Sunil Padiyar is the Chief Technology Officer (CTO) of Trintech, a global leader in AI Financial Close solutions.
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Enterprise resource planning (ERP) systems sit at the center of modern finance. They standardize processes, centralize data and promise a single source of truth. But ERP systems were never designed to answer the one question that matters most: Can you actually trust the numbers?
For years, the ERP has been positioned as the backbone of financial transformation. It delivers structure and scale. But in today’s environment, a system of record is only the starting point. This is because truth is not just captured; it has to be proven.
Finance teams are no longer just closing the books; they are defending them. Every reporting cycle requires reconciling across fragmented systems, validating balances across entities, enforcing controls, tracking approvals and assembling audit-ready evidence. All of this must happen faster, with fewer resources and under greater scrutiny.
As organizations scale, so does the complexity: more systems, more data and more dependencies. The result is a growing confidence gap.
In a 2025 survey of 200 CFOs and senior finance leaders by Cherry Bekaert, 55% identified data accuracy and consistency as a major challenge, and 84% ranked improving data quality and integration as a top priority. This is not a failure of ERP. It is a mismatch between what ERP was built to do and what finance is now expected to deliver.
The ERP plays a critical role, but it is a specific one. It records transactions, structures financial data and ensures debits and credits are captured correctly. The financial close serves a different purpose. It is about validation.
The close is where finance proves that the numbers are accurate, complete and defensible. It requires reconciliation, substantiation, workflow orchestration, audit trails and exception management across systems and teams.
Put simply, the ERP records the numbers and the close proves them. That distinction reveals what many organizations are missing: a true system of control.
Three forces are making this gap more visible:
1. Complexity has outgrown ERP-native capabilities. Most enterprises operate across multiple ERPs, subledgers and external systems. Reconciliation is no longer linear. It is multi-source, high-volume and constantly changing.
2. Expectations around control have increased. It is no longer enough to be accurate. Finance leaders must demonstrate how numbers were validated, who approved them and what evidence supports them.
3. Manual work remains embedded in the process. Spreadsheets, email approvals and disconnected workflows still fill critical gaps. Over time, these workarounds reduce visibility and introduce risk in the exact place where finance leaders need confidence.
Individually, these challenges are manageable. Together, they slow the close, strain teams and erode trust in the output.
AI in finance is often framed as a tool for faster insights or better reporting, but that is not where the biggest impact is emerging. The real opportunity is in confidence. However, confidence in finance doesn’t come from speed alone.
In practice, getting there requires more than layering AI on top of existing processes. Organizations need to start with data readiness, ensuring financial data is consistent, governed and reconciled across systems before introducing intelligent automation.
Equally important is embedding AI directly into close workflows, not as a parallel tool, so that controls, approvals and audit trails remain intact. And throughout, human-in-the-loop oversight must be preserved. The goal is not to replace judgment, but to augment it with systems that are transparent, explainable and aligned to the control expectations of finance. This comes from systems that can explain, validate and audit every decision as it happens.
A strategic approach to AI can help transform the financial close from a reactive validation step into a proactive control layer embedded directly into the workflow. Intelligent matching can reduce manual reconciliation, risk-based prioritization can surface what actually needs attention, and real-time visibility can identify bottlenecks before they delay the close.
In finance, AI cannot be a black box. It must operate as a glass box, showing its reasoning, attaching evidence and producing outputs that auditors can trace, validate and trust.
This shift is only possible when governed data, intelligent reasoning and workflow orchestration come together as a unified system. Instead of validating after the fact, finance teams can operate with control during the process. That shift is where transformation happens.
For years, ERP modernization has been treated as a strategic advantage. Today, it is the baseline. The real advantage lies in what happens after transactions are recorded. It is defined by how effectively organizations validate, control and operationalize financial data across their ecosystem.
When finance teams can close faster without sacrificing control, reduce manual effort while improving accuracy and deliver audit-ready results with confidence, they can expand beyond efficiency to become more trusted. And trust is not just a soft metric. It drives speed. Speed drives decisions, and decisions drive outcomes.
The ERP will remain the foundation of enterprise finance, but the organizations that pull ahead will not be the ones with the most advanced systems of record. I believe they will be the ones that build strong systems of control around them. They will turn financial data into something leaders can rely on with certainty instead of assumptions.
In modern finance, the real competitive advantage is not having the numbers; it is believing them.
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