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Undetected errors and poor data quality are quietly eroding business profitability, according to a new analysis by MindBridge, which warns that organisations are pushing ahead with AI adoption while overlooking the financial damage caused by unreliable information.
Widespread Financial Impact From Data Errors
The study found that more than 90% of organisations have experienced a direct financial impact from undetected errors, with around 62% describing the effect as moderate to severe. The research, which examined the energy, manufacturing and retail sectors, highlights what MindBridge describes as a significant “data paradox”.
When asked about the biggest benefits AI could deliver, respondents pointed to “improving accuracy and trust” (retail 54%; energy 45%; manufacturing 34%) and “reducing repetitive manual work” (retail 44%; energy 48%; manufacturing 53%). Yet nearly 90% of participants (88.6%) said data issues are already causing delays in critical financial workflows.
Sector‑Specific Disruptions
The energy sector showed a notable disconnect between confidence and operational reality. While 68.5% of energy professionals said they were “confident” or “very confident” in their data for financial decisions, 88.6% reported delays caused by data quality issues, with 50.6% describing those delays as moderate to significant.
In terms of financial impact, 40% of energy respondents said undetected errors had a major or severe effect on their business, compared with 31% in retail and 20% in manufacturing.
Retail reported the highest level of operational disruption, with 94% of professionals saying data issues caused delays in their work. Energy followed at 89%, and manufacturing at 83%. Retail leaders also expressed the greatest concern about the risks of rapid automation, with nearly 44% worried that critical risks or unusual activity could go unnoticed as processes are streamlined. Budget pressures remain a barrier, with 43.5% citing funding constraints as the main obstacle to AI adoption — significantly higher than energy (31%) and manufacturing (28.2%).
Although manufacturing reported fewer frequent delays (7.9%), the sector faces persistent daily friction, with 45% saying they experience “some delays”, compared with 39% in retail and 38% in energy.
Trust, Explainability And The “Data Paradox”
Across all three industries, the findings challenge the assumption that AI is primarily being used to reduce headcount. Only 6% of respondents viewed AI as a tool for workforce reduction. Instead, businesses see automation as a way to improve accuracy and reclaim time lost to manual processes.
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.”
He added: “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.”
DeWitt said organisations need AI systems that can “show their work and can explain every transaction, data point, or calculation”, arguing that finance must move away from traditional sampling towards explainable AI capable of continuously processing all 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,” he said.
Source: manufacturingmanagement.co.uk
