CONCEPTUAL ANALYSIS OF FACTORS AFFECTING SUCCESS IN DATA-DRIVEN BUSINESS PROCESSES: AN ARTIFICIAL INTELLIGENCE-BASED MODEL PROPOSAL
DOI:
https://doi.org/10.5281/zenodo.17119495Keywords:
Artıfıcal Intelligence, Business Process Management, Critical Success Factors, Data-Driven Decision Making, Digital TransformationAbstract
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 in a layered structure that includes input components such as senior management support, data governance, data quality, data literacy, and technical infrastructure; an AI-based process mechanism consisting of machine learning, predictive analytics, process automation, and recommendation systems; and 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
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