Critical Thinking and Skepticism in AI-Augmented CPA Work

Overview

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.

Highlights

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

Prerequisites

A general understanding of accounting, auditing, tax, or professional CPA practice is recommended; prior technical knowledge of artificial intelligence is not required.

Designed For

This course is designed for CPAs, auditors, tax professionals, accountants, controllers, finance leaders, and firm managers who use or oversee AI-assisted professional work and need to strengthen verification, judgment, and accountability.

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