AI-Augmented BI Data Analysis and Decision Quality in Pakistan's Climate Crises: The Mediating Role of Swift Trust and Moderating Role of Organizational Culture

The 2022-2025 floods in Pakistan displaced 38 million people, exposing severe civil-military coordination failures, yet technology-adoption research remains concentrated in corporate settings and rarely examines how AI-augmented analytics shape decisions under extreme time pressure. This study extends a corporate analytics-trust model to Pakistan's humanitarian context, testing whether AI-Augmented BI Data Analysis improves Decision Quality and Collaborative Performance through Swift Trust, and whether Organizational Culture moderates these relationships. Data from 250 emergency responders across disaster authorities, military units, NGOs, and commercial partners were analyzed using PLS-SEM with bootstrapping. AI-Augmented BI significantly predicted Decision Quality (β = 0.415) and Swift Trust (β = 0.524); Swift Trust partially mediated the BI-Decision Quality link (β = 0.193) and strongly drove Collaborative Performance (β = 0.485). Flexible culture amplified analytics-to-trust formation (β = 0.185), while control-oriented culture weakened analytics' translation into collaborative performance (β = -0.224). By integrating technology adoption, inter-organizational trust, and organizational culture within a climate disaster governance framework, the study advances both humanitarian operations theory and practice. These findings indicate that technical infrastructure alone cannot resolve humanitarian coordination failures; effective climate crisis governance requires pairing AI-driven analytics with deliberate trust-building and flexible organizational design, offering actionable guidance for disaster authorities balancing hierarchical command with agile, network-based collaboration