Recent research by MindBridge shows that organizations are moving quickly to use AI in finance, but basic data problems are still slowing them down. MindBridge found that more than 90% of small and midsize businesses face setbacks because of data quality, even though leaders hope AI will improve accuracy and efficiency. The study calls this a “data paradox.” According to CPA Practice Advisor, companies are investing in automation to build trust in their financial data, but almost 89% say data problems are already disrupting important work. More than 90% report that undetected errors directly affect their finances, and 62% say the impact is moderate to severe.  

This gap is especially clear in sectors like energy, where about 69% of professionals in that field feel confident in their data, but nearly as many report delays caused by data quality issues. Retail faces the biggest challenges, with 94% mentioning delays and more concerns that automation could hide risks. Budget limits are also a problem, as 44% of retail leaders say a lack of resources is a main barrier to using AI.  

Stephen DeWitt, CEO of MindBridge, said the problem is structural, not just a minor issue. “Nearly 90% stalled by data quality issues is not a minor friction point,” he said. “It is a structural gap between the pace of AI adoption and the controls designed to govern it.” The research suggests that for most industries, having accurate and well-managed data is more important for successful AI adoption than moving quickly.