人工智慧發展對產業與就業之挑戰

Challenges of Artificial Intelligence Development to Industry and Employment

本文係藉由人工智慧（Artificial Intelligence，AI）對國內勞動市場影響之資料蒐整分析初探性研究。本文研究結果與結論顯示，各行業均有其導入AI技術等相關需求。透過金融服務業及醫療保健社會工作服務業之訪談分析之結果，普遍認為AI主要應用於重複性及一定程度複雜之工作，對AI創造新職位持觀望態度。另未因導入AI而造成明顯之工作替代效果，反而透過互補效果，提升對AI專業人才之需求，進而創造就業機會；此外，AI於工作中之角色及產業應用，反而促使工作內容更加明確化，進一步優化產業分工。短期建議：1.「強化職業訓練機制以因應及滿足職場跨域人才需求」；2.「企業AI導入輔助方案：針對不同年齡層、性別與產業別的工作重組支援」；3.「擴大AI自動化訓練量能：工會參與與技能培訓機制強化」；4.「建立AI自動化入場輔導機制，並協助中高齡勞工適應及學習AI技術」及5.「依企業規模分類，建構並推廣AI成功導入之經營模式」。長期建議：1.「建置AI自動化人才資料庫，以因應數位轉型與跨域人才需求」；2.「建構完整的AI教育體系，確保『以人為本』之發展架構」，及AI倫理之重要性，應由國小至高等教育階段均導入，及3.「驅動產業創新並強化國際市場連結，擴大AI產業生態系統」。未來，可再進行更細緻產業分工與識別之調查，以滾動修正AI對我國產業之效益及協助。

This paper presents an exploratory study based on the compilation and analysis of data concerning the impact of Artificial Intelligence (AI) on the domestic labor market. The research findings and conclusions indicate that all industries have varying degrees of demand for the adoption of AI technologies. Based on interview analyses conducted in the financial services industry and the human health and social work services sector, AI is generally perceived as being primarily applied to repetitive tasks and tasks with a certain level of complexity. Respondents remain cautious regarding AI’s potential to create entirely new job positions. Moreover, the introduction of AI has not resulted in significant job displacement. Instead, through complementary effects, it has increased demand for AI-related professional talent, thereby creating employment opportunities. On the contrary, it has led to clearer delineation of job content and further optimization of the division of labor within industries. Short-term recommendations include: 1. Strengthening vocational training mechanisms to respond to and meet cross-disciplinary workforce demands; 2. Implementing enterprise-level AI adoption support programs, with job restructuring assistance tailored to different age groups, genders, and industries; 3. Expanding capacity for AI automation training through enhanced union participation and skills development mechanisms; 4. Establishing guidance mechanisms for AI automation adoption, while assisting middle-aged and older workers in adapting to and learning AI technologies; and 5. Developing and promoting successful AI adoption business models categorized by enterprise size. Longterm recommendations include: 1. Establishing an AI automation talent database to address digital transformation and cross-disciplinary talent needs; 2. Building a comprehensive AI education system that ensures a“human-centered”development framework, with AI ethics integrated across all educational stages from primary to higher education; and 3. Driving industrial innovation and strengthening international market linkages to expand the AI industrial ecosystem. In the future, more detailed investigations into industrial divisions of labor and classification may be conducted to dynamically revise and refine assessments of AI’s benefits and supportive roles for Taiwan’s industries.