Generative AI at Work

Checked 30 Sept 2026. Non-commercial or no-derivatives license: full text kept internally; only metadata and abstract are indexed and used.

Abstract 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.

• Field and laboratory evidence conflict on whether AI assistance builds durable human skill: Brynjolfsson et al. 2024 "find evidence that AI assistance facilitates worker learning," with the largest gains for less-experienced, lower-skilled workers, whereas Wu et al. 2025 find gains vanish in solo work and boredom rises—so the conditions under which AI collaboration converts into lasting human learning remain unidentified. Trace →

• Supervision and doctoral education