← Blog Before you buy
AI for auditors: practical uses, limits, and controls
AI for auditors can help with data analysis, document review, planning, workpapers, and reports. The decision is where to use it, how to measure its answers, and how to document human review.
Key takeaways
- AI fits best in five repeatable uses: document classification, support review, anomaly detection, draft findings, and search across internal standards.
- An AI result isn't enough audit evidence by itself; the auditor must review the source, procedure, and conclusion.
- Before real audit use, you need an evaluation set with correct cases, edge cases, and expected errors.
- Reviewing a full population with AI doesn't remove materiality or professional judgment; it changes how exceptions are found.
What can AI do for auditors without replacing your judgment?
AI helps when the task has clear inputs, repeatable criteria, and an output an auditor can review. It shouldn't issue the audit opinion, decide materiality, or close findings by itself.
In audit work, AI is useful for reading long documents, extracting data, comparing policies against evidence, grouping similar transactions, finding outliers, and drafting text. It can also search approved manuals, contracts, minutes, invoices, certificates, and prior workpapers.
The limit is responsibility. A system can suggest that an invoice doesn't match a purchase order. The auditor decides whether that affects an assertion, whether more procedures are needed, and how to document the conclusion.
The difference between a generic AI tool and an audit-ready system is control. A useful system records the documents it consulted, the model version, the instructions, the answer, and the reviewer. If it searches controlled documents, a RAG system can answer from internal sources instead of model memory.
The IIA published an update to its AI auditing framework on September 13, 2024. It covers governance, management, reasonable assurance, traceability, transparency, and accountability in the IIA's Artificial Intelligence Auditing Framework. The same idea applies when a firm uses AI in its own work. The system has to be governed, measured, and monitored.
Where does AI for auditors help most?
Start with tasks where the output can be checked and doesn't close conclusions on its own.
| Part of the cycle | Practical use | Control needed |
|---|---|---|
| Planning | Summarize minutes, policies, contracts, and business changes. | Check omitted facts and keep reviewed sources. |
| Document review | Extract dates, amounts, signers, clauses, and terms. | Sample extractions against the original document. |
| Tests of details | Flag unusual transactions, duplicates, and unexpected relationships. | Document rules and review each exception. |
| Workpapers | Draft narratives, matrices, cross-checks, and evidence references. | Review before saving as engagement support. |
| Reports | Draft findings, causes, effects, and recommendations. | Adjust scope, tone, support, and conclusion. |
On July 22, 2024, the PCAOB reported that GenAI use in audits was limited but moving fast. The firms it spoke with focused mostly on administrative and research activities, with potential in planning and execution in the observations published by its staff.
How do you validate AI for audit before real use?
Validation isn't solved by trying prompts. You need known cases, expected answers, and a decision about which errors are unacceptable.
- Define the exact task. Don't validate “AI for audit” in general. Validate one task: classifying contracts, extracting invoice data, summarizing minutes, or detecting unusual journal entries.
- Build an evaluation set. Include normal cases, hard cases, low-quality documents, real exceptions, and cases where the correct answer is “there isn't enough information.” At Quarl, we use this method to compare answers before and after changes. See how we measure whether a RAG system answers well.
- Measure dangerous errors. Count errors that can change a conclusion. Examples include inventing a source, omitting an exception, extracting the wrong amount, or marking a risky item as low risk.
- Test traceability. Every answer should show where it came from. If the system summarizes a contract, it should point to the contract and section. Without a source, treat the answer as a draft.
- Make a go/no-go decision. Before production, document the allowed task, limits, roles, and review rules. The NIST AI RMF Core organizes AI risk management into four functions: govern, map, measure, and manage. After mapping, it recommends enough context for an initial go/no-go decision in the AI RMF Core.
Can AI results be audit evidence?
They can support evidence, but they don't become sufficient evidence because a tool produced them. The question is whether the procedure meets its objective.
An AI summary can guide the auditor. The support is still the original document, accounting record, confirmation, contract, or authorized source. If the system found an exception, the workpaper should show the data reviewed, the criterion, the output, and the auditor's decision.
Illustrative example, not a client: a firm reviews accounts payable for a company operating in Colombia. The inputs are vendor invoices, purchase orders, warehouse receipts, and the accounting subledger. The system extracts tax ID, invoice number, date, amount, vendor, and related order. Then it flags value differences, duplicate invoices, and incomplete support. The result is a list of exceptions with links to support. The team can test, discard, expand, or document findings.
This also applies to full-population analysis. The IAASB explains that an auditor may perform procedures manually or using automated tools and techniques. It also says performance materiality still applies when a tool is applied to an entire population in its FAQ on technology, exceptions, and materiality. Reviewing more data doesn't remove professional judgment.
For the result to be auditable, the system must leave a trail. Store model version, instructions, documents consulted, execution date, user, output, human decision, and later changes. If it integrates with an ERP, document system, or audit software, that traceability must travel through the integration. Many implementations need good integrations and APIs, not a chat interface alone.
Which option fits best for your audit team?
The right option depends on data, risk, traceability, and usage frequency. Many teams shouldn't build from scratch.
| Option | When it makes sense | Risks to review |
|---|---|---|
| Features in audit software | The platform already includes analytics, extraction, or assistants. | Permissions, traceability, data residency, limits, and exports. |
| Automation platform | Email, folders, spreadsheets, ERP, or documents need coordination. | Credentials, change control, silent errors, and failed sources. |
| RAG system | Teams need answers from policies, contracts, manuals, or workpapers. | Retrieval quality, citations, updates, and unsupported answers. |
| Custom development | The case needs sensitive data, ERP logic, roles, or quality metrics. | Data quality, connectors, security, testing, and governance. |
Don't choose based on tool rankings. Test the real task with your documents, measure errors, and define who is responsible for each output.
What we do
Frequently asked questions
What can AI do for an auditor without replacing professional judgment?
It can read documents, extract data, detect exceptions, summarize information, prepare drafts, and search internal sources. The auditor still defines the procedure, evaluates evidence, applies materiality, and signs the conclusion. AI speeds up reviewable work. It doesn't take professional responsibility.
Can AI results be audit evidence?
An AI result can support a procedure, but it isn't enough by itself. The auditor must review the original source, data reliability, procedure objective, and generated output. Workpapers should keep sources, parameters, response, and human review.
How do you validate that AI for audit answers well?
Start with one task. Build an evaluation set with real cases, difficult cases, and expected answers. Measure conclusion-changing errors, not overall accuracy alone. Before production, document limits, authorized users, review rules, and criteria for stopping use.
Does AI let auditors review the full population of transactions?
In some cases, it can analyze a full population and flag exceptions, duplicates, or unusual patterns. That doesn't remove materiality or professional judgment. Reviewing more data changes how items are selected, but the auditor still evaluates sufficiency.
What's the difference between using AI in audit and auditing AI systems?
Using AI in audit means applying tools to procedures, document review, or analysis. Auditing AI systems means evaluating governance, data, controls, performance, and monitoring of a system used by the audited entity. The objectives and evidence are different.
Written by
Quarl engineering team
We are an AI engineering team in Medellín. We build and run systems that reach production — automations, assistants and agents — and for three years we ran an assistant for two million users across ten countries.
See how we work →Want to apply this in your company?
In fifteen minutes we tell you whether it is worth it, free and with no pitch. The proposal arrives within 48 hours.