Operational Risks in AI Lifecycle Management
AI adoption continues to accelerate across industries, promising efficiency gains, enhanced decision-making, and new revenue streams. However, organizations are increasingly exposed to operational risks that, if unmanaged, can result in financial losses, regulatory penalties, reputational damage, and ethical violations. These risks are not confined to deployment—they permeate every stage of the AI lifecycle, from data collection to continuous monitoring. Effective AI governance requires a holistic understanding of these risks and the implementation of proactive risk management strategies.