Generative AI and Customer Engagement in Digital Marketing: A Systematic Review and Evidence Map of Affective vs. Behavioral Outcomes, Disclosure Effects, and Human-in-the-Loop Moderation

The rapid advancement of generative artificial intelligence (AI) has transformed digital marketing, redefining how brands create, personalize, and deliver content to engage consumers. As organizations increasingly rely on AI-driven systems for customer interaction, understanding how these technologies influence customer engagement has become a critical area of inquiry. This study systematically reviews research published between 2022 and 2025 to evaluate the impact of generative AI on customer engagement, distinguishing between affective outcomes (e.g., satisfaction, trust, and commitment) and behavioral outcomes (e.g., click-throughs, shares, purchases, and retention). Following PRISMA guidelines, a comprehensive search across major databases (Scopus, Web of Science, and Google Scholar) identified 528 records, of which 64 articles were assessed for eligibility, and 33 studies met the final inclusion criteria for qualitative synthesis. Findings indicate that generative AI tools, particularly large language models (LLMs) for conversational marketing and generative adversarial or diffusion models for visual content, generally associated with positive behavioral engagement outcomes, such as improved attention, interactivity, and conversion rates, although these effects vary depending on context, platform, and implementation. However, affective engagement outcomes remain mixed; while personalization and novelty foster satisfaction and delight, authenticity concerns often hinder trust and emotional connection. Two moderating factors, AI content disclosure and human-in-the-loop (HITL) oversight, emerged as critical influences. Transparent disclosure can enhance credibility in some contexts but evoke skepticism in others, while human oversight consistently reinforces brand authenticity, ethical quality, and consumer confidence. Practical implications include guidance on effective AI disclosure strategies, when and how to be transparent about AI generation and the importance of maintaining a human touch in AI-augmented marketing. The review also identifies key research gaps, including long-term effects on brand loyalty, cross-cultural differences, and ethical and legal implications, and proposes a future research agenda to advance knowledge in this rapidly evolving field.