Reconceptualizing Advertising Measurement in the Age of Generative Artificial Intelligence

Generative artificial intelligence (GenAI) is transforming advertising by enabling large-scale creative automation, dynamic personalization, and AI-mediated consumer decision journeys. Existing measurement systems were developed for human-authored and relatively stable communication environments and do not capture how GenAI systems cocreate brand touchpoints with advertisers or mediate consumer evaluations of brands. In this study, we develop an integrated triadic framework for understanding the GenAI-mediated advertising landscape and reconceptualizes the action–mindset–behavior–finance measurement architecture to reflect an advertising process that is increasingly recursive, nonlinear, and adaptive. In doing so, we distinguish between traditional metrics that remain relevant but require reconceptualization and novel metrics unique to GenAI-assisted advertising systems. In addition, we extend the framework beyond firm-centered outcomes to incorporate stakeholder-oriented metrics relevant to employees, regulators, investors, and society. The framework offers guidance for scholars and managers seeking to evaluate GenAI advertising while maintaining transparency, trust, and competitive integrity. The study concludes with a future research agenda and crosscutting priorities addressing measurement, governance, accountability, and methodological challenges associated with GenAI-assisted advertising.