Published: November 6, 2026
Author: Thilo Richter – Chief Product and Technology Officer at DataSnipper
Every year, auditors spend approximately 6 billion hours on manual verification tasks, including data extraction, document reconciliation, requesting and following up on evidence, and maintaining audit documentation.
Meanwhile, the number of professionals available to perform these tasks is declining, while the volume of evidence and the scope of regulatory requirements they must examine continue to grow. This widening gap between the amount of work requiring verification and the capacity to perform it is what we call the verification gap.
We all know that AI is becoming increasingly capable of completing tasks in minutes that previously took auditors hours, from reviewing documents and reconciling figures to identifying exceptions across large volumes of evidence.
However, in auditing, completing tasks faster is only part of the challenge. Results must still be accurate, traceable, and supported by evidence that auditors can independently verify.
The value of AI depends on the ability to verify the work it performs. Speed means little if auditors cannot determine where an answer came from, understand how a result was produced, or confidently take responsibility for the conclusion.
At DataSnipper, when designing Agentic AI experiences, we always ensure that auditors can quickly understand how an answer or conclusion was reached.
Rather than simply asking AI questions and receiving answers, auditors can delegate clearly defined tasks to AI Agents. These Agents can process documents, compare information, identify exceptions, and link findings back to the original evidence, while auditors retain oversight and responsibility for the final assessment.
However, understanding what the technology can do is one thing. Knowing whether it is suitable for your team is another.
Is Agentic Automation suitable for smaller audit firms? Can it handle both one-off and recurring audits? And what happens when evidence is scattered across multiple documents, systems, and client workflows?
These are the questions we are exploring together with Microsoft. They are also the questions audit teams consistently raise in our conversations about bringing Agentic Automation into real-world audit workflows.
So, rather than focusing on what AI might theoretically deliver, let's explore six questions we frequently encounter in practice.
1. Is Agentic Automation suitable for audit firms of different sizes?
It is easy to assume that Agentic AI is primarily intended for the largest audit firms, where high engagement volumes and dedicated technology teams make it easier to justify investments in automation.
However, firm size alone does not fully reflect the technology's potential value. What matters most is the nature of the work being performed.
How much time does your audit team spend collecting evidence, testing transactions, reconciling information, or repeating procedures across engagements? And how much of that work could be delegated to AI without compromising audit quality?
At RSM Cayman, Excel Agents helped reduce year-end expense and investment testing processes from 4–6 hours to approximately 30 minutes.
At FGMK, testing time per selected sample decreased from 30–45 minutes to approximately 5 minutes in a complex, high-volume testing process.
Start with the audit procedures that consume the most time, rather than focusing on the size of your organization.
2. Is Agentic Automation only useful for recurring audits?
Repeating the same audit procedures creates opportunities to save time across multiple engagements.
However, one-off audits can also involve substantial work in reviewing documents, reconciling figures, and verifying evidence.
The key difference is that Agentic Automation does not require the entire audit engagement to be repetitive. It only requires clearly defined tasks, evidence requirements, and review criteria.
Therefore, the question remains worth asking: How much manual work can be reduced within this particular audit engagement?
3. Is Agentic Automation suitable for audit firms with many small clients?
Smaller audit engagements often come with more limited budgets, but the volume of essential verification work does not always decrease proportionately.
Audit teams still need to collect evidence, test transactions, and document their conclusions, often across dozens of clients with different systems and processes.
This is where Agentic Automation can make a difference.
Even relatively small reductions in manual work per engagement can generate significant cumulative benefits across a large client portfolio.
Of course, the economic benefits depend on the audit procedures involved and the effort required to configure them.
The opportunity lies not only in automating large audit engagements but also in improving the efficiency of smaller audits without compromising work quality.
4. What types of documents and systems can Agentic Automation work with?
Audit evidence rarely arrives in a single, well-organized format.
It is often scattered across Excel spreadsheets, PDF files, Word documents, and information provided by different clients or teams.
The ability to process multiple file types is important, but it is only part of the challenge.
Equally important are the scale at which AI Agents can process evidence, the accuracy with which they interpret information, and whether their conclusions are genuinely supported by relevant source documents.
Ultimately, the ability to read hundreds of documents means little if auditors cannot trust the results or trace them back to the original evidence.
However, it is important to distinguish between supporting a document format and integrating directly with a system.
Before getting started, audit teams should verify which file types AI Agents can process, how evidence is accessed, and whether the results remain traceable and easy to review.
5. Can AI Agents support follow-up activities, action management, and reporting?
Verification work does not end when evidence is provided.
Internal audit teams still need to identify missing information, investigate exceptions, document findings, and continue following up and communicating with relevant stakeholders.
AI Agents can support parts of this process by requesting supporting documentation, checking evidence against predefined criteria, and flagging exceptions for auditor review.
Just as importantly, AI Agents can maintain links between findings and their supporting evidence, making it easier for auditors to understand and substantiate audit results.
However, this does not mean that all follow-up activities, corrective actions, or reporting processes can be fully automated.
The real value lies in reducing the manual work associated with verification, allowing auditors to focus on resolving issues and exercising professional judgment.
6. What if our internal audits are too specialized to automate?
Internal audits rarely follow a single standardized approach.
Differences in risks, business units, and evidence requirements mean that even similar audit engagements may involve substantially different procedures.
However, a customized audit does not necessarily mean that every task within it is unique.
Activities such as collecting evidence, reviewing documents, reconciling figures, and examining exceptions can still be performed according to clearly defined instructions.
With Agentic Automation, auditors define the objectives, scope, and boundaries of the work. AI Agents can then support specific verification tasks, while professionals remain responsible for interpreting findings and approving final conclusions.
The goal is not to standardize every audit.
Instead, it is to automate clearly definable tasks without sacrificing the flexibility required in internal auditing.
From AI potential to practical value in auditing
Agentic Automation delivers value when AI Agents take on time-consuming verification tasks, evidence remains verifiable, and auditors retain control.
As the six questions above demonstrate, achieving this requires organizations to understand their workflows, evidence requirements, and the professional judgments involved.
I believe that the value of auditors' professional judgment will continue to grow over the coming years.
As AI Agents take over mechanical tasks, the role of auditors becomes even more important because they can devote more time to work that genuinely requires professional judgment.
However, this is only possible when every result can be traced back to its source.
EGP Vietnam
DataSnipper consulting and implementation partner in Vietnam
Contact us to schedule a live demo:
Thai Nguyen
0912 500 085
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6 Questions Audit Teams Ask Before Trying Agentic Automation
