Artificial intelligence as a driver of customer value in B2C services

The purpose of this article is to develop a conceptual explanation of how AI capabilities are translated into customer value, to identify the boundary conditions that determine whether this translation is successful, and to systematize the consumer outcomes associated with AI-enabled service encounters. The study applies a structured theoretical synthesis of recent research in AI marketing, service management, value co-creation, customer experience and AI-powered service quality. The analysis distinguishes three levels: technological capabilities, customer-facing mechanisms and perceived value outcomes. AI creates value when capabilities such as learning, prediction, personalization and automation improve relevance, responsiveness, expertise, convenience, decision simplicity and proactive support, or when they reduce time, search and cognitive effort. At the same time, the same capabilities may generate new sacrifices through privacy concerns, opacity, algorithmic bias, inappropriate personalization, automation failure and reduced human warmth. On this basis, the article introduces the concept of AI-enabled net customer value, understood as the customer’s assessment of AI-generated benefits relative to AI-generated sacrifices. A conceptual framework is proposed in which the effect of AI on value is conditioned by AI-customer ability fit, privacy assurance, transparency, reliability, perceived empathy, task complexity and the stage of the customer journey. The framework also separates customer value from downstream outcomes such as satisfaction, trust, engagement, loyalty, purchase intention, recommendation and continued use. The study contributes to the literature by integrating value creation and value erosion within a single B2C service framework and by explaining why identical AI capabilities may produce positive, neutral or negative value across customer and service contexts.