The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LLMs), has the potential to augment human experts' workflows in this specialized domain. Our case study investigated the utility of LLMs in drafting replies to stakeholder inquiries and supporting case handlers. We conducted a preliminary study (observations and interviews), a survey, and a text similarity analysis (LLM-as-a-judge and Semantic Embedding Similarity). We discover that while LLM drafts can streamline workflows, they often require significant modifications to meet the specific demands of maritime communications. Though LLMs are not yet mature enough for safety-critical applications without human oversight, they can serve as valuable augmentative tools. Final decision-making thus must remain with human experts. However, by leveraging the strengths of both humans and LLMs, fostering human-AI collaboration, industries can increase efficiency while maintaining high standards of quality and precision tailored to each case.

翻译：海事行业需要多元利益相关方之间的有效沟通以应对复杂且安全关键性的挑战。工业人工智能，包括大语言模型（LLMs），有潜力在这一专业领域增强人类专家的工作流程。我们的案例研究探讨了LLMs在起草利益相关方问询回复和支持案件处理人员方面的效用。我们开展了一项初步研究（观察与访谈）、一项问卷调查以及文本相似性分析（LLM作为评判者与语义嵌入相似性）。我们发现，虽然LLM生成的草稿能够简化工作流程，但通常需要进行大量修改才能满足海事通信的特定需求。尽管LLM在无人监督的情况下尚不足以应用于安全关键性场景，但它们可以作为有价值的增强工具。因此，最终决策权必须保留在人类专家手中。然而，通过结合人类与LLM的优势，促进人机协作，各行业能够在保持针对每个案例的高质量与精确标准的同时，提升工作效率。