted content, on the other hand, is created with the help of AI but is then reviewed or edited by a person.
| Human-created | AI-assisted | AI-generated | |
| Who creates it | A graphic designer and copywriter | AI drafts, then a human reviews and decides | Mostly the AI system |
| Human oversight | Full | High | Low or none |
| Speed and cost | Slower, more expensive | Faster, moderate cost | Fastest, cheapest |
| Risk for consumer trust | Low | Moderate | Highest |
Why are brands using it? The benefits of generative AI in marketing campaigns
Generative AI allows marketers “to quickly and cost-effectively develop advertisements” (Grigsby et al., 2025, p. 1). Its main advantages are:
- Efficiency: teams can produce first drafts, creative concepts and many content variations in minutes.
- Personalization at scale: text from messages can be adapted to different audience segments, languages or stages of the customer journey.
- Creative experimentation: marketers can test more ideas before investing in a campaign and spend more time on strategy and creative direction.
However, faster production does not automatically equal better performance. A study of Airbnb listings found that AI-written descriptions helped underperforming listings, but that “AIGC could even backfire for high-performing listings” (Fan et al., 2026, p. 1531) (AIGC = artificial intelligence-generated content).
Does AI-generated content affect consumer trust?
Trust is at the heart of the relationship between a brand and its customers. AI-generated content has four potential risks:
- Loss of authenticity: Consumers may feel that a brand’s communication no longer reflects genuine human creativity, personality, or values. In three experiments, brands that automate content creation with AI were seen as “less authentic than brands that do not use GenAI” (Brüns and Meibner, 2024, p.9). When consumers believe one was written by AI, “positive word of mouth and customer loyalty are reduced” (Kirk & Givi, 2025, p. 1).
- Misleading representations: One of the main risks of AI-generated images in advertising is that they can show products, people, or situations that do not exist or greatly differ from reality.
- Generic brand identity: when many brands rely on the same tools, their content can start to sound and look alike.
- Errors and accountability: AI can invent facts or details (called “hallucinations”), and the brand remains responsible for everything it publishes.
However, it would be wrong to conclude that all AI content damages trust. Kirk and Givi (2025) found that the negative effect is weaker for factual messages, such as product information, and when AI only edits a text written by a person. Similarly, negative reactions are less important “if GenAI is used to assist humans in content creation rather than to replace them through automation” (Brüns & Meißner, 2024, p. 1).
Should brands disclose AI-generated content?
THIS is the central dilemma.
Consumers want transparency. A study found that 75% of them favour disclosures on AI-generated ads (Dentsu, 2023, as cited in Grigsby et al., 2025). However, in experiments on service advertising, “AI disclosures result in lower trust and less positive ad attitudes” (Grigsby et al., 2025, p. 1).
Disclosure does not have to have negative consequences, though. The same study shows that trust can be preserved when brands carefully choose which elements are AI-generated, for example, “a real dentist at an AI-generated office” (Grigsby et al., 2025, p. 1). Hiding AI use is also risky, since Kirk and Givi (2025) point to “the risk to firms of not disclosing AI use in content creation if it is later exposed or seriously suspected by consumers” (p. 12).
Brands must also respect the law. When it comes to AI-generated advertising rules in Canada, the Competition Act applies to AI-generated ads like any other advertising. “All representations, in any form whatsoever, that are false or misleading in a material respect are subject to the Act” (Competition Bureau Canada, 2026).
How brands can use generative AI without losing trust
Here are five best practices for using AI in marketing content:
- Keep humans involved in creative decisions: Use AI for brainstorming, first drafts, and repetitive tasks, but let people control the brand’s voice, emotional messages, and final decisions.
- Fact-check everything: verify product descriptions, statistics and visuals before publishing.
- Protect the brand’s identity: set clear guidelines for tone, visual style and acceptable AI use.
- Be transparent where it matters: communicate human involvement, for example with a hashtag (Brüns & Meibner, 2024, p. 10).
- Measure more than production efficiency: track brand perception, trust, engagement and complaints, not only time and cost savings.
Frequently asked questions (FAQ)
Does AI-generated marketing content affect brand trust?
It can. Research shows that it can lower perceived authenticity, word of mouth, and loyalty, especially for emotional messages (Brüns & Meibner, 2024; Kirk & Givi, 2025).
Should brands disclose AI-generated marketing content?
Yes. It is important to do so, especially when content could mislead. Disclosure can lower trust in some cases, but hiding AI use is also risky, and Canadian law prohibits misleading advertising.
Is AI-generated content better than human-created content?
Not necessarily. AI is faster and cheaper, but humans make content feel more authentic. The best results usually come from humans and AI working together.
Conclusion
So, where should a brand draw the line?
AI-generated marketing improves the speed and flexibility of creation, but efficiency alone does not necessarily equal success. Consumer trust depends on authenticity, transparency, and the quality of the content. AI is not good or bad for marketing by itself. The real challenge is deciding where it adds value and where human judgement and intervention remain essential.
For now, the line seems clear: we should let AI assist, but let humans decide.
References
Brüns, J. D., & Meißner, M. (2024). Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity. Journal of Retailing and Consumer Services, 79, Article 103790. https://doi.org/10.1016/j.jretconser.2024.103790
Competition Bureau Canada. (2026, February 19). False or misleading representations and deceptive marketing practices. https://competition-bureau.canada.ca/en/deceptive-marketing-practices/types-deceptive-marketing-practices/false-or-misleading-representations-and-deceptive-marketing-practices
Fan, N., Li, X., Liu, C., & Fan, Z.-P. (2026). The power of AI-generated content: Evidence from the peer-to-peer accommodation market. Journal of Travel Research, 65(5), 1519–1538. https://doi.org/10.1177/00472875251332951
Grigsby, J. L., Michelsen, M., & Zamudio, C. (2025). Service ads in the era of generative AI: Disclosures, trust, and intangibility. Journal of Retailing and Consumer Services, 84, Article 104231. https://doi.org/10.1016/j.jretconser.2025.104231
Kirk, C. P., & Givi, J. (2025). The AI-authorship effect: Understanding authenticity, moral disgust, and consumer responses to AI-generated marketing communications. Journal of Business Research, 186, Article 114984. https://doi.org/10.1016/j.jbusres.2024.114984