Аннотация
On the largest e-commerce platforms, artificial intelligence is no longer a supplementary tool but the operating logic through which value is created, delivered, and captured. Marketing research has treated the personalization side of that shift; service research has treated the automation side. The business-model consequence has been left under-theorized: how the three canonical components of value creation are rewired when customer behavior analytics runs continuously in the loop. This paper builds a conceptual framework linking three tiers of AI-enabled analytics (descriptive, predictive, and generative) to three business-model components (value proposition, value delivery, and value capture). Drawing on secondary evidence from Amazon, Alibaba, Shein, and Shopee, we argue that AI-enabled analytics does not merely optimize existing offerings; it changes what value is being created, from products to continuously personalized experiences. Three governance tensions follow—transparency, privacy, and algorithmic dependence—which we read not as side effects but as constitutive of the reshaped model. The paper contributes a bridge between the marketing-analytics literature and business-model theory, together with a set of testable propositions for later empirical work.