For many audit teams, PBC requests and workpaper preparation remain among the most frustrating parts of the engagement. Even when firms use a portal or standardized request list, the process often dissolves into a series of disconnected steps: documents uploaded in different formats, inconsistent naming conventions, multiple versions circulating at once, and time-consuming efforts to trace evidence back to its source. The cumulative effect is a workflow burdened by manual sorting, repetitive checks, and avoidable rework. This inefficiency has long been accepted as part of the audit, but it doesn’t have to be.
As the profession explores the next generation of AI-enabled workflows, document analysis is emerging as one of the clearest areas where firms can gain immediate traction. While broader agentic AI models are still in development and represent a more future-state vision, document analysis is a tangible, near-term capability that illustrates what is possible when AI begins to take on the most manual aspects of audit documentation.
The role of document analysis today
Document analysis agents can assist with the heavy lifting that happens the moment a file arrives from a client. The agent analyzes an uploaded document, determines what type of file it is, extracts key information, and routes it into the appropriate category within the engagement. These are all tasks that auditors currently perform manually, often thousands of times across an engagement cycle.
By interpreting documents more quickly and consistently than humans can, document analysis agents can reduce the tedious work of naming, sorting, classifying, and re-checking client files. Just as importantly, agents will be able to help surface potential issues earlier. Missing pages, mismatched totals, or out-of-period items can be flagged during intake rather than during late-stage review, shifting error detection to the earliest and most efficient phase of the audit.
Although document analysis is just one component of a full agentic system, it lays the foundational building blocks for one. It demonstrates how AI can begin to take responsibility for repeatable, rules-based steps while auditors retain full control of judgment and decision-making. It also shows how a single intelligent capability can begin to reshape the experience of PBC intake from the moment a file enters the workflow.
This process consumes an enormous amount of time, and the inefficiencies compound as engagement complexity increases.
