Beliefs about Bots: How Employers Plan for AI in White-Collar Work

TL;DR

This study reveals that employers underestimate automation risks in white-collar jobs, but providing information increases their risk perception and influences productivity and training plans without immediate hiring changes.

Contribution

It offers experimental evidence on how information affects employer beliefs and plans regarding AI automation in high-skill work, highlighting underestimated risks and future task expectations.

Findings

Employers underestimate automation potential in white-collar roles.

Information increases risk perception but does not change short-term hiring plans.

Updated beliefs lead to higher productivity and training intentions.

Abstract

We provide experimental evidence on how employers adjust expectations to automation risk in high-skill, white-collar work. Using a randomized information intervention among tax advisors in Germany, we show that firms systematically underestimate automatability. Information provision raises risk perceptions, especially for routine-intensive roles. Yet, it leaves short-run hiring plans unchanged. Instead, updated beliefs increase productivity and financial expectations with minor wage adjustments, implying within-firm inequality like limited rent-sharing. Employers also anticipate new tasks in legal tech, compliance, and AI interaction, and report higher training and adoption intentions.