Core Viewpoint - The article discusses the challenges and potential of integrating AI, particularly large language models (LLMs), into the Failure Mode and Effects Analysis (FMEA) process, emphasizing the need for a systematic approach to enhance efficiency while maintaining professional judgment [1][4][12]. Group 1: Challenges in Traditional FMEA - FMEA is often seen as crucial but is cumbersome due to scattered information and reliance on manual analysis, leading to inefficiencies and potential omissions [1][2]. - The traditional FMEA process has not fundamentally changed despite advancements in industry standards, continuing to depend heavily on human analysis and documentation [2][3]. Group 2: AI Integration Potential - New AI technologies, especially LLMs, can efficiently process and organize large volumes of textual information, prompting a reevaluation of whether FMEA must rely solely on human effort [1][2]. - LLMs excel at understanding and structuring complex text, which can alleviate the burden of data organization in FMEA, allowing experts to focus on decision-making [2][4]. Group 3: Systematic Approach for AI + FMEA - A structured methodology is necessary to effectively integrate AI into the FMEA process, ensuring that professional judgment is not compromised while reducing manual workload [4][12]. - The proposed "AI + FMEA framework" breaks down the FMEA process into five clear steps, from information collection to integrating results into existing information systems [5][6]. Group 4: Practical Implementation - Emphasizing the design of information systems is crucial; FMEA should be part of the enterprise knowledge system rather than a one-time task [7][10]. - The framework aims to transform scattered experiences into a sustainable system capability, enhancing FMEA's role as a long-term management tool [7][12]. Group 5: Validation of AI's Effectiveness - The effectiveness of AI in FMEA should be validated through real-world data, such as user comments, to assess its practical value [8][9]. - Initial findings indicate that LLMs can quickly identify potential issues but should not replace expert judgment in final assessments [9][12]. Group 6: Long-term Sustainability - Successful implementation of AI in FMEA requires careful consideration of data security, model training, and ongoing validation in real industrial contexts [12][10]. - The focus should be on how to effectively utilize AI rather than whether to use it, ensuring a clear division of labor between AI and human experts [12][10].