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Generative AI at Work

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arxiv 2304.11771 v2 pith:4VSHKHHO submitted 2023-04-23 econ.GN q-fin.ECq-fin.GN

classification econ.GNq-fin.ECq-fin.GN
keywords agentsassistancelessworkersdataevidenceexperienceexperienced
verification ladder T0 review T1 audit T2 compute T3 formal
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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. 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 relatively rare problems, where human agents have less baseline training and experience. Finally, we provide evidence that AI assistance improves the experience of work along two key dimensions: customers are more polite and less likely to ask to speak to a manager.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Checking Problem: What must be true before AI ships in a regulated firm

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Across 72 LLM workflow configurations in regulated finance, 56% survived a production bar after passing a demonstration, and requiring confidence scores plus citations roughly halved the human review burden.

  2. MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems

    cs.AI 2025-08 reject novelty 4.0 of 10

    The authors claim their MultiFluxAI orchestration framework achieves 95% accuracy and 0-10 ms responses by combining rule-based routing, caching, and graph knowledge stores for multi-service RAG queries.

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