Reshaping How Enterprises Build and Risk-Manage AI

Abstract

Intelligent AI systems introduce risks that extend beyond traditional concerns of data privacy, security, and compliance to include the potential for physical harm. These systems can generate misleading outputs based on spurious correlations learned during training, and such outputs may be persuasive in misleading ways or paired with disturbing imagery. The resulting hazards can lead to financial loss, reputational harm, regulatory non-compliance, and violations of ethical or corporate standards. While these risks deserve the same rigor historically applied to physical and software safety—if only for liability reasons—no comprehensive framework currently exists for identifying, evaluating, and mitigating hazards across enterprise AI systems. Consequently, regulators and corporate oversight bodies lack operating practices rooted in established product governance and risk management methods, and existing literature, though it touches on relevant safety considerations, does not provide a practical blueprint for governing enterprise AI products across their lifecycle.
To close this gap, we first present a detailed taxonomy of AI hazards relevant to enterprise settings. These hazards are organized into logical categories that serve as building blocks for enterprise AI hazard analysis and mitigation. We then introduce an intelligent product governance framework, mapping these hazard categories onto three supporting pillars for governing AI-enabled enterprise products and services. The following sections examine the foundational role of operating-model transformation and role specification in guiding enterprise AI risk management and lifecycle hazard mitigation, emphasizing that both the technical and business dimensions of AI-enabled products must address these considerations in an integrated and interdependent way.