Empowering Modern Auditors with Real-World Agentic AI
An eGuide with practical, realistic examples
For years, audit has been stuck in a cycle of compromises: limited samples, retrospective analysis, and rigid tools that demand workarounds.
Traditional audit tools work great for a recurring process—but as new risks emerge, processes need to be changed, and new ones launched, in order to keep up. This is often what makes audits so tedious, and human-driven. For auditors, it's always been assumed that this work—mapping new threats to new processes, creating new rules—is too complex and judgement-based to automate.
Agentic challenges this assumption.
Agentic AI goes beyond simple task automation by independently planning, reasoning, and taking action to achieve a goal—much like, in limited capacities, an auditor can figure out what steps to take rather than just follow instructions.
This guide illustrates how agentic AI creates an opportunity to offload parts of the audit process that traditionally required human logic, such as finding patterns, adjusting to new risks, or building test logic from scratch, while still allowing humans to retain oversight and judgment at key decision points.
Traditional audit tools work great for a recurring process—but as new risks emerge, processes need to be changed, and new ones launched, in order to keep up. This is often what makes audits so tedious, and human-driven. For auditors, it's always been assumed that this work—mapping new threats to new processes, creating new rules—is too complex and judgement-based to automate.
Agentic challenges this assumption.
Agentic AI goes beyond simple task automation by independently planning, reasoning, and taking action to achieve a goal—much like, in limited capacities, an auditor can figure out what steps to take rather than just follow instructions.
This guide illustrates how agentic AI creates an opportunity to offload parts of the audit process that traditionally required human logic, such as finding patterns, adjusting to new risks, or building test logic from scratch, while still allowing humans to retain oversight and judgment at key decision points.