Four Practical Ways Higher Education Auditors Can Use AI Today

Artificial intelligence has quickly moved from buzzword to boardroom conversation across higher education. AI is impacting the audit profession, and internal auditors can use AI responsibly, practically and strategically to strengthen audit activities without sacrificing professional judgment or compromising the security of sensitive institutional data.

In colleges and universities facing increased regulatory scrutiny, staffing constraints, cybersecurity concerns and the pressure to do more with less, AI offers an opportunity to support auditors in expanding coverage, improve efficiency and identify risks that traditional methods may miss.

The good news? Internal audit teams don’t need to become data scientists to begin using responsible AI effectively. And with proper human oversight, AI outputs can be verified and refined before they ever reach a workpaper or report.

Here are four practical ways higher education internal auditors can begin using AI in their audit functions today.

1. Use AI for Transaction and Anomaly Detection

One of the most valuable applications of AI in internal audit is identifying unusual transactions, patterns or behaviors hidden within large datasets. Traditional audit sampling only reviews a small portion of transactions. AI-driven tools can evaluate entire populations of data and flag anomalies that warrant additional investigation. Additionally, many AI tools can operate on de-identified or aggregated data, reducing exposure of sensitive student or employee records.

AI doesn’t require perfect data to be useful. Even institutions with fragmented or inconsistent systems can begin with targeted analysis of a single data source (such as purchasing card transactions or payroll records) and expand from there.

Examples in a university environment might include:

• Duplicate payments
• Unusual purchasing card activity
• Payroll irregularities
• Journal entries posted outside normal business hours
• Transactions just below approval thresholds
• Unauthorized vendor changes

AI models excel at recognizing patterns and highlighting transactions that deviate from normal activity. This helps auditors focus their time on higher-risk areas instead of manually sorting through thousands of records. This capability is particularly valuable in decentralized university environments where colleges, departments and auxiliaries may all operate with different processes and varying levels of oversight.

The concept of AI-powered anomaly detection and transaction analysis is highlighted throughout government audit guidance and training materials, including examples focused on identifying unusual payments and fraud indicators.

2. Automate Audit Documentation and Administrative Tasks

Internal auditors often spend significant time on repetitive administrative work rather than higher-value analysis. AI can help reduce that burden by automating repetitive tasks such as documentation, data collection, recalculations and report generation while reducing manual effort and human error.

Generative AI tools can assist with:

• Drafting audit programs
• Creating interview questions
• Summarizing policies and procedures
• Preparing first drafts of reports
• Developing testing checklists
• Organizing workpapers
• Translating complex regulations into plain language summaries

For example, an auditor reviewing p-card controls could use AI to quickly generate a tailored testing checklist or summarize a lengthy policy manual before audit testing begins.

Importantly, AI should support – not replace – auditor review. Every output still requires professional review and validation, as AI should be used as a drafting partner, not a final authority, but the efficiency gains can be substantial.

3. Strengthen Risk Assessment and Audit Planning

Most internal audit departments struggle with the same challenge: limited resources and too many risks to cover. AI can improve annual risk assessments by analyzing historical data, operational trends, prior findings, financial information, cybersecurity incidents and compliance activity to identify areas with elevated risk profiles. AI-supported risk modeling can help internal audit move toward a more continuous and data-informed approach to assurance rather than relying exclusively on annual snapshots.

Rather than relying solely on surveys and management interviews, AI-assisted risk assessment can help auditors:

• Identify emerging operational risks
• Detect trends across departments
• Prioritize high-risk functions
• Evaluate changes in spending patterns
• Monitor compliance indicators continuously

In higher education, this might include identifying elevated risks related to federal grants, research compliance, athletics spending, student financial aid, cybersecurity and third-party vendors. AI-assisted audit planning and predictive risk modeling are increasingly recognized as valuable tools for identifying areas with higher likelihoods of material misstatement or control weaknesses.

4. Monitor Compliance and Controls Continuously

Many university audits still operate on periodic testing cycles (quarterly, annually, every other year, or after issues arise). AI enables a shift toward continuous monitoring. With the right data integrations and automation workflows, AI tools can monitor transactions and control activities in near-real time and alert internal audit when predefined thresholds or exceptions occur.

Examples include:

• Monitoring segregation of duties conflicts
• Flagging unauthorized system access
• Tracking grant spending outside allowable periods
• Identifying procurement policy violations
• Monitoring unusual payments

Continuous monitoring does not eliminate audits. Instead, it gives internal audit better visibility into where issues may be developing before they become material findings. For institutions navigating increasing compliance obligations with limited staffing, this can significantly enhance oversight capabilities without requiring large increases in personnel.

AI’s ability to support compliance monitoring, real-time alerts and continuous transaction review makes it a powerful tool for identifying control weaknesses and fraud indicators.

Why AI Will Not Replace Internal Auditors

Every conversation about AI eventually arrives at the same concern: “Will AI take audit jobs?” The short answer is no.

AI will absolutely change how internal auditors work. But it will not replace the judgment, skepticism, communication and governance responsibilities that define the profession.

Internal audit is fundamentally a human discipline built on:

• Professional skepticism
• Ethical judgment
• Contextual understanding
• Relationship management
• Institutional knowledge
• Governance insight
• Communication with leadership and boards

AI can analyze patterns, flag anomalies, summarize policies and draft findings, but it can’t understand organizational culture, determine intent, navigate political sensitivities across campus stakeholders or build trust with audit committees or management teams. Internal auditors do far more than test transactions. They provide strategic insight and independent perspective.

The most effective AI implementations in higher education are human-centered; designed around auditor expertise, institutional context and professional judgment. The future is not “AI versus auditors.” It is auditors who effectively use AI versus auditors who do not.

Moving Forward: Start Small, Stay Practical

Institutions who move beyond AI hype focus on practical implementation that strengthens oversight, improves efficiency and supports better decision making. Internal audit departments that embrace AI thoughtfully can improve efficiency while strengthening their role as strategic advisors to university leadership.

Here’s a practical starting point. Choose one repetitive task from your current audit cycle (such as summarizing a policy manual or generating a testing checklist) and pilot an AI tool on a single engagement.

The James Moore Higher Education and Digital teams work with colleges and universities to evaluate practical, responsible uses of AI within their operations. Our team can assist with:

• AI readiness assessments for internal audit departments
• Development of AI governance frameworks, including data privacy and responsible use policies
• Audit data analytics strategy, including data readiness and quality assessments
• Customizing continuous monitoring and anomaly detection automation
• Higher education and athletics-specific audit considerations

Whether your department is exploring AI for the first time or looking to build more advanced data automation capabilities, we can start with a focused data and AI readiness conversation to identify your highest-impact opportunities.

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