Evaluation of AI Tools in Terms of Sustainable Workforce Productivity and Their Impact on Organizational Outcomes Using an Intuitionistic Fuzzy Approach
Jul 2026· Journal of Operations Intelligence· Vol 4, pp. 23-41· 0 citations· 57 references
TL;DR
A multi-criteria decision-making framework for evaluating six AI tools against eight sustainability-oriented criteria indicates that improving individual productivity and enhancing employee engagement and motivation are the most influential evaluation criteria.
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.
The mining industry faces increasing challenges in enhancing productivity within complex operational environments characterized by the interaction of technical, organizational, human, and external factors. Nevertheless, existing studies frequently examine these determinants independently, thereby limiting a comprehensive understanding of their relative importance and combined influence on operational performance. This study develops a decision framework to identify and prioritize the factors affecting productivity in medium-scale mining in the Coquimbo Region, Chile, through the application of the Analytic Hierarchy Process (AHP) based on expert judgment obtained from active mining operations. The AHP model was structured using two criteria, seven subcriteria, and twenty-three decision factors, whose consistency and reliability were assessed through the consistency ratio (CR) and Cronbach’s alpha coefficient. The results revealed a marked predominance of Internal factors (75.0%) over External factors (25.0%), indicating that productivity is influenced primarily by variables that can be managed at the organizational level. Among the evaluated subcriteria, Work planning achieved the highest priority (32.0%), whereas Scheduling and control (10.2%), Human factors (6.4%), and Working conditions (5.2%) emerged as the most influential decision factors. Furthermore, sensitivity analysis confirmed the robustness and stability of the model. Beyond establishing a prioritization of productivity determinants, this study provides a decision-support framework that can assist mining companies in strengthening productivity management and improving operational performance in medium-scale mining.
Multi-Criteria Decision-Making (MCDM) has become an indispensable analytical approach for supporting organizational performance evaluation and strategic management in increasingly complex and uncertain business environments. Modern organizations operate under multiple, often conflicting objectives involving financial performance, operational efficiency, sustainability, innovation, stakeholder satisfaction, risk mitigation, and competitive positioning. Conventional single-criterion evaluation techniques frequently fail to capture these multidimensional relationships, necessitating structured decision-making frameworks capable of integrating qualitative and quantitative information. MCDM methodologies enable systematic prioritization of strategic alternatives by incorporating diverse evaluation criteria, expert judgments, uncertainty considerations, and organizational objectives into a unified decision-support process. Recent developments integrating hybrid optimization techniques, fuzzy environments, and data-driven analytics have significantly enhanced decision transparency, robustness, and adaptability across industries. This paper investigates the role of contemporary MCDM approaches in improving organizational performance assessment and strategic decision-making by examining methodological developments, practical implementation frameworks, evaluation indicators, and performance outcomes. The study further proposes an integrated conceptual framework that facilitates objective strategic prioritization, efficient resource allocation, and sustainable organizational competitiveness. The findings are expected to provide valuable theoretical insights and practical guidance for researchers, policymakers, and organizational leaders seeking evidence-based strategic management solutions.
Dr. Padmavathi SM¹, S² Dr.Suma, B. Dr.LakshmiR et al.· Journal of Intelligent Decis...· 0 citations
Industrial sustainability assessment needs a transparent and operational framework that can manage multidimensional indicators, expert uncertainty, weighting complexity, and managerial interpretation. This research presents the Sustainable Industrial Measurement (SIM) Model, an AI-assisted sustainability intelligence architecture for manufacturing organizations. The model comprises literature-based indicator synthesis, Fuzzy Delphi Technique (FDT), Pareto 80/20 screening, Group Analytic Hierarchy Process (Group AHP), Utility Value Analysis and the web-based AI-enabled decision support system (AI-DSS) under the Triple Bottom Line (TBL) framework. In FDT validation by consensus, threshold and fuzzy score criteria, 33 experts accepted 64 indicators. The Pareto screening reduced the set to 50 high-impact indicators, consisting of 10 economic, 22 social and 18 environmental indicators. The priority weights were derived from the group AHP weighting by 21 experts and checked for consistency. The environmental and economic indicators represent the dominant sustainability priorities. The weighted structure was embedded in the web-based AI-DSS to enable automated scoring, visualization, gap diagnosis and AI-based managerial recommendations. Thirty industrial practitioners reported excellent perceived usability of the SIM Model, with a System Usability Scale score of 86.0. However, the evaluation assessed usability only, not the accuracy, effectiveness, or organizational impact of AI-assisted recommendations for manufacturing sustainability decisions and future implementation.
P. Boonkanit, Thirachet Paengteerasukkamai· Sustainability· 0 citations
A research framework for analysing the added value of generative AI in logistics organizations, with a focus on “difficult to automate” tasks and processes is presented.
Gerald Schneikart, Walter Mayrhofer· Engineering review· 0 citations
The application of Artificial Intelligence (AI) in decision-making processes is changing the way organizations approach and make decisions.Organizations are increasingly turning to Artificial Intelligence (AI) to analyze complex data, detect trends, predict outcomes, and consider various options in their decision-making process. Organisation performance assessment, however, encompasses numerous both qualitative and quantitative criteria, uncertainty, conflicting priorities of management and imprecise expert opinions – which cannot be fully captured by conventional decision making techniques. This study presents a new AI assisted Fuzzy Multi-Criteria Decision-Making (F-MCDM) model to assess the performance of an organization and to assist in making strategic management decisions. The proposed framework is based on the use of AI-based analytical capabilities and fuzzy decision-making techniques that can handle the vagueness of managerial assessments and prioritize strategic options in a systematic manner. The relative importance of organisational performance criteria are determined by using the fuzzy Analytic Hierarchy Process (FAHP), and the fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the strategic alternatives. It looks at aspects such as financial performance, operational efficiencies, innovation capability, customer orientation, human-resource effectiveness, and strategic adaptability as well as digital capability. The aim of the proposed approach is to enhance the decision consistency, transparency of decision making, strategic alignment and managerial responsiveness. The study illustrates that AI and fuzzy MCDM are able to offer an intelligent decision-support mechanism to organisations in an uncertain and competitive environment.
Mukul Agarwal, Ms. Aman Gupta, Martina Kant et al.· Journal of Intelligent Decis...· 0 citations
This study evaluates institutional models for adapting labour markets to the growing adoption of automation and artificial intelligence (AI). As AI continues to transform labour markets, identifying effective institutional responses has become increasingly important. Six institutional adjustment models were assessed against eight evaluation criteria using a hybrid fuzzy-rough decision-making framework that integrates the SiWeC (Simple Weight Calculation) and WASPAS (Weighted Aggregated Sum Product Assessment) methods based on expert judgments. The proposed approach explicitly incorporates uncertainty and imprecision into the evaluation process, thereby reducing subjectivity in decision-making. The results indicate that innovation and productivity incentives and institutional feasibility are the most influential evaluation criteria. A case study conducted in the Republic of Croatia further shows that the mass retraining and participatory AI capital models provide the most suitable institutional responses. The study contributes both methodologically, by proposing a robust fuzzy-rough evaluation framework, and practically, by supporting evidence-based policy decisions for labour market adaptation in the era of AI.
Jurica Bosna, A. Puška, Darko Božanić· Journal of Soft Computing an...· 0 citations
