AI Persuasion in Marketing: A Systematic Review of Algorithmic Influence on Consumer Decision-Making

Abstract

This article examines how artificial intelligence (AI) systems in marketing shape consumer decision-making, focusing on algorithmic personalization, recommendation, and conversational agents. The study’s role is to integrate fragmented evidence from 2016-2021 into a coherent picture of “algorithmic persuasion” and its implications for autonomy, privacy, and fairness. Using a systematic literature review of peer-reviewed empirical studies, the article identifies how AI increases message relevance, compresses consideration sets, and alters interaction experiences, thereby enhancing short-term outcomes such as attention, attitudes, and behavioral intentions. The results are synthesized narratively by grouping studies according to AI application type, persuasion mechanism, and consumer response patterns, and by comparing findings across contexts and methods. The review finds that AI-based persuasion is consistently effective but ambivalent: it improves decision efficiency while simultaneously creating vulnerabilities related to perceived surveillance, manipulation, and overdependence on algorithmic guidance. These findings underscore the need for ethically informed design, transparency safeguards, and longitudinal, cross-cultural research on AI-mediated persuasion.