From Robotic Process Automation to Agentic AI: A Systematic Review, Taxonomy, and Capability Assessment Framework for Intelligent Automation in Enterprise Accounting

Abstract

Enterprise accounting is undergoing a structural transition from rule-based robotic process automation (RPA) toward agentic artificial intelligence: systems built on large language models that plan multi-step workflows, invoke tools, and adapt their execution autonomously. Research on this transition remains fragmented across information systems, accounting, and artificial intelligence venues, and no unified conceptual structure yet characterises the emerging class of agentic accounting systems. We conduct a PRISMA 2020-compliant systematic literature review, screening 2,387 retrieved records down to 60 included studies, and synthesise the technological trajectory of intelligent automation across four generations RPA, intelligent process automation (IPA), hyperautomation, and agentic AI and across core accounting subfunctions including procure-to-pay, order-to-cash, record-to-report, reconciliation, audit, and tax. Applying Nickerson et al.'s method, we derive a six-dimensional taxonomy spanning autonomy level, learning capability, process scope, human oversight, integration depth, and accounting subfunction. We extend the taxonomy into a capability assessment framework with five maturity levels (L0–L4) per dimension and ground it in four archetypal system classes, from the classic RPA bot to the autonomous multi-agent reconciliation system. The review identifies five critical research gaps: agent governance, hallucination mitigation, benchmark scarcity, multi-agent orchestration standards, and explainability and outlines a research agenda for trustworthy agentic accounting systems.