AI Agents for Audit and Finance: A Practical Starting Point

Audit and finance teams are operating in a different reality than they were even a few years ago.

Transaction volumes are higher. Reporting requirements are broader. Regulatory scrutiny is increasing, especially around revenue recognition, leases, and ESG disclosures. At the same time, teams are expected to deliver faster, with fewer resources, and with a higher standard of documentation and defensibility.

It is no surprise that many audit and finance leaders are now asking the same question: where does AI actually fit in our work?

Why AI matters in audit and finance today

Audit and finance functions are inherently evidence-driven. Every conclusion must be supported. Every number must trace back to a source. And every process must withstand review.

That makes the industry uniquely well suited to AI, when applied correctly.

AI is not about replacing professional judgment. It is about supporting work that is already rules-based, repetitive, and time-consuming, such as:

• Matching documents to transactions
• Extracting structured data from unstructured files
• Recalculating values based on defined rules
• Identifying anomalies that warrant closer review
• Reconciling large data sets across systems

As data volumes grow, performing these tasks manually becomes increasingly risky, not because auditors lack skill, but because scale introduces complexity humans were never meant to handle alone.

Why many teams are still hesitant

Despite the potential, adoption remains cautious.

Audit and finance teams often hesitate because they are concerned about:

• Loss of transparency or traceability
• Over-reliance on black-box models
• Disruption to established workflows
• Regulatory and inspection expectations
• Trust in outputs they cannot easily validate

These concerns are valid. And they are precisely why AI adoption in this space needs to be deliberate, controlled, and auditor-led.

Where AI Agents make sense as a first step

For teams exploring AI for audit and finance, the most effective starting point is not end-to-end automation. It is assistance.

AI Agents are emerging as a practical way to support specific tasks within existing workflows helping teams scale work without changing how conclusions are reached.

Rather than replacing judgment, AI Agents can:

• Assist with document matching and data extraction
• Support larger sample sizes without added effort
• Surface outliers and inconsistencies earlier
• Reduce manual evidence gathering
• Improve consistency and traceability

Importantly, this approach keeps humans in control by reviewing, validating, and deciding at every step.

Getting started without overcommitting

For organizations that are cautious by design, the question is not whether to use AI, but how to start responsibly.

A sensible starting point is to apply AI where:

• Rules are well-defined
• Outputs are easily reviewable
• Evidence remains fully traceable
• Existing tools and workflows are preserved

This allows teams to build confidence, demonstrate value, and expand thoughtfully over time.