Written By: Adam Pittman, Senior Editor, ERP Today and SAPinsider

This article originally appeared on SAPinsider and is republished with permission.

AI can accelerate core financial close functions. But its promise has intensified pressure on finance teams to close the books faster, surface problems earlier, and give executives a more continuous view of performance. The trouble is that every output — manual or AI-generated — must still withstand review, audit, and regulatory scrutiny.

That is why Trintech states in its white paper, Fast Isn’t Enough: Why Trust Will Define the Future of AI in Finance, that “finance does not have an AI problem. It has a trust problem.”

AI-generated work must remain explainable, traceable, reviewable, and protected before finance teams can rely on it. The white paper addresses the natural follow-up question: How can companies use AI to do more of the close without losing control?

AI Outputs Need an Owner and an Audit Trail

AI does not reduce finance’s responsibility for reported results. It increases the burden of demonstrating how those results were produced.

“If a number cannot be explained, it cannot be defended,” the report states. Finance teams must validate the output while also tracing the data, logic, and actions behind it.

Trintech defines three operating requirements for trusted AI: responsible, secure, and human-governed. Responsible AI produces outcomes that finance can explain and audit. Secure AI keeps data access, use, and storage within approved controls. Human-governed AI preserves clear decision ownership, approval workflows, and the ability to intervene.

Putting those principles into practice requires clear rules for reviewing AI outputs, recording actions, assigning exception ownership, and granting final approval.

The report shows how the Trintech AI Platform puts that model into practice. Embedded AI supports reconciliation, matching, journals, and close management. Agentic AI performs work such as variance analysis, exception prioritization, and accrual validation across Trintech and connected systems.  Outputs can then remain reviewable, traceable, and controlled, with finance retaining oversight.

The Close Shifts From Search to Evaluation

AI changes where finance teams enter the process. The system identifies, categorizes, and explains potential discrepancies before presenting them to finance. Finance professionals then assess the explanation and decide what action to take.

The same division of work can apply to journal entries and anomaly detection. AI can prepare a proposed entry with its source data, rationale, approval route, and change history. It can also flag unusual activity and provide the evidence behind the alert. Finance then reviews the proposed entry or alert, checks the source data and supporting evidence, and decides what action to take. This moves the team from finding and assembling information to evaluating it.

This approach becomes more useful when applied across the close, rather than to one entry or alert at a time. Linking reconciliations, journals, approvals, and reporting allows the system to identify dependencies and bottlenecks before they affect reporting deadlines. Finance teams can then focus on the issues most likely to threaten accuracy, or delay the close, instead of manually tracking every task.

The report says governed AI can shorten investigations, reduce exception backlogs, and improve audit readiness. Trintech’s platform supports those outcomes by explaining exceptions, preparing close outputs, and moving them through controlled workflows.

Confidence Metrics Show When AI Is Ready to Scale

The gains described above are difficult to evaluate through time-to-close alone. A process can finish sooner while finance still has to recheck explanations, rebuild audit evidence, or resolve inconsistent results. Trintech’s broader AI financial close framework expands the definition of success: “Success is no longer defined by how fast you close the books. It’s defined by how quickly you generate value from your data.”

Before finance can judge whether AI is improving the close, the underlying process must be sound. Trintech says that starts with clean data, optimized workflows, and teams prepared to use the technology. On that foundation, automation, predictive insights, adaptive governance, and new KPIs can support a continuous close; without it, AI may simply accelerate flawed processes and unreliable data.

The report makes the measurement question more concrete. It recommends tracking the percentage of AI outputs reviewed and approved, the consistency of explanations and validation, the completeness of audit evidence, and the reliability of results across reporting periods. Traditional measures such as cycle time, transaction cost, and productivity remain useful, but they no longer stand alone.

Those measures help finance determine how much review each use case still requires. Inconsistent explanations or incomplete evidence keep human checks high. As outputs become more reliable and traceable, teams can focus their attention on higher-risk exceptions rather than reviewing every result.

What This Means for SAPinsiders

• Review can become risk-based. As outputs prove consistent and traceable, finance can reduce blanket checks and direct attention toward higher-risk exceptions. Governance then becomes a way to allocate scarce expertise more effectively.
• Trust metrics can guide expansion. Explanation consistency and evidence completeness can serve as thresholds for moving use cases into broader operation. They also give teams a practical basis for improving weaker processes before scaling them.
• The close must be prepared for AI. Clean data, optimized workflows, and trained teams determine whether automation produces earlier insight or faster errors. Investment in those foundations should therefore precede wider deployment across the close.