By now, 88% of organizations have started formally using AI in at least one business function. While most finance leaders recognize the potential of this technology, many remain cautious about implementing it. And that’s understandable.

Finance teams deal with sensitive data, work under strict regulatory requirements, and are on the hook for maintaining accuracy, control, and audit readiness across every process. A hasty AI implementation, no matter the scope, can introduce serious risk, ranging from data exposure to regulatory violations to unreliable financial reporting.

But that doesn’t mean finance organizations should avoid AI altogether. When governance, oversight, and accountability are front and center, safe AI implementation in finance is very much possible.

In this guide, we’ll share a practical framework for finance leaders looking to safely implement AI. We’ll walk through key governance principles, guidance for evaluating AI vendors, and a phased approach that enables finance teams to realize AI’s benefits without increasing organizational risk.

Why AI has become essential for modern finance teams

In a few short years, AI has gone from an experimental technology to one that’s widespread. Organizations that implement it safely and strategically see major gains, ranging from less manual work and greater efficiency to faster innovation and better decision-making. 

For finance in particular, AI has become essential. Today, half of finance teams manage 5,000 or more invoices every month, arriving in formats ranging from printouts to PDFs to EDI feeds. As volume grows and complexity increases, many teams are expected to meet the same tight deadlines and maintain high levels of accuracy and control. And they’re expected to do it all with lean teams and manual processes that weren’t built to scale.

AI has quickly become the key to keeping pace with growing workflow demands. It can automate repetitive tasks such as invoice capture, coding, matching, and anomaly detection, freeing up time for strategic work. And, unlike manual work, it can scale as invoice volume and business demands continue to grow.

But while AI can drive real results in finance, teams must ensure it’s implemented responsibly, securely, and in compliance with requirements. 

Why AI adoption is especially risky for finance organizations 

Organizations don’t always have visibility into how AI models use data and produce outputs, which exposes them to risks, including data exposure, compliance violations, poor decision-making, and poor auditability. 

The risk is particularly high in finance, where teams often handle highly sensitive data, including banking information, employee records, payment details, purchasing data, and other forms of personally identifiable information (PII). If an AI system improperly uses or exposes any of that data, the organization can face some serious consequences.

At the same time, finance teams are subject to a long list of regulatory and reporting requirements. Depending on the business and location, that might include SOX controls, GDPR requirements, SEC reporting obligations, industry-specific retention rules, and internal audit standards. Any AI tool that touches financial workflows must be aligned with those governance structures. 

What’s more, explainability and audit trails are especially critical in finance. Finance leaders can’t rely on “black box” AI systems that produce outputs without any insight into how they were generated. Leaders need to know the rationale behind outputs, as well as clear, real-time records of what happened, why it happened, and who approved it. 

There’s no denying that AI can be especially risky in finance organizations. But with the right governance framework, safe AI implementation is within reach.

The four pillars of an effective finance AI governance framework

An AI governance framework is a set of policies, controls, and processes that help ensure finance organizations use AI safely, responsibly, and in alignment with their financial, regulatory, and operational requirements. 

There are four key pillars of an effective finance AI governance framework. 

1. Data access controls

Finance AI systems are only as secure as the data environments they live within. Strong data access controls help ensure sensitive financial information is only available to the right people, systems, and workflows – no more and no less. These controls include defining role-based permissions, limiting access to PII, and developing clear policies around how data is stored, shared, and retained. 

Strong data access controls are especially important when organizations adopt AI tools that integrate with ERP systems, AP workflows, procurement tools, and payment platforms. These systems all contain sensitive data, and without the right controls, it could be exposed.

For example, consider a finance team implementing an AI assistant that can summarize invoice and payment data across systems. If permissions aren’t properly configured, an employee outside of the finance team could potentially access sensitive information by asking the assistant a question. The employee isn’t necessarily trying to access this information for malicious intent. But because there wasn’t proper governance during implementation, they can access information they shouldn’t be able to.  

Finance teams are used to applying strict controls to payment approvals, user permissions, and segregation of duties. AI systems must follow the same principles, rather than creating parallel workflows with weaker oversight.

2. Model auditability

It goes without saying that accuracy is important in finance. But teams also need visibility into the why and how behind decisions, recommendations, and outputs. Model auditability is the ability to trace, review, and explain AI outputs after the fact. 

This is important because finance workflows often require supporting documentation, approval histories, and evidence trails for auditors, regulators, and internal stakeholders. If an AI tool flags a transaction as suspicious, finance teams should know why. If it provides a recommendation, they should be able to understand the rationale.

Consider an AI system that incorrectly flags legitimate invoices as duplicates for several weeks. If the team doesn’t have audit logs, they’ll have a hard time determining when the issue started, what triggered it, and how many vendors it affected. Piecing together the answer will require time and guesswork, which creates delays and increases audit risk. 

Strong auditability requires version tracking, activity logs, approval histories, and explainable decision logic. Finance leaders should be able to reconstruct what an AI system did at any point in time, much like they would with any other financial process or internal control.

3. Human-in-the-loop review

While AI can increase the efficiency and scalability of finance processes, human judgement will always be essential, especially when it comes to handling exceptions, high-risk transactions, and policy-sensitive decisions. Human-in-the-loop review ensures AI supports finance teams, rather than operating on their own. 

Finance data is often nuanced, and AI models don’t always understand. For example, an invoice might come through with a total that appears outside of the norm for a specific vendor. AI may flag it as an anomaly, but a finance professional knows that the unusual amount is legitimate due to seasonality. With this context, the human can make an informed final decision. 

Often, finance teams over-automate workflows right out of the gate. For instance, a team might implement AI-driven invoice approvals to reduce manual work, only to later discover the system approved invoices that should have been subject to further review. Even if the system performs well most of the time, a small number of bad approvals can have real consequences. 

When implementing AI, the aim should never be to replace humans. It should be to reduce manual work while preserving oversight and control where financial risk is involved. 

4. Vendor vetting

There’s no shortage of new AI tools coming to market and existing platforms layering on AI features. But not all AI tools are created equal, and it’s important to properly vet the vendors behind any technology. Treat the process like any other form of due diligence, with careful attention to security, controls, reliability, and long-term operational fit. 

Failure to thoroughly properly vet vendors can have serious consequences. Consider a finance team that spends a good portion of time coding invoices. The team decides to purchase a low-cost AI tool that automates invoice coding, without realizing the vendor stores financial data in an unsecured environment. Sure, eliminating manual work and freeing up time for other work seems appealing. But the long-term compliance and security risks far outweigh the benefits. 

What finance teams should look for when evaluating AI tools

When evaluating AI tools, finance teams should pay attention to security, auditability, reliability, integration capability, and operational risk. 

Data storage and security

Finance teams deal with highly sensitive financial data, such as invoices, payment details, vendor records, and employee information, and AI systems might touch any of it. It’s important to ask the right questions to understand where data will live and how it’ll be protected. Find out whether data is encrypted, how long it’s retained, whether it’s used for model training, and what controls are in place to prevent unauthorized access. 

Compliance and certifications

Any potential vendor that handles financial workflows should be able to demonstrate strong practices through certifications such as SOC 2. So be sure to ask. Of course, a certification doesn’t guarantee security. But they do serve as evidence that a vendor has formal controls for data handling, access management, and security monitoring. 

Explainability 

Finance, perhaps more than any other function, needs to understand the “why” behind AI actions and outputs. For example, if an invoice is flagged as suspicious, they need to know the reasons behind it. Or, if a transaction was categorized in a certain way, they need to know why. When evaluating your options, look for tools that provide transparent reasoning, traceable workflows, and clear activity histories. 

ERP and workflow integrations

When finance teams head into vendor evaluations, most already have established tools and workflows. Adding the wrong AI tool can create more complexity and add more manual work and system handoffs. 

Be sure that any AI tool integrates seamlessly with your ERP, AP automation system, procurement tools, and payment infrastructure. Strong integrations help maintain data consistency while reducing manual work.

Audit logs and oversight 

Finance teams depend on audit trails to support compliance and financial accountability. When evaluating AI vendors, make sure all actions are traceable, including approvals, edits, recommendations, and user activity. 

Evaluating AI tools isn’t about choosing a flashy technology that shows well during the demo. Instead, it’s about finding a durable platform that can operate safely within a controlled financial environment. The right tools will increase operational efficiency without introducing more risk.

A 3-phase approach to a safe AI implementation 

Implementing AI doesn’t mean overhauling every workflow right out of the gate. In fact, going all in all at once isn’t a safe approach.

Instead, it’s best practice to take a phased approach. Start small, establish strong governance right from the start, and scale from there. 

Phase 1: Start with low-risk, high-volume tasks 

Time-consuming tasks such as invoice matching, data extraction, and invoice routing are all good contenders. They’re high-volume, process-driven, and carry relatively low financial risk.

Starting with these types of tasks allows teams to evaluate AI performance in real environments without introducing unnecessary risk. It also allows teams to get comfortable with new technology while seeing measurable gains early on.

In the first phase of implementation, AI should support humans, not work autonomously. AI can help speed up processes and provide recommendations. But human oversight and judgement is still needed to validate accuracy, reliability, and operational fit.  

Phase 2: Build the governance and policy layer

During this stage, finance teams should solidify policies related to data access, approval authority, vendor management, audit logging, and acceptable use cases across the organization. 

It’s also important to clearly define ownership and accountability. There are several teams involved in governing AI, including finance, IT, security, procurement, and compliance, and it isn’t always clear who’s responsible for what. Defining ownership helps ensure alignment and accountability as AI adoption grows. 

Phase two is also a great time to standardize the vendor evaluation process. That way, all future AI initiatives will follow a consistent model. 

Phase 3: Scale with continuous monitoring in place 

Once finance teams refine their initial initiatives and see measurable gains, many look to expand AI into even more financial workflows. But that doesn’t mean they can (or should) fully shift those initial projects to autopilot. 

Ongoing monitoring is a key component of any AI implementation. Finance teams should regularly review model performance, approval accuracy, exception rates, audit logs, and workflow outcomes to ensure systems are working as expected. Teams should also periodically review vendor security practices, integration reliability, and changes to regulatory compliance. 

If governance processes aren’t maintained, organizations may encounter issues at this phase such as model drift, over-automation, or workflow inconsistencies. Continuous oversight helps teams spot potential issues early on before they grow into bigger problems. 

Common mistakes finance teams make when adopting AI (and how to avoid them)

Even the best finance teams can run into challenges when implementing AI. That’s not surprising. AI is a relatively new, rapidly evolving technology, and many organizations are still trying to figure out how to drive value without introducing risk.

Understanding the most common pitfalls is the first step towards avoiding them and building a stronger foundation for AI adoption.

Skipping vendor security review

When an AI tool promises impressive results, it’s easy to rush to implement. But if finance teams don’t understand how data is stored, protected, or used, they can unintentionally introduce security and compliance risks into critical financial workflows. 

Treating governance as an afterthought

Many teams rush to implement AI as quickly as possible, and they push conversations about governance to another day. That can cause serious problems down the road. Right from the start, it’s important to develop clear policies around data access, auditability, approvals, and accountability. This helps prevent risk from growing as AI adoption expands. 

Over-automating tasks that still require human judgement

Not every finance decision should be fully automated, especially right out of the gate. Approvals, exception handling, and policy-sensitive workflows all require human oversight, and removing it too early increases the risk of errors and compliance gaps. 

Ignoring change management

AI tools won’t deliver results if adoption is low. Organizations must provide ongoing training and clear communication about how the system works, why it’s being introduced, and what’s in it for employees. With the right support, the new system will feel like an opportunity rather than a disruption. 

Ignoring model drift over time 

AI systems aren’t static. Models, workflows, and operational conditions all evolve, and ongoing monitoring is key to maintaining trust and reliability over time. 

Are you ready to build an AI-ready finance function? 

While AI adoption continues to grow throughout the business, many finance leaders remain cautious. And that’s understandable. But safe implementations that balance innovation with safety and security are within reach.

Finance organizations must approach AI implementations the same way they approach any other operational change: with clear controls, thoughtful implementation, and ongoing oversight. Teams that establish strong governance right from the start will be well positioned to expand AI adoption confidently as technology and the regulatory landscape continue to evolve.