From Content Automation to Capability Trap: Rethinking Marketing Capabilities in the Era of Generative AI

Abstract

The rapid diffusion of generative artificial intelligence (GenAI) has intensified claims that AI systematically enhances marketing capabilities. While prior research emphasizes efficiency gains, personalization, and agility, it largely assumes a linear relationship between AI integration and strategic performance. This article challenges that assumption by developing a conceptual framework of the Generative AI Capability Trap. Drawing on dynamic capabilities theory, competence trap logic, and emerging research on digital authenticity, the relationship between GenAI intensity and marketing distinctiveness is theorized as inherently non-linear. Moderate AI integration may amplify sensing and seizing capabilities, expand creative throughput, and improve short-term performance. However, excessive reliance may compress symbolic variance, reinforce exploitation bias through KPI-driven optimization, and gradually erode adaptive creative capacity. This erosion dynamic is driven by three mechanisms—pattern convergence, creative deskilling, and algorithmic reinforcement—and is conditioned by boundary factors related to organizational learning orientation, creative governance, and industry dynamism. By reframing generative AI as a dual-edged strategic infrastructure, this study extends capability theory to probabilistic systems and introduces variance preservation as a critical lens for evaluating AI-enabled marketing transformation.