What Is AI and Machine Learning: A Simple Guide for Auditors

Table of Contents

• The Fundamentals: What Are AI and Machine Learning in Auditing?
• The Evolution of AI and ML in Auditing
• How AI Enhances Document Review
• AI’s Role in Workpaper Preparation
• AI in Fieldwork Execution
• Improving Audit Report Drafting with AI
• AI in Different Audit Domains
• Mitigating AI Risk: Ethical and Governance Considerations for Auditors
• Governance and Readiness for AI Adoption
• The Future of AI in Auditing
• Final Thoughts

The Fundamentals: What Are AI and Machine Learning in Auditing?

Artificial intelligence (AI) and machine learning (ML) are changing the auditor’s toolkit. Auditors can use AI to simulate intelligent reasoning. ML, a subset of AI, can enable these systems to learn directly from audit documentation to improve precision. These technologies allow auditors to automate repetitive tasks, instantly analyze entire populations of data to spot trends and anomalies, and make better-informed judgment calls.

This article dives deep into how AI applications and ML models are enhancing efficiency and quality across every phase of auditing, from engagement initiation to recommendation follow-up.

The Evolution of AI and ML in Auditing

AI’s integration into professional fields like auditing has increased over the past few years, driven by advances in computing power, data availability, and algorithmic sophistication. The adoption of AI began with basic automation and has evolved into intelligent systems capable of interpreting context and generating insights.

According to PwC, internal audit is transitioning from traditional models to “human-led, agent-powered” frameworks. These agentic systems operate as digital teammates, sensing risk and executing workflows within defined guardrails. This marks a departure from point-in-time testing toward continuous assurance and real-time analytics.

The Public Company Accounting Oversight Board (PCAOB) has acknowledged this shift. In its 2024 Spotlight publication, the Board summarized outreach findings from audit firms and companies exploring generative AI (GenAI) in financial reporting. While adoption has been relatively slow, the momentum is undeniable.

How AI Enhances Document Review

Document review is one of the most time-consuming aspects of auditing. Auditors must sift through policies, contracts, financial statements, and emails to identify relevant information and assess compliance. AI transforms this process by automating extraction, classification, and summarization.

Some key benefits of AI in document review include:

• Speed: AI can process hundreds of pages in seconds.
• Consistency: Algorithms apply uniform logic across documents.
• Insight: ML models can detect anomalies and trends that may be missed manually.

These efficiencies can streamline document review and allow auditors to focus on analysis rather than manual extraction. They also align with Deloitte’s findings that AI-driven processes elevate audit quality and productivity.

Tools like AuditPal AI, for example, allow auditors to upload PDF and Word documents and interact with them using natural language. Depending on the audit domain, auditors can use AuditPal AI in the following ways:

• Internal auditors can ask: “What are the key controls in this policy?” to quickly identify control objectives and design elements.
• Financial auditors can prompt: “Summarize the revenue recognition disclosures,” helping them assess compliance with accounting standards.
• IT auditors can extract system configurations and control settings from technical documentation to support cybersecurity and access reviews.
• Performance auditors can identify key performance indicators (KPIs) in strategic plans or budget documents to evaluate program effectiveness.

AI’s Role in Workpaper Preparation

Workpapers are the backbone of audit documentation. They explain the procedures that the audit team performed, the evidence they obtained, and the conclusions they reached. Preparing workpapers manually involves extensive editing, formatting, and referencing — tasks that are ripe for automation.

The PCAOB has recognized AI’s potential in this area. In a 2025 speech, Board Member Christina Ho emphasized that AI can redefine audit quality by enabling deeper insights and faster documentation.

Among other things, AI can assist auditors by generating templates, organizing data, and suggesting content based on prior audits or uploaded documents.

For example:

• Internal auditors can use AI to document walkthroughs of procurement processes.
• Financial auditors can auto-generate lead sheets from trial balances.
• IT auditors can create control matrices from system access logs.
• Performance auditors can draft summaries of program effectiveness based on uploaded metrics.

AuditPal AI offers prompt templates that guide auditors through drafting control testing summaries, walkthrough narratives, and risk assessments. These templates adapt to user input, ensuring consistency and completeness.

AI in Fieldwork Execution

Fieldwork involves testing controls, performing walkthroughs, and gathering evidence. Traditionally, this requires manual sampling, interviews, and spreadsheet-based data analysis. AI can enhance the fieldwork phase by instantly analyzing transactional data, identifying outliers, and suggesting areas for deeper testing. As PwC notes, the responsible use of AI during fieldwork can also enhance stakeholder confidence and support governance.

Here are some potential AI applications during the fieldwork phase:

Improving Audit Report Drafting with AI

Audit reports must clearly communicate findings, conclusions, and recommendations to stakeholders. That’s because they serve as the final deliverable of an audit engagement and influence decision-making. Drafting these reports requires precision, consistency, and alignment with supporting evidence — tasks that can be time-consuming and prone to human error.

AI tools like AuditPal AI can streamline the reporting phase by assisting auditors in several key ways:

• Narrative generation: AI can draft well-structured paragraphs based on fieldwork results, control testing outcomes, and documented exceptions.
• Tone and consistency: AI can ensure the report maintains a professional and consistent tone throughout, regardless of how many contributors are involved.
• Evidence alignment: AI can cross-reference findings with uploaded workpapers to ensure the conclusions are properly supported.
• Fact-checking and editing: AI can highlight inconsistencies, suggest revisions, and help auditors refine language for clarity and impact.

These capabilities reduce the time spent on writing and editing, improve the quality of audit reports, and help ensure the timely delivery of reports that meet stakeholder expectations.

AI in Different Audit Domains

AI’s versatility allows it to support a wide range of audit types. While the core benefits are universal, the applications vary by domain.

• Internal Auditing: Internal auditors use AI to monitor controls, assess risk, and provide advisory insights. AI agents can flag emerging risks in real time and suggest mitigation strategies. As PwC notes, internal audit is evolving into a hybrid model where digital teammates operate alongside human auditors.
• Financial Auditing: In financial audits, AI helps validate transactions, test controls, and assess disclosures. According to Deloitte, AI-generated outputs must meet stakeholder expectations for accuracy and reliability, which requires strong governance.
• IT Auditing: IT auditors assess system controls, cybersecurity, and data integrity. AI can analyze access logs, detect anomalies, and simulate attack scenarios. It also helps document technical findings in plain language for non-technical stakeholders.
• Performance Auditing: Performance auditors evaluate program effectiveness, efficiency, and economy. AI can analyze budget data, compare outcomes to benchmarks, and identify areas for improvement. For example, AI might flag a department consistently underperforming against its KPIs.

Mitigating AI Risk: Ethical and Governance Considerations for Auditors

Despite its benefits, AI introduces risks that auditors must manage. These include data privacy, model bias, and regulatory compliance. Responsible use requires governance, transparency, and human oversight, according to PCAOB.

The limited adoption of GenAI in the auditing industry is primarily because firms remain cautious. That’s why AuditPal AI operates within defined guardrails, ensuring that auditors remain in control of decisions and that outputs are traceable to source documents.

In terms of the risks with the use of AI, the key considerations for auditors include:

• Data security: AI systems must comply with data protection regulations such as GDPR and CCPA. Sensitive client data should be encrypted and access-controlled.
• Model transparency: Auditors should understand how AI models reach conclusions. Black-box algorithms may pose challenges for audit documentation and defensibility.
• Bias and fairness: ML models trained on biased data can perpetuate inequities. Auditors must evaluate training data sources and monitor outputs for unintended bias.
• Regulatory alignment: AI use must conform to standards set by oversight bodies like PCAOB, AICPA, and IIA. This includes maintaining professional skepticism and documenting sufficient appropriate audit evidence.

Governance and Readiness for AI Adoption

Successful AI adoption in auditing requires more than just technological innovation. It demands a governance framework that supports ethical use, accountability, and continuous improvement. Audit firms must establish AI governance programs that include risk assessment, model validation, and stakeholder engagement.

The key steps to prepare for AI adoption include:

• Assess audit team readiness: Evaluate digital literacy, openness to change, and training needs.
• Define use cases: Identify audit tasks where AI can add the most value, such as document review or control testing.
• Establish controls: Implement policies for data handling, model oversight, and output validation.
• Pilot and iterate: Start with small-scale pilots to test AI tools in real audit scenarios.
• Engage stakeholders: Communicate with clients, regulators, and internal leadership about AI goals and safeguards.

AuditPal AI supports this journey by offering intuitive tools that integrate seamlessly into existing workflows. Its conversational interface, document analysis capabilities, and prompt templates make it accessible to auditors across domains and experience levels.

The Future of AI in Auditing

AI is poised to become a standard tool in the auditor’s toolkit. As systems become more sophisticated, auditors will shift from manual execution to strategic oversight. The profession will evolve toward continuous assurance, real-time analytics, and hybrid human-machine collaboration.

The emerging trends include:

• Predictive auditing: ML models will forecast risk areas before fieldwork begins.
• Continuous monitoring: AI will track transactions and controls in real time, enabling proactive intervention.
• Multimodal analysis: Future tools may combine text, image, and voice data to provide richer audit insights.
• Regulatory evolution: Standards bodies are exploring how to adapt audit guidance to AI-enhanced workflows.

Audit firms and regulators are already preparing for this future. The PCAOB is evaluating whether changes to standards are needed to accommodate AI-driven audits. Meanwhile, consulting firms are investing in AI platforms that integrate with audit methodologies across domains.

For auditors, the message is clear: AI isn’t a threat. It’s an opportunity to deliver deeper insights, faster turnaround, and greater value to stakeholders.

Final Thoughts

From document review to report drafting, AI and ML can enhance the efficiency and accuracy of audit work. By adopting AI responsibly and strategically, auditors can adapt to the evolving demands of stakeholders and regulators.

Whether you’re conducting a financial statement audit, evaluating internal controls, assessing IT systems, or measuring program performance, AI can help you work more efficiently.

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