A Conceptual Framework on the Relationship between Artificial Intelligence Adoption, Data-Driven Decision-Making and Zakat Management Efficiency

Abstract

Zakat plays a vital role in Islamic social finance, serving as a mechanism for poverty alleviation and social welfare. However, its management continues to face challenges including inefficiency, limited transparency, and low stakeholder trust. Conventional approaches often hinder timely collection and equitable distribution, underscoring the need for innovative, Shariah-compliant technological solutions. This study develops a conceptual framework that examines the relationship between Artificial Intelligence (AI) adoption, data-driven decision-making (DDDM), and zakat management efficiency. Drawing upon General Systems Theory (GST), the framework positions DDDM as a mediating mechanism that translates AI-driven technological capabilities such as predictive analytics, real-time monitoring, and machine learning into evidence-based, ethical, and transparent decision-making processes. Using a narrative review of Scopus-indexed literature (2019– 2024), the study synthesizes theoretical and empirical insights to demonstrate that AI adoption enhances institutional efficiency primarily through the mediating role of DDDM, which strengthens accountability, fairness, and governance in zakat administration. The study contributes theoretically by extending systems theory into Islamic social finance and practically by providing policymakers and zakat institutions with a Shariah-aligned model for responsible AI integration that promotes transparency, trust, and socio-economic justice.