Managing Responsible AI Personalization Through the Value–Trust–Agency Framework

Abstract

Artificial Intelligence (AI) personalization is shifting marketing from periodic targeting toward continuous prediction, recommendation, content generation, and interaction. Yet many firms still evaluate these systems mainly through predictive accuracy, click-through, conversion, and other short-horizon metrics. This creates a management problem: personalization can improve relevance while simultaneously eroding trust, increasing privacy concerns, or restricting perceived choice when interactions feel opaque, intrusive, or manipulative. This conceptual article develops the Value–Trust–Agency (VTA) framework through an integrative synthesis of research on AI in marketing, customer experience, personalization, privacy, consumer autonomy, algorithm acceptance, and human–AI collaboration. The synthesis identifies three jointly necessary conditions for sustainable AI personalization: experienced value, warranted trust, and meaningful agency. Building on these conditions, the article develops six research propositions, a customer-journey design matrix, a balanced measurement architecture, and a closed-loop governance process linking strategic purpose, data discipline, model design, deployment, human oversight, and organizational learning. The framework shows why predictive accuracy creates only potential value: durable customer and firm value depends on whether personalization remains useful, trustworthy, and contestable across the journey. For managers, VTA provides a practical basis for deciding when to personalize, what safeguards to apply, how to allocate human oversight, and which performance indicators should complement conventional response metrics.