84% of execs trust AI is accurate, but 26% also say errors have reached board or external audiences

Despite high confidence from leaders in AI outputs, more than a quarter of executives said internal audits have detected AI errors that reached external audiences or the board. 

This is according to recent data from Cloud-based financial reporting technology company Workiva, which found that 84% of executives in the finance, audit, and sustainability fields are either somewhat (39%) or very (45%) confident in the accuracy of AI outputs in an annual report, even without human review. This is likely a false confidence, as 26% of those same executives also said internal audits have detected AI errors that reached external audiences or the board. Junko Swain, chief accounting officer at Workiva, noted that the results indicate that AI errors are not some hypothetical problem but an actual risk that organizations must control. 

"As a CAO, that statistic sets off alarms but it's not surprising. That number tells you AI errors aren't hypothetical risks and Business leaders still need human oversight and data lineage, especially in fields like internal and external financial reporting as well as accounting processes where errors can lead to real consequences," she said in an email. 

This does not seem to be due to people not recognizing that misinformation can be dangerous, as poll respondents already seemed well aware of the risk. The report noted that the proportion of those citing misinformation as a top external threat grew from 24% to 31% in the U.S. (globally the rise was from 23-26%.) Swain thought the contradiction might be due to executives feeling confident about AI in the abstract, but not as much when discussing their specific organization. 

She pointed to another stat in the report which said only 11% felt their own data was AI-ready, and that 71% said poor data quality has at least moderately impacted AI in financial and sustainability reporting. AI is only as good as the data fed into it, and so if a company's data is inaccurate or incomplete, the output will sound plausible but still be wrong. The AI does not fix the data quality problem but amplifies it. This in mind, she felt there is likely a relationship between lack of data-readiness and AI errors. 

"It would surprise me more if there wasn't a relationship between that and the 26% of internal auditors finding AI errors. Garbage in, garbage out. That's been true in finance, risk, and audit long before anyone was talking about AI. What makes this harder now is that AI outputs often look polished even when the inputs are flawed. Generic AI tools don't have the capability to trace numbers back to their source, which means errors can be harder to catch and harder to explain after the fact," she said. 

What is alarming about this, according to Swain, is that the results suggest that many organizations are trusting AI outputs without asking the harder question of "where did this data come from, and can I stand behind it?" 

"I can't emphasize this enough: data governance is not a back-office concern. It is the foundation that determines whether finance professionals can responsibly expand their use of AI, and governance means more than asking whether the data is accurate. You also have to ask who is allowed to consume this data. Data lineage and clear ownership aren't just problems for IT. In finance, data governance isn't optional. It's the prerequisite. If you can't answer, 'Is this data accurate, and is the right person using it?' then you don't have the controls in place to confidently stand behind your AI-assisted outputs," she said. 

This is not the first, or even second or third research finding that shows a severe mismatch between how confident organization are in their governance and data integrity and the actual realities. Most recently, a similar study from Top 50 firm Schellman, in a report, found that 90% of AI governance professionals say their organizations have already allocated funding specifically for AI governance and 74% believe they could pass an AI compliance audit today. However, only 27% described their governance programs as fully mature. Perhaps as a consequence, 65% of respondents said their organizations have experienced AI-related incidents or near-misses in the past 24 months. 

But we can go back even further. A study from this past May by corporate performance management solutions provider OneStream found that 79% of executives believe their data governance can support large-scale AI adoption. However, further data shows they're not actually as confident as they say, as 61% said they second-guess their data at least once a month, and 11% question it daily. 

They are probably right to question their data's quality, as 72% say bad data cost their organization $500,000 or more, with more than one-third (37%) reporting damages over $1 million. Downstream impacts include delayed reporting and closing (cited by 44%), lost revenue opportunities (41%), a lack of trust in automated insights (38%) and compliance issues (35%). 

A similar confidence mismatch was found in a December 2025 survey from data extraction solutions provider Parseur, which noted that, on the one hand, 88% of U.S. business leaders said they are very or somewhat confident in the accuracy of the data feeding their analytics and AI systems but, on the other, 69% of them reported discovering errors in document-derived data at least sometimes, if not often or very often. Parseur said this implies finding mistakes in the data is a regular occurrence.  

Overall, the Workiva results, combined with all the others, indicates there might be a pattern of overconfidence in AI controls and governance considering the errors identified.