IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2607.28222.html

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

Author

Listed:
  • Brian Jabarian
  • Luca Henkel

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.

Suggested Citation

  • Brian Jabarian & Luca Henkel, 2026. "Voice AI in Firms: A Natural Field Experiment on Automated Job Interviews," Papers 2607.28222, arXiv.org, revised Sep 2026.
    • Handle: RePEc:arx:papers:2607.28222
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2607.28222
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. John Conlon & Peter Schwardmann, 2026. "AI Sycophancy and Decisions," Papers 2607.28133, arXiv.org.
    2. Hanna Brosch & Christina Langer & Philipp Lergetporer & Sandra Mueller & Harald Pfeifer & Nele Tschoepe, 2026. "Task Expansion with Generative AI: Experimental Evidence on Middle-Skilled, Early-Career Workers," CESifo Working Paper Series 13045, CESifo.
    3. John Conlon & Peter Schwardmann, 2026. "Creative destruction: Human-AI competition in an experimental market," Papers 2610.18921, arXiv.org.

    More about this item

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M51 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Firm Employment Decisions; Promotions
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2607.28222. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.