Tag Archives: drug discovery

Architectural Innovation, Organizational Restructuring, and the Role of AI: Evidence from the Rise of Antibody-Drug Conjugates

Kwon, Angela Eunyoung, Jaecheok Park, Gene Moo Lee. “Architectural Innovation, Organizational Restructuring, and the Role of AI: Evidence from the Rise of Antibody-Drug Conjugates,” Work-in-progress.

  • Presentations: KrAIS (2026)

Architectural innovations reconfigure the linkages among existing components and thereby pose distinct challenges to established organizational structures. This study examines the rise of antibody-drug conjugates (ADCs) in oncology as a case of architectural innovation and investigates how this technological shift shapes organizational restructuring among pharmaceutical firms. Drawing on the theory of architectural innovation (Henderson & Clark,1990), we argue that ADCs disrupt established organizational routines and patterns of knowledge coordination, prompting firms engaged in oncology research to redesign internal roles, interfaces, and coordination mechanisms. We further theorize two moderators of firms’ adaptive effectiveness. First, AI capability functions as an architectural competence by facilitating cross-domain information processing and enabling knowledge recombination. Second, modality breadth provides absorptive capacity grounded in diverse drug development experience. This research-in-progress aims to contribute to the literature on architectural innovation and organizational restructuring by showing how firms adapt their internal structures to technological change, while also highlighting the emerging role of AI in firm-level knowledge coordination.

How Does AI Change Drug Development? Evidence from Clinical Trial Phases and Drug Types

Kwon, Angela Eunyoung, Jaecheok Park, Gene Moo Lee. “How Does AI Change Drug Development? Evidence from Clinical Trial Phases and Drug Types,” Working Paper.

  • Presentations: KrAIS (2025), CIST (2025), INFORMS (2025), UBC (2025), WISE (2025)

We examine how pharmaceutical firms’ AI capabilities influence drug development outcomes, focusing on clinical trials. Clinical trials progress through three phases that differ in regulatory scrutiny and evidentiary requirements. We measure firm-level AI capabilities using job postings and clinical trial outcomes using the number of trials initiated across phases. We find no significant overall effect of AI capabilities on clinical trial activity. However, this average relationship masks meaningful heterogeneity. AI capabilities are associated with increases in incremental innovation (refinement trials) but not radical innovation (new trials). These effects are stronger for biologics, where market incentives are high, than for small-molecule drugs, where learning hurdles are relatively low. AI capabilities also matter more in early-phase trials, where regulatory barriers are lower, and have no detectable influence in Phase III. This study contributes to the healthcare IS literature by identifying the nuanced and context-dependent business value of AI in drug development. It also offers practical guidance for pharmaceutical firms and policymakers on where AI investments are most likely to enhance R&D productivity.