Leveraging Augmented Reality and wearable technology for knowledge-intensive training is thought to offer huge potential for improving human performance. The recent introduction of the technology means that much of this potential is untapped, though efforts are needed to understand what makes it useful, entertaining, and easy-to-use. The research presented in this article investigates the implementation of a combined hardware and software application in three use-cases: aviation, medical and space. Following the validation of metrics for a questionnaire, data was collected from 142 participants, and a structural equation model, based on UTAUT2, was proposed in order to interpret the data. Following model improvement, two constructs show significant factor loading and latent variable correlation, Interoperability and Augmented Reality / Wearable Technology Fit. Model optimisation was conducted, and a variety of goodness-of-fit indices are reported. The two additional constructs are found to be covariant and impact the UTAUT2 variables performance expectancy, effort expectancy and facilitating conditions, in some cases explaining more than 85% of the variance in those constructs (p < 0.001). A root mean square error of approximation of 0.047 after a 1000-fold Monte Carlo cross-validation indicates a good fit between the model and the data. In all other fit indices, a moderate power has been observed.