"AI in the audit" describes two separate shifts that happen to be arriving at once: the IRS using machine learning to choose who gets examined, and outside auditors using AI to test far more of a company's data than they used to. Neither one changes what good records look like — but both raise the cost of sloppy ones.
This is general information, not individual tax advice — the right treatment depends on your specific situation.
Two Different Audits, Both Changing
A tax audit is the IRS examining a return. A financial-statement audit is an independent CPA firm giving an opinion on a company's financials for lenders, investors, or a board. The technology stories overlap, but the audiences and the stakes are different.
How the IRS Is Using AI
Using Inflation Reduction Act funding, the IRS built a large-partnership compliance effort that uses machine-learning models to select returns for examination. It opened exams on dozens of the largest U.S. partnerships and a set of large corporations, and has signaled a broader expansion. The stated focus is large partnerships, big corporations, and high-income individuals — areas the IRS says were under-audited — not small businesses.
Does This Reach Small Businesses?
Directly, not much: the AI selection programs are aimed at large, complex filers. Indirectly, the direction of travel matters. The IRS already runs automated document matching — comparing your return against the W-2s, 1099s, and K-1s others filed about you — and mismatches generate notices without a human deciding to look. As models improve, the bar for "this return is internally consistent and matches third-party data" effectively rises for everyone.
It also matters if you own an interest in a partnership that is in scope. Adjustments at the partnership level flow through to the partners, so a large-partnership examination can reach your personal return even though you were never selected. Owners of tiered pass-through structures should expect more questions about basis, allocations, and related-party terms than in the past.
How Financial-Statement Auditors Use AI
Traditional audits tested samples — a few dozen invoices out of thousands. AI-assisted tools let auditors analyze the entire population of transactions, flag the handful that are unusual, and focus their questions there. For a well-run business this can mean a smoother audit. For one with inconsistent coding or weak documentation, it means more exceptions surfaced and more follow-up requests.
What Actually Gets Flagged
Anomaly detection tends to surface the same things a sharp reviewer would, just more completely: round-dollar entries, transactions booked just under an approval threshold, sudden margin or expense-ratio swings, journal entries posted at odd times or by unusual users, related-party activity, and amounts that do not reconcile to third-party records. None of these are automatically problems — but each one now reliably draws a question.
The Defense Is Boring: Clean, Contemporaneous Records
The preparation for both kinds of audit is the same and unglamorous. Reconcile accounts monthly. Keep documentation attached to transactions as they happen, not reconstructed later. Make sure the return ties to the books and the books tie to the bank. Code transactions consistently period over period. A business whose records are clean and internally consistent has little to fear from better analytics; a business relying on "we'll explain it if they ask" has more exposure than it used to.
How VarStan Helps
We keep clients' books reconciled and audit-ready year-round, make sure returns and financials agree, and prepare the documentation that answers the questions analytics tools raise — before an examiner or an auditor asks them. If you are heading into a financing round or an audit, we can get the records into the shape that keeps it short.