Artificial Intelligence and Strategic Marketing Decisions: A Review of Current Research
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Abstract
Artificial intelligence (AI) has moved from a peripheral analytical tool to a central force shaping how organizations formulate and execute strategic marketing decisions. This review synthesizes current research on the integration of AI into strategic marketing, examining how machine learning, natural language processing, predictive analytics, and generative systems influence segmentation, targeting, positioning, customer relationship management, and resource allocation. Drawing on peer-reviewed literature, the paper organizes findings around four analytical dimensions: the conceptual foundations linking AI to marketing strategy, the application of AI across the strategic marketing process, the antecedents and organizational capabilities that enable adoption, and the ethical, managerial, and performance-related outcomes of AI deployment. The review finds broad consensus that AI enhances decision speed, personalization, and predictive accuracy, while also revealing persistent tensions around data governance, algorithmic transparency, consumer trust, and the changing role of human managerial judgment. A methodological account of the narrative review process is provided, along with three summary tables consolidating definitions, application domains, and reported outcomes. The paper concludes that AI is best understood not as a replacement for strategic marketing judgment but as an augmenting capability whose value depends on organizational readiness, data quality, and ethical stewardship. Directions for future research are proposed, emphasizing longitudinal performance studies, generative AI in strategy formation, cross-cultural adoption dynamics, and frameworks for responsible AI-enabled marketing.
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