How to Get Started in Using Artificial Intelligence in Internal Audit

For years, the internal audit profession has watched artificial intelligence from the sidelines — intrigued, occasionally skeptical, and increasingly aware that the sidelines are not a place to stay for long. Now, with generative AI reshaping industries and predictive analytics powering risk management across functions, internal auditors are asking a new question: Where do we begin?

The answer is both simpler and more nuanced than it seems. AI in internal audit doesn’t start with buying expensive software or hiring data scientists. It starts with curiosity and a willingness to experiment.

To get there, though, internal audit teams must move beyond the hype and into the practical steps that turn AI from an abstract buzzword into a concrete capability.

The Mindset Shift: From Risk to Opportunity

Internal audit’s relationship with new technology has often been cautious, even defensive. When data analytics first entered the scene, many auditors saw it primarily as a tool to catch errors more efficiently — not as a way to change how the function operated. The same is happening now with AI.

Yes, AI introduces new risks: data quality, privacy, ethics, bias, model complexity, and something called hallucinations. But it also offers a rare opportunity to amplify internal audit’s impact — to analyze more data, generate insights faster, and engage management in smarter, more forward-looking conversations. Done right, AI can help internal audit teams tackle everyday tasks quicker leaving more time for higher level projects, such as advisory work. And, by the way, AI can help with those too!

The first step, then, isn’t technical at all. It’s cultural. Internal audit leaders must cultivate a learning mindset, one that encourages teams to explore, test, and even fail a little in controlled ways.

“You don’t need to have a perfect AI strategy on day one,” says the chief audit executive of a large financial institution I recently spoke with. “You just need to start asking what AI can do for you — not to you.”

This mindset shift reframes AI as a partner in the audit process rather than a threat to it. Once that door is open, the journey can begin.

Understanding What AI Can and Can’t Do

Before jumping in, it’s worth grounding in what “AI” really means for internal audit. The term covers a broad range of technologies — from classic machine learning models that detect anomalies, to natural language processing tools that can summarize reports, to generative AI systems like ChatGPT that can draft, analyze, and reason over text.

For internal audit, that means opportunities in three broad areas:

• Efficiency: Automating repetitive work like drafting findings, summarizing interviews, or preparing risk assessments.
• Insight: Identifying unusual patterns in data or predicting areas of emerging risk.
• Engagement: Communicating findings in clearer, more compelling ways through AI-assisted writing and visualization.

What AI can’t do (at least not yet) is replace internal auditor judgment. It doesn’t understand organizational culture, tone, or ethics the way a seasoned internal auditor does. It can support those skills, but never substitute for them. That distinction is crucial. AI is a tool that augments human insight. The internal auditors who will thrive in this new era are the ones who learn to use it as a multiplier of their professional expertise.

Start Small: The Power of a Pilot

If AI feels overwhelming, that’s because it is — until you start. The best entry point for internal audit isn’t a massive, organization-wide rollout. It’s a pilot project: something small, low-risk, and measurable. Pilots let you experiment, learn, and refine before scaling.

Here are a few ideas that have worked for early adopters:

• Report drafting assistance: Use a generative AI tool to create first drafts of audit reports or executive summaries. Feed it bullet points or notes and see how it structures the narrative.
• Control testing prioritization: Use AI or machine learning to analyze past testing results and identify which controls may have a higher probability of failure in the next cycle.
• Risk sensing: Experiment with an AI tool that scans news, social media, or internal reports to flag emerging risks relevant to your organization.
• Audit planning: Use a language model to summarize policy documents, risk registers, or board minutes to identify recurring themes.

The key is not to over-engineer the pilot. The goal is to learn what’s possible — and to discover how AI fits within your existing workflows. Once the pilot produces tangible results (time savings, improved insight, or simply enthusiasm from staff), you’ll have the momentum needed to expand.

Choose the Right Tools and Keep It Simple

The AI landscape is vast, confusing, and evolving faster than any procurement cycle can keep up with. The good news? You don’t need to buy a specialized “AI for audit” platform to get started.

In fact, most teams begin with tools they already have access to:

• Microsoft Copilot (embedded in Excel, Word, and Teams) can automate report writing, summarize data, and even generate basic risk matrices.
• ChatGPT or Gemini can support brainstorming, report drafting, and quick text analysis — provided you don’t input confidential data.
• Power BI or Tableau already offer AI-enhanced analytics like anomaly detection or natural language queries.

The guiding principle: use what’s safe, familiar, and accessible. You can always move to more advanced or specialized tools once you’ve built internal competence.

Before adoption, though, work closely with IT and data governance teams to ensure compliance with company policies. Never input sensitive data into public AI tools. Many organizations now have approved “sandbox environments” or internal AI models for experimentation — that’s the ideal place to start.

Data: The Foundation of AI

AI is only as good as the data it learns from. For internal audit, this is both an opportunity and a challenge. Internal auditors often sit on a treasure trove of structured and unstructured data — past audits, control results, issue logs, policies, meeting minutes, even emails. Yet much of that data is scattered across systems and stored in inconsistent formats.

If your function isn’t ready to run machine learning models tomorrow, that’s fine. A critical early step is simply getting your data house in order.

Ask questions like:

• Where is our audit data stored?
• Is it standardized and searchable?
• Can we easily access historical findings, ratings, or risk information?

Start building a data inventory — a map of what data you have, where it lives, and who owns it. This exercise will pay dividends long before you introduce AI, because it will improve the consistency and transparency of your internal audit process overall. Once data quality improves, AI becomes a natural next step rather than a technical leap.

Upskilling the Team

AI doesn’t replace internal auditors; it reshapes what they do. That’s why the next phase of getting started involves skills development — not necessarily in coding, but in digital literacy, prompt design, and critical interpretation.

A good starting point is simple training: short internal workshops or “AI coffee chats” or “lunch and learns” where internal auditors can test tools together and share what they learn.

Some teams are creating AI champions — one or two individuals within the department who take the lead on experimentation, document best practices, and serve as internal mentors.

Focus training on three core areas:

1. Understanding AI fundamentals: how models work, what bias looks like, and how to evaluate reliability.
2. Prompting and interacting: how to ask the right questions to get useful responses from generative AI tools.
3. Critical thinking: how to interpret AI output and integrate it responsibly into audit workpapers or reports.

Remember, the goal isn’t to turn auditors into data scientists. It’s to make them confident users and evaluators of AI-generated insight.

Governance and Guardrails

Before long, the “Can we use AI?” conversation will evolve into “How do we use it responsibly?” That’s where governance comes in. AI’s potential to amplify both good and bad outcomes means internal audit must think carefully about ethics, transparency, and accountability from the outset.

Start by establishing clear usage principles — simple, understandable guidelines for your team. Examples might include:

• Always verify AI-generated output before using it in any deliverable.
• Do not enter confidential or personally identifiable data into unapproved tools.
• Document when AI tools are used in the internal audit process and how their outputs were validated.

Internal audit functions that get ahead of these guardrails now will have an advantage later, especially as regulators and standards-setters begin formalizing expectations around AI assurance and governance.

In fact, AI governance itself is emerging as a new internal audit universe area. By experimenting internally, audit teams gain firsthand understanding of what responsible AI looks like — knowledge that will be invaluable when evaluating the organization’s own AI controls.

Internal audit doesn’t have to walk the AI path alone. In fact, some of the most successful programs are those that partner closely with other functions, such as risk, compliance, IT, data analytics, and HR.

For example, if your company’s data science team is already building predictive models for operations or customer behavior, reach out. Ask how their methods might apply to risk indicators or control monitoring. Offer to pilot a joint project. These partnerships create win-wins: internal audit gains technical insight, and the business gains a trusted partner who understands both risk and opportunity.

Moreover, collaboration builds internal credibility. When senior leaders see audit applying AI in thoughtful, responsible ways, it strengthens the function’s image as an innovative, value-adding advisor rather than a backward-looking compliance checker.

Learning from Early Adopters

Across industries, a handful of audit functions have already begun experimenting, and their experiences offer valuable lessons.

One multinational manufacturer started by training an internal AI model on past audit reports to identify recurring control weaknesses. Within weeks, the model could flag emerging risk patterns that had taken humans months to detect.

A financial services firm used generative AI to draft summaries of audit interviews. Auditors reported that the tool saved hours of note-taking time, freeing them to focus on deeper analysis.

In another example, a healthcare organization implemented AI to cross-reference policy documents with regulatory updates. What used to take two auditors a week now happens in minutes.

None of these pilots were perfect. Each required human validation and iterative refinement. But they all demonstrated the same point: the path to meaningful AI use starts small and grows through experimentation.

Once a pilot succeeds, the temptation is to rush toward full adoption. Resist that impulse.

Scaling AI within internal audit requires careful planning — not just technically, but strategically. Ask:

• How will AI outputs integrate into our existing audit methodology?
• What controls will ensure quality and reliability?
• What training will be needed as we expand use cases?
• How do we measure success beyond time savings — for example, improved insight or stakeholder satisfaction?

Think of AI adoption as a maturity curve, not a project. Early stages focus on experimentation and learning; later stages emphasize governance, integration, and optimization. As you move up that curve, consider formalizing your efforts into an AI roadmap — a simple plan that outlines goals, milestones, and accountability. It doesn’t need to be complicated; it just needs to guide consistent progress.

The Human Element

For all the technology talk, AI’s success in internal audit ultimately comes down to people. Internal auditors are, at their core, truth-seekers — professionals trained to ask hard questions, test evidence, and think independently. Those same qualities are exactly what make them valuable in the age of AI. AI may write faster or analyze more data, but it lacks intuition, ethics, and context, which are the very things that define internal audit’s professional identity.

The future isn’t AI versus internal auditors. It’s internal auditors who know how to use AI versus those who don’t. And that’s a skill gap every organization can close with time, curiosity, and guidance. As one internal auditor recently said at a major professional conference: “It’s not that AI is going to put internal auditors out of their jobs. But it may very well put internal auditors who don’t know AI in the unemployment line.”

So, if you’re reading this and wondering, Where should I actually start? Here’s a roadmap in plain language:

1. Get curious. Pick one AI tool (like ChatGPT or Copilot). Use it to summarize an audit report or brainstorm risk themes.
2. Talk to IT. Find out what’s approved and what data can safely be used.
3. Run a pilot. Choose one task and test whether AI saves time or improves quality.
4. Document the results. Note what worked, what didn’t, and what surprised you.
5. Share the story. Talk about your experiment with peers or leadership. Curiosity is contagious.

That’s it. That’s the first mile of the AI journey. It’s not a revolution, but a steady walk toward new capability.

The New Frontier of Assurance

As AI becomes more embedded in organizations, internal audit’s mandate will expand again, from using AI in audit to auditing AI itself.

AI models that drive credit decisions, supply chain forecasts, or HR screening will all require assurance around fairness, transparency, and governance. Audit teams that experiment now will be the ones best positioned to provide that oversight later. In that sense, learning AI today isn’t just about improving efficiency. It’s about future-proofing the profession.

AI won’t transform internal audit overnight. But it will, inevitably, reshape the profession, much as data analytics did a decade ago. The question is not if but how quickly each audit function adapts.

The good news is that internal auditors already have the right instincts for this transformation. You’re trained to evaluate evidence, test controls, and challenge assumptions. Those same skills make you uniquely equipped to explore AI with a healthy balance of curiosity and skepticism.

Imagine an internal audit function that can proactively identify emerging risks, provide real-time assurance, and offer truly predictive insights that shape strategic decision-making. This is the future AI promises. It’s a future where auditors are freed from mundane tasks, empowered to ask deeper questions, and positioned as true strategic advisors.

The journey may seem long, but every great journey begins with a single step. For internal audit, that step is often a small, focused pilot project, a commitment to learning, and a willingness to embrace the intelligence revolution. The time to start your AI awakening is now. The value it can unlock for your organization, and for the internal audit profession itself, is simply too significant to ignore. 

Joseph McCafferty is editor & publisher of Internal Audit 360°