Data Quality is Not Being Prioritized on AI Projects, a Trend that 96% of U.S. Data Professionals Say Could Lead to Widespread Crises

• AI investments are being built on flawed data as business leaders struggle to make quality a priority. 81% of AI professionals say their company still has significant data quality issues, yet 85% believe leadership isn’t addressing these issues. 90% of data professionals at the director or manager level are even more likely to agree that company leaders are not paying adequate attention to bad or inaccurate data, compared to executives (76%) and those in non-manager roles (77%). Poor quality data leads to unreliable AI outputs, financial waste, and increased risk

• Executives may not see the depth of the problem. Non-management employees (27%) are more likely than executives (17%) to say major AI data quality issues remain. 90% of directors and managers—those closest to AI implementation—believe leadership is failing to focus on the issue.

• Big businesses see the crisis coming—and claim they’re prepared. 77% of companies with $5B+ in revenue expect poor AI data quality to cause a major crisis. Yet, 65% of that same group say their AI strategy is ‘on the right path,’ revealing either confidence or a blind spot in readiness.

• Industry disruptions like DeepSeek are prompting second thoughts. Nearly half (47%) of AI professionals now worry their company has overinvested in costly, inefficient AI models, forcing a reassessment of their AI approach.

More Work to Be Done

While AI adoption is accelerating, this survey underscores a major gap: most companies are investing in AI without placing enough priority on the quality of the data that powers it, and it isn’t just management that has a blind spot when it comes to recognizing the scale of data-quality problems. The issues can be major and extend company-wide. Four out of five (81%) professionals, who deal with data, analytics, and AI models, know their organization has more to do to overcome quality issues, with either a moderate or a large amount of work.

“As companies rush to implement AI, they risk building on flawed data, leading to biased models, unreliable insights, and poor ROI,” said Drew Clarke, EVP & GM, Data Business Unit at Qlik. “Our research makes it clear: AI success isn’t just about deploying models—it’s about ensuring the data powering those models is trusted and reliable.”

AI models built on flawed data will produce unreliable results, financial waste, and increased business risk. Those closest to AI implementation see the dangers—but leadership remains focused on AI investment without ensuring data quality.