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

自動化から拡張へ：企業財務機能におけるエージェント型AI採用の制約認識フレームワーク (AI 翻訳)

Khaleel Ahmed Jalaluddin

Veredas do Direito📚 査読済 / ジャーナル2026-04-13#その他対象セクター: finance
DOI: 10.18623/rvd.v23.5632
原典: https://doi.org/10.18623/rvd.v23.5632

🤖 gxceed AI 要約

日本語

本論文は、企業財務機能におけるAIエージェント採用の枠組みを提案する。2020年から2026年の文献レビューに基づき、自動化から拡張への移行を分析し、制約認識型の5層フレームワークを提示する。財務報告や内部統制などの制約を考慮し、段階的拡張を提唱する。

English

This paper proposes a constraint-aware framework for adopting agentic AI in corporate finance, based on a literature review from 2020-2026. It distinguishes automation, augmentation, and bounded autonomy, and suggests a five-layer framework aligning use-case selection, autonomy design, controls, oversight, and metrics. The focus is on graduated augmentation with human accountability.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本の財務・経理部門でもAI活用が進むが、本論文はESG・気候関連開示には直接触れず、GX文脈での関連性は限定的。ただし、SSBJ対応を含む開示プロセスへのAI適用を検討する際の参考にはなる。

In the global GX context

While the paper addresses AI adoption in finance, it lacks explicit ESG or climate disclosure focus. It may inform how AI can support sustainability reporting processes, but its direct relevance to global GX frameworks like ISSB or CSRD is limited.

👥 読者別の含意

🔬研究者:AI in finance governance frameworks; potential to extend to ESG reporting.

🏢実務担当者:Guidance on implementing AI agents in finance with control considerations.

📄 Abstract（原文）

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.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。