The Secret to Success in the AI Financial Close? Proper AI Governance

As AI adoption in finance continues to ramp up, some organizations are reaping the benefits while others are on damage control. In fact, one Trintech customer achieved a nearly error-free AI-powered financial close. The secret to their AI success? Clean data, careful orchestration and strong AI governance.  

Without the right guardrails, even the most advanced AI solutions can introduce risk rather than reduce it. But with a solid AI governance framework, finance teams can ensure AI operates safely, ethically, and effectively—transforming the close process into one that’s both intelligent and trusted. 

Why is AI Governance Important? 

Governance is the bedrock of trust in AI adoption. In finance, where precision and auditability are non-negotiable, governance ensures that AI systems enhance accuracy instead of amplifying errors. 

Without clear oversight, AI can introduce inconsistencies, bias, and compliance risks. But with strong governance in place, CFOs can unlock AI’s full potential while protecting data integrity and user confidence. 

AI governance: 

• Builds confidence and trust in AI-driven data and insights 

• Accelerates adoption by helping teams feel secure using AI tools 

• Ensures compliance and control through transparent, auditable processes  

Ultimately, finance professionals need to trust the AI they work with—because that data informs financial reporting, performance analytics, and strategic decision-making. 

What Is Shadow AI? 

While AI governance is gaining attention, its opposite—shadow AI—is quietly spreading across organizations. 

Shadow AI refers to the widespread use of unapproved or unsanctioned AI tools outside official oversight. It’s more common than most leaders realize: research shows that over 90% of organizations use shadow AI, while only about 40% have official AI licenses or governance policies in place. 

Shadow AI can expose sensitive financial data, lead to inaccurate reporting, and create compliance violations. When employees use AI tools that haven’t been vetted or secured, organizations lose visibility into how information is being generated, shared, and stored. 

To mitigate these risks, CFOs must champion responsible innovation—encouraging exploration while setting boundaries that protect both data and decision-making integrity. 

The Risks of Lacking AI Governance in the Financial Close 

AI is only as good as the structure supporting it. Without a formal AI governance framework, even well-intentioned automation can quickly become a liability. In the AI financial close, lack of oversight can lead to: 

• Unreliable numbers – Poorly governed AI can amplify errors instead of correcting them, undermining financial accuracy. 

• Data security risks – Unauthorized tools increase exposure to leaks or breaches of sensitive financial data. 

• Lack of auditability – When AI outputs can’t be traced, audit trails break down and compliance risk rises. 

For finance teams, governance isn’t bureaucracy—it’s protection. Auditors won’t accept “the AI told us to do it” during an audit. Proper governance ensures that automation, AI, and analytics all work together with compliance and control. 

Building an AI Governance Framework 

To implement responsible AI use in finance, CFOs and their teams need an AI governance framework. This provides a structured approach that balances innovation with accountability. 

Here are three key steps to building one: 

1. Create an AI Center of Excellence (CoE) 
   Establish a cross-functional team that defines governance policies, reviews new AI tools, and provides training. The CoE should include leaders from finance, IT, risk, and compliance who collaborate to ensure AI aligns with business goals and ethical standards. 

2. Ensure Audit-Ready Data Lineage 
   Transparency is critical in the AI financial close. Maintain clear documentation that tracks where data originates, how it’s transformed, and how AI models use it. This not only streamlines audits but also builds confidence in AI-generated insights. 

3. Set Clear Usage Policies 
   Define what tools are approved, what purposes they can serve, and what data they can access. Block unapproved applications and reinforce compliance with periodic reviews. Strong guardrails create freedom within structure—allowing innovation to thrive safely. 

Conclusion: Building Trust, Not Fear, Around AI 

In a 2025 Workday survey, 82% of organizations reported expanding or testing AI, but employees voiced a consistent theme: they want AI as a collaborator, not a boss. While 63% favor investment in AI, nearly 70% remain uncomfortable with the idea of AI making decisions in sensitive areas such as HR, finance, or legal contexts. The message is clear: CFOs must champion AI adoption with empathy and clarity, framing it as an augmentation of human expertise rather than a replacement for leadership judgment. 

Strong AI governance builds that trust. Without it, even well-designed systems will face resistance, eroding the potential return on investment. Finance leaders who communicate openly about how AI supports decision-making—while maintaining human accountability—create confidence across teams and stakeholders. This shift isn’t about technology alone; it’s about building a culture where employees feel empowered, rather than threatened, by innovation. t by embedding AI within the workflows, governance, and data that drive real results. 

Written By: Lindsay Rose