This study examines the impact of artificial intelligence (AI) on employment trends in the finance and manufacturing industries, utilizing a comprehensive dataset of 120,844 AI-related news articles, 69,807 articles on jobs in the finance industry, and 65,907 articles on jobs in the manufacturing industry collected from Naver News between January 1, 2017, and August 31, 2024. To uncover the complex relationships between AI and employment, this study employed text mining techniques, including TF-IDF analysis, topic modeling, and association rule analysis. Topic analysis showed three dominant themes in the manufacturing sector, primarily driven by macroeconomic policies, industry-level employment trends, and workforce dynamics. In the finance industry, four topics were found, highlighting the role of corporate finance, digital transformation, government regulations, and global economic fluctuations in shaping employment patterns. Additionally, association rule analysis identified telecommunications, information, industry, and enterprise as key intermediary nodes linking AI to job markets in both finance and manufacturing. The extracted association rules illustrate distinct AI-driven employment pathways, confirming AI’s significant impact on job structures across industries based on high-confidence values. These findings contribute to a deeper understanding of AI’s sector-specific implications, offering insights for policymakers, industry leaders, and researchers navigating the evolving landscape of AI-driven labor markets.