aiqbench analysis
This field experiment reveals a critical nuance in deploying generative AI assistants for human agents: while average metrics like speed improve, the tool disrupts high-performing workers by encouraging premature disengagement from complex chats. The findings suggest that successful integration requires tailored rollouts rather than blanket adoption to prevent workflow interruptions among top-tier performers.
Generative AI in Action: Field Experimental Evidence from Alibaba's Customer Service Operations
arXiv:2603.29888v2 Announce Type: replace-cross Abstract: In collaboration with Alibaba, we study how a generative AI assistant affects service performance in e-commerce after-sales operations. In a large-scale field experiment, human agents providing digital chat support were randomly assigned access to a gen AI assistant. The assistant drafts issue diagnoses and solution proposals in the opening stage only; agents can adopt, modify, or disregard them. Because of this discretion, we estimate the effects of both gen AI access and usage. On average, gen AI improves service speed and subjective service quality, measured by customer ratings, but has no significant effect on objective service quality, measured by customer retrials. These gains come from more than automation. Gen AI reshapes agent-customer interactions: treated agents respond faster and take a more proactive role, while customers provide less input; both patterns persist into later chat stages. These average effects, however, mask heterogeneity across agents. Lower-performing agents benefit the most, indicating that gen AI can narrow performance gaps. Top-performing agents experience declines in both subjective and objective service quality. This decline is consistent with workflow disruption: among top performers, gen AI use increases shift-away time, response delays, and immediate customer retrials, suggesting weakened service continuity in the focal chat. Successful gen AI deployment therefore requires careful performance evaluation and rollout tailored to agent skill.
Abstract:In collaboration with Alibaba, we study how a generative AI assistant affects service performance in e-commerce after-sales operations. In a large-scale field experiment, human agents providing digital chat support were randomly assigned access to a gen AI assistant. The assistant drafts issue diagnoses and solution proposals in the opening stage only; agents can adopt, modify, or disregard them. Because of this discretion, we estimate the effects of both gen AI access and usage. On average, gen AI improves service speed and subjective service quality, measured by customer ratings, but has no significant effect on objective service quality, measured by customer retrials. These gains come from more than automation. Gen AI reshapes agent-customer interactions: treated agents respond faster and take a more proactive role, while customers provide less input; both patterns persist into later chat stages. These average effects, however, mask heterogeneity across agents. Lower-performing agents benefit the most, indicating that gen AI can narrow performance gaps. Top-performing agents experience declines in both subjective and objective service quality. This decline is consistent with workflow disruption: among top performers, gen AI use increases shift-away time, response delays, and immediate customer retrials, suggesting weakened service continuity in the focal chat. Successful gen AI deployment therefore requires careful performance evaluation and rollout tailored to agent skill.
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
arXiv:2603.29888 [cs.HC]
(or arXiv:2603.29888v2 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2603.29888 Focus to learn more arXiv-issued DOI via DataCite
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