PHILADELPHIA, PA — A new survey by Qlik® has uncovered significant risks in AI initiatives due to poor data quality and misaligned leadership priorities. The findings, based on insights from 500 U.S. professionals working with AI, paint a concerning picture for the future of AI investments.
Despite the accelerated adoption of AI technology, a startling 81% of AI professionals admit their organizations are plagued by data quality issues. Even more concerning, 85% believe leadership is failing to prioritize solutions, leaving AI models vulnerable to unreliable outputs, financial waste, and heightened business risks.
Those closest to AI implementation feel the effects most acutely. 90% of data directors and managers, who work directly with AI systems, agree that leadership is underestimating the depth of the data quality problem. Executives, by comparison, appear less aware of the issue, with only 76% recognizing leadership inaction. Non-management employees share a similar outlook, with 77% identifying inadequate focus from the top.
The survey also highlights a disconnect in awareness of the problem’s scale. 27% of non-management employees cited major AI data concerns, compared to only 17% of executives.
Big businesses are already bracing for potential fallout. 77% of companies generating $5 billion or more in revenue anticipate that poor data quality could spur a major crisis. Yet, 65% of those same companies claim their AI strategy is “on the right path,” suggesting either confidence in their approach or a blind spot in their preparedness.
Industry trends are intensifying scrutiny of AI investments. Nearly half (47%) of AI professionals worry their organizations have overinvested in inefficient AI models, prompting a reevaluation of strategies and addressing costly inefficiencies.
More Work Needed to Address Data Challenges
The survey makes it clear that addressing data quality lags behind AI adoption. Four out of five respondents believe their organizations still have a “moderate or large amount of work” left to overcome data challenges. This gap impacts companies across sectors, as poor-quality data results in biased models, flawed insights, and diminished return on investment.
“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 of Qlik’s Data Business Unit. “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.”
Survey Methodology
The Qlik AI survey was conducted by Wakefield Research between February 4–18, 2025, among 500 U.S. data and analytics professionals working with AI models at companies with annual revenues exceeding $500 million USD.
Qlik’s findings highlight the urgent need for companies to address data quality if they are to fully unlock AI’s potential while minimizing associated risks. Without immediate leadership intervention, the promises of advanced AI capabilities could fall short, jeopardizing business outcomes across industries.
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