This entry investigates how artificial intelligence (AI) is redefining subscription models in retailing by converting personal data into a core driver of prediction, personalization, and automated delivery. As AI becomes embedded in sectors built on recurring payments and data-rich interactions, subscription models have evolved from fixed offerings into adaptive systems that learn from consumer behavior. Extending established distinctions among access, replenishment, and discovery subscriptions, this entry details how AI strengthens these formats when applied as a predictor to analytics, to drive automation, and to empower hyper-personalized recommendations. AI as a predictor of analytics enables firms to forecast preferences, usage patterns, and churn likelihood, enabling advanced structures such as “ship-then-shop,” in which curated assortments are sent before purchase decisions. This system enhances match quality and reduces consumer search effort, making the model viable at scale only when prediction accuracy is high. AI-driven automation further reduces friction by autonomously managing selection, billing, replenishment, and delivery timing, transforming subscriptions into self-regulating services that adjust continuously to user needs. AI-powered hyper-personalization recommendations extend these capabilities by integrating behavioral, demographic, textual, and temporal signals to generate recommendations that are contextually relevant and emotionally resonant, mitigating decision fatigue and sustaining engagement. Using examples from streaming services, fashion, and meal-kit platforms, this entry analyzes how AI reshapes consumer experience through subscription plans by improving efficiency, reducing mismatches, and increasing customer lifetime value. This entry ends by identifying key trends in subscription plans for the market and business.