Emerging trends challenges and research opportunities in artificial intelligence applications in marketing

Abstract

This study presents a systematic review of Artificial Intelligence (AI) applications in digital marketing by integrating two complementary text-mining techniques—Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis—to identify emerging trends, challenges, and research opportunities. Drawing on 381 peer-reviewed journal articles published between 2021 and 2025 from the ScienceDirect and Taylor & Francis databases, the analysis uncovers five dominant research themes: AI-driven personalization and customer engagement, big data analytics and predictive modeling, generative AI applications in digital content creation, ethics and privacy concerns, and AI deployment in B2B and SME marketing contexts. A temporal analysis shows a sharp increase in publications after 2020, peaking in 2024, reflecting the rapid mainstreaming of AI in marketing scholarships. Sentiment analysis reveals predominantly positive academic attitudes toward AI’s role in enhancing efficiency and personalization, yet a growing concern with ethical risks such as algorithmic bias, privacy, and trust erosion. By demonstrating how the field has evolved from technical optimization toward socio-ethical reflection, this review contributes both methodologically and theoretically. It extends the Technology Acceptance Model (TAM) by incorporating transparency and accountability as moderating factors in consumer and organizational adoption. Furthermore, it identifies critical gaps—particularly the lack of cross-cultural comparative studies and limited exploration of AI’s intersection with emotional branding—and offers practical guidance for researchers, marketing practitioners, and policymakers seeking to design trust-oriented, responsible AI strategies.

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