A Risk Assessment Framework for Cognitive Process Automation in Audit

ABSTRACT Cognitive process automation (CPA) is the process of automating knowledge-intensive tasks that require reasoning, interpretation, and decision-making using artificial intelligence (AI) agentic workflows. Although CPA offers significant potential in audit, auditors often struggle to determine what tasks are suitable for CPA and manage the risks associated with CPA implementation. This study proposes an AI risk reporting tool that operationalizes an AI Risk Assessment Framework for CPA deployment in auditing. Drawing on Task-Technology Fit Theory and Cognitive Load Theory, our framework includes three sequential stages: assessing audit task suitability for CPA, quantifying the cognitive load measure to proxy for AI implementation risk, and translating the measured risk into prescriptive human oversight requirements. This study contributes to the literature by proposing a framework to bridge the current governance gap in CPA deployment and its associated risk. JEL Classifications: C88; D81; M15; M41; M42; O33.