Digital twins couple a physics- and/or data-driven model with operational data to mirror, predict, and optimize the behavior of a physical system across its lifecycle. For many digital-twin use cases, such as online calibration, model-based control, design space exploration, or uncertainty quantification, fast and reliable gradient information with respect to model parameters is a key feature, because it allows scalable gradient-based optimization and a sound basis for sensitivity rankings. However, finite-difference sensitivities are often too expensive and can be numerically brittle in high-dimensional, nonlinear models. This work positions Variational Design Sensitivity Analysis (VDSA), as a practical tool in the context of digital-twin modeling. The approach is based on an enhanced kinematic viewpoint rigurously separating geometry and physics, cf. e.g. [1]. Starting from the governing equations, direct and adjoint sensitivity relations that provide high-quality gradients of selected quantities of interest with respect to defined parameter sets are derived. The central benefits include numerical efficiency, high accuracy (independent on numerical parameters), and flexibility in view of discretization and implementation. Beyond purely physics-based models, the proposed sensitivity framework is also applicable to data-driven or hybrid digital twins, for instance by enforcing an Artificial Neural Networks (ANNs) to learn gradient information additionally to the primal solution, for instance in terms of Sobolev Training, cf. [2]. This enables gradient-informed training and calibration as well as sensitivity-based model interrogation. Moreover, VDSA provides the derivative information required for uncertainty analysis, supporting local uncertainty propagation and sensitivity-driven parameter screening to identify reliable interval bounds, cf. e.g. [3]. Furthermore, possible extensions towards multiscale, multiphysics settings are discussed, in which coupled processes across scales, e.g. micro-macro couplings, cf. e.g. [4] or component-system interactions that demand efficient, consistent gradients for calibration, control, and design under uncertainty.