Yogi Goel, Co-Founder & CEO of Maxima.
In many finance organizations, AI is becoming embedded in core accounting workflows during the end-of-month process. Systems are now classifying transactions, generating journal entries, performing reconciliations, and drafting variance explanations based on underlying data.
But for auditors, AI’s growing role in accounting introduces a new layer of scrutiny. When financial reporting outputs are partially system-generated, the core questions become ones of completeness, accuracy, and trust. Can the output be traced back to its source? Was it reviewed and approved by a human? And is there enough documentation to withstand regulatory scrutiny?
The answers to those questions depend less on the AI itself and more on how it was deployed and governed. Which means auditors need to know what to look for before they ever open the file.
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What auditors need to know
Auditors are no strangers to friction at the close. By the time financials arrive, and they are rarely on time, supporting data has come in across dozens of spreadsheets with no clear thread between them, and individual entries often have no rationale attached. The reasoning is living in someone’s head or buried in an email chain, with nothing but a number and a category to show for it.
AI is starting to change some of that, but the fundamentals of what auditors are looking for haven’t changed.
High judgment areas, like reserves and accounting estimates, still require the same professional skepticism they always have. AI can produce precise, well-supported outputs, but auditors still need to understand how the assumptions were made and whether the numbers would hold up under different conditions.
Three questions auditors should be asking
As AI becomes more commonplace in financial reporting, auditors will need to add a few new questions to their standard inquiry to make sure they have the full picture:
- Was the AI following rules or making judgment calls? There’s a meaningful difference between AI that follows strict, predefined logic and AI that freely interprets available data to reach a conclusion. The first is testable and consistent. The second introduces the possibility of errors or fabricated details that have no basis in the underlying records. Auditors should understand which type of system they’re dealing with before evaluating the output.
- Can the output be traced back to its source? Traceability has always been a core part of the audit process, but AI changes what that trail looks like. Auditors should be able to follow the thread from the final number all the way back to the underlying data, the logic that was applied, and any overrides or adjustments made along the way. If that documentation doesn’t exist, assessing the output for completeness, accuracy, and management bias becomes very difficult.
- Is there enough documentation to withstand regulatory scrutiny? AI-driven outputs need to be supported by governance that goes beyond internal sign-off. Auditors should be asking whether the documentation is thorough enough to hold up under external review, and whether the controls in place would satisfy a regulator looking at the same work.
Understanding what to ask is one part of the equation. The other is recognizing how AI creates some practical opportunities to work differently.
What changes and what doesn’t
Auditors will still do the core work they’ve always done: assessing risk, testing controls, tracing transactions to source documents, and evaluating management judgments. That part of the job isn’t going anywhere. That said, when an AI system is configured well, there are a few shifts in workflow that can make the job meaningfully easier.
Take sample-based auditing as an example, which was designed for a world where reviewing every transaction wasn’t feasible. A typical audit of vendor payments might involve pulling five to ten entries from a population of hundreds. It’s a practical constraint versus a preference, and auditors have always known that sampling leaves gaps.
When financial data has been prepared through a well-configured system, auditors can test the entire population rather than a subset of it. Because the system applies the same logic consistently, testing a representative set of rules gives auditors confidence in the full dataset rather than just the sample they happened to pull.
Audit trails can also improve in a meaningful way. Traditional spreadsheet workflows often leave fragmented version histories and limited visibility into how a number evolved. A properly configured AI system can log data sources, rule changes, and user overrides automatically. That makes it easier for auditors to trace how a balance was constructed without reconstructing weeks of manual work.
What doesn’t change is judgment. AI can apply a rule consistently across 100,000 rows, but it can’t determine whether management’s assumptions are reasonable, conservative, or biased. Nor can it replace professional skepticism when something does not align with the economic reality of the business.
A confusing time, with a clear opportunity
There’s no shortage of confusion around AI right now, and accounting is no exception. What’s becoming clearer is that AI works best in accounting when it’s domain specific, tightly scoped, and operating close to structured financial data. In those cases, outputs tend to be more consistent, more traceable, and less dependent on manual spreadsheet work under deadline pressure. For auditors, that creates a real opportunity.
That doesn’t mean auditors will need to become AI engineers, but they do need to understand how these tools fit into the control environment, how outputs are generated, and where human judgment still plays a role. Those who approach AI with curiosity will find that many of the fundamentals they already rely on still apply.
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Yogi Goel is the Co-founder, CEO and CFO of Maxima. Yogi has spent the past 20 years serving the office of the CFO in roles that span accounting, banking, and enterprise leadership. He began his career as an auditor at EY before moving to Wall Street, where he worked in M&A and IPOs at Citi and Barclays and helped take several companies public. Before starting Maxima, Yogi spent seven years at Rubrik as a strategic finance leader working directly with the CEO and CFO. He joined when the company was at $5M in ARR and played a key role in scaling it to $900M in ARR, culminating in its IPO on the New York Stock Exchange in 2024.
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