The audit profession is entering an era of fundamental change driven by artificial intelligence.

For the first time, the profession has a path to achieve long-standing goals: better margins, stronger client experiences, improved accuracy, deeper insights, and more meaningful work for young professionals.

The year ahead will redefine both how audit work is done and the value it delivers. Those who act decisively will gain the advantage; those who wait risk falling behind.

Here are five audit practices set to be transformed by AI in the year ahead:

1. Manual data extraction from client documents

Staff auditors spend days manually transcribing data from PDFs and client documents. Contracts, invoices, financial statements, and lease agreements are typed into spreadsheets and re-entered into multiple workpapers. This is a fundamental misallocation of human capital and a direct hit to practice margins.

AI is ending this cycle. Modern audit platforms can now read complex PDFs and automatically extract data into structured, audit-ready formats. What once took a staff auditor two days now takes minutes. Automation empowers teams to move from data entry to analysis and professional judgment where auditors actually add value.

2. Reformatting client data across audit workpapers

Audit teams routinely reformat the same client data across multiple workpapers and tools. A lease schedule arrives in Excel and is rebuilt several times for different procedures, reviews and sign-offs. Hours multiply, errors compound, and version control becomes a risk in itself. This friction exists because legacy tools were never designed to work together.

Modern audit infrastructure eliminates this rework. Data flows through the engagement once, automatically transforming to meet each procedure’s requirements while maintaining a single source of truth.

Firms removing this friction are recapturing 20% to 30% of engagement hours previously lost to reformatting. That capacity either redeploys to higher-value work or drops directly to margin.

3. Searching email for audit evidence

When approvals and supporting documentation live in email chains, audit teams waste senior-level time reconstructing what should be immediately available. The risk isn’t just inefficiency, it’s incomplete documentation and regulatory exposure.

On modern platforms, the audit trail is embedded directly into the workflow. Changes are logged with attribution, approvals are immutable, 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 that operate within modern systems differentiate on engagement quality and professionalism.

4. Sampling as a constraint rather than a choice

Sampling has historically been a necessity because testing full populations was too time-consuming and expensive. While statistically defensible, it was always a compromise.

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

This shift has real implications for audit quality and regulatory scrutiny. Full-population testing uncovers anomalies missed by sampling and eliminates reliance on inference. Firms that continue to default to sampling will increasingly be asked to justify why.

5. Schedule compression driven by client close delays

Every auditor knows the pattern: client closes slip, and audit timelines compress. Firms absorb the pressure, rushing fieldwork and review to meet filing deadlines. The result is burnout, quality risk and strained relationships.

Real-time matching and anomaly detection change this dynamic. When reconciliations happen continuously, discrepancies are flagged immediately, not weeks later. By the time fieldwork begins, the numbers are cleaner and the audit trail is complete.

AI adoption stakes, challenges

Together, these changes reflect a fundamental shift in how audits are enabled. As manual work disappears, audit quality, speed and predictability increasingly depend on the systems and data structures that sit upstream of the engagement.

Every CFO has a stake in how this unfolds. Audit outcomes are no longer determined solely by an audit firm’s methodology; they are increasingly shaped by the technology stack that underpins the audit.

Finance teams seeing the strongest results start with a small number of high-impact use cases, demand explainable and auditable outputs, and scale deliberately as confidence grows. A phased adoption approach helps ensure AI investments translate into measurable value, stronger controls, and lasting impact.

Data is another critical factor. As a former CFO, I know how disruptive audits can be when data is fragmented and evidence lives across disconnected systems. Those conditions introduce risk, uncertainty, and unnecessary strain on finance teams.

Organizations that invest in clean, connected, AI-enabled financial infrastructure don’t just experience smoother audits; they close faster, gain continuous visibility into their data, and reduce reliance on manual workarounds during audit season. In this environment, audit technology becomes a strategic lever for CFOs — one that extends beyond compliance to influence cost, quality and predictability.