The digital transformation experienced within the advertising ecosystem is addressed through a holistic perspective grounded in AI-supported advertising management. The traditional advertising model—based on mass media channels and a broad, homogeneous understanding of target audiences—is increasingly being restructured in line with data-intensive, algorithm-driven, and dynamic systems. This transformation extends beyond media selection and message delivery, encompassing profound changes in strategic planning, analytical evaluation, and operational implementation processes. Through big data analytics, machine learning, and automation mechanisms, artificial intelligence enables multidimensional analysis of consumer behavior, facilitates predictive insights, and optimizes advertising distribution processes in real time.

Within this framework, AI-supported advertising management is positioned not merely as a technical tool that automates media buying or targeting processes, but as a comprehensive managerial paradigm that renders decision-making mechanisms data-driven. The integrated processing of structured and unstructured customer data enhances the accuracy of consumer insights, promotes the widespread adoption of micro-segmentation practices, and enables the production of personalized content. Programmatic systems and real-time bidding mechanisms ensure more efficient allocation of advertising budgets while allowing instantaneous and performance-oriented measurement. Consequently, advertising management moves away from intuition-based practices toward a measurable, comparable, and continuously optimizable structure.