Agentic AI Adoption in Finance Operations: Examining Its Impact on Managerial Decision- Making, Operational Efficiency, and Organizational Performance

Abstract

The ability of AI agents to reason, plan, co-ordinate, and execute tasks collectively defines Agentic AI capability. This paper examines the impact of Agentic AI upon financial management, using the context as to how it enhances the level of management productivity, financial decision quality, and organization performance. Ten researchers were selected for a systematic literature review, which were then analyzed under several thematic dimensions; aided by relevant industry reports. Five primary themes were identified through analysis: Autonomous Financial Activities, Productivity and/or Operational Efficiency, Quality of Financial Decisions, Organization Performance and Trust and Governance. The study found that Agentic AI is useful in financial analysis, investment research, reporting, risk monitoring and even complicated financial functions, showing the expanding industry's interest toward Autonomous AI in organization along with issues of implementation challenges, data quality, reliability, and governance. Synthesis evidence leads to suggestions for an Agentic AI-Financial Management Framework that articulates Autonomous AI capability into financial process automation, management productivity, decision quality and organizational performance. Findings indicate that human supervision, organization readiness and appropriate governance is necessary for its responsible adoption. Findings offer a manager-oriented perspective for empirical studies on strategic implications of Agentic AI for financial institutions, beyond those discussed within accounting, finance, information management and marketing literature.