Evaluation of AI Tools in Terms of Sustainable Workforce Productivity and Their Impact on Organizational Outcomes Using an Intuitionistic Fuzzy Approach

Abstract

The adoption of artificial intelligence (AI) offers significant opportunities to improve organizational performance. However, organizations must leverage its potential to enhance productivity without compromising employees' long-term well-being. This study addresses this challenge by developing a multi-criteria decision-making framework for evaluating six AI tools against eight sustainability-oriented criteria. Expert judgments provided by academic researchers from the Republic of Croatia were used to assess both the importance of the evaluation criteria and the performance of the AI tools in supporting sustainable organizational efficiency. Given the uncertainty and imprecision inherent in expert evaluations, an intuitionistic fuzzy framework was employed. The SWARA (Stepwise Weight Assessment Ratio Analysis) method was applied to determine the criteria weights, while the MABAC (Multi-Attributive Border Approximation Area Comparison) method was used to rank the AI tools. The results indicate that improving individual productivity and enhancing employee engagement and motivation are the most influential evaluation criteria. The findings further show that AI tools supporting employee learning and development, together with decision-support capabilities, represent the most suitable solutions for sustainable organizational efficiency. The robustness of the proposed framework was confirmed through comparative and sensitivity analyses. The study provides practical guidance for managers seeking to balance technological advancement with the long-term development and well-being of human resources.