This prospective, single-center qualitative study explored healthcare providers’ perspectives on AI-enabled precision dosing tools, specifically, the CURATE.AI platform, using an interview-based analysis to identify barriers and promoters to clinical adoption. The researchers conducted in-depth, semistructured interviews with n=16 providers (physicians, pharmacists, and nurses) from Alexandra Hospital in Singapore between August and December 2023. The AI model discussed, CURATE.AI, is a dosing optimization system that personalizes chemotherapy treatment using individual dose–response data and does not require large population-level datasets. Interview transcripts were coded deductively and inductively using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) framework, expanded to include trust and risk perception as an additional construct. Analysis revealed five key domains influencing implementation: performance expectancy, effort expectancy, facilitating conditions, social influence, and trust/risk perception. Participants viewed AI dosing tools as potentially useful for routine or structured treatments and time-saving in high-volume clinics. They cited expected benefits such as improved dosing accuracy, safety, and workflow efficiency, but expressed reservations about AI’s applicability to complex clinical cases, loss of clinician autonomy, and liability in adverse outcomes. Concerns also included alert fatigue, integration challenges with existing systems, and the need for training and transparency to build trust. Most participants anticipated that AI use in medicine was inevitable, though they emphasized the importance of human oversight and clear accountability frameworks. Limitations include the single-center design, possible selection bias, and absence of patient perspectives.