Generative AI at Work

Erik Brynjolfsson, Danielle Li and Lindsey Raymond
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Danielle Li: MIT

Research Papers from Stanford University, Graduate School of Business

Abstract: New AI tools have the potential to change the way workers perform and learn, but little is known about their impacts on the job. In this paper, we study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents. Access to the tool increases productivity, as measured by issues resolved per hour, by 14% on average, including a 34% improvement for novice and low-skilled workers but with minimal impact on experienced and highly skilled workers. We provide suggestive evidence that the AI model disseminates the best practices of more able workers and helps newer workers move down the experience curve. In addition, we find that AI assistance improves customer sentiment, increases employee retention, and may lead to worker learning. Our results suggest that access to generative AI can increase productivity, with large heterogeneity in effects across workers.

JEL-codes: D8 J24 M15 M51 O33 (search for similar items in EconPapers)
Date: 2023-11
New Economics Papers: this item is included in nep-ain, nep-eff, nep-hrm and nep-tid
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Citations: View citations in EconPapers (25)

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https://www.gsb.stanford.edu/faculty-research/working-papers/generative-ai-work

Related works:
Working Paper: Generative AI at Work (2024)
Working Paper: Generative AI at Work (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:4141

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