Collaborative Intelligence in Accounting: A Human + AI Complementarity Framework for Professional Work
DOI:
https://doi.org/10.64044/vpm19y22Keywords:
generative AI; large language models (LLMs); financial reporting; period-end close; human–AI collaboration; AI governanceAbstract
Background: Public discussion of generative artificial intelligence (AI) in accounting often swings between the allure of full automation and job-displacement anxiety, yet the most immediate reality in organizations is human + AI work: AI accelerates drafting, summarization, and pattern detection while professionals remain accountable for judgment, materiality, and defensibility in financial reporting and analysis.
Methods: This paper synthesizes recent research and practitioner guidance (2023–2025) to develop a practical model for designing human–AI collaboration, sometimes described as collaborative intelligence, in the financial reporting function (often referred to as controllership), including period-end close, financial statement preparation, variance explanation, management reporting narratives, and accounting policy documentation.
Results: The paper develops the C³ Framework—Complementarity, Controls, and Competencies—which maps accounting tasks by task structure and judgment/materiality to recommend collaboration modes, specifies five mandatory control points for high-judgment use cases (source grounding and traceability, independent verification and tie-out, contradiction testing, escalation and approval, and audit-trail logging), and proposes a role taxonomy that clarifies review responsibility, escalation thresholds, and evidence retention.
Conclusions: The C³ Framework provides implementable design patterns and testable propositions intended to help accounting leaders capture productivity gains from human + AI work while preserving accountability, consistency, and alignment with governance expectations in high-stakes reporting contexts.
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