As AI systems transition from pilot phases to full-scale production, organizations often face hidden risks in governance, data management, operations, and change management that can hinder sustainable growth. EisnerAmper outlines a six-pillar risk management framework—covering governance, business strategy, cybersecurity and data privacy, technology and cloud infrastructure, people and change, and data practices—that helps organizations identify and address potential friction points early, ensuring responsible and scalable AI adoption aligned with established standards like NIST and ISO. Early assessment under this framework is critical for sustaining effective AI systems as usage expands.
