Evaluation and Mitigation of the Limitations of Large Language Models in Business Decision-Making

Large Language Models (LLMs) have emerged as transformative tools in business decision-making, offering capabilities such as automation, market analysis, and customer insights. However, their integration into business processes presents significant limitations, including biases in training data, lack of domain-specific knowledge, interpretability issues, and ethical concerns. These challenges can affect the quality and reliability of business decisions, potentially leading to biased or erroneous outcomes. This paper evaluates the key limitations of LLMs in business contexts and explores strategies to mitigate these issues. Proposed solutions include diversifying training data to reduce biases, fine-tuning models with industry-specific expertise, implementing explainable AI techniques for better transparency, and establishing ethical guidelines to safeguard against misuse. By addressing these limitations, businesses can harness the full potential of LLMs, ensuring more effective, fair, and accountable decision-making processes. This research provides a comprehensive approach for mitigating the risks associated with LLMs in business environments.