이강돈  서재민  오세미

Vol. 40,  No. 5, pp. 154-165, Oct.  2025
10.14341/JKOSOS.2025.40.5.154

Generative AI AI agent agentic AI SHE management scenario-based analysis Risk Prediction AX(AI Transformation)

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This study investigated the applicability and effectiveness of GenAI and AI agent technologies in the field of safety, health, and environment (SHE). By employing a scenario-based analysis methodology, it designed five industry-representative SHE scenarios to simulate AI integration in strategic planning, decision-making, field operations, document processing, and risk prediction. The results revealed that AI can not only contribute significantly to the automation of repetitive and high-risk SHE tasks, which renders it suitable for improving accident prevention, but also enhance the effectiveness of safety training. In particular, AI systems powered by no-code platforms such as Make, n8n, and Cursor can provide real-time data-driven decision support and strategic responsiveness, and hence, they can be highly beneficial to organizations, especially SMEs seeking SHE digital transformation. This study systematized AI applications across five categories, namely strategy planning, decision support, operational safety, data analytics, and risk reduction, and considered implementation cases. These cases showed that AI integration can help create an intelligent, proactive safety management ecosystem beyond mere automation. Specifically, the findings highlight limitations of scenario-based analyses, such as the absence of empirical validation and environmental constraints. To overcome these limitations, future studies should adopt hybrid methodologies along with cross-impact analysis, and field data and expert verification should be combined for broader applicability. Notably, this research identifies AI technologies, especially GenAI and autonomous agents, as a transformative factor for SHE management, potentially reshaping practices to involve the use of collaborative, ethical, and explainable AI systems. Governance frameworks for data integrity, ethical accountability, and human-AI interaction are necessary for sustainable deployment. AI is not only a powerful tool for effective accident prevention but also a foundational infrastructure for the development of a more efficient, responsive, safety-oriented organizational culture.