Managing the risks of generative AI: a framework for enterprise risk management

Ahmad Haidar and Christine Balagué

Abstract

As organizations increasingly integrate generative artificial intelligence (GenAI) into core business functions, a new landscape of managerial and operational risk is emerging that remains insufficiently explored in academic research. By developing a conceptual framework for mitigating these risks, based on a semi-systematic literature review of 76 peer-reviewed articles from Web of Science, this study aims to identify how GenAI is reshaping enterprise risk management. We apply keyword co-occurrence analysis, a quantitative clustering technique conducted using VOSVIEWER, to identify five key constructs that underpin risk emergence in managerial contexts: the enterprise readiness gap; novice risk work; shadow GenAI governance; unethical GenAI; and innovation drift. These constructs are systematically mapped to 10 typologies of GenAI-related risks (eg, data-related, legal, human-GenAI interaction) and further refined into 36 distinct observed risks (eg, loss of control, hallucinations, customer well-being concerns), highlighting how these risks materialize in practice. The framework outlines a system of relationships that explains how these risks manifest across six core management functions: strategy; human resources; operations; finance; marketing; and legal compliance. The review highlights that GenAI risks are both strategic and operational, presenting five propositions that map GenAI risk patterns to guide enterprise risk managers in scenario-based risk modeling.