A question comes to mind whenever I see an artificial intelligence tool accomplish in minutes what used to take an accountant days: if a machine can reconcile accounts, analyze contracts, and detect unusual transactions, what is left for the accountant?

This question concerns not only accountants, but everyone who prepares them for the profession: universities, professional bodies, and audit firms. I found a thought-provoking answer in an article published by the American magazine “CPA Journal,” written by Dr. Sunil Kumar, associate professor of accounting at Roger Williams University, on educating the next generation of Certified Public Accountants in the age of artificial intelligence.

Who is a Certified Public Accountant?

In the United States, a Certified Public Accountant (CPA) is an accountant who has obtained a professional license after passing exams and meeting specific experience requirements, qualifying them for duties such as auditing financial statements. The points raised there apply in essence to accountants in Saudi Arabia as well, whether they are preparing for the Saudi Organization for Auditors and Accountants’ fellowship or entering the workforce directly.

What has changed in an accountant’s work?

The article notes that artificial intelligence now handles data entry, account reconciliation, contract analysis, and the detection of anomalies in audit work. The accountant’s role is no longer to perform these tasks by hand, but to oversee and assess the machine’s outputs.

The author cites KPMG’s 2023 announcement of a $2 billion investment in artificial intelligence and cloud services as evidence of the scale of change within professional firms.

But the gap, as the author describes it, is clear: employers are looking for AI, automation, and data analytics skills, while students are still focused on financial reporting and auditing in their traditional forms. According to studies cited in the article, fewer than half of academic programs teach predictive analytics, cybersecurity, or digital skills. Students also report getting little practical experience with AI tools, and many faculty members lack that experience themselves.

What skills will the accountant of the future need?

The article divides these skills into three areas, a framework I find effective:

Technical skills: managing and cleaning data; programming fundamentals in languages such as Python, R, and SQL; the principles of machine learning; and using AI-powered accounting and auditing platforms.

Analytical skills: statistical modeling, data visualization, predictive analytics, and, most importantly, the ability to critically evaluate AI outputs in terms of their reliability, bias, and materiality.

Professional and human skills: communication, telling the story behind the data, understanding how businesses work, adaptability, professional skepticism, and ethical thinking.

Professional skepticism is a core auditing concept. It means that auditors approach evidence with an inquisitive mindset and do not accept information simply because it has been presented to them. It seems to me that this particular skill will become even more valuable, because information may now come from a machine that speaks with complete confidence—even when it is wrong.

How should we teach these skills?

The article does not call for relying solely on separate elective courses, but for integrating artificial intelligence into core courses themselves. Auditing students, for example, can use anomaly-detection tools, while financial accounting students can automate ratio analysis or draft disclosures. The article also suggests comparing AI-generated results with manual assessments, so students can see for themselves how results change with the quality of the data and the assumptions used.

The author proposes building education in three layers: a foundational layer covering data literacy, statistics, and AI principles; an applied layer that uses tools in different areas of accounting; and an ethical governance layer addressing accountability, bias, and professional responsibility. The author also recommends case-based projects, such as evaluating AI-generated audit reports against professional ethics rules; partnerships with audit firms; micro-credentials and training boot camps; and training for faculty themselves.

Responsibility cannot be delegated to a machine

The article spends considerable time on ethics, which, in my view, is its most important point. Artificial intelligence may carry hidden biases, and it may rely on “black box” models whose decision-making processes are unclear. A model trained on historical fraud data may repeat the errors in how that data was classified.

That is why the author urges accountants to ask where the data came from, how it was verified, and whether it can be audited, while also paying attention to confidentiality and data privacy risks when using cloud-based tools. The article is clear about responsibility: the professional remains responsible for AI outputs. It quotes the U.S. Public Company Accounting Oversight Board (PCAOB) as saying that “technology does not lessen the burden of proof.”

There is an old saying in computing: “garbage in, garbage out.” Artificial intelligence does not change this rule; it amplifies its effects by producing an incorrect result faster and with greater confidence.

So perhaps the best way to describe the accountant’s future is not as a profession that will disappear in the face of machines.

What seems more realistic to me is that accountants will move from producing numbers to standing behind them: asking where the data came from, testing the model’s logic, and taking responsibility for the result before everyone. The skill once measured by how quickly someone could process entries will be measured by how accurately they judge what the machine produces.

That is why I believe this discussion is worth considering—not as an American issue concerning only CPAs there, or as a ready-made formula suited to every university, but as a window onto the broader transformation of the profession:

From an accountant who prepares the numbers to one who ensures they are correct.

Disclaimer: This article is for informational purposes. Its aim is to explain the topic in clear, accessible language. The information it contains comes from published sources and has been adapted and simplified; it does not represent an official position or statement by any organization. This information may be updated after publication, so readers are advised to consult official sources for full details.