  Advances in artificial intelligence (AI) have enabled unprecedented
capabilities, yet innovation teams struggle when envisioning AI concepts. Data
science teams think of innovations users do not want, while domain experts
think of innovations that cannot be built. A lack of effective ideation seems
to be a breakdown point. How might multidisciplinary teams identify buildable
and desirable use cases? This paper presents a first hand account of ideating
AI concepts to improve critical care medicine. As a team of data scientists,
clinicians, and HCI researchers, we conducted a series of design workshops to
explore more effective approaches to AI concept ideation and problem
formulation. We detail our process, the challenges we encountered, and
practices and artifacts that proved effective. We discuss the research
implications for improved collaboration and stakeholder engagement, and discuss
the role HCI might play in reducing the high failure rate experienced in AI
innovation.
