Can AP Trust AI? What makes automation audit-ready?

People in accounts payable know that AI holds incredible potential for financial operations, but there’s still some hesitation. As Director of Product at Tipalti, I spend my days working directly with teams operating at that intersection of finance and technology. 

I recently sat down with two leaders: Jacqui Long, an AP and finance leader based in Denver, and Joseph Voss, a strategic CFO and CPA, in a webinar where we explored what it really takes to move from AI skepticism to AI confidence.

We discussed how to approach AI in your own accounts payable operations to build trust and make automation audit-ready.

Why finance teams are skeptical of AI in AP

When I speak with CFOs and AP leaders, what comes up most often is that they aren’t ready to let go completely. They want guardrails and audit checks in place. The key fear is that something might slip through, like a payment going out without a proper review. 

But at a recent Tipalti customer advisory board meeting, I was surprised to find that most finance teams are already using AI a lot. So it isn’t a fear of AI itself; finance leaders just want to make sure they’re using it the right way. This caution is completely healthy. Finance has the ultimate fiduciary responsibility to stakeholders, so their financial systems and data must have an uncompromised level of integrity.

What makes AI finance-grade

So, what actually makes an AI tool finance-grade versus a general-purpose tool?

Finance teams deal with complex datasets and highly repetitive tasks like invoice coding and statement reconciliation. This makes AI a natural fit, but context is the critical differentiator. It’s way more than pattern matching. You can’t just dump data into genai like ChatGPT and expect it to code the next invoice correctly. 

Finance-grade AI must be built within the specific rules, guardrails, and workflows of a finance environment.

Finance teams should exercise caution when building custom finance AI from generic tools. The risk is high. Jacqui gave an example of a team using a non-compliant AI tool for contract and invoice analysis that ended up exposing sensitive financial data to an unvetted platform. That damaged vendor trust and created risk before they course-corrected to enterprise-grade tools.

What makes AI automation audit-ready

Trust in AI comes from building systems with transparency baked in from day one.

At Tipalti, everything AI does is fully visible—it has that explainability. Customers can always see what was AI-driven versus human-driven. Our legal and compliance teams review every AI tool we use, especially before customer exposure. We want to show the thought process behind every action and accept continuous feedback so you can trust the output and not second-guess every decision.

Here’s what this looks like in practice: complete, real-time visibility at every stage: ingestion, coding, approval, and payment. Every human or AI agent change, exception, or override is logged by who made it and when. Transaction trails are viewable and reversible, keeping human judgment at the center. 

This explainability is non-negotiable. CPAs and auditors require baseline reliability and auditable systems to validate financial statements.

Balance automation with human oversight

The immediate value of automation in AP is reducing repetitive work. Teams can spend more time on higher-value financial activities. 

Jacqui shared that automation freed her team from mundane data entry. They went from being AP clerks to strategic staff and senior accountants who manage accruals, reclassifications, cash forecasting, and vendor relationships.

To keep the right level of oversight, Jacqui uses this risk-based approach:

• High-risk/low-confidence items: Routed directly to a human for review before any action occurs.
• Low-risk candidates: Routine tasks like invoice coding and workflow routing can be automated with exceptions and low-confidence outputs flagged for human review.
• Non-negotiable checkpoints: SOX requirements and audit checkpoints always retain strict human oversight.

Keeping a human in the loop isn’t just about safety; it’s about building trust in the system over time. When finance leaders ask me how to balance AI experimentation with guardrails, my first rule is simple:

Never exceed human-defined policy controls. 

We believe finance professionals must be able to review and override AI-powered decisions, such as approvals and payments, so Tipalti’s AI works with the built-in controls that are a part of our platform. For example, if AI is coding a bill, there should be a checkpoint before it’s paid. Human checkpoints give teams confidence.

To build a sustainable framework for human oversight, here is how you can tackle it:

Use a risk-based approach: High-risk tasks or low-confidence outputs must be routed to a person before anything happens, while lower-risk items (like routine invoice coding or workflow routing) can run autonomously. Non-negotiables, such as SOX requirements and policy-driven audit checkpoints, always require human oversight.

Set internal approval rules for access: Having AI tools doesn’t mean everyone in your organization needs the same level of AI autonomy, so implement role-based access control. 

Avoid review overload: If you try to automate a massive dataset all at once, you won’t eliminate your workload; you’ll simply shift the burden to reviewing thousands of outputs. Start small, gain confidence, and then scale.

Monitor performance: Autonomy isn’t binary. Continue to monitor performance through API dashboards, re-evaluate confidence thresholds, and adjust oversight as the system learns and earns trust.

Watch out for autonomy triggers: You need to pull back AI autonomy if exception rates rise, duplicate payments occur, or audit flags appear. If your team completely stops questioning or flagging anomalies because they blindly assume the AI is correct, you’ve lost an essential human intelligence layer.

How to evaluate AI vendors and build an implementation roadmap

When evaluating AI vendors, I always advise finance leaders to ask these three questions:

1. Why does it do it that way? 
   You need to understand the underlying logic, not just the output.
   
   
2. How is the AI monitored, measured, and improved? 
   Ask them about accuracy metrics and data privacy.
   
   
3. What is the tool not doing with AI? 
   AI should solve a real problem rather than be applied just for its own sake.

Leaders must also check for auditability, SOC 2/SOX compliance, and secure data environments before signing off.

A 4-step roadmap for adopting AI in AP

When you’re looking to trust AI and make automation audit-ready, here is the roadmap I recommend:

Step 1: identify top pain points. Start with clear ROI use cases like invoice coding, data consolidation, or statement matching. Don’t try to automate everything at once; the default is to ask yourself whether something can be automated. Even if it can’t, revisit it later, as capabilities are added and trust in AI is earned.

Step 2: run a controlled pilot test. Test the software using real company data, not just vendor demo data, to push edge cases and prove accuracy.

Step 3: measure results and expand. Track traditional metrics like cycle times, error rates, and processing costs per invoice, but also measure audit readiness and how productively your team uses their freed-up time.

Step 4: revisit autonomy. Autonomy isn’t an on/off switch—it’s a dial. Review guardrails regularly. As AI evolves and earns trust, you can open up more automation, but revisit practices if exception rates climb, audit flags arise, or your team stops questioning anomalies.

Make automation audit-ready

AI is not smarter, but it’s making us more efficient, enabling us to focus on high-risk, complex tasks. Healthy automation augments human judgment rather than replaces it. Trust in AI is a process. You don’t move from AI skepticism to AI confidence overnight, but if you focus on transparency and put in place finance-grade guardrails and a phased roadmap, your team can eliminate those repetitive, time-consuming tasks that really slow down operational efficiency.

Want to hear our full conversation that also covers handling complex multi-entity environments and how to get internal buy-in on trusting AI in your AP processes?

Watch the full webinar on demand.