Date Start Date: 9/15/2026 Start Time: 12:00 PM EST End Date: 9/15/2026 End Time: 1:00 PM EST Contact Name: Ethan Castillo Email
Artificial intelligence is rapidly reshaping the internal audit profession, creating new opportunities to expand coverage, improve efficiency, and elevate the strategic value of audit functions. Yet many audit leaders are still asking the same questions: Where should we start? What can AI realistically do today? And how do we adopt it while maintaining quality, trust, and professional judgment? Join Grant Thornton and Fieldguide for a practical discussion on how AI, automation, and modern audit technology are transforming the internal audit operating model. Our speakers will explore where AI can deliver meaningful value across the audit lifecycle, how leading organizations are balancing innovation with governance, and what skills and capabilities internal audit teams need to succeed in the years ahead. Whether you're evaluating AI for the first time or looking to scale existing initiatives, this session will provide actionable insights on implementation considerations, audit quality, workforce implications, and the future of assurance delivery. Attendees will learn: How AI and automation are reshaping internal audit operations Which audit activities are ready for AI-enabled support today The role of human judgment, oversight, and professional skepticism in AI-enabled audits Key considerations related to data security, governance, and platform trust Practical approaches for modernizing internal audit while maintaining audit quality and accountability Learning objectives Describe how AI and automation are reshaping the internal audit operating model, including opportunities to expand coverage, accelerate delivery, and increase advisory value. Identify which parts of the internal audit lifecycle are ready for AI-enabled support today, and distinguish those activities from areas that still require human judgment, professional skepticism, and review. Differentiate general-purpose AI tools from purpose-built audit agents, including implications for workflow integration, evidence grounding, audit trail, and human oversight. Evaluate practical approaches for using AI-generated outputs in internal audit while preserving workpaper quality, reviewability, independence, and accountability. Recognize key data, security, platform trust, talent, and adoption considerations that internal audit leaders should address when introducing AI-enabled audit technologies. Visit the Website
