Audit workflows and agentic AI: How to make your process more effective

Learn how agentic AI transforms audit team members from document chasers into decision-makers

Quick: Name the most painful part of the traditional audit process.

Is it chasing documents and then going back for Round 2 after realizing the client uploaded the wrong form? How about asking clients for access to password-protected files? Or is it emailing the client yet again to get answers to questions that are holding up the entire process?

All these friction points slow down the audit timeline, creating delays that ripple across the entire process.

The good news is that there is a better, saner way to conduct an audit. The answer is agentic AI. When agentic AI is deployed smartly, it transforms audit teams from document chasers to judgment-driven reviewers.

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First things first: What is agentic AI?

Unlike generative AI tools that wait for a user to ask a question, agentic AI is more proactive. These tools can manage multiple tasks independently, with human supervision. They include:

• Taskers: Useful for automating low-value, repetitive tasks
• Automators: Capable of running entire processes end-to-end
• Collaborators: Offer intelligent guidance during complex workflows
• Orchestrators: Function as project managers, coordinating the work of multiple agents to deliver an outcome

Where in the audit process is agentic AI most helpful?

Agentic AI excels at time-intensive tasks. It is especially useful at the beginning of the audit workflow, but can assist auditors throughout the process.

It replaces fragmented workflows with connected, insight-driven processes, improves the overall collaborative experience for the client, and paves the way for a smoother, faster path to the “ready for review” stage.

Here are a few examples of how to utilize agentic AI during each stage of the traditional audit workflow:

Transform onboarding from manual, fragmented processes to an intelligent workflow

• Automate, organize, and standardize client onboarding, eliminating manual entry and inconsistencies by pulling data straight from the source.
• Automatically populate systems and workpapers, reducing manual setup tasks.
• Intelligently assemble binders with auto tagging and organization, removing the need for cross-referencing.

Result: The audit binder is transformed from a storage folder into a connected, structured knowledge base.

Build a real-time, data-driven foundation for task risk assessment

• Use current and historical client data and external research to build risk-based audit plans.
• Analyze entire data sets to uncover patterns and anomalies before fieldwork begins, assigning risk scores that trace back to source evidence and triggering PBC requests for additional data.
• Generate a targeted audit plan and control documentation.

Result: The auditor shifts from data gatherer and document writer to critical thinker. Meanwhile, audit planning becomes grounded in actual data rather than assumptions.

Take the pain out of the data collection and ingestion phase

• Generate tailored PBC request lists based on specific engagement, identified risks, and client characteristics.
• Send automated reminders to clients to submit necessary documents and track client data submission in real time.
• Ingest data from multiple formats and sources such as Excel, PDF, or OCR files, saving auditors from having to manually reformat or consolidate data before analyzing it.

Result: The data collection process goes from manual bottlenecks to an intelligent, streamlined, and proactive process.

Transform the audit execution and fieldwork phase

• Intelligently extract key data from client documents such as invoices, contracts, and schedules.
• Flag exceptions and anomalies in real-time and trigger requests for supporting evidence.
• Execute testing procedures from start to finish.

Result: Auditors can focus on applying expert judgment rather than manual work.

Make sure data is truly ready for review and sign-off

• Perform automated pre-review checks to ensure all procedures are complete, all required documentation is present, and the audit is compliant.
• Identify gaps or inconsistencies in workpapers.
• Ensure no disclosure or compliance requirements are missed.

Result: Faster, higher-quality reviews, over which the expert retains full control and responsibility

Reduce the manual effort required to finalize the audit

• Automatically organize, categorize, and index workpapers and documents according to firm standards.
• Verify that all procedures are completed, sign-offs and approvals are captured, and checklists are finished.
• Create a locked, auditable final file, maintaining version control and a full audit trail.

Result:  A more structured, automated, and forward-looking process that satisfies compliance and audit defense needs while preparing for future audits

Which audit work should not be left to AI?

Although agentic AI can be very helpful during the audit process, there must always be an expert in the loop. AI cannot and should not replace the judgment of skilled auditors, sign off on audits, guarantee perfect accuracy, or fully automate entire audits. This means that human auditors remain responsible and accountable for their conclusions and, as a result, need to review and validate the final results output.

Regulatory and risk considerations

To ensure that AI audit tools can pass regulatory muster, the following must be in place:

• Accountability. Full responsibility lies with the experts who sign off on an audit.
• Explainability. AI output needs to be traceable, source-linked, and reviewable.
• Audit trail integrity. Full logging of all actions is required, just like in a traditional audit. AI tools cannot be allowed to call audibles.
• Data security. Adequate safeguards are necessary to prevent confidential client data from being leaked into public AI models.

In short, AI itself must be auditable.

What to know before implementing agentic AI in the audit process

Keeping with the human oversight theme, establishing proper governance should always be the first step in implementing AI.

Step 1: Lay the groundwork

Avoid headaches later by setting firm standards early on.

• Identify and procure secure tools for handling confidential client data.
• Codify data usage policies.
• Decide which audit duties will and will not be entrusted to AI.

Step 2: Ensure data readiness

AI is only as good as the data it is being asked to process. So before implementing it, centralize document access and clean all structure and unstructured data.

The connected audit:  Learn how to create a strategic framework for agentic workflows

Step 3: Skill up

AI implementation is not merely an information technology department initiative. To succeed, it needs firm-wide buy-in, from managing partners down to first-year staff.

Before investing thousands or millions of dollars in technology, firm leadership should consider this crucial point: AI tools do not automatically add value all on their own.

Powerful tools need to be paired with parallel training to prevent frustration and adoption resistance.

How to begin experimenting with AI audit tools

It is perfectly acceptable to start small and implement AI in phases.

Provided it can be done safely without compromising data security, consider choosing a single, high-friction part of the audit workflow as an AI pilot project.

Prime candidates for this type of automated work include Prepared by Client (PBC) coordination, evidence extraction, or risk summaries.

Once the work is complete, human auditors should check the AI output for accuracy.

Measure ROI by keeping track of time saved, reduction of rework, and quality improvements, in addition to anecdotal metrics like staff feedback.

Endgame: Letting humans do what they do best

The point of implementing agentic AI into the audit process is not to replace humans with machines.

The ultimate goal is to offload the painful drudge work, letting human auditors use their knowledge and experience to better serve their clients’ and firms’ future needs.