Artificial intelligence (AI) is rapidly reshaping how psychology is practiced, from assessment and case formulation to intervention planning, monitoring, and documentation. Yet the field faces a strategic choice: deploy AI as a substitutive “automated therapist,” or develop AI copilots that augment psychologists’ judgment while preserving the relational and ethical core of professional work. In this article, we synthesize how contemporary AI-especially Machine Learning and Large Language Models- maps onto psychologists’ core tasks and discuss the implications for clinical quality, scalability, and innovation in real-world settings. We then present Psypilot as a case study of the copilot paradigm: an AI-powered clinical assistance platform designed to support Precision Mental Health. We critically examine key risks and governance challenges such as automation bias, data representativeness and fairness, privacy and secondary use, transparency, and accountability under emerging regulatory frameworks, and translate them into practical design and training recommendations. By framing AI as workflow-embedded decision support rather than autonomous care, this contribution advances responsible innovation and clarifies the competencies psychologists need to thrive in an AI-driven professional landscape.
Roca et al. (Fri,) studied this question.