Enterprise AI Governance and Risk Management: An Integrated Framework for Controlled AI Adoption in Cross-Functional Business Workflows

This paper presents a focused narrative review and conceptual framework for managing risks associated with AI adoption in cross-functional enterprise workflows. Organizations considering AI-assisted document processing, reporting, knowledge work, customer operations, and decision support must address accountability, output reliability, human oversight, monitoring, and operational performance. Drawing on established AI governance guidance and selected literature, this paper proposes an integrated five-stage approach: Govern, Assess, Control, Measure, and Improve. The proposed framework connects governance responsibilities to workflow-level risk assessment, proportionate controls, human review and escalation, operational measurement, and continuous improvement. Candidate measures include output quality, cycle time, rework, human-intervention rates, incident severity, reviewer workload, and operating cost. These measures are presented as suggestions for evaluation rather than as observed results. The five-stage framework is an author-proposed synthesis, not an official standard or an empirically validated model. This paper is a focused narrative review, not a systematic review, field study, or experimental evaluation. Further work is needed to assess its applicability and effectiveness in real organizational settings.