Artificial Intelligence is rapidly reshaping how Internal Audit functions operate, yet adoption across the profession remains inconsistent and fragmented. While some teams are experimenting with basic prompting, others are beginning to automate audit procedures and explore agentic AI capabilities—creating a widening gap in performance and readiness.

This session brings together three forward-practicing Internal Audit leaders who are actively implementing AI within their functions to provide a practical, experience-based roadmap for adoption. Through a structured framework of three maturity stages—early, intermediate, and advanced—participants will learn how to move from initial experimentation to scalable, value-driven implementation.

Panelists will share real-world examples, including how to prioritize use cases, build team-wide AI literacy, navigate organizational constraints, and design repeatable processes that drive measurable efficiency gains. Attendees will leave with actionable guidance tailored to their current level of AI maturity and a clear path to accelerate adoption within their teams.

Learning Objectives:

After attending this session, participants will be able to:

1. Identify the current state of AI adoption within Internal Audit and key barriers limiting broader implementation
2. Differentiate between early, intermediate, and advanced stages of AI maturity within an audit function
3. Apply practical strategies to move from basic AI experimentation to structured, scalable use cases
4. Evaluate common organizational constraints (e.g., data access, security, tooling) and approaches to navigate them
5. Implement techniques to improve AI literacy and drive adoption across audit teams
6. Assess the potential impact of AI-driven efficiencies on audit workflows, resource allocation, and future skill requirements

Field of Study: Internal Audit
Program Level: Intermediate
CPE Credit: 1