AI-Driven Personalization in Digital Marketing: A Systematic Review

Abstract

Purpose - This review examines AI-driven personalization as a distinct digital marketing technology and clarifies how it creates marketing value while also generating privacy and governance concerns. Methodology - Using PRISMA-style review logic, the study screened peer-reviewed journal articles and major review papers published between 2020 and 2025, with limited early-2026 updating. From 176 identified records, 39 studies were retained and coded by application domain, analytical theme, AI mechanism, focal outcome, constraining factor, and managerial implication. Findings - The literature shows that AI-driven personalization generally improves perceived relevance, usefulness, convenience, engagement, and purchase intention when personalization is timely, contextually appropriate, and supported by credible first-party data. These benefits are not automatic. The same systems may intensify privacy concern, perceived intrusiveness, algorithmic opacity, and authenticity doubts. Across the reviewed studies, trust emerges as the main mediating factor connecting personalization with engagement, satisfaction, loyalty, and purchase intention. Novelty - The article integrates technical, consumer, and governance perspectives into one explanation of how AI capability becomes marketing value. Significance - The review offers an evidence map and a practical basis for a more responsible and effective personalization strategy.