Human plus AI in Accounting: Early Evidence from the Field

• 成果类型: Article
• 署名作者: Choi, Jung Ho; Xie, Chloe L.
• 署名单位: Stanford University; Massachusetts Institute of Technology (MIT)
• 刊物名称: JOURNAL OF ACCOUNTING RESEARCH
• ISSN/ISSBN: 0021-8456; 1475-679X
• DOI: 10.1111/1475-679x.70052
• 发表日期: 2026-06
• 页码: 1333-1373
• 关键词: generative AI; Accounting; bookkeeping; accountant; LABOR; technology adoption; Large Language Model; human plus AI; disclosure; tasks

摘要

This paper provides early evidence on the integration and impact of generative artificial intelligence (GenAI) in accounting at the accountant and task levels. Using survey data from 277 professional accountants, we document substantial heterogeneity in adoption patterns, perceived benefits, and concerns about GenAI. Using proprietary field data from an AI-enabled accounting platform serving 79 small- and medium-sized enterprises, we analyze over 200,000 transaction-level records. We document that GenAI adoption is associated with significant productivity gains and systematic reallocation of effort away from routine data entry toward business communication and quality assurance tasks. GenAI use is also associated with improvements to financial reporting quality, evidenced by more granular ledgers and faster month-end closing. Examining human-AI interaction, we find that accountants selectively intervene when AI confidence scores are low, consistent with complementarity between professional expertise and AI. A framed field experiment further shows that while AI assistance improves classification accuracy on average, reliance on non-consensus AI recommendations can increase the risk of error. Overall, our findings highlight both the promise and the risks of GenAI in accounting and suggest that, in practice, AI is most effective as a tool that augments-rather than replaces-professional judgment.