Chain-of-Thought (CoT) prompting is a technique used to enhance the reasoning capabilities of large language models (LLMs) by structuring responses into sequential steps, which improves accuracy and coherence in complex tasks. Introduced by Wei et al. in 2022, CoT is particularly effective for tasks requiring multi-step thinking, such as scientific reasoning, finance decision-making, and healthcare diagnosis. It involves explicit and implicit instructions, and can be applied in various forms like zero-shot, automatic, and multimodal CoT, each with unique advantages for different enterprise applications. While CoT greatly benefits problem-solving and adaptability, its limitations include increased computational demands on smaller models and the need for precise prompt engineering to ensure efficiency and accuracy. Despite these challenges, CoT remains a versatile tool for enterprises aiming to improve AI-driven reasoning and decision-making processes across diverse fields.