Do AI Voice Agents Conduct Better
Job Interviews Than Humans?

This paper studies whether AI automation can improve organizational outcomes by reducing variance when collecting information. We conducted a large-scale natural field experiment in which 70,000 job applicants were randomly assigned to be interviewed by human recruiters or AI voice agents. In both conditions, 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. These results demonstrate that automating information collection with AI can enhance decision quality through standardization.

70,000 applicants randomly assigned to one of three interview conditions in a large-scale natural field experiment.

This study provides causal evidence that AI can improve real hiring outcomes at scale — not by replacing human judgment, but by improving the information that feeds into it.

The findings have implications for how firms organize their hiring processes, how workers experience recruitment, and how policymakers think about AI adoption in labor markets.

This research is part of the Artificial Process Outsourcing program, a five-year initiative studying how AI reorganizes workflows and decision-making in firms.