AbstractThe integration of artificial intelligence (AI) in enterprise finance presents transformative opportunities for enhancing operational efficiency and reducing costs amid increasing complexity and volume of financial processes. Despite the technological advancements, significant challenges persist in systematically implementing AI-driven automation that effectively addresses operational cost reduction while ensuring compliance, security, and user acceptance. This research aims to develop and validate a comprehensive framework that guides enterprises through the adoption of AI automation specifically targeted at lowering operational expenses in finance functions. Employing a mixed-method approach, the study synthesizes insights from a systematic literature review coupled with qualitative data obtained through expert interviews with finance professionals, AI specialists, and organizational strategists. The framework incorporates key dimensions including technological integration, organizational change management, ethical governance , and performance evaluation metrics. Empirical validation through expert feedback highlights the interplay of legacy system complexities, user adoption as a critical cost driver, ethical considerations, and the necessity of assistive AI roles supported by rigorous measurement of cost benefits. The proposed framework advances theoretical understanding by bridging multidimensional factors into a cohesive model and offers practical value by providing actionable guidance for enterprises seeking to optimize AI-driven automation strategies in finance. Ultimately, this study contributes to enhancing financial performance and organizational resilience by delivering an empirically grounded, adaptable roadmap for reducing operational costs through AI automation in enterprise finance.
Paras Pandey (Wed,) studied this question.