ABSTRACT Deep‐technology ventures, particularly in medical artificial intelligence (AI), face a persistent “valley of death” where promising innovations fail to find viable business models despite technical merit. While the NSF I‐Corps program addresses this through structured customer discovery, little empirical research validates how specific market signals drive strategic adaptation. This study examines strategic pivoting via a mixed‐methods case study of a university‐spun medical AI venture. Analyzing 148 stakeholder interviews using a sequential mixed‐methods design, we identify a “double pivot” trajectory: an initial shift to a local maximum followed by a radical reorientation triggered by economic feedback. Quantitative analysis reveals that while technical and regulatory barriers remained stable, funding constraints exhibited a strong trend toward increased prevalence (74% → 92%, p = 0.056) during the critical pivot phase. These findings reframe I‐Corps as a hypothesis‐invalidation engine that accelerates the rejection of nonviable models rather than merely validating initial ideas. The study contributes a replicable framework for isolating compelling market signals from background noise, offering practical guidance for academic entrepreneurs to distinguish user enthusiasm from economic viability.
Khanzada et al. (Thu,) studied this question.