Friend or Foe? Teaming Between Artificial Intelligence and Workers with Variation in Experience

Weiguang Wang (), Guodong (Gordon) Gao () and Ritu Agarwal ()
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Weiguang Wang: Simon Business School, University of Rochester, Rochester, New York 14627
Guodong (Gordon) Gao: Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202
Ritu Agarwal: Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202

Management Science, 2024, vol. 70, issue 9, 5753-5775

Abstract: As artificial intelligence (AI) applications become more pervasive, it is critical to understand how knowledge workers with different levels and types of experience can team with AI for productivity gains. We focus on the influence of two major types of human work experience (narrow experience based on the specific task volume and broad experience based on seniority) on the human-AI team dynamics. We developed an AI solution for medical chart coding in a publicly traded company and conducted a field study among the knowledge workers. Based on a detailed analysis performed at the medical chart level, we find evidence that AI benefits workers with greater task-based experience, but senior workers gain less from AI than their junior colleagues. Further investigation reveals that the relatively lower productivity lift from AI is not a result of seniority per se but lower trust in AI, likely triggered by the senior workers’ broader job responsibilities. This study provides new empirical insights into the differential roles of worker experience in the collaborative dynamics between AI and knowledge workers, which have important societal and business implications.

Keywords: artificial intelligence; human-AI teaming; worker experience; productivity; healthcare; medical coding (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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