Paper Number
ICIS2025-2254
Paper Type
Complete
Abstract
Large Language Models (LLMs) are increasingly integrated into various professional tasks, yet their impact on knowledge-intensive occupations remains underexplored. This study examines how the emergence of LLMs influences the performance of financial analysts, a key profession reliant on information processing. We find the onset of LLM adoption significantly improves the forecast accuracy of sell-side analysts, with the most pronounced effects observed among lower-ability analysts. Cross-sectional analyses reveal that these effects are stronger for analysts with fewer resources or less experience and in firms with superior information environments. Further analyses show that lower-quality analysts increase the frequency and informativeness of their forecasts following LLM adoption. These findings suggest that LLMs provide greater marginal benefits to analysts with weaker quality by enhancing their information-processing capabilities. Overall, our study highlights the complementary role of LLMs in knowledge-intensive decision-making and underscores their potential to reduce quality disparities, thereby leveling the playing field of opportunities.
Recommended Citation
Yang, Xu and Hu, Nan, "LLMs: Leveling the Field or Amplifying Elitism?" (2025). ICIS 2025 Proceedings. 25.
https://aisel.aisnet.org/icis2025/gen_ai/gen_ai/25
LLMs: Leveling the Field or Amplifying Elitism?
Large Language Models (LLMs) are increasingly integrated into various professional tasks, yet their impact on knowledge-intensive occupations remains underexplored. This study examines how the emergence of LLMs influences the performance of financial analysts, a key profession reliant on information processing. We find the onset of LLM adoption significantly improves the forecast accuracy of sell-side analysts, with the most pronounced effects observed among lower-ability analysts. Cross-sectional analyses reveal that these effects are stronger for analysts with fewer resources or less experience and in firms with superior information environments. Further analyses show that lower-quality analysts increase the frequency and informativeness of their forecasts following LLM adoption. These findings suggest that LLMs provide greater marginal benefits to analysts with weaker quality by enhancing their information-processing capabilities. Overall, our study highlights the complementary role of LLMs in knowledge-intensive decision-making and underscores their potential to reduce quality disparities, thereby leveling the playing field of opportunities.
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