The use of generative artificial intelligence in digital marketing communications: the relationship between transparency, content authenticity, and brand trust

Abstract

This article focuses on the use of generative artificial intelligence in digital marketing communications and, more specifically, on how disclosing its involvement shapes perceived transparency, content authenticity, and brand trust. The relevance of the topic lies not only in the rapid adoption of generative models for advertising copy, images, videos, and personalised messages. It also stems from an unresolved consumer response: disclosure may signal openness and responsible conduct, yet at the same time reduce the perceived human contribution behind a marketing message. The study draws on a structured review of academic publications from 2021 to 2026 and applies systems analysis, comparison, theoretical synthesis, classification, and conceptual modelling. AI-use disclosure is treated as a multidimensional communication practice rather than a simple distinction between labelled and unlabelled content. Its dimensions include the extent of algorithmic involvement, the specific content element affected by AI, the wording and level of detail, the timing and visual prominence of the disclosure, and the way human-AI collaboration is framed. Four general disclosure models are identified: minimal formal disclosure, functionally specified disclosure, human-centred disclosure, and extended responsible disclosure. The article introduces the principle of sufficient and proportionate transparency, according to which disclosure should accurately reflect the actual role of the technology, correspond to the risk of consumer misinterpretation, and avoid unnecessary technical overload. A dual-path conceptual model is proposed to explain why the same disclosure may produce different outcomes. The positive path operates through perceived transparency, reduced information asymmetry, and stronger perceptions of brand openness. The negative path operates through a possible decline in perceived authenticity, sincerity, humanness, and creative effort. Brand trust integrates these competing mechanisms and, in turn, influences attitudes towards advertising, willingness to engage with content, purchase intention, and recommendation intention. The relative strength of the two paths depends on the degree of automation, meaningful human oversight, the emotional or rational nature of the message, the product category, brand positioning, prior trust, and consumer AI literacy. The study contributes by conceptualising AI-use disclosure as a multidimensional element of digital marketing communication, developing a structured disclosure typology, and linking transparency and authenticity within a single explanatory framework. Its practical value lies in supporting corporate policies on generative AI, disclosure wording, allocation of responsibility between specialists and algorithms, and verification of marketing materials.