Description

Artificial intelligence (AI) tools are moving away from analytical tools toward semi-autonomous and autonomous agents that enable cross-functional collaboration and skills sharing.

This session spotlights this important shift, how it challenges traditional governance models, and explores the implications for accountability, oversight, and professional judgment.

This webcast will cover:

- Modern, enterprise-wise AI governance operation models

- Risk oversight

- Regulatory and assurance readiness in environments where AI is embedded across products, platforms, and ecosystems

Instructor: Justin Headley,Ray Cheung,Ray Sang

Target Audience

This program is designed for professionals responsible for governance, risk, and oversight across all industries, including:

· CPAs in public practice and industry

· Audit, tax, and advisory partners and managers

· Board members and executive leadership

· Risk management, compliance, and ethics leaders

· Technology, data, and innovation leaders

· Professionals working within or serving TMT organizations

Course Objectives

· Identify AI governance risks across industries, including agentic and autonomous systems.

· Recognize how AI agents affect accountability, transparency, and decision authority.

· Distinguish effective governance practices for traditional AI versus agent-based AI.

· Recognize AI governance principles across the AI lifecycle and organizational boundaries.

· Estimate readiness for regulatory, assurance, and stakeholder expectations.

Subjects

· Enterprise AI governance across industries

· Agentic AI and autonomous decision-making

· AI-to-AI (agent-to-agent) interaction risks

· Accountability, explainability, and oversight

· Regulatory and assurance implications of AI