While advancements in Artificial Intelligence are transformative and rapid, a comprehensive framework of measurement of the quantity and the quality of these transformations become increasingly pressing. In this paper, we generalize graph theory into a framework to compare traditional and AI-empowered pathways in order to calculate a comparable value. First, we define important and identifiable variables that can encapsulate other measurable parameters, namely cost, emotion, and time, weighting them accordingly. Secondly, we consider error as a multivariate parameter and construct a measurable objective function for cost and optimization. Furthermore, we adjust the composite risk index and associate it with error, as an additional measurable parameter of the transition from traditional to AI-empowered workflows. To illustrate the proposed framework, we present a case study in the medical domain and more specifically in the patient journey.