Voice disorders offer a high-impact application for artificial intelligence (AI), bridging voice science, clinical medicine, and machine learning. Yet developing robust voice AI models faces distinctive challenges: small and imbalanced datasets, variability in language and recording conditions, and the need for interpretability and ethical oversight in healthcare deployment. In this chapter, we introduce the factors that need to be considered to develop a voice AI. It outlines a four-pillar framework: data, modelling, evaluation, and governance. It builds on the previous chapters, which discussed potential biomarkers for voice disorders. We discuss trade-offs between feature-driven and data-driven approaches, the role of foundation and real-time models, and the imperative of reproducibility and post-deployment monitoring.