This study reports the findings of qualitative interview sessions conducted with ICU clinicians for the co-design of a system user interface of an artificial intelligence (AI)-driven clinical decision support (CDS) system. This system integrates medical record data with wearable sensor, video, and environmental data into a real-time dynamic model that quantifies patients' risk of clinical decompensation and risk of developing delirium, providing actionable alerts to augment clinical decision-making in the ICU setting. Co-design sessions were conducted as semi-structured focus groups and interviews with ICU clinicians, including physicians, mid-level practitioners, and nurses. Study participants were asked about their perceptions on AI-CDS systems, their system preferences, and were asked to provide feedback on the current user interface prototype. Session transcripts were qualitatively analyzed to identify key themes related to system utility, interface design features, alert preferences, and implementation considerations. Ten clinicians participated in eight sessions. The analysis identified five themes: (1) AI's computational utility, (2) workflow optimization, (3) effects on patient care, (4) technical considerations, and (5) implementation considerations. Clinicians valued the CDS system's multi-modal continuous monitoring and AI's capacity to process large volumes of data in real-time to identify patient risk factors and suggest action items. Participants underscored the system's unique value in detecting delirium and promoting non-pharmacological delirium prevention measures. The actionability and intuitive interpretation of the presented information was emphasized. ICU clinicians recognize the potential of an AI-driven CDS system for ICU delirium and acuity to improve patient outcomes and clinical workflows.