Editor's Note: Artie Minson is the CEO of New York-basedTrulliona company that provides an AI-driven accounting and audit platform for finance teams. He previously served as co-CEO of WeWork and CFO of Time Warner Cable and AOL. This article reflects the author's personal views.

The audit industry is entering an era of fundamental transformation driven by artificial intelligence.

For the first time, the industry is poised to achieve long-held goals: higher profit margins, better client experiences, greater accuracy, deeper insights, and more meaningful work for young professionals.

The coming year will redefine how audit work is performed and the value it delivers. Those who act decisively will gain an advantage, while those who wait may fall behind.

Here are five audit practices that AI will reshape in the coming year:

1. Manually extracting data from client documents

Auditors often spend days manually transcribing data from PDFs and client documents into spreadsheets, re-entering it across multiple workpapers. This is a fundamental misallocation of human resources and directly erodes firm profitability.

AI is putting an end to this cycle. Modern audit platforms can read complex PDFs and automatically extract data into structured, audit-ready formats. Work that once took auditors two days can now be completed in minutes. Automation enables teams to shift from data entry to analysis and professional judgment—the areas where auditors truly create value.

2. Reformatting client data across audit workpapers

Audit teams often need to reformat the same client data across multiple workpapers and tools. For example, a lease schedule arrives in Excel and is repeatedly rebuilt for different procedures, reviews, and sign-off requirements. This leads to multiplied hours, accumulating errors, and version control itself becoming a risk. This friction stems from legacy tools never designed to work together.

Modern audit infrastructure eliminates this duplication. Data flows through the business only once, automatically transforming to meet each procedure's requirements while maintaining a single source of truth.

Firms that eliminate this friction can reclaim the 20% to 30% of engagement hours previously lost to reformatting. That capacity is either redeployed to higher-value work or converted directly into profit margins.

3. Searching emails for audit evidence

When approvals and supporting documents live in email threads, audit teams waste senior staff time reconstructing information that should be immediately available. The risk is not just inefficiency, but also incomplete documentation and regulatory exposure.

On modern platforms, the audit trail is embedded directly into the workflow. Changes are logged with attribution, approvals are tamper-proof, and evidence is searchable. What once took days of "inbox archaeology" now takes seconds. The result is stronger, more defensible audit evidence.

This also changes client expectations. Firms adopting modern systems differentiate themselves on engagement quality and professionalism.

4. Sampling shifts from choice to constraint

Sampling has historically been a matter of necessity, because testing the entire population was too time-consuming and costly. Though statistically defensible, it has always been a compromise.

AI removes this constraint. Full-population testing is now as economical as sampling and more defensible. Instead of testing 100 transactions, firms can test all 10,000 with the same effort.

This shift has practical implications for audit quality and regulatory scrutiny. Full-population testing uncovers anomalies that sampling misses and eliminates reliance on extrapolation. Firms that continue to default to sampling will increasingly be asked to explain why.

5. Time compression caused by client closing delays

Every auditor knows the pattern: client closing is delayed, and the audit timeline gets compressed. Firms come under pressure to rush fieldwork and reviews to meet filing deadlines. The result is burnout, quality risk, and strained relationships.

Real-time matching and anomaly detection change this dynamic. When reconciliations run continuously, discrepancies are flagged immediately rather than weeks later. By the time fieldwork begins, the data is cleaner and the audit trail is complete.

Risks and challenges of AI adoption

Overall, these changes reflect a fundamental shift in how audits are empowered. As manual work disappears, audit quality, speed, and predictability increasingly depend on the systems and data structures upstream in the business.

Every CFO has a stake in this transformation. Audit outcomes are no longer determined solely by the audit firm's methodology, but increasingly by the technology stack underpinning the audit.

Finance teams that achieve the best results typically start with a few high-impact use cases, demand explainable and auditable outputs, and scale deliberately as confidence grows. A phased adoption approach helps ensure that AI investments translate into measurable value, stronger controls, and lasting impact.

Data is another critical factor. As a former CFO, I know firsthand how disruptive an audit can be when data is fragmented and evidence is scattered across different systems. These conditions introduce risk, uncertainty, and unnecessary pressure on finance teams.

Organizations that invest in clean, connected, AI-driven finance infrastructure not only get a smoother audit experience; they also close books faster, maintain continuous data visibility, and reduce reliance on manual workarounds during audit season. In this environment, audit technology becomes a strategic lever for CFOs—its impact extending beyond compliance to cost, quality, and predictability.