This paper presents a novel approach named Persona-Grouping-Intelligence (PGI), which has been crafted to tackle the challenges posed by GPT models when applied to real-world business issues. PGI leverages the inherent capabilities of the GPT model to comprehend intricate language structures and generate responses that are contextually relevant. The experiment occurred in a business scenario where human intelligence was being underutilized due to less optimized business processes. The primary objective of this approach is to leverage GPT models to reduce the workload on humans in tasks that are extensive, monotonous, and repetitive. Instead, the focus is redirected toward decision-making activities. Remarkably, the experiment yielded an accuracy rate of 93.81% in validating 4,000 responses generated by the model, underscoring the effectiveness of the PGI strategies. Effectively addressing the issue of underutilized human intelligence, this paradigm shift aligns business environments with dynamic machine intelligence, enabling them to navigate the intricacies of real-world challenges. This approach facilitates the practical utilization of these models to tackle actual problems. The methodology offers an opportunity to reshape the fundamental structure of business processes by seamlessly integrating human decision-making with adaptable machine intelligence. Consequently, this optimization enhances operational efficiency and elevates strategic decision-making across diverse business contexts.

翻译：本文提出了一种名为“人格分组智能”（Persona-Grouping-Intelligence, PGI）的新方法，旨在解决GPT模型应用于实际业务问题时面临的挑战。PGI利用GPT模型理解复杂语言结构并生成上下文相关响应的内在能力。实验在一个人工智能因业务流程优化不足而被低效利用的商业场景中进行。该方法的主要目标是利用GPT模型减少人类在大量、单调及重复性任务上的工作负荷，从而将精力重新聚焦于决策活动。值得注意的是，实验在验证模型生成的4000个响应时达到了93.81%的准确率，充分证明了PGI策略的有效性。通过有效解决人类智能利用率低下的问题，这一范式转变使商业环境与动态机器智能协同运作，从而应对现实世界挑战的复杂性。该方法促进了这些模型在解决实际问题中的实际应用。通过将人类决策与适应性机器智能无缝融合，该技术提供了重塑业务流程基本结构的机会。最终，这一优化提升了运营效率，并增强了跨业务场景的战略决策能力。