AI-Powered Decision Support Systems for Trade Union Strategic Planning: Integrating Cloud Analytics with Collective Bargaining Intelligence
Аннотация
AI-powered decision support systems (DSS) are increasingly used to support strategic planning and collective bargaining, yet few have been designed specifically for the governance context of trade unions. The proposed analytical hierarchy process (AHP) model is intended to be integrated with the architecture of cloud analytics because it captures the decision–coordination interdependencies and enables more data-driven insights for negotiators in labor–management dialogues. This empirical evaluation and regression modeling, made using Gephi and high-speed data visualization with network clustering, consist of the analysis of a representative section of union activities across two sectors and organizations over a period of five years. The aim of this study was to facilitate evidence-based decision making in the framework of collective bargaining processes. Quantitative and qualitative methods were applied to identify correlations, to estimate priorities, propose ways that artificial intelligence could best support bargaining strategies for both employees and employers, and establish indicators and ranking criteria for a transparent negotiation environment. Through a focus on knowledge mapping, we show how reading communication flows can inform strategic reasoning about bargaining dynamics and add to computational understanding of influence structures in industrial relations. These outcomes, validated with AHP–SEM regression and network statistics, resulted in the development of TUS-AIDSS – a cloud-based decision platform to improve transparency and bargaining efficiency. The findings showed that predictive accuracy and decision reliability increase when union analysts collaborate with AI tools and their data interfaces, and when organizations synchronize their digital infrastructures for negotiations at multiple levels after model calibration.