AI in Modern Marketing Practice: A Critical Examination of Applications and Emerging Trends

Abstract

Purpose, The primary purpose of this study is to examine the transformative role of Artificial Intelligence (AI) in modern marketing by systematically reviewing recent literature. Specifically, the paper aims to synthesize current AI applications, evaluate their effectiveness in enhancing marketing outcomes, and identify the key challenges and future research directions for their strategic integration.

Design/methodology/approach, This research utilizes a literature-based review approach, involving the systematic collection and critical synthesis of scholarly articles, books, and reputable industry reports published between 2015 and 2025. Databases such as Scopus, Web of Science, and Google Scholar were searched using keywords related to "artificial intelligence in marketing," "machine learning," and "generative AI" to ensure a comprehensive, evidence-based understanding of the field's most recent developments.

Findings, AI technologies, including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Generative AI (GenAI), are fundamentally reshaping marketing functions. Key applications include predictive analytics (forecasting demand/churn), hyper-personalization (recommendation engines and CRM), customer service automation (chatbots), and content creation/optimization (GenAI). The central finding is that AI significantly enhances decision-making, increases customer engagement, and improves operational efficiency, but its full potential is constrained by implementation challenges such as algorithmic bias, data privacy concerns, and organizational expertise gaps.

Originality/value, This review offers a timely and comprehensive synthesis of the complex landscape of AI in marketing, bridging the divide between technological advancements and practical application. By mapping current usage against emerging ethical and practical challenges, the paper provides a crucial reference point for both academics seeking to identify knowledge gaps and practitioners aiming to strategically leverage AI for sustained competitive advantage.