The biggest risk AI poses to accounting is not automation. It’s eroding the skill that keeps the system honest. As firms deploy AI to handle reconciliations, classifications and routine testing, they automate the work that once taught junior accountants how to recognize when something is wrong. Without deliberate training, the next generation may trust the machine more than their own judgment.
The major accounting firms are rolling out AI agents that classify transactions, analyze financial statements and walk clients through tax document uploads. The Big Four have announced AI investments measured in hundreds of millions to billions of dollars. Those investments will make the profession more responsive, more efficient and more scalable. I don’t question any of that.
What I question, as someone who has spent decades training accountants and now leads the organization that represents accounting’s academic community, is whether anyone is investing at the same scale in the skill that makes all of it trustworthy: the ability to look at a confident, polished AI-generated output and say, “I don’t buy it.”
Professional skepticism is the skill that separates an accountant from an algorithm. And right now, the profession is attempting to automate the very work that was central to practicing that skill.
Why Skepticism Was Never Really Taught
Professional skepticism is what U.S. auditing standards define as “a questioning mind, being alert to conditions that may indicate possible misstatement due to fraud or error, and a critical assessment of audit evidence.” It is a skillset that separates an accountant from a calculator.
Teaching skepticism in a deliberate, structured way in academic settings can be challenging. More often, skepticism develops through hands-on experience by working through repetitive, detail-oriented tasks that build pattern recognition over time. Activities such as reconciliations, transaction testing and tracing numbers cultivate an instinct for when something doesn’t look correct. While the classroom provides a strong technical foundation, the workplace is where professional judgment and true skepticism develop more fully.
AI is automating the job part. The grunt work that turned junior accountants into seasoned professionals increasingly falls to machines. So if skepticism was largely learned by doing, what happens when the doing disappears?
The Divide No One Expected
A study from Stanford and MIT makes the stakes concrete. Researchers ran a controlled experiment with 99 professional accountants. AI assistance improved accuracy by nearly 18 percentage points — a real gain. However, when the AI flagged its own uncertainty, experienced accountants dug deeper. Less experienced ones didn’t. They accepted uncertain output at face value.
The implications were even more concerning. When the AI offered an unusual classification, participants followed it, even though those suggestions were less accurate. The tool’s authority overrode their own judgment. Researchers call this automation bias, and it’s not new. What is new: 88% of more than 1,000 surveyed audit professionals now say AI risks undermining professional judgment. Former PCAOB Acting Chair George Botic warned that while AI can relieve auditors of administrative tasks, “it may also compromise reasoning and judgment,” and he called on auditors to grapple with how to uphold professional skepticism as AI reshapes their work.
This matters to anyone who relies on audited financial statements to invest, lend or run a business. The profession is producing more accountants than it has in years. Enrollment rose 12% in fall 2024. Top programs report near-perfect placement rates at starting salaries around $80,000. The Bureau of Labor Statistics projects 124,200 openings per year over the next decade. None of that matters if the people entering the pipeline can’t tell when the AI is wrong.
What’s Actually Working
When I taught at Wake Forest University, teaching skepticism meant discussing it in class and assigning a case study, hoping students would notice the red flag on page four. Many didn’t. Not because they were careless, but because classroom cases feel like classroom cases. You know there’s an answer. You know the professor put it there and that it centers on the subject matter you have been discussing. The stakes are artificial.
The best programs today have figured out how to make the stakes feel real. The EY Academic Resource Center puts students inside AI-powered audit simulations where the AI sometimes gets it wrong — and students don’t know when. They can’t default to “find the answer the professor hid.” They must develop the discipline of verification: check the source data, test the logic, question the classification. It is closer to what a first-year auditor actually does than anything we had 10 years ago.
What has changed isn’t the technology. It’s the pedagogy. The “sage on the stage” is gone. Faculty across our American Accounting Association membership are flipping classrooms, working through problems alongside students, weaving AI into the curriculum from principles courses onward and using the tools as both subject and stress test. AACSB’s Accounting Accreditation Standard A5, newly titled “Digital Agility” in the new standards taking effect July 1, 2026, focuses on technology agility. Standard 5.2 states that learners must “demonstrate the ability to evaluate the appropriateness of technologies for specific accounting tasks, interpret technology-enabled outputs with accuracy and skepticism, and communicate findings clearly and ethically”.
Research published in our journals shows that skepticism traits, a questioning mind and the drive to verify can measurably improve through instruction designed this way.
When educators ask me what to cut from the curriculum to make room, I give them the same answer every time: nothing. Change how you teach it.
The Question The Profession Must Answer Now
The profession has roughly two to three years before the current generation of AI-native graduates reaches the level where they supervise others. If those professionals learned to review AI output without ever doing the underlying work manually, without building the instincts that tell you when a number feels wrong, we will have a gap in the middle of the profession that no amount of technology can fill.
Firms, educators and regulators all own a piece of this. The IAASB is developing guidance on what skepticism means when AI is in the workflow.
Let me be direct: the window for getting this right is shorter than most people think. Professional skepticism is the foundation the entire financial reporting system is built upon. We can either teach it deliberately or find out what happens when a generation of accountants that never learned to question the machine rises into the managerial ranks of our profession.
Yvonne Hinson, PhD, CPA is CEO of the American Accounting Association and a former executive with the Association of International Certified Professional Accountants. A longtime Wake Forest University professor, department chair, and Dean of the Charlotte campus, she has held key academic and professional board roles and was named one of the Most Powerful and Influential People in Accounting (2024, 2025).
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