Systems and software models and variability are established disciplines to help engineers manage complexity and design flexible systems. These models are expert-built abstractions, often parameterized and composable, to answer engineering questions and guide system development or its runtime execution. In contrast, AI-enabled systems rely mainly on machine-learned models, which are derived purely from data, to handle complex prediction tasks, such as scene recognition or decision making. Machine-learned models have enabled amazing progress in AI, but suffer from the lack of specifications and human interpretability, which is a serious problem in safety-critical applications, such as automated driving. Illustrated with examples from automated driving, this talk will contrast expert- and data-driven models in terms of their strengths and limitations, discuss the role of expert-driven models in AI-enabled systems, explore the integration of both types of models, and identify future directions to achieve best of both worlds.