A new analysis by MindBridge, a leader in financial intelligence, revealed that undetected errors and poor data quality are quietly eroding business profitability, even as CFOs are increasingly looking at implementing AI to stop the financial damage.
While business leaders are eager to deploy AI as a ‘silver bullet’ for efficiency, the research shows that poor data quality is already creating a massive drag on operations. Over 90% of organizations report a direct financial hit from undetected errors, with approximately 62% describing the impact as moderate to severe.
The study, which examined the energy, manufacturing and retail sectors, highlights a significant “data paradox”. When asked about the biggest benefits AI could provide, the top reasons were “improving accuracy and trust” (retail 54%; energy 45%; manufacturing 34%) and “reducing repetitive manual work” (retail 44%; energy 48%; manufacturing 53%). Despite this push for modernization, nearly 90% of respondents (88.6%) admitted that data issues are actively causing delays in critical financial workflows.
There is a surprising disconnect in the energy industry where professionals report the highest levels of trust in their data despite significant operational friction. While 68.5% of energy respondents claim to be “confident” or “very confident” in their data for financial decisions, 88.6% of energy teams experience delays due to data quality, with 50.6% describing those delays as moderate to significant.
When it comes to the financial impact on their business, 40% of energy professionals admit that undetected errors have a major or severe impact, compared to 31% in retail and 20% in manufacturing.
The study also shows that the retail industry is experiencing the most severe operational disruptions, with a staggering 94% of retail professionals reporting that data issues cause delays in their work. In comparison, energy reports 89% and manufacturing reports 83%.
Retailers are also the most anxious about the risks of rapid automation. Nearly 44% of retail leaders expressed concern that critical risks or unusual activity could go unnoticed as they push to streamline operations. Despite these pressures, the sector is struggling to fund solutions; 43.5% cited budget and resource constraints as their primary barrier to AI adoption – significantly higher than the 31% in energy and 28.2% in manufacturing.
While manufacturing may not be experiencing too many frequent delays due to data issues (7.9%), the industry is facing persistent, daily friction, with 45% of professionals reporting that there are “some delays”, compared to retail’s 39% and energy’s 38%.
Across all three industries, the data debunks the common assumption that AI is being used primarily to reduce headcount. Instead, businesses view automation as a way to reclaim their time and improve accuracy. Only 6% of respondents see AI primarily as a means of reducing headcount. This aligns with the central finding that trust and explainability, not speed alone, are the foundation of an AI-enabled business.
Stephen DeWitt, CEO of MindBridge, said: “The ‘data paradox’ represents a critical friction point for the autonomous enterprise. Our study shows that while teams are racing toward an AI-powered future, they are being held back by data errors and issues that create significant financial and operational drag. Nearly 90% stalled by data quality issues is not a minor friction point. It is a structural gap between the pace of AI adoption and the controls designed to govern it.”
“This ‘data paradox’ is most visible in the disconnect between trust and reality, where leaders are confident and trust their data, but the hard facts show otherwise. Undetected errors are producing real financial damage, at scale, and largely out of sight.”
“CFOs, CIOs, and boards need AI systems that show their work and can explain every transaction, data point, or calculation. To achieve this, we need to move away from traditional sampling of financial data towards explainable AI that continuously processes 100% of transactions. Finance is becoming autonomous, but automation without governance is a risk. True digital transformation isn’t just about speed; it requires accountability at scale.”
