How Internal Audit Can Begin Leveraging AI: A Practical Guide

The Mindset Shift: From Risk to Opportunity

Internal audit's traditional caution with new technology is understandable, but AI presents a unique opportunity to enhance the function's impact. While AI introduces risks like data quality, privacy, and bias, it also offers the chance to analyze more data, generate insights faster, and engage in more forward-looking conversations with management. The initial step isn't technical; it's cultural. Internal audit leaders must foster a learning mindset, encouraging teams to explore, test, and even fail in controlled environments.

Understanding What AI Can and Can't Do

For internal audit, AI encompasses various technologies, from machine learning for anomaly detection to natural language processing for summarizing reports and generative AI for drafting and analysis. These tools can enhance:

• Efficiency: Automating repetitive tasks like report drafting or interview summaries.
• Insight: Identifying unusual data patterns or predicting emerging risks.
• Engagement: Improving communication of findings through AI-assisted writing and visualization.

However, AI cannot replace human judgment, understanding of organizational culture, or ethical considerations. It serves as a tool to augment human insight, not substitute it.

Start Small: The Power of a Pilot

The most effective way to begin with AI is through small, low-risk, and measurable pilot projects. These allow for experimentation, learning, and refinement before scaling. Examples of successful pilot projects include:

• Using generative AI for drafting audit reports or executive summaries.
• Prioritizing control testing by analyzing past results to identify high-risk areas.
• Employing AI tools to scan external and internal sources for emerging risks.
• Summarizing policy documents or risk registers for audit planning.

The goal of a pilot is to understand AI's potential within existing workflows and build momentum for broader adoption.

Choose the Right Tools and Keep It Simple

Internal audit teams don't need specialized AI platforms to start. Many can begin with tools they already have, such as:

• Microsoft Copilot for automating report writing and data summarization.
• ChatGPT or Gemini for brainstorming and text analysis (with caution regarding confidential data).
• Power BI or Tableau for AI-enhanced analytics like anomaly detection.

Collaboration with IT and data governance teams is crucial to ensure compliance and safe usage, ideally within approved sandbox environments.

Data: The Foundation of AI

AI's effectiveness hinges on the quality of data. Internal audit possesses a wealth of data, but it's often scattered and inconsistent. A critical early step is to organize and standardize this data. Creating a data inventory helps map out available data, its location, and ownership, which improves overall internal audit processes even before AI is fully implemented.

Upskilling the Team

AI will reshape the internal auditor's role, necessitating new skills in digital literacy, prompt design, and critical interpretation. Training should focus on:

• Understanding AI fundamentals, including bias and reliability.
• Effective prompting for generative AI tools.
• Critical thinking to interpret AI output responsibly.

The aim is to empower auditors as confident users and evaluators of AI-generated insights, not to turn them into data scientists.

Governance and Guardrails

Responsible AI usage requires clear governance and ethical guidelines. Internal audit functions should establish principles such as:

• Always verifying AI-generated output.
• Avoiding input of confidential data into unapproved tools.
• Documenting AI tool usage and validation processes.

Proactive establishment of these guardrails will position internal audit to provide assurance on AI governance as it becomes more prevalent within organizations.

Learning from Early Adopters and Scaling

Successful AI adoption involves learning from others and strategic scaling. Examples from other organizations show AI's potential in identifying control weaknesses, drafting interview summaries, and cross-referencing policy documents. When scaling, consider how AI outputs integrate into existing methodologies, what controls ensure quality, and how success is measured beyond just time savings. Developing an AI roadmap can guide consistent progress.

The Human Element and Future-Proofing

Ultimately, AI's success in internal audit depends on people. Internal auditors' core skills of critical thinking, evidence evaluation, and independent judgment are invaluable in the age of AI. The future involves auditors who can effectively leverage AI, not those replaced by it. Learning AI today is not just about efficiency; it's about future-proofing the profession and expanding internal audit's mandate to include auditing AI itself, ensuring fairness, transparency, and governance in AI models across the organization.

The journey begins with curiosity, small pilot projects, and a commitment to continuous learning, unlocking significant value for both the organization and the internal audit profession.