  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.
