CONCEPTUAL ANALYSIS OF FACTORS AFFECTING SUCCESS IN DATA-DRIVEN BUSINESS PROCESSES: AN ARTIFICIAL INTELLIGENCE-BASED MODEL PROPOSAL

In recent years, the integration of artificial intelligence (AI) into business process management (BPM) has gained significant momentum, particularly in data-driven organizational structures. This study proposes a method-based conceptual model that integrates organizational critical success factors (CSFs) with AI-enabled process management components. The model is designed to include input components such as senior management support, data governance, data quality, data literacy, and technical infrastructure. It also incorporates an AI-based process mechanism consisting of machine learning, predictive analytics, process automation, and recommendation systems. This structure is constructed to produce output variables such as decision quality, operational efficiency, process success, and agility. Adopting a feedback-driven and adaptive system logic, the model aims to develop learning-based process cycles. The model's components are structured through a comprehensive literature review covering the past five years and evaluated comparatively with cognitive BPM models and AI-enabled process frameworks. The findings indicate that aligning AI technologies with organizational readiness factors can enhance strategic decision quality and digital transformation success. This conceptual framework offers both an academic contribution and a practical roadmap for institutions that want to implement AI-supported processes.