5 Ways Generative AI Is Changing Internal Audit Work

If you’ve spent any time in internal audit, you already know the pressure. 

You’re expected to cover more risk areas every year. You’re working with the same headcount you had two years ago. And somewhere between growing portfolios and rising inspection demands, you’re supposed to produce audit work that can survive a regulator’s questions, an audit committee review, and the professional skepticism of everyone in the room. 

How do you close that gap? How do you move faster without cutting corners or compromising the conclusions you’re responsible for?

Generative AI won’t fix a broken resource model overnight. But if you deploy it with intention, it changes what’s actually possible within the hours you have. 

These five use cases lay out exactly where generative AI fits into your audit workflow. For teams considering more tailored automation beyond off-the-shelf tools, Launchpad Lab offers AI agent development services that can help turn specific audit workflows, review processes, and internal knowledge systems into more efficient AI-powered solutions.

Audit Planning and Risk Scoping

Before fieldwork even starts, you’re already buried. The pre-engagement workload quickly mounts and a large portion of it is work that doesn’t require your best judgment. Examples include scoping memos, audit announcements, background research on business units and interview guides. All it takes is time which you don’t have. 

This is something generative AI does well. Give it the appropriate process context and it will generate a draft interview guide or scoping memo from which you can actually work. More usefully, it can simultaneously draw from a variety of data sources such as operational signals, financial trends and previous findings to provide you with a more comprehensive picture of the risks true location before allocating resources. 

This is especially important if you collaborate with internal audit consultants: AI provides a better starting point for all parties involved in the engagement and it does so more quickly than any manual preparation method would. You still have the final say over where to focus your attention. AI simply provides you with better inputs. 

Document Extraction and Controls Testing

You’ll likely feel the most relief here. Data extraction from contracts invoices and GL records has traditionally been one of the slower aspects of fieldwork—not because it’s difficult but rather because it’s repetitive and there is a lot of it. That timeline can be greatly shortened by AI. 

Additionally, it can directly link extracted evidence to particular test steps so by the time you’re ready to document the relationship between your findings and their implications has already been established. Instead of starting from scratch, you are checking and verifying. It’s not just speed that this provides. More test coverage is achieved with the same amount of work. By testing the entire population you can gain a level of confidence that is never possible with sampling alone. 

Journal Entry Analysis and Anomaly Detection

Because it’s really hard to know when you’ve done enough manually, searching a transaction population for anomalies is easy to overdo. What sticks out is flagged. You examine what appears strange. 

However, you are working with a human-scale perspective on a dataset that is frequently not human-scale. The equation is altered by AI. When you run it over your journal entry population it highlights transactions that merit further investigation such as duplicate vendors’ peculiar approval sequences and entries that meet the materiality threshold at a strangely regular rate. You have ceased your search. You’re sorting through a list of priorities and determining which ones need further attention. 

The final product still requires your opinion. AI recognizes trends. You determine if those patterns have any significance. It is precisely this division of labor that makes this use case possible. 

Workpaper Documentation and Reporting

Documentation expands to fill whatever time you give it, and it’s rarely the part of the job that generates real professional value. The value is in what you found and what it means. And that is documented in the workpaper. 

When you record walkthroughs, you can use a secure internal AI tool to run the transcript and receive a draft document in a matter of minutes. Your senior reviewer can focus on the content of the discussion rather than whether or not the notes accurately reflect it. 

Besides that, you’ll discover that AI contributes to reducing the output quality disparity between junior and senior employees. Since the baseline is already there to build from rather than because AI is doing the thinking more, newer team members will be able to produce documentation that is closer to the level you’d expect from someone with more experience. 

Continuous Monitoring and Controls Surveillance

Traditional audit is a point-in-time exercise. You cover a period, reach conclusions, and the next cycle starts months later. The controls environment keeps changing in between.

Continuous monitoring is where AI makes the most impact if your company views risk assessment and internal audit as an ongoing discipline. It can perform rolling analysis of transactional data and operational signals identifying possible control failures as soon as they appear rather than waiting six months for the next scheduled engagement to reveal them. 

Keep in mind that replacing the audit cycle is not the goal here. The goal is to fortify the areas in between cycles. You don’t have to wait for the next audit to discover what changed because you can see how the controls environment is changing in real time. 

Wrapping Up

The key to successfully implementing any of these use cases is understanding where AI truly performs and developing a review procedure prior to scaling. You must be aware of who reviews AI output before it is included in a workpaper, what happens if something doesn’t seem right and where human approval is located in the workflow. You can’t control AI without it. 

But that’s no excuse to wait for ideal circumstances to solve that. Start with a high volume and a clear review path, then build from there. The functions with the most advanced tools are not the ones experiencing the most significant outcomes. They were the ones who built the governance protocols first and allowed these use cases to abide by them.