Voice AI in Firms: A Natural Field Experiment on Automated Job Interviews

Abstract

We study AI agents as information-collection technologies: automated systems that elicit decision-relevant signals from humans through live interactions. We test how such AI automation impacts information collection and organizational outcomes using a natural field experiment with 70,000 applicants applying for real jobs. Applicants were randomly assigned to be interviewed by either human recruiters or AI voice agents. Afterward, human recruiters evaluate the interviews and make hiring decisions. Applicants interviewed by AI agents are 12% more likely to receive job offers, and these gains translate into higher job starts and worker retention, with no decline in the productivity of hired workers. Analyzing interview transcripts reveals that AI voice agents achieve controlled variance: their interviews are more structured and consistent while remaining responsive to individual applicants, which is associated with more hiring-relevant information collected. Our results suggest that a key advantage of AI automation lies in environments where information collection is delegated across many human workers and repeated such that variance in task execution becomes noise in decision-relevant signals, which AI compresses through adaptive standardization.