The rapid integration of artificial intelligence (AI) into global communication practices has fundamentally transformed the creation, adaptation, and circulation of advertising messages. Contemporary advertising discourse increasingly relies on neural language models, automated copywriting platforms, and multimodal generative systems capable of producing persuasive content tailored to specific audiences. As a result, the field of translation studies now faces new challenges related not only to cross-linguistic transfer but also to the semiotic complexity and algorithmic nature of AI-generated promotional texts. While traditional approaches to advertising translation emphasize creativity, cultural adaptation, and functional equivalence, the new phase of AI-assisted communication requires rethinking these strategies through the lens of digital discourse, algorithmic authorship, and data-driven personalization.

AI-generated advertising discourse, regardless of language, demonstrates several characteristic features that significantly influence translation decisions. First, such texts often rely on predictive linguistic modeling, which produces high-probability lexical combinations optimized for emotional impact and user engagement. According to Kress and van Leeuwen, persuasive discourse in commercial communication must be understood as a multimodal phenomenon involving linguistic, visual, and contextual dimensions [1]. AI systems, particularly large language models, replicate these dimensions through the algorithmic synthesis of human-like discourse patterns. However, the translation of this algorithmically optimized language into other linguistic and cultural environments requires human intervention, especially when the original text contains implicit cultural scripts, subtle pragmatic cues, or references generated from datasets that may not include sufficient cultural diversity.

The second important characteristic is hyper-personalization. Recent studies indicate that AI-driven advertising often incorporates individualized rhetorical mechanisms, such as micro-targeted emotional appeals and demographic-tailored vocabulary [2]. When such content is transferred cross-linguistically, translators must consider the target culture’s norms, the acceptability of personalization strategies, and the ethical implications of data-based messaging. The question of translator responsibility becomes even more complex in cases where AI-generated texts adopt persuasive or manipulative techniques that may not align with local cultural expectations or advertising regulations.

Another challenge arises from the stylistic hybridity typical for AI-generated promotional content. As Lotman argues, any text functions within a semiotic space shaped by the interaction of multiple codes [3]. AI-generated advertising discourse demonstrates hybridity by blending informational, conversational, and affective codes. The translation strategy must therefore account for multimodal semiotic convergence, especially in cases where the AI model produces slogans, metaphors, or emotionally charged descriptions based on training data that reflect English-centric marketing traditions. This is particularly perceptible in the overuse of certain persuasive clichés or generalized emotional constructs that may sound unnatural or overly dramatic in languages with different rhetorical norms.

Furthermore, as Venuti emphasizes, translation involves a constant negotiation between domestication and foreignization [4]. In AI-generated advertising texts, this negotiation becomes multidimensional: the translator works not only between languages and cultures but also between human and algorithmic authorship. Unlike human-created advertising messages, AI-generated content may lack intentionality, coherent branding voice, or stable emotional positioning. This ambiguity complicates the translator’s task because maintaining functional equivalence requires identifying implicit communicative intentions that may not be explicitly present in the AI-produced discourse.

The integration of AI into advertising also raises the question of machine creativity versus human creativity. Scholars such as He and Deng argue that modern AI systems imitate creative language production but operate through statistical approximation rather than subjective intention [5]. Consequently, the translator must not only interpret the linguistic meaning of the AI-generated message but also compensate for potential gaps in emotional resonance, cultural imagery, or brand-specific creative tone. When AI-generated metaphors, slogans, or neologisms rely on dataset-specific cultural associations, their direct translation may lead to semantic distortion or communicative inadequacy. Therefore, translators increasingly adopt hybrid strategies that combine post-editing with creative rewriting to achieve cultural and stylistic alignment.

In addition to linguistic and pragmatic concerns, ethical considerations now occupy a central place in translation research. Shankar and Mittelstaedt note that AI-generated advertising can unintentionally reinforce social stereotypes or biases embedded in training data [6]. When translating such content, professionals must remain attentive to implicit evaluative meanings, culturally sensitive terminology, and possible ethical misalignments. The translator thus becomes a mediator not only of textual meaning but also of responsible communication values.

The evolution of multimodal AI tools, including image-based generators, further expands the scope of translation challenges. As Forceville demonstrates, metaphor in advertising frequently operates across visual and verbal channels [7]. When AI systems generate paired text-image content, translators must interpret how verbal meaning interacts with visual symbolism. The adaptation of such multimodal advertising requires a deep understanding of cultural semiotic systems, visual rhetoric, and audience perception norms. Often, the translation of AI-generated slogans must be accompanied by modifications to corresponding visuals to maintain coherence and persuasive impact.

Finally, advancements in neural machine translation have introduced the concept of machine-to-machine translation cycles, where AI-generated advertising content is automatically translated and later post-edited by human specialists. According to Koehn, while neural models achieve high levels of fluency, they still lack pragmatic awareness and cultural sensitivity [8]. The translator’s expertise thus remains essential in ensuring that the advertising message retains its intended persuasive function and aligns with target-market expectations.

Overall, the translation of AI-generated advertising discourse represents a dynamic and interdisciplinary field situated at the intersection of linguistics, communication studies, semiotics, and digital media. The increasing influence of AI on commercial communication demands that translators develop new competencies, including understanding algorithmic text generation, evaluating multimodal AI outputs, and addressing ethical implications. As global advertising continues to evolve through the integration of intelligent technologies, the role of the translator becomes even more critical: not merely as a linguistic mediator but as a cultural, ethical, and creative interpreter capable of navigating the complexities of human-machine communication.