Across machine learning, physics, quantum theory, and the biological sciences, many systems exhibit the same fundamental transition: a probabilistic representation of possible states gives rise to a single, externally accessible outcome. Although these processes differ in substrate, scale, and mechanism, they share a minimal structural form involving a description of alternative possibilities, constraints that determine when one becomes admissible, and the resulting realized state. This article develops a substrate-independent framework that formalizes this shared structure. We define a realization condition as the minimal set of constraints under which a probabilistic state description yields a definite, externally accessible outcome, and argue that the role traditionally attributed to an “observer” in quantum theory can be understood in these functional terms. We examine four distinct domains — large language models, information-based physical theories, quantum measurement, and biological decision making — and show that each instantiates this transition in a structurally homologous way without implying mechanistic or ontological equivalence. The framework clarifies the functional role of observation in quantum theory, provides a common vocabulary for cross-domain comparison, and identifies structural expectations that may guide empirical work on uncertainty resolution and outcome formation. The framework is deliberately limited to describing the form of this transition rather than its underlying mechanisms, but it offers a basis for interdisciplinary analysis and a foundation for further theoretical development.