This course provides a concise overview of critical thinking and professional skepticism in AI-augmented CPA work. As artificial intelligence increasingly performs drafting, analysis, calculation, research, and other preparation tasks, CPAs are shifting toward higher-value roles involving review, verification, challenge, and professional judgment. Participants will examine professional standards, human cognitive biases, AI failure modes, evidence requirements, questioning techniques, vendor claims, documentation practices, and firm-level controls. The course emphasizes a central principle: AI output should be treated as an unverified claim requiring appropriate human challenge before professional reliance. This event may be a rebroadcast of a live event and the instructor will be available to answer your questions during the event.
Learning Objectives
After attending this presentation, you will be able to...
- Explain how professional skepticism applies to AI-augmented CPA work
- Identify cognitive biases and AI failure modes that can impair professional judgment
- Analyze AI-generated outputs using appropriate evidence and reliability principles
- Apply questioning techniques to challenge AI conclusions, assumptions, citations, and calculations
- Evaluate vendor claims, benchmarks, and AI system performance with appropriate skepticism
- Design risk-based verification and documentation procedures for AI-assisted work
- Develop practices that strengthen skepticism, accountability, and professional judgment within CPA firms
Major Topics
The major topics that will be covered in this course include:
- Professional skepticism and existing CPA standards
- Six traits of the skeptical professional mind
- Automation bias and other AI-related cognitive biases
- Evidence and verification rules for AI outputs
- Practical questioning and challenge techniques
- Vendor claims, benchmarks, and AI due diligence
- Review, documentation, accountability, and skeptical firm culture
