New Study Uncovers Critical Data Governance Gaps in AI Adoption by Finance Executives

Major Findings on AI and Data Trust Among Finance Executives

A recent survey conducted by OneStream, a leader in enterprise finance management solutions, examined the alarming state of data governance as organizations increasingly adopt artificial intelligence (AI) technologies. The research sampled over 350 senior Finance and IT executives including CFOs and CTOs from the U.S., U.K., and France. Notably, the findings signal critical gaps in data trust and governance that could undermine AI’s potential benefits.

Key Insights from the Study

According to the study, an overwhelming 96% of executives acknowledge the necessity of accurate data for business success. Yet, nearly half (47%) confessed to making significant business decisions based on inaccurate or outdated financial data within the last year. The implications of such oversight are severe, with 72% of the queried executives reporting financial losses exceeding $500,000 as a direct consequence of poor data quality.

In fact, 37% have incurred losses exceeding $1 million due to bad data. This reflects a critical disconnect where organizations, under immense pressure to scale AI implementations — estimated to hit over $2 trillion in expenditure by 2026 — find themselves relying on unstable data foundations. Such an environment raises questions about the efficacy of AI technologies and their ability to deliver trustworthy insights.

Underestimating Risks Leads to Blind Spots

Despite the recognition of these risks, companies continue to deploy AI tools at an accelerated pace. Strikingly, those who have made decisions based on flawed data are four times more likely to utilize ten or more AI applications. However, confidence in these AI systems is fragile, with 95% of executives expressing apprehensions regarding AI-related risks, including concerns surrounding the reliability of automated insights and the risk of financial misreporting.

Generation Gap: A Mixed Bag of AI Fluency and Data Flaws

The study highlights a generational divide in how leaders engage with AI tools. Younger executives (ages 25-44) often employ multiple AI tools for decision-making, yet this demographic also reports being more exposed to the consequences of bad data — with 51% stating they have made material decisions based on inaccurate information. In stark contrast, the seasoned executives revealed lower rates of reliance on faulty data but still stressed the importance of strong data governance structures.

The findings portray a critical gap where technology fluency does not guarantee sound decision-making. Although AI can expedite data analysis, it cannot compensate for the required business context to discern when information is misleading. Therefore, the need for a blend of technical skills alongside deep institutional knowledge is paramount.

Bridging the Gap: The Finance and IT Collaboration

While Finance and IT leaders largely concur on the importance of robust data governance, the study unveils significant discord regarding who should take charge. A staggering 89% of respondents believe there is alignment between Finance and IT, but discrepancies emerge over data ownership responsibilities. Around 32% of CFOs cite the absence of clear data ownership as a critical hurdle for effective governance.

To mitigate these gaps and propel successful AI implementation, it is essential for organizations to foster a cooperative relationship between Finance and IT. Data governance must transcend mere programs to become foundational to AI-driven decisions, as organizations with strong alignment report being 5.5 times more likely to trust their data comparably. Such collaboration could lead to the responsible and effective use of AI.

Conclusion

With the stakes exceptionally high in terms of financial repercussions from poor data governance, the current landscape demands a strategic approach to unify Finance and IT in a quest for superior data accuracy. Understanding that AI implementation comes with inherent risks necessitates that organizations prioritize trust in their data practices. If not addressed, this critical gap could thwart the anticipated benefits of AI and in turn, amplify flawed decision-making processes.

The call to action for today’s finance leaders is clear: earning trust in data has never been more vital, and it will require deliberate governance frameworks and clarity of ownership to navigate this critical challenge effectively.