6 Questions Audit Teams Ask Before Trying Agentic Automation

We know that AI is becoming more capable of performing work that used to take auditors hours in minutes, from reviewing documents and reconciling figures to identifying exceptions across large volumes of evidence.  

But in audit, completing a task faster is only part of the challenge. The result still needs to be accurate, traceable, and supported by evidence that an auditor can independently verify. AI is only as valuable as the work you can verify. Speed means little if auditors can't see where an answer came from, understand how it was reached, or confidently stand behind the conclusion. 

Instead of simply asking AI a question and receiving an answer, auditors can delegate defined tasks to AI agents. Those agents can work through documents, compare information, identify exceptions, and connect their findings back to the underlying evidence, while auditors retain oversight and final judgment. 

But understanding what technology can do is one thing. Knowing whether it will work for your team is another. Does it make sense for smaller firms? Can it handle one-off engagements as well as recurring audits? And what happens when evidence is scattered across different documents, systems, and client workflows? 

So rather than focusing on what AI could do in theory, let's tackle six of the questions we keep hearing in practice. 

1. Does agentic automation make sense for firms of different sizes?

It's easy to assume that agentic AI is primarily for the largest audit firms, where high engagement volumes and dedicated technology teams make automation easier to justify. But firm size alone doesn't tell you much about the potential value; it's about the work being done. How much time does your team spend gathering evidence, testing transactions, reconciling information, or repeating procedures across engagements? And how much of that work could be delegated without compromising audit quality? 

Start with the procedures consuming the most time, rather than the size of your organization. 

2. Is agentic automation only useful for recurring audits?

Repeating the same procedures creates opportunities to save time across multiple engagements. But one-off audits can be just as demanding when it comes to reviewing documents, reconciling figures, and verifying evidence. 

The distinction is that agentic automation doesn't need an entire engagement to be repeatable. It needs clearly defined tasks, evidence requirements, and review criteria. 

The question is still worth asking: how much manual work could be reduced within this particular engagement? 

3. Does agentic automation make sense when you have lots of smaller clients?

Smaller engagements often come with tighter budgets, but the underlying verification work doesn't always shrink proportionally. Teams still need to collect evidence, check transactions, and document their conclusions, often across dozens of clients with different systems and processes. 

That's where agentic automation could make a difference. Reducing even a modest amount of manual work per engagement can add up across a large client portfolio. 

Of course, the economics depend on the procedures involved and the effort required to set them up. The opportunity isn't just in automating large audits. It's in making smaller engagements more efficient without compromising the quality of the work. 

4. What documents and systems can agentic automation work with?

Audit evidence rarely arrives in one neat format. It's spread across Excel spreadsheets, PDFs, Word documents, and information provided by different clients and teams. 

Working with different file types is important, but it's only part of the equation. What matters just as much is the scale at which AI agents can process evidence, how accurately they interpret it, and whether their conclusions are grounded in the right source material. After all, being able to read hundreds of documents means little if an auditor can't trust the result or trace it back to the evidence.  

But there's an important distinction between supporting a document format and integrating directly with a system. Before getting started, teams should check which files an agent can process, how evidence is accessed, and whether the results remain traceable and easy to review. 

5. Can AI agents help with follow-ups, action tracking, and reporting?

Verification doesn't end when the evidence arrives. Internal audit teams still need to identify missing information, investigate exceptions, document findings, and follow up with stakeholders. 

AI agents can help with parts of this process by requesting supporting documents, checking evidence against defined criteria, and flagging exceptions for review. Just as importantly, they can keep findings connected to their sources, making it easier for auditors to understand and defend the results. 

That doesn't mean every follow-up, remediation action, or report can be managed automatically.

The real value is reducing the manual work around verification, so auditors can focus on resolving issues and making judgments. 

6. What if our internal audits are too bespoke for automation?

Internal audits rarely follow a single template. Different risks, business units, and evidence requirements mean that even similar engagements can involve very different procedures. 

But a bespoke audit doesn't mean every task within it is unique. Evidence collection, document checks, reconciliations, and exception reviews can still follow clear instructions. 

With agentic automation, auditors define the objective, scope, and boundaries of the work. AI agents can then support specific verification tasks, while professionals retain responsibility for interpreting findings and signing off on conclusions. 

The goal isn't to standardize every audit. It's to automate the work that can be clearly defined, without losing the flexibility that internal audit requires. 

Moving from AI potential to practical value

Agentic automation works when agents take on the time-consuming verification work, the evidence stays visible, and auditors stay in control. As these six questions show, getting there takes a clear understanding of the workflows, evidence, and professional judgment involved. 

I think the next few years will raise the value of auditor judgment. When agents handle the mechanical work, the auditor’s job gets bigger, because they spend their time where judgment counts. That only holds if every result traces back to its source.