Researchers have begun to use the synergy of psychiatry and neuroscience to identify biomarkers that can be used to diagnose mental health disorders, predict their progression, and forecast treatment efficacy. However, biomarkers have achieved limited success to date, potentially due to a narrow focus on specific aspects of brain signals. This highlights a critical need for methodologies that can fully exploit the potential of neuroscience to transform psychiatric practice. In recent years, there has been emerging evidence of the ubiquity and importance of moment-to-moment neural variability for brain function. Single-neuron recordings and computational models have demonstrated the significance of variability even at the microscopic level. Concurrently, studies involving healthy humans using neuroimaging recording techniques have strongly indicated that neural variability, which in the past was dismissed as undesirable noise, is an important substrate for cognition. Given the cognitive disruption seen in several psychiatric disorders, neural variability is a promising biomarker in this context, and careful consideration of design choices is necessary to advance the field. In this review, we provide an overview of the significance and substrates of neural variability across different recording modalities and spatial scales. We also review the existing evidence that supports its relevance in the study of psychiatric disorders. Finally, we advocate for future research to investigate neural variability within disorder-relevant, task-based paradigms and longitudinal designs. Supported by computational models of brain activity, this framework holds the potential for advancing precision psychiatry in a powerful and experimentally feasible manner.