Evaluating project manager competencies using explainable artificial intelligence

Nowadays, in various industries, particularly those related to information technology and telecom, AI-driven projects have increased. The implementation of these projects poses fundamental challenges, and the presence of competent project managers can enhance the likelihood of their success. Therefore, the primary goal of this article is to develop a model for evaluating the competencies of AI-driven project managers. In this context, after reviewing conventional models in this field and conducting surveys with experts based on the IPMA model, evaluation indicators for AI-driven project managers were identified. Subsequently, using the decision tree algorithm and optimizing class weights with the Ant Colony Optimization algorithm, a decision support system was developed for continuous and real-Time evaluation of AI-driven project managers. The article's findings indicate that indicators such as Change and Transformation, Resourcefulness, and Time are among the most important competencies for project managers. Moreover, the decision tree model optimized with the Ant Colony Optimization algorithm can accurately identify the competencies of project managers with a 94% accuracy rate, outperforming other algorithms. Additionally, a sensitivity analysis of various model features, conducted using the Shapley Additive exPlanations Algorithm, reveals the impact of each feature on the competencies of project managers.