Designing an AI Governance Policy for Finance Teams

An AI governance policy for finance teams needs a concrete starting point, not abstract principles. That starting point is whatever regulation already governs the data your organization handles, before AI enters the picture at all. 

Start With the Regulation That Already Governs You

Every AI governance policy needs to point back to that existing regulation and build on top of it. A university handling student records has to account for FERPA, and a healthcare organization handling patient data answers to HIPAA. A municipality carries its own state and local reporting requirements on top of whatever federal guidance touches its grant funding. Building your AI use cases on top of that baseline, instead of treating AI governance as a separate track, is how you stay compliant instead of writing policy in a vacuum. 

Decide Who Owns the Policy Before You Write It

Ownership tends to land differently depending on how a company is structured. IT often ends up in the mix by default, since anything touching data privacy, security or system access usually sits there already. Larger organizations sometimes create a dedicated AI leadership role, but a lot of mid-size firms don’t have the headcount for that, which means responsibility often splits between IT and the CFO’s office.

What matters more than the org chart line is that somebody owns writing and updating the policy, running the training that goes with it and answering questions when people aren’t sure how it applies to them. If IT owns AI governance without real input from the CFO’s office, two problems show up fast. Either the policy misses something specific to how accounting and finance data works, or the CFO’s team never adopts the tools the way leadership wanted, because they weren’t part of setting the rules in the first place. James Moore Digital’s approach to AI governance controls starts from that same premise. The controls environment your finance team already has is the foundation for the AI policy, expanded to cover how AI touches those same processes. 

What Human in the Loop Looks Like Day to Day

Every automated process needs some level of human oversight, but how much depends on the risk of what’s being automated. That’s the core of the DIO model, the framework built around scoring detection risk and impact together to determine the right level of oversight for a given process.

Take an internal audit function using AI to speed up sample selection for a fraud-related test. The human has already decided where the risk sits and how big the sample needs to be, then hands the selection task to AI, which pulls supporting documentation and flags which items match the criteria before the human performs the testing. That’s a low-risk use of automation because the human never left the loop, just moved from doing the mechanical work directly to reviewing the AI’s output before acting on it.

The mistake is trying to automate an entire process end to end instead of identifying which specific piece is worth handing off. Start with the portions least likely to be missed if something goes wrong, and build from there.

Design Audit Trails From the Start

When an examiner asks to see how AI was used in a reconciliation or a forecast, “we followed the policy” isn’t an answer on its own. The policy needs to require that the AI component log what it did, including timestamps, successes and failures. When a human overrides an AI output, that override and the reason behind it need documentation too.

Handled well, this approach gives finance teams more visibility into what happened than a fully manual process ever produced, since the requirement to log gets built into the automation from the start instead of relying on someone remembering to write it down. That has to be planned into the technical specifications up front. Retrofitting an audit trail after the fact rarely produces the same quality of documentation.

Where Policy Written on Paper Falls Apart in Practice

The most common failure shows up after the policy is written, when real life stops matching what’s on paper. Oversimplifying the process is the first problem. Ideal-world policies rarely account for what happens when the one person approved to sign off is out sick or traveling, and if someone else steps in without documenting why, the organization is now breaking its own policy without a record of it.

Segregation of duties creates a second, less obvious risk once AI enters the picture. If an AI agent takes over part of one person’s role and another person controls or works alongside that agent, the organization can end up recreating the exact access problem the original segregation was designed to prevent. The policy has to account for what access the AI agent itself carries, since that access can quietly extend to whoever is operating it.

Training is the third gap. Writing a strong policy doesn’t guarantee people follow it without training, and the same logic applies to AI, though the training looks different. Rather than walking a model through a webinar, the work is testing outputs, monitoring for drift and adjusting the skill files or instructions the AI is working from when its behavior stops matching the policy. You’re training the AI the same way you’d retrain a person whose work started drifting from the standard.

Set the Big Picture Before You Draft a Single Policy

Before any of this gets written down, it helps to step back further. Start by naming the organization’s real goals for using AI. Then map what kinds of data are involved and whether any of it carries its own compliance requirements, including whether AI is arriving through third-party software already in use. From there, decide how much decision-making authority can sit with managers before something has to go to a committee or the C-suite.

The piece that gets skipped most often is cost-benefit. AI shouldn’t get adopted because competitors are doing it. It needs a business purpose with a return you can measure, track and revisit if the numbers start moving the wrong direction. A policy built around all of this from the start holds up. Skip these questions and the policy tends to need a rewrite every time reality doesn’t match the plan.

Build a Governance Policy Your Team Will Follow

Designing an AI governance policy for finance teams comes down to giving your team a clear, workable structure for who owns what, how much oversight each process needs and what happens when something goes wrong. James Moore Digital helps finance leaders build governance shaped around how their team operates, in an industry regulators haven’t necessarily written the rulebook for yet.  Visit James Moore Digital to talk through where your policy stands today.

 

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