Will AI play a dominate role in the field of auditing? This is the million-dollar question (no pun intended) today particularly with uncertainty about the long-term viability of AI in question. For many auditors who’ve already taken the plunge, Christina Ho’s Board Member of PCAOB, address at the 2025 MindBridge Vision event could be a sigh of relief. Ho described PCAOB’s vision of incorporating responsible AI and auditing, emphasizing that auditors will increasingly use AI to enhance judgment, drive efficiency, and increase oversight instead of viewing it as a threat or substitute.
For those on the fence, the truth is that only time will tell. However, if most technology forecasters are to be believed – and while not discounting a larger part of the auditing world a confident prediction would be that AI is here to stay. This overarching dominance of AI will inevitably play a role in various aspects of auditing, something that is being termed as a ‘use case’ with various auditing workflow automations coming into the fold. For auditors, there would be no better time to stay updated on the impending changes to the audit lifecycle and quickly evolve internal processes.
This blog gives a high-level overview of the audit lifecycle, and how firms can stay prepared or evolve to the changing AI. It also looks at the response of audit regulatory bodies towards the continuous development of AI and why that is significant for firms.
Connecting the Audit Lifecycle with AI
Before we begin, the audit lifecycle is the formal series of events that auditors undertake to review financial statements, identify risks, and issue assurance regarding the accuracy and fairness of an organization’s financial reporting. The process classically consists of planning, risk assessment, testing, gathering evidence, reporting, and review.
Traditionally, most of the audit cycle was based on manual processes, sampling methods, and even human judgment. Though these are still important, with the inclusion of artificial intelligence in auditing, each step has changed significantly, making it more efficient and accurate. It is also important for audit firms to know how AI models are going to interact with the auditor’s day-to-day tasks.
1. Planning
The planning phase is the cornerstone of the audit. In this phase, auditors develop an understanding of the client’s business context, internal control, and areas of risk. It includes establishing audit goals, defining work scope, assigning resources, and deciding timelines. An effective audit plan enables adequate utilization of time and ensures that the team concentrates on matters with the highest materiality.
How is AI used for optimizing planning?
Artificial intelligence in auditing can elevate audit planning by independently scanning tremendous amounts of structured and unstructured data from sources such as previous audit reports, governance and compliance databases, control assessments, and issue logs.
Agentic AI, the latest form of AI, formulates sub-goals, considers several planning options, and continuously improves recommendations as new data arrives. It determines high-risk domains, forecasts possible issues, and dynamically changes resource allocation, essentially serving as an independent co-planner for auditors.
With agentic AI, auditors get more timely, more accurate, and responsive planning. Automation saves time on manual review of data, identifies regions of greatest material importance, and keeps audit plans in context as new info emerges. This enables auditors to concentrate on professional judgment, strategic decision-making, and monitoring all complex or judgment-heavy areas, enhancing both efficiency and audit quality.
Use Case: AI-Driven Account Reconciliation Planning
An auditor responsible for planning a client’s accounts payable reconciliation needs to determine what accounts to test and how to use the team’s resources. Agentic AI autonomously examines the previous payments made, vendor agreements, and past audit results, flagging those accounts with abnormal payment trends or inaccuracies.
For example, the AI identifies a vendor account with a history of late payments and varying invoice amounts. It recommends giving top priority to this account for thorough testing and proposes the ideal sample size and method. The auditor applies these observations to allocate team members, plan reconciliations effectively, and concentrate on judgment-driven reviews, minimizing manual data verification and enhancing audit effectiveness and quality.
2. Risk Assessment
After planning, auditors conduct a risk assessment to determine where material misstatements are likely to arise. This involves analysis of internal controls, risk of fraud, and analysis of external factors that would affect the financial position of the client.
How AI enhances Risk Assessment
The multi-model AI system can keep track of financial and operational information in real time, spotting anomalies, strange patterns, and departures from expected behavior. It processes intricate relationships between structured and unstructured information, including transactions, contracts, emails, and regulatory filings.
The AI can also make multiple risk scenarios and actively suggest audit procedures, dynamically revising risk profiles as new evidence becomes available. It effectively serves as an autonomous partner, pointing out issues before they become problems.
Auditors are provided with a live, evidence-driven risk perception, enhancing fraud or material misstatement detection. Dependence on sampling is minimized, and new risks can be dealt with in real-time. In effect, the AI promotes transparency, facilitates regulatory compliance, and liberates auditors from the need to manually review data, enabling them to concentrate on judgment-based analysis.
Use Case: AI-Driven Inventory Risk Assessment
An auditor evaluating inventory for a retail customer usually samples warehouse locations and merchandise categories to review. AI can process historical records of inventory, sales trends, and supplier shipment data, pointing out products with exceedingly high write-offs or differences between quantities on hand and sales.
For instance, AI can identify a given SKU that indicates recurring shrinkage at a single distribution center versus others. The auditor can then give high-priority testing to this SKU, concentrate on potential areas of internal control deficiency, and suggest additional verification procedures. This focused method minimizes manual sampling, enhances risk coverage, and properly investigates high-risk inventory items.
3. Fieldwork and Testing
Fieldwork, which is regarded as the heart of audit, entails gathering and testing evidence to ensure the validity of financial statements. Auditors scrutinize transactions, verify balances, check supporting documents, and conduct analytical procedures to ascertain that figures reported are real.
How AI Enhances Fieldwork and Testing
Gen AI enhances field work by constantly reviewing financial and operational information, detecting anomalies, strange patterns, or transactions that do not align with expectations. It may target high-risk areas, suggest focused testing procedures, and collect evidence from disparate systems. It can also simulate test scenarios and revise testing approaches in real-time according to fresh discoveries, acting as a smart assistant during the whole process.
Use Case: AI-Driven Fieldwork and Testing
An auditor auditing payroll would typically sample employee records to check salaries, overtime, and deductions. Gen AI processes the entire payroll dataset, finding anomalies like duplicate payments, unusual salary jumps, or overtime surges in certain departments.
For instance, AI can alert multiple employees with overtime on the borderline of approval limits during the same pay period. The auditor can then concentrate testing on the alerted entries, check supporting documentation, and review possible control weaknesses. This process minimizes manual work, maximizes coverage of high-risk transactions, and enhances accuracy and reliability in the payroll audit.
4. Evaluation and Reporting
Once the auditors have finished the testing phase, they review their results to ascertain whether there are any material misstatements. On the basis of this review, they draft an audit report, where they render their opinion regarding whether or not the financial statements are fairly presented in relation to the respective accounting standards.
How does AI enhance evaluation and reporting?
Agentic AI collates and examines evidence from fieldwork in real-time and is able to target areas where additional attention is needed. Agentic AI can produce summaries, emphasize exceptions, and recommend possible disclosures or modifications. It can select key findings, model reporting situations, and revise recommendations as more evidence comes in, allowing auditors to concentrate on judgment and interpretation while still having a comprehensive, data-driven review process.
To auditors, it makes the evaluation and reporting process more efficient by carrying out evidence collection and analysis automatically, minimizing effort, and enabling an ability to concentrate on professional judgment. In real-time, insights and responsive recommendations enable faster identification of reporting inconsistencies or disclosure requirements, whereas scenario simulations enable more assured, well-supported audit conclusions. In all, agentic AI is a strategic partner that increases efficiency, accuracy, and audit quality.
Use Case: AI-Driven Evaluation and Reporting
A traditional auditor examining journal entries would sample transactions for irregularities.
AI searches the entire set, identifying anomalous entries like big one-time postings, round-dollar transactions, or business-cycle outside-of-normal postings.
For instance, it can pinpoint a high-value entry on the final day of the reporting period with no supporting documentation. The auditor reviews such flagged entries, verifies evidence, and decides if adjustments are necessary, enhancing precision, saving time in manual review, and preventing high-risk items from receiving attention in an inconsistent manner.
5. Review and Continuous Improvement
The last phase of the audit cycle is a review of the process and results to guarantee quality and adherence to standards like those established by the PCAOB. Interfirm reviews or peer reviews, where audit firms look for areas of improvement regarding methodology or implementation, are sometimes performed by internal quality reviews.
How does AI enhance Review?
Agentic AI collates and examines fieldwork evidence in real time, pinpointing patterns, discrepancies, and areas that need more probing. It can compile summaries, flag exceptions, and recommend potential disclosures or modifications. Agentic AI ranks key findings, poses reporting scenarios, and refines recommendations as new evidence is uncovered, allowing auditors to concentrate on judgment and interpretation while still enforcing a comprehensive, data-driven review process.
For auditors, agentic AI enhances speed and precision through automated review and analysis of audit evidence. It points out critical anomalies, marks red flags, and offers adaptive suggestions so that auditors can devote their expertise to professional judgment and critical decision-making. Through reporting scenario simulations and segregation of major findings, AI assists auditors in making more sound conclusions while minimizing time on repetitive, data-driven work.
Use Case: AI-Driven Quality Review
During the post-audit review, an audit manager normally reviews workpapers, sampling choices, and documentation to ensure compliance with standards. Agentic AI automatically analyzes these documents, pointing out areas where procedures were omitted, sampling was inadequate, or anomalies were inadequately explored.
For instance, the artificial intelligence in auditing review can highlight consistently recurring problems in revenue testing across several clients, and the team can modify methodology or add training accordingly. Fears are learned, processes are improved, and future audits are more efficient and reliable.
Conclusion
AI is becoming an important tool for auditors, speeding up routine tasks, detecting threats early, and analyzing complex data. But don’t be mistaken, it will never replace humans because their expertise, judgment, and ethics cannot be duplicated.
The attention of auditors should be on the safe and responsible use of AI, allowing auditors to concentrate on work requiring experience and expertise. The future is not about humans versus machines but about teamwork, with machines doing the heavy lifting while humans make the key decisions.
