Paradigm shift in the management of drug development projects: artificial-intelligence-enabled parallel scenario execution for managing uncertainty.

Abstract

Artificial intelligence (AI) is transforming project management beyond efficiency gains by enabling parallel scenario execution, in which multiple outcome-contingent deliverables are prepared before key uncertainty-resolving events and rapidly converged once results are revealed. By extending set-based concurrent engineering, this approach addresses traditional implementation barriers such as resource constraints and combinatorial complexity through large-scale AI-driven preparation and automation. Drawing on late-stage clinical development and regulatory submission workflows as an illustrative example, we show how parallel scenario execution can reduce post-event lead times, improve preparedness for low-probability outcomes, reduce replanning burden, and support more robust decision-making under uncertainty. We discuss implementation considerations, including scenario prioritization, governance, quality assurance, AI resource allocation, and the essential role of human oversight.