The Propensity of AI Adopters to Undergo Training: A Decision Tree-Based Analysis

Abstract

This study explores factors influencing employees' willingness to undergo training in artificial intelligence (AI) within the workplace. Utilizing decision trees, the paper identifies key predictors and patterns that determine employees' readiness to embrace AI training. The analysis is based on a large-scale survey dataset from the Organization for Economic Co-operation and Development (OECD), capturing a wide range of demographic and job-related variables. Key findings reveal that prior training experience, positive attitudes towards AI, job role, and education level are significant predictors of training propensity. The decision tree model achieved high accuracy and reliable performance metrics, demonstrating its effectiveness in predicting training readiness. These insights offer valuable guidance for organizations aiming to enhance their workforce's AI capabilities through targeted training programs. The study highlights the importance of designing customized training initiatives to foster AI adoption and ensure a smooth transition to AI-driven workflows.

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