Mapping artificial intelligence in marketing: insights through a two-decade analysis (2000–2024)

Purpose Integrating Artificial Intelligence (AI) into marketing has driven significant transformations, reshaping consumer engagement, campaign management and business strategies. Although previous reviews have examined AI in marketing, most are limited to specific applications or relatively short time frames. There is a gap in recent bibliometric analyses that capture AI research's intellectual evolution and rapid diversification in this field. This study addresses this need by mapping the development of AI in marketing from 2000 to 2024, identifying emerging trends, key contributors and persistent knowledge gaps. Design/methodology/approach This study conducts a bibliometric analysis of 2,105 articles published between 2000 and 2024, using the PRISMA method and drawing on Scopus and Web of Science data. Bibliometrix and VOSviewer were employed to identify the most influential journals, authors and countries and to uncover thematic clusters through co-occurrence and co-citation network analyses. Findings The results reveal an exponential growth in AI-related marketing research since 2017, with 2024 marking the highest annual publication volume. Key research areas emerging from this trend include AI ethics, generative AI applications and the strategic importance of social presence in fostering brand-consumer engagement. This study not only provides a comprehensive overview of AI's evolution and current landscape in marketing but also identifies persistent gaps, such as algorithmic bias and content authenticity, that require further investigation, thereby highlighting critical priorities for future research and practical implementation. Originality/value By providing the most up-to-date, systematic and longitudinal bibliometric mapping of AI research in marketing, this study substantially extends and updates previous literature reviews. It delivers a foundation for both scholars and practitioners, clarifying how the field has evolved, particularly towards generative AI and ethical considerations, and establishing a clear research agenda for addressing unresolved challenges, with specific future research avenues aligned with the latest trends.