The shift from rule-based to data-driven decision making fundamentally changes how interfaces operate and evolve. Traditional interfaces rely on explicit rules created by designers: "If users click X, then show Y." These rules remain static until manually updated by developers. Data-driven AI interfaces, however, build their own internal models through exposure to user behavior. Instead of following fixed paths, they identify patterns across interactions and adjust their responses accordingly.^[1]

This distinction affects every aspect of the design process. Rule-based systems require designers to anticipate all possible user needs and create paths for each scenario. Data-driven systems require designers to establish frameworks for learning and create mechanisms for appropriate adaptation. For users, this shift means interfaces become more personalized but potentially less predictable, creating new challenges for establishing trust and setting appropriate expectations.