Most current analyses of AI adoption assume that adoption occurs at the level of a task or decision. Some have questioned whether this task-level approach is suitable and whether a system-level analysis is more appropriate. The paper presented in this ZEW Lecture on Economic Policy provides a formal analysis by considering multiple decisions/tasks that may be part of a modular or non-modular system. Modeling AI as superior prediction of external variables, the authors find that reliance on AI increases decision variation which, in turn, raises challenges if decisions across the organisation interact. Modularity softens that impact and hence, can facilitate AI adoption. However, it does this at the expense of achieving synergies. By contrast, when there are mechanisms for inter-decision coordination, AI adoption is enhanced when there is a non-modular environment. Consequently, the authors show that there are important cases where AI adoption will be enhanced when it can be adopted beyond tasks but as part of a designed organisational system.
Joshua Gans, a born Australian, is a Professor of Strategic Management at the University of Toronto. Since 2013, he has been Chief Economist of the University’s Creative Destruction Lab. Prior to that, he had teaching assignments at the Melbourne Business School and the University of New South Wales. In 2011, Joshua was a visiting researcher at Microsoft Research (New England). Joshua holds a Ph.D. from Stanford University mentored by this year’s Nobel Prize winner Paul Wilgrom and an honors degree in economics from the University of Queensland. In 2012, Joshua was appointed Research Associate of the NBER in the Productivity, Innovation and Entrepreneurship Program.
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