Large language models (LLMs) have achieved remarkable performance in language
understanding and generation tasks by leveraging vast amounts of online texts.
Unlike conventional models, LLMs can adapt to new domains through prompt
engineering without the need for retraining, making them suitable for various
business functions, such as strategic planning, project implementation, and
data-driven decision-making. However, their limitations in terms of bias,
contextual understanding, and sensitivity to prompts raise concerns about their
readiness for real-world applications. This paper thoroughly examines the
usefulness and readiness of LLMs for business processes. The limitations and
capacities of LLMs are evaluated through experiments conducted on four
accessible LLMs using real-world data. The findings have significant
implications for organizations seeking to leverage generative AI and provide
valuable insights into future research directions. To the best of our
knowledge, this represents the first quantified study of LLMs applied to core
business operations and challenges.

大型语言模型（LLMs）在理解和生成任务方面取得了显著的表现，但其在偏见、上下文理解和对提示的敏感性方面存在限制，因此对其在实际应用中的准备性产生了担忧。本文通过对四个可访问的 LLMs 使用真实世界数据进行的实验，深入研究了 LLMs 在业务流程中的实用性和准备性。研究结果对希望利用生成式人工智能的组织具有重大影响，并为未来的研究方向提供了宝贵的见解。据我们所知，这是第一项将 LLMs 应用于核心业务运营和挑战的定量研究。