Organizational drivers of AI-driven business intelligence capability in marketing: Evidence from digitally transforming Jordanian commercial banks
TL;DR
The technological infrastructure, organizational culture, managerial engagement and analytics maturity must all work together to create AI-driven marketing intelligence as a strategic tool for the organization.
Abstract
Banking is now going through a rapid digital transformation which is generating a highly data driven marketing landscape, owing to the presence of Artificial Intelligence (AI). Although banks make significant investments in AI-powered Business Intelligence (BI) systems, many of them face challenges with turning data resources into actionable marketing intelligence. The current study aims to explore the drivers of organizational and technological development in terms of embedding the capability of using artificial intelligence in BI among the Jordanian commercial banks. The study focuses on four hypothesized drivers, which are grounded in the Resource Based View (RBV) and Dynamic Capabilities Theory (DCT): Analytics-Driven Culture, AI Readiness, Executive Support, and AI Analytics Maturity. A structured online questionnaire was used to collect the data from the commercial banks in Amman, Irbid, Zarqa and Aqaba which consisted of marketing managers, analytics professionals and IT decision makers. The results of the valid answers were analyzed by using the Structural Equation Modeling (SEM) technique with IBM SPSS Amos 24 software. Findings: The measurement model showed good reliability and validity. The structural model showed an acceptable fit (χ²/df = 2.41, CFI = 0.952, TLI = 0.946, GFI = 0.918, AGFI = 0.901, RMSEA = 0.048, SRMR = 0.041). All four drivers had significant and positive relationships with AI enabled BI capability. AI Analytics Maturity was the strongest predictor (β = 0.369, p < 0.001), followed by Analytics-Driven Culture (β = 0.323, p < 0.001), AI Readiness (β = 0.289, p < 0.001), and Executive Support (β = 0.226, p < 0.001). The four drivers accounted for 64.7% of the variance in AI-driven BI capability (R² = 0.647). Conclusions: The technological infrastructure, organizational culture, managerial engagement and analytics maturity must all work together to create AI-driven marketing intelligence as a strategic tool for the organization. The findings contribute to the existing body of knowledge on RBV/DCT in a context that has not been explored in the banking sector literature, and offer practical advice to bank managers who are driving digital transformation efforts.