INVESTIGATING THE IMPACT OF AI ON THE WORKFORCE AND THE FUTURE OF WORK IN THE REGION: A MACHINE LEARNING PERSPECTIVE

This study examines the evolving impact of Artificial Intelligence (AI) on workforce dynamics within a regional context. The analysis of structured data, which includes job positions alongside industry categories, collected from Kaggle.com, AI implementation metrics, automation uncertainty assessments, skill requirements, compensation amounts, and work projection estimates, enables the application of machine learning approaches that generate insights for decision support. The purpose of this study is twofold: to analyze the impact of AI on work structures and identify automated jobs, as well as vulnerable sectors, to inform recommendations that help plan education systems and workforce development. Three clustering approaches, including K-Means and DBSCAN, along with Agglomerative Clustering, were implemented to categorize different jobs based on their AI acceptance levels, automation probability, and pay ranges. The performance analysis, as indicated by silhouette scores, revealed that Agglomerative Clustering generated meaningful clusters at a score of 0.289, while both K-Means and DBSCAN recorded scores of 0.262 and 0.093, respectively. The developed clusters enable researchers to identify vulnerable positions while proposing new career options and uncovering stable competencies, which include digital aptitude as well as emotional capability and troubleshooting abilities. The study directly provides answers to major research questions about how AI affects particular sectors while revealing portable skills across industries. Through the integration of cluster analytics and workforce analytics, this study provides policymakers, educational institutions, and workforce planners with strategic information, enabling a resilient labor market that is prepared for the future.