As AI becomes embedded across operations, oversight structures are struggling to keep pace. The legal, financial, compliance, and enterprise risk implications of AI adoption are mounting.
This webinar, presented in collaboration between Crowe and Saul Ewing, is specifically tailored for leaders tasked with navigating governance and accountability frameworks for AI. The session will include a walk-through of a practical AI governance playbook covering ownership structures, life cycle documentation, data and third-party vendor risks, and ongoing human oversight as well as a review of the 2026 regulatory and legal landscape. Presenters will also discuss a realistic maturity model to benchmark an organization’s AI governance posture and plan next steps.
Crowe and Saul Ewing professionals will close with a Q&A to help participants translate AI governance structures into measurable outcomes that enable both risk reduction and innovation.
Attendees will leave with a clear framework for action, guidance on evolving responsibilities across functions, and defensible strategies for embedding AI accountability into enterprise governance and operating practices.
After attending this session, you should be able to:
- Identify key accountability and liability touchpoints for enterprise AI use across legal, compliance, finance, and risk functions, and assess how these responsibilities map to governance structures
- Distinguish between major 2026 AI regulatory and legal themes, such as privacy, intellectual property, third-party risk, and ethics, and align them with practical enterprise risk and compliance strategies
- Design an AI governance approach that includes life cycle documentation, role-based approvals, data and vendor oversight, and human monitoring, and use a maturity model to prioritize integration into enterprise risk management or board-level reporting
