How Artificial Intelligence is Reshaping Internal Audit
Artificial intelligence (AI) has moved beyond hype, and internal audit leaders are starting to explore how it can transform assurance and advisory work. While many organizations are still in the initial stages, AI offers the opportunity to increase efficiency, accuracy, and insight. By automating routine tasks and surfacing insights from large datasets, AI can free auditors to focus on higher-value analysis, risk evaluation, and strategic decision-making.
Where AI Fits in Internal Audit
AI is not a replacement for auditors, but instead a tool to amplify their impact and improve audit quality. Some of the most promising applications include:
Note: The following mini-cases are illustrative examples and do not represent actual client engagements. AI rapidly processes large volumes of transaction data, executes sample tests, and highlights key results for auditors to analyze. This accelerates the identification of anomalies and exceptions.
Automated Testing
AI rapidly processes large volumes of transaction data, executes sample tests, and highlights key results for auditors to analyze. This accelerates the identification of anomalies and exceptions.
Mini-case: In a SOC engagement, AI analyzed 1,000 transactions in under 30 minutes and highlighted anomalies that would have taken an audit team several days to identify manually, enabling auditors to focus on interpretation and follow-up.
Audit Program Drafting
AI can assist in building audit programs or drafting test steps within software platforms, reducing planning time. It can also review relevant guidance, regulations, and best practices to recommend enhancements to audit procedures, ensuring coverage of areas that may not have been originally included or that need updating.
Mini-case: An internal audit team used AI to draft test steps for a recurring operational audit, cutting program development time in half while maintaining quality and consistency. It also suggested updates based on the latest guidance, bridging gaps between existing procedures and what should be covered for optimal audit effectiveness.
Risk Assessments
AI can identify patterns, anomalies, or potential areas of concern across large datasets, providing insights that support both assurance and advisory work.
Mini-case: A financial services client leveraged AI to analyze payment transactions across multiple branches, revealing previously unnoticed risk trends that informed a targeted internal audit plan.
AI Agents
AI agents can coordinate multi-step audit tasks, such as assembling test packs, monitoring anomalies, or gathering documentation for audits and exams. With human-in-the-loop checkpoints, agents save time, increase coverage, and free auditors to focus on higher-value analysis. Early wins include continuous exception monitoring in accounts payable and automated financial statement review.
By combining these capabilities, AI enables internal audit teams to work smarter, improving both speed and quality while providing leadership with actionable insights. While automated testing, audit program drafting, and risk assessments are among the most impactful applications today, AI’s potential extends far beyond these areas.
Additional use cases include continuous auditing, fraud detection, predictive analytics, natural language processing, workflow automation, automated PBC lists and requests, and generative AI for reporting. The possibilities with AI in internal audit are truly limitless, and as the technology continues to evolve, new use cases will emerge, further transforming how audit teams deliver value.
Pros and Cons of AI for Internal Audit
Organizations exploring AI adoption in internal audit should weigh both opportunities and potential challenges:
Efficiency Gains: Automating repetitive tasks can save hours of manual work, allowing auditors to focus on analysis and interpretation. Demonstrating these time savings with role-specific examples helps increase employee buy-in and excitement for AI adoption. For example, if a payroll auditor could use AI to automatically match transactions, it would free up time for deeper analysis.
Improved Coverage: AI can handle larger datasets than traditional sampling methods, providing broader insight into transactions and processes. Employees are more likely to embrace AI when they see how it enhances the quality and depth of their work.
Skill Set Shifts: Auditors will need stronger analytical and critical thinking skills to evaluate AI-generated results accurately, rather than taking outputs at face value. Hands-on workshops, pilot projects, and step-by-step guides can help employees gain confidence and adopt new tools effectively.
Accuracy and Reliability: While AI improves with feedback, outputs are not always correct. Validation and oversight are essential to avoid errors or misinterpretations. Open discussions and clear guidance can reduce fear or resistance by emphasizing that AI enhances, rather than replaces, human expertise.
By understanding both the benefits and potential challenges, particularly employee adoption, organizations can deploy AI in a way that reinforces audit quality while minimizing resistance and maximizing value.
Practical Steps for Internal Audit Teams
To leverage AI responsibly, internal audit leaders should consider these steps:
Evaluate Use Cases: Identify areas where automation or AI could meaningfully reduce manual effort or enhance insight. Examples include transactional testing, program drafting, or preliminary risk analysis.
Tool Assessment: Assess AI tools for compatibility, cost, and integration with client systems. Not all solutions offer the same functionality or connectivity, so careful evaluation is important.
Strengthen Skills: Ensure the audit team has the analytical skills needed to interpret AI outputs and make informed decisions.
Establish Governance: Define how AI will be used, establish safeguards for accuracy and ethical use, and clarify how outputs are reviewed.
Pilot Small: Start with small, controlled projects to test AI tools and measure effectiveness before scaling adoption across the audit function.
These steps allow organizations to begin their AI journey in a measured, responsible way while building confidence and experience in its use.
Exploring AI With Clark Schaefer Consulting
AI equips teams with smarter tools to deliver higher-value insights. Organizations that experiment now will be better positioned to harness AI responsibly and maximize its impact as capabilities continue to evolve.
Clark Schaefer Consulting helps organizations explore AI use cases in internal audit. We guide teams through adoption, governance, and optimization as we continue to build our own experience and capabilities. If you are interested in learning more or discussing how AI might fit into your audit function, we welcome the opportunity to collaborate and share what we are discovering.
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