ESMT European School of Management and Technology, Germany
Artificial intelligence (AI) is diffusing rapidly throughout the economy, raising questions regarding its impact on human workers. Current theories predict that the degree to which humans are replaced (automation) or supported (augmentation) will depend on characteristics of the task such as its degree of routinization and manual vs. cognitive nature. Yet, existing empirical evidence tends to come from aggregate occupation data or individual case studies, with limited research using larger samples of projects or tasks. Moreover, whether and how AI is implemented may not only depend on task characteristics but also on managers’ objectives, which may go beyond narrow efficiency to include benefits resulting from human employment itself (“employment goals”). We provide novel empirical evidence using data from more than 1,200 research projects that involve crowd members in different tasks such as data collection, data analysis, or creative problem solving. We confirm that the use of AI is associated with task characteristics, although the patterns are more nuanced than those shown in work on prior automation technologies such as computers and robots. Moreover, we find that managers pursuing employment goals are less likely to use AI for automation, while they are just as likely as others to use AI to augment the work of human workers. We contribute to the literature on AI adoption in organizations by providing rare evidence from project-level data, and by highlighting that the path towards “good AI” or “bad AI” is, partly, a matter of human choice.