Authors: Vijayalakshmi M, Assistant Professor Jayashree K
Abstract: This chapter examines the impact of artificial intelligence (AI) on marketing decision-making, with particular attention to the data-driven strategies that modern businesses now use to plan, execute, and evaluate marketing activity. As organisations increasingly delegate analytical and, in some cases, operational decisions to machine learning systems, the nature of marketing management is shifting from intuition-led judgement toward evidence-based, algorithmically informed choice. Drawing on theoretical frameworks including the Technology Acceptance Model (TAM), the Resource-Based View (RBV) of the firm, Bounded Rationality theory, and Dynamic Capabilities theory, this chapter develops a conceptual model showing how AI capabilities, encompassing predictive analytics, customer segmentation, content and campaign automation, and conversational AI, influence the speed, accuracy, and confidence of marketing decisions, and how these in turn shape commercial performance. The chapter reviews relevant literature, analyses real-world case studies across retail, streaming, hospitality, and e-commerce sectors, and proposes a framework linking AI capability dimensions to decision quality and business outcomes. Findings suggest that AI does not simply automate existing marketing tasks but fundamentally reconfigures how decisions are made, who makes them, and on what evidentiary basis. The chapter also identifies key challenges to AI adoption in marketing decision-making, including data quality dependency, algorithmic bias, loss of human judgement and creativity, organisational readiness gaps, and ethical and regulatory uncertainty. Future directions, including generative AI in campaign creation, agentic marketing systems, explainable AI for marketing accountability, and human-AI collaborative decision models, are discussed. This chapter contributes to the growing literature on AI-enabled marketing management and data-driven strategy, and holds practical implications for marketing managers, business leaders, and management students seeking to understand how intelligent systems are reshaping the discipline of marketing.
DOI: http://doi.org/
