Generative AI at Work

By Erik Brynjolfsson, Danielle Li, Lindsey Raymond. Social Science Research Network, 2023

1,316 citations recorded by the source corpus.

We study the staggered introduction of a generative AI–based conversational assistant using data from 5,172 customer-support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average, with substantial heterogeneity across workers. The effects vary significantly across different agents. Less experienced and lower-skilled workers improve both the speed and quality of their output, while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for moderately rare problems, where human agents have less baseline experience but the system still has adequate training data. Finally, we provide evidence that AI assistance improves the experience of work along several dimensions: customers are more polite and less likely to ask to speak to a manager.

Subfields: Demography, Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/5a23a489bb9e742edacc8b8e778b06e1594365d3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. S. Reddy
2. Anil R. Doshi
3. Oliver P. Hauser

In Economics and Econometrics

1. Shakked Noy
2. Whitney Zhang
3. S. Reddy

In Sociology and Political Science

1. Shakked Noy
2. Whitney Zhang
3. Gero Strobel
4. S. Reddy
5. Leonardo Banh
6. Anil R. Doshi
7. Oliver P. Hauser

Citations in the corpus

• Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024, methodology): “However, generative AI technologies like GANs and LLMs are complex to understand and implement [8].”
• Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation (2024, neutral): “These technologies belong to the realm of Generative AI (GAI), a branch of Machine Learning (ML) that can create new content, including text, images, music, or video, by learning patterns from existing data (Brynjolfsson, Li, and Raymond 2023).”
• Experimental evidence on the productivity effects of generative artificial intelligence (2023, result): “These results are consistent with other studies showing productivity-enhancing and equalizing effects of recent AI technologies (8, 15, 16, 18).”
• Generative artificial intelligence (2023, background): “…GET3D (Gao et al., 2022), and Point-E (Nichol et al., 2022), which generate realistic and complex 3D models that facilitate a range of applications from product design and architecture to virtual reality and game development with their customers (Brynjolfsson et al., 2023; Mondal et al., 2023).”
• Generative AI enhances individual creativity but reduces the collective diversity of novel content (2023, neutral): “…( 3 ), increase quality and efficiency of production of typical white-collar work ( 4 ), promote productivity in customer support relations ( 5 , 6 ), speed up programming tasks ( 7 ), and enhance persuasion messaging ( 8 ), little is known about generative AI’s potential impact on a…”
• When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settings (2024, unknown): “…Marlow and Wood, 2021; Cabanac et al., 2021), journalism (Petridis et al., 2023), story generation (Valentini et al., 2023; Xie et al., 2023; Zhao et al., 2023), as chatbots on social media (Sadiko˘glu et al., 2023) and in the workplace at large (Brynjolfsson et al., 2023; Eloundou et al., 2023).”