THE TRANSITION FROM AUTOMATION TO AUGMENTATION: A CONSTRAINT-AWARE FRAMEWORK FOR AGENTIC AI ADOPTION IN CORPORATE FINANCE FUNCTIONS

Resumen

Corporate finance functions are moving through a new digital inflection point. Earlier waves of automation in finance centered on robotic process automation, rules engines, workflow tools, and predictive analytics that standardized high-volume transactions and reduced cycle times. The current wave is different because large language models, generative AI, and agentic systems can interpret unstructured data, coordinate across tools, and recommend or execute multi-step decisions. Yet the finance function cannot adopt autonomy in the same way as less regulated business areas. Financial reporting, planning, controllership, treasury, tax, internal audit, and investor-facing processes are constrained by internal controls, segregation of duties, auditability, data lineage, policy compliance, and accountability to boards, regulators, and external assurance providers. This review paper examines how the literature from 2020 to 2026 explains the shift from automation to augmentation and develops a constraint-aware framework for adopting agentic AI in corporate finance. Following a structured review and content analysis of recent academic studies, standards, and practitioner reports, the paper synthesizes four themes: the evolution of AI use cases in finance and accounting; the distinction between automation, augmentation, and bounded autonomy; the organizational and technical constraints that shape deployment; and the governance mechanisms needed for reliable value capture. The review argues that the most realistic pathway for finance is not unrestricted autonomy but graduated augmentation, in which AI agents expand analytical capacity, accelerate close and planning cycles, and improve stakeholder alignment while humans retain accountability over material judgments and irreversible actions. Building on the synthesis, the paper proposes a five-layer framework that aligns use-case selection, autonomy design, control requirements, human oversight, and performance metrics. The framework helps organizations match agentic capability to task criticality, data quality, reversibility, and regulatory exposure. The paper contributes a finance-specific conceptualization of agentic adoption, unique research objectives, an implementation sequence for CFO organizations, and a future research agenda focused on control redesign, explainability, operating models, and the changing role of finance professionals.