The use of generative AI in marketing is expanding rapidly, with many companies using it to create content efficiently. While previous research has mainly focused on human-created content, it often overlooked specific challenges of AI-generated content (AIGC), such as its tendency to appear "clinical" or lacking real emotion. Furthermore, existing theories suggest that the effectiveness of a tone depends on the context, such as the product type. Therefore, this study compared emotional and factual tones in AIGC in terms of perceived message clarity and purchase intention. In addition, the role of the product type (hedonic versus utilitarian) was investigated to see how it changes the effect of tone. In sum, the research’s aim could be summarized as follows: "To what extent is the tone of AI-generated content (emotional vs. factual) associated with consumers’ purchase intention, considering the mediating role of perceived message clarity and the moderating role of the type of products (hedonic vs. utilitarian) between the tone and perceived message clarity?". To examine this, an online experiment was conducted, collecting data from 136 participants. The results showed that a factual tone generally led to higher perceived message clarity compared to an emotional tone. However, no significant main effect was found of tone on purchase intention, indicating that tone alone does not directly drive sales. Crucially, an interaction effect was found between the tone and the product type. This means that for utilitarian products, a factual tone increased clarity, whereas for hedonic products, an emotional tone was more effective. Furthermore, perceived message clarity positively influenced purchase intention and played a medianing role in the relationship between the tone of the content and purchase intention. The mediation was found to be a full mediation, as the direct effect of tone was not significant, indicating that tone only influences purchase intention by making the message clearer. These findings led to several recommendations for brands, marketing managers, and AI developers. First, marketing managers should adopt a "context-aware" strategy by matching the tone to the product category. Specifically, brands should use a factual tone for utilitarian products to reduce ambiguity and an emotional tone for hedonic products to create a better connection. In addition, marketers should treat message clarity as a key performance indicator (KPI) when testing AI content. AI developers could support this by creating tools that automatically suggest the right tone based on the product type. Lastly, as clarity was identified as the main driver of purchase intention, brands should focus on ensuring their AI-generated messages are coherent and easy to understand.