An empirical study of augmented analytics adoption and its impact on firms' agility through AI-based models

Augmented analytics extends business analytics by combining artificial intelligence, machine learning, and natural language processing. However, organizations differ in their ability to adopt these technologies and derive value from them. This study examines the organizational conditions associated with augmented analytics adoption and the role of firm agility in linking adoption to competitive advantage. The proposed model draws on Task-Technology Fit, the Resource-Based View, and Dynamic Capability Theory. It includes six resource-technology fit dimensions as antecedents of augmented analytics adoption and tests firm agility as a mediator. Survey data from a final analytic sample of 287 respondents across several business sectors in Jordan were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that the fit dimensions do not contribute equally to adoption, with data, individual, cultural, and analytics-capability fit showing the clearest relationships. The results also show that the relationship between augmented analytics adoption and competitive advantage operates mainly through firm agility. The study therefore suggests that the value of augmented analytics depends not only on adopting the technology but also on aligning organizational resources and using analytical insights to support timely organizational responses.