As medical researchers and pharmaceutical companies race to develop treatments and a vaccine for COVID-19, they show how repurposing knowledge, resources, and technology already at hand can be a valuable innovation strategy.

How does organizational decision-making change with the advent of artificial intelligence (AI)-based decision-making algorithms? This article identifies the idiosyncrasies of human and AI-based decision making along five key contingency factors: specificity of the decision search space, interpretability of the decision-making process and outcome, size of the alternative set, decision-making speed, and replicability. Based on a comparison of human and AI-based decision making along these dimensions, the article builds a novel framework outlining how both modes of decision making may be combined to optimally benefit the quality of organizational decision making. The framework presents three structural categories in which decisions of organizational members can be combined with AI-based decisions: full human to AI delegation; hybrid-human-to-AI and AI-to-human-sequential decision making; and aggregated human-AI decision making.