Empirical analysis reveals that AI investments improve analyst forecast speed and redirect effort toward private information discovery, highlighting human-AI complementarity in finance.
We examine how analysts’ information acquisition and processing differ when analysts have access to AI resources, focusing on investment banks’ AI investments. We propose and test a two‐step framework, which is informed by in‐depth interviews with analysts. First, consistent with AI facilitating automation‐assisted public information processing, we show that AI investments are associated with more timely earnings forecasts following 10‐K filings, particularly after the implementation of iXBRL, which increases the machine readability of filings. Second, we show that analysts reallocate the time and capacity freed by automation toward acquiring and incorporating private information, supported by several sets of evidence: AI investments (1) are associated with higher quality and bolder earnings forecasts, particularly when private information is more important and accessible to analysts; (2) are associated with an expansion of analyst coverage to new firms and industries; and (3) are associated with higher information‐seeking efforts, particularly greater participation in earnings conference calls. Additionally, exploiting the launch of AskResearchGPT at Morgan Stanley, an in‐house generative AI designed for research, we find results consistent with our main analyses. Overall, our study provides insights into the potential for AI to reshape the human value of sell‐side analysts.
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Shanthikumar et al. (2026) studied this question.
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