Why Data Quality Powers the Success of AI in Accounting

If you spend any time in finance circles today, you’ll hear a familiar anecdote: AI is changing everything. Yet as someone who has spent more than two decades inside the mechanics of accounting—building controls, safeguarding funds, and governing the integrity of financial results—I’ve learned that AI’s future in our industry rests on something far more fundamental than flashy algorithms. It rests on trust.

In the past few years, the most critical function of the accounting team has shifted dramatically. Yes, we still ensure the accuracy of financial reporting, maintain internal controls, and see our organization through the month-end close cycle. However, we are now responsible for shaping how AI impacts the daily workflows of our team.

As this becomes our new reality, one truth has become impossible to ignore: the entire system only works if the data is clean, consistent, and traceable. In other words, data quality is what underpins the success of AI in our work.

The Data Quality Concerns Behind the AI Trust Gap

A recent study commissioned by Tipalti, The State of AI in Finance: Exploring the AI Trust Gap, revealed that 55% of finance professionals are “extremely optimistic” about the benefits of AI. However, 48% expressed either extreme or moderate concerns about the associated risks, with data quality being a significant issue.

As someone responsible for financial accuracy, this statistic resonates. Finance teams are eager to adopt AI but rightfully question whether the data powering these models is reliable, consistent, and ready for auditing. This study also found that 57% of finance professionals identified data quality and standardization as major obstacles to AI adoption.

For finance professionals, trust erodes when data is isolated or manipulated through disconnected and inconsistent systems. Unfortunately, this scenario reflects the legacy architecture that many accounting teams still rely on, which often involves manual workflows, fragmented processes, and challenges in maintaining data integrity.

Why Data Quality Is the Foundation for AI in Accounting

It’s no surprise that 45% of finance professionals expressed a desire for improved data lineage and quality controls before they could fully trust AI. Without a clear and traceable path showing the origins of data, finance teams struggle to rely on AI outputs with confidence.

Additionally, the State of AI report offered another significant insight: when asked what their organizations should have done earlier or better to ensure the success of AI, finance professionals did not say more sophisticated models. Instead, they emphasized the need for foundational improvements in data quality and integration.

This is a key message from today’s industry leaders, and one I observe frequently within my own organization. The advancement of AI in accounting will not be determined by the complexity of algorithms, but by the maturity of the data environment that supports them.

Ultimately, addressing data quality challenges is not merely beneficial; it’s a crucial component to success—and the deciding factor in whether the accounting function can trust AI enough to implement it at scale.

How to Ensure Data Quality

The most effective way to improve data quality is to simplify and centralize your financial architecture. Today’s AI models perform best when they operate on clean, standardized, connected data. Here’s what that looks like in practice:

1. Make Sure Your Tools Talk to Each Other

Technology solutions that naturally sync with each other (AP, procurement, global payments, ERP systems, etc.) reduce the risk of data loss, formatting errors, and reconciliation inconsistencies. When your systems connect seamlessly, your data becomes automatically standardized.

2. Unify Your Accounting Tech Stack

End-to-end solutions give your team a clear, consistent view of all financial data. When your AP automation tool is fully integrated with your ERP and other key accounting systems, data can be traced seamlessly from invoice capture through reconciliation. This unified structure is exactly what AI models need in order to deliver reliable, accurate insights.

3. Know Exactly Where Your Data Comes From

Track your financial data from its source to the financial statements, embedding automated checks—matching, validation, duplicate detection, coding recommendations, and audit trails—so it remains accurate, consistent, and fully traceable across your finance systems.

4. Cut Your Manual Bottlenecks

Where manual processes thrive, data integrity suffers. AI performs best in structured, automated environments rather than in systems dependent on repetitive human intervention or ad-hoc workarounds. Automating your workflows ensures data is consistent, reliable, and ready to support AI-driven insights.

5. Get Your Data Right, Get AI Right

AI doesn’t magically fix bad data. It accelerates whatever exists—good or bad. Spend the time to clean, standardize, and map your data before AI ever touches it. Done well, this creates an accounting environment where AI can operate with precision, not guesswork.

Solving the AI Trust Gap

As accounting professionals, we know AI’s effectiveness depends on the quality of the data powering it. Without clean, standardized, and traceable data, even the most advanced tools cannot deliver reliable results.

Yet achieving that level of quality remains a challenge. Accounting teams face fragmented systems, manual processes, and inconsistent data that make trust in AI difficult to establish. As today’s finance professionals confirmed, concerns around data quality remain top of mind, underscoring just how critical it is to get right.

The foundation of accounting has always been trust, underpinning every number and every report. In the age of AI, that trust must extend to the data feeding these tools. By prioritizing quality first, finance teams can unlock AI’s full potential and build a function that is more accurate and auditable than ever before.