A new survey commissioned by Parseur reveals a striking contradiction in how organizations view their data: while 88% of U.S. business leaders say they are very or somewhat confident in the accuracy of the data feeding their analytics and AI systems, the same 88% report discovering errors in document-derived data at least sometimes.

The findings suggest that confidence in data quality may be masking widespread accuracy issues. Nearly seven in ten respondents (69%) reported finding errors sometimes, often, or very often, indicating that data issues are not isolated incidents but a routine part of day-to-day operations.

These data quality gaps carry significant consequences. Respondents linked document data errors to incorrect forecasts, financial reporting issues, customer or supplier disputes, compliance or audit findings, operational delays, revenue loss and increased fraud exposure. Many described the impact of these errors as moderate or severe, highlighting the operational and financial risks of unreliable data.

The survey results arrive amid widespread AI adoption across business functions. While organizations continue to expand their use of AI-driven tools, the data feeding those systems often originates from documents such as invoices, purchase orders, contracts, and customer forms. Errors in data inputs can quietly undermine AI outputs, analytics, and downstream decisions, even when overall confidence in data remains high.

"What this survey shows is a confidence illusion," said Sylvestre Dupont, co-founder and CEO of Parseur, an intelligent document processing platform. "Organizations believe their data is healthy, but persistent errors tell a different story. As companies rely more heavily on AI, data accuracy becomes foundational. That's why organizations need better support around how data is captured and validated at the point of entry."

The survey also identified clear "danger zones" in document accuracy. Invoices were cited most often as error-prone (21%), followed by purchase orders (18%) and customer-facing documents (17%). Respondents also flagged contracts, intake forms and logistics documents as frequent sources of errors, underscoring that data quality challenges extend beyond a single workflow.