The Ultimate Guide for the AI-Curious Auditor

Audit teams are under pressure to deliver more work, cover more risk, and document everything clearly, often with fewer people and tighter timelines. AI is increasingly part of how teams respond to that pressure.

For many auditors, the challenge is no longer whether AI belongs in audit. The challenge is understanding how to evaluate it, how to use it responsibly, and how to avoid tools that create more review work instead of less.

This guide explains how auditors are using AI today, what is changing next, and how to assess AI in finance tools in a way that aligns with audit standards, regulatory expectations, and day-to-day audit work.

TL;DR: Key takeaways

Auditors should choose AI that supports evidence collection, testing, and review in a traceable and reviewable way, fits existing Excel-based workflows, and keeps professional judgment with the auditor.

AI is most effective when used for data extraction, matching, cross-referencing, and monitoring tasks, while auditors remain responsible for materiality, risk assessment, and conclusions.

The why behind AI use in auditing tasks

Audit and finance trends in 2026 show a huge shift. The scope continues to expand across financial reporting, controls, ESG, IT risk, and regulatory compliance, while timelines and documentation expectations have not eased. At the same time, teams are expected to deliver this work with fewer experienced people. Recent studies show:

83% of senior finance and accounting leaders according to Personic's 2024 Pulse Survey said there’s a talent shortage.
70% of internal auditors, IIA's 2025 North American Pulse of Internal Audit finds that growing regulatory compliance requirements are straining their current audit plans.
Within 3 years, KPMG's global AI in finance report cites that 83% expect to use AI widely in financial reporting.

Therefore, simply adding more manual work or more checklists is not sustainable for most teams. This is why AI is gaining attention in audits now. Not as a replacement for judgment or experience, but as a way to handle volume.

Tasks such as requesting documents, searching through large files, extracting data, reconciling information, and preparing documentation consume a significant share of audit time. AI can support these areas so auditors can spend more time reviewing, interpreting, and deciding.

Key terms used throughout this guide

By the time auditors start evaluating AI more seriously, confusion often comes less from the technology itself and more from the language around it. Different vendors, articles, and even regulators use the same terms in different ways.

To make the rest of this guide easier to interpret, here is how we use a few common AI-related terms in an audit context.

Artificial intelligence (AI) A broad category of technology that can analyze data, recognize patterns, and support tasks that would otherwise require significant human effort. In audit, AI is used to assist with evidence-heavy work, not to replace professional judgment.

Audit automation Technology used to reduce manual steps in audit processes, such as document handling, reconciliations, and routine checks. Automation focuses on efficiency and consistency rather than decision-making.

Machine learning A subset of AI that improves outputs over time by learning from data. In audit, machine learning is often used to identify similarities, patterns, or anomalies based on historical information.

Generative AI AI that can generate text, summaries, or responses based on prompts. In audit workflows, this type of AI is typically used to support drafting, summarizing, or organizing information, with auditor review required.

Agentic AI An emerging approach where AI supports a defined objective by coordinating multiple steps, such as searching documents, extracting information, and organizing results. In audit, agentic AI is expected to assist with preparation and review tasks while keeping judgment and approval with the auditor.

Data extraction The process of pulling specific information, such as amounts, dates, or terms, from documents and placing it into a structured format. In audit work, extracted data should remain clearly linked to its source.

Document matching The comparison of information across documents or datasets to identify matches or differences, such as tying invoices to ledger entries or reconciling balances.

Anomaly detection The identification of data points or transactions that fall outside expected patterns. In audit, anomalies are indicators that require review, not conclusions on their own.

Human-in-the-loop A working approach where AI supports tasks, but humans review outputs, apply judgment, and make final decisions. This principle is central to responsible use of AI in audit.

Explainability / traceability The ability to understand how an AI-supported output was produced and to trace it back to the underlying data or source documents. This is essential for audit review, inspection, and accountability.

What use cases AI is commonly used for in audit

In practice, teams are not adopting AI because it is new. They are adopting it because the current way of working no longer scales to the expectations placed on audit functions today.

Across audit firms and internal audit teams, AI is most often used in areas where large volumes of structured and unstructured data slow work down. Common uses include:

Extracting information from documents

AI can be used to extract relevant information from documents such as invoices, contracts, bank statements, and confirmations. This often includes amounts, dates, counterparties, and key terms.

When implemented carefully, AI Extractions can place extracted values directly into audit workpapers while maintaining a clear link to the source document. This has the potential to reduce manual copying and help reviewers understand where figures originate. Over time, teams may extend this approach to additional document types and more complex data fields can place extracted values directly into audit workpapers while maintaining a clear link to the source document. This has the potential to reduce manual copying and help reviewers understand where figures originate. Over time, teams may extend this approach to additional document types and more complex data fields.

Requesting and validating documents from clients

AI can support the process of requesting documents from clients by helping structure requests, track responses, and validate submissions as they come in. Instead of relying on manual follow-ups and static checklists, AI-powered PBC workflows can help auditors see which documents are missing, incomplete, or not aligned with the original request.

This approach has the potential to reduce back-and-forth during audit preparation and improve evidence readiness before testing begins, as seen in AI-powered PBC and document request workflows.

Matching and reconciling large datasets

AI can support matching data across multiple sources, such as tying invoices to general ledger entries or reconciling subledgers to balances. Rather than checking small samples, auditors can review a larger population and focus attention on items that do not match.

As these processes mature, AI has the potential to reduce time spent confirming expected matches and allow more focus on understanding exceptions.

Supporting control testing and evidence checks

For control testing, AI can help verify whether required evidence is present and complete. This may include checking approvals, timestamps, or required documents against defined control criteria.

The output typically highlights gaps or deviations for auditor review. Auditors then assess whether issues are valid, determine their impact, and document conclusions. This supports consistency in testing while keeping professional judgment with the auditor.

Linking reported figures back to evidence

AI can help connect figures in financial statements or summaries to the documents that support them. Instead of searching through folders and files, auditors can trace reported numbers directly to their source.

This can improve consistency in documentation and reduce review time, particularly during financial statement and disclosure reviews.

Searching and reviewing lengthy documents

Beyond extraction, AI can support audit work by reducing the need to manually search through long or complex documents. Instead of reading documents page by page, auditors can ask focused questions and receive suggested answers that reference the relevant numbers, text, or sentences in the source material.

Reflected in tools such as DocuMine, AI allows auditors to spend more time validating information and applying judgment, rather than locating evidence. It is increasingly used for contracts, loan agreements, policies, and similar documents where relevant information may be spread across many pages.

Applying agentic AI to audit outcomes

As teams become more familiar with task-level agentic AI, some are beginning to apply agentic AI to support outcomes that span multiple steps. In audit, this can include coordinating searches, checks, and validations toward a defined objective, while keeping review and approval with the auditor.

Early examples include agent-style approaches to disclosure testing and Excel-based workflows, where AI supports preparation and validation while auditors remain responsible for interpretation, judgment, and sign-off. This builds on existing extraction and review capabilities and has the potential to make audit workflows more connected and review-focused over time.

Highlighting unusual patterns and transactions

AI can scan transaction data to surface patterns that fall outside expected ranges, such as duplicate payments, unusual timing, or unexpected values. These items are flagged for auditor attention rather than classified automatically.

Over time, this approach can help teams identify issues earlier and apply judgment where it matters most.

Keeping findings and documentation organized

AI can help populate issue trackers, summaries, and references as audit work progresses. This reduces manual status updates and helps keep documentation aligned across the audit file.

Auditors retain control over wording, classification, and sign-off, while AI supports structure and consistency in the background.

Read more about the AI use-cases for AI in accounting and internal audit.

Where human judgment remains central in AI-supported audits

Area of audit responsibility	How AI supports the work	Where human judgment is required
Materiality and audit strategy	Analyzes large datasets and past audit information to surface trends and areas of focus	Determining materiality thresholds, defining audit scope, and deciding how much assurance is required based on business context and stakeholder expectations
Risk assessment and prioritization	Highlights potential risk areas based on data patterns, control outcomes, or historical issues	Assessing likelihood and impact of risks, determining relevance to audit objectives, and prioritizing work based on professional judgment
Interpretation of exceptions and anomalies	Flags unusual transactions, mismatches, or deviations from expected patterns	Evaluating whether an exception represents an error, control deficiency, fraud risk, or valid business explanation
Review of AI outputs	Produces draft results, extracted data, or flagged issues for review	Challenging outputs, validating accuracy, understanding limitations, and taking accountability for conclusions
Documentation and audit trail	Links outputs to source documents and structures evidence consistently	Deciding what documentation is sufficient, how findings are explained, and ensuring evidence supports conclusions
Ethical reasoning and independence	Surfaces data and patterns without context or intent	Applying professional skepticism, considering bias or data limitations, and making ethical decisions consistent with audit standards
Communication of findings	Assists with organizing information or drafting summaries	Explaining conclusions clearly to management, audit committees, and regulators, and standing behind those conclusions

How the use of AI in audit is changing in 2026

Audit teams are moving beyond single-task automation toward more coordinated use of AI.

Shift 1: From isolated tasks to outcome-based workflows

Instead of running one tool for one step, teams increasingly use AI to complete sequences of related tasks such as preparing testing documentation or reviewing disclosures, with review points built in.

Shift 2: Agent-based support inside audit workflows

Agentic AI refers to AI that can follow instructions, complete multiple steps, and return outputs for review. In audit, this often appears as Excel-based agents that assist with reconciliations, disclosure checks, or document review.

Shift 3: Greater focus on disclosure and reporting reviews

As reporting requirements grow, AI is increasingly used to scan financial statements and notes for missing or inconsistent information, helping auditors focus review effort where it matters most.Shift 4: Continuous rather than periodic testing

Shift 4: Continuous rather than periodic testing

AI enables more frequent testing and monitoring, which supports earlier identification of issues during the audit cycle.

See what's shaping the future of audit and finance

Ethical considerations when using AI in audit

As AI becomes part of everyday audit work, ethical questions show up in practical ways. Most of them come down to how auditors maintain control, transparency, and accountability.

Keep outputs traceable

Auditors need to understand where AI-supported results come from and how they link back to source evidence. This matters even more as AI supports complex tasks like disclosure reviews. Clear traceability helps ensure that work remains reviewable and defensible, as outlined in building trustworthy AI in audit through data traceability.

Keep humans accountable

AI can support searching, checking, and organizing information, but responsibility for conclusions stays with the auditor. Review points, validation, and sign-off remain essential, especially as agent-style AI supports more steps in a workflow.

Question outputs, not just errors

AI reflects the data and rules it is given. Auditors still need to apply professional skepticism, consider data quality, and assess whether results make sense in context. An unusual result is a signal, not an answer.

Use AI carefully in disclosures and reporting

When AI supports disclosure and reporting reviews, accuracy is only part of the picture. Auditors still decide what is material, complete, and appropriate to disclose.

How audit teams move from understanding AI to choosing tools

At this point, you have seen how AI shows up in audit work today, where it can support scale, and where professional judgment remains essential. The remaining challenge is practical: translating that understanding into decisions that hold up during real audits.

These questions are explored in more depth in our flagship event Connect 2025 in NYC with experts like Mike Levy, CEO at Cherry Hill Advisory, and Terence Artus Jr., Director at Citrin Cooperman to discuss their playbook for choosing technology that lasts. Read on to get the takeaways, or watch the session video.

FAQ

How do auditors use AI in practice?

Auditors use AI to extract data from documents, match transactions, cross-reference evidence, flag anomalies, and support review tasks while retaining responsibility for conclusions.

What is agentic AI in audit?

Agentic AI refers to AI that can perform multiple related steps toward an outcome, such as preparing disclosure checks or reconciliations, with auditors reviewing and approving results.

Is AI audit software compliant with audit standards?

AI can support compliance when outputs are traceable, reviewable, and documented according to applicable standards. Auditors remain responsible for final judgments.

What should auditors look for when choosing AI?

Key factors include traceability, Excel integration, documentation quality, security controls, and ease of adoption across teams.

Can AI replace auditors?

No. AI can automate repetitive and data-heavy tasks, but audit opinions, professional skepticism, and judgment must remain with licensed auditors.

Is AI allowed in audits?

Yes, when used appropriately. Regulators allow AI as a tool, but auditors remain responsible for audit quality, documentation, and conclusions.