Retailers are facing the most severe operational disruption from poor data quality among major industries, according to new research from MindBridge, as businesses accelerate investment in AI-driven transformation.
The study, which surveyed 640 professionals across retail, manufacturing and energy, highlights what it describes as a growing “data paradox” where organisations are pushing toward AI-enabled efficiency while being held back by unreliable data foundations.
Across all sectors, more than 90 per cent of organisations reported a direct financial hit from undetected errors, with 62 per cent describing the impact as moderate to severe. At the same time, 88.6 per cent said data quality issues are actively causing delays in critical financial workflows.
For retailers, the impact is even more pronounced. The research found that 94 per cent of retail professionals experience delays due to data issues, which represents the highest of any sector surveyed.
Retail leaders are also among the most concerned about the risks associated with rapid automation. Nearly 44 per cent said they are worried that errors, risks or unusual activity could go unnoticed as AI is deployed to streamline operations.
Despite this, many retailers are struggling to invest in solutions. Over 43 per cent cited budget and resource constraints as the primary barrier to AI adoption, significantly higher than in energy and manufacturing, suggesting a widening gap between ambition and execution.
The report also reveals a disconnect between perception and reality in data confidence. Businesses increasingly view AI as a tool to improve accuracy and trust (cited by 54 per cent of retail respondents as a key benefit) underlying data challenges largely remain unresolved.
This contradiction is mirrored across industries but is particularly acute in retail, where complex supply chains, high transaction volumes, and fragmented systems create persistent data challenges.
Notably, the research challenges the assumption that AI adoption is primarily about cost-cutting. Just 6 per cent of respondents said reducing headcount was the main driver, with most organisations instead focused on improving accuracy, reducing manual work, and reclaiming time.
Stephen DeWitt, CEO of MindBridge, said the findings expose a structural issue in how businesses are approaching AI transformation: “Nearly 90 per cent stalled by data quality issues is not a minor friction point. It is a fundamental gap between the pace of AI adoption and the controls designed to govern it.”
He added that the disconnect between perceived data confidence and actual performance is creating hidden financial risk at scale, with undetected errors continuing to erode profitability.
For retailers, the findings serve as a warning that AI investment alone will not deliver results without stronger data governance. As the sector pushes toward more automated, real-time decision-making, ensuring data accuracy and transparency is emerging as a critical foundation for success.
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