AI Adoption Surges Despite Data Trust Issues
AI adoption in finance is accelerating at a rapid pace, yet a new study reveals that many organizations are building their artificial intelligence initiatives on shaky data foundations. According to a recent survey by OneStream, nearly half of over 350 finance and IT executives admitted to making major business decisions based on inaccurate or outdated financial data within the past year. As companies increase their investments in AI—with global spending forecasted to exceed $2 trillion in 2026—the risks associated with unreliable data are becoming more pronounced.
Financial Risks and the Cost of Poor Data
The study highlights significant financial consequences for organizations relying on flawed data. Approximately 72% of surveyed executives reported that bad data cost their company $500,000 or more in the last year, while more than a third suffered damages exceeding $1 million. The repercussions extend beyond financial loss: 44% experienced delayed reporting and closing processes, 41% lost revenue opportunities, and 38% lost trust in automated insights. Compliance issues were cited by 35% of respondents, underscoring the broad impact of poor data quality on AI adoption in finance.
The Paradox of AI Tool Usage and Data Confidence
Interestingly, executives who have been affected by bad data are four times more likely to use ten or more AI tools than their peers. Despite widespread concerns—95% of respondents expressed worries about AI-related risks such as flawed decisions and financial misreporting—companies continue to scale their AI efforts. Tom Shea, CEO of OneStream, notes, “Unless companies have data they can trust, AI will only accelerate and amplify bad decisions.” He emphasizes the need for finance and IT to align on business logic and financial context to ensure AI tools deliver value rather than propagate errors.
Data Governance: Confidence vs. Reality
The survey exposes a critical gap between executives’ confidence in their data governance and the reality of their data practices. While 79% believe their governance is robust enough for large-scale AI adoption and 85% report having a formal data governance program, 61% second-guess their data at least monthly, and 11% question it daily. Only 19% of organizations source the majority of their AI inputs from a single, centralized system. Additionally, just half have implemented consistent quality controls or automated reconciliation processes, with 32% citing disconnected systems as a major barrier to effective governance.
Generational Divide in AI Usage and Risk Exposure
Younger leaders, particularly those aged 25–44, are driving the use of AI tools in finance. Over 82% of this group use three or more AI tools for decision-making, compared to 69% of older executives. However, this increased adoption comes with heightened risk: more than half have made significant decisions based on bad data, and they are over four times more likely to report severe financial or compliance impacts. This suggests that AI fluency alone is not enough—strong data governance and business context are essential to mitigate risk.
Bridging the Divide Between Finance and IT
The study also reveals differing perspectives between finance and IT leaders regarding data governance. While 89% of executives claim alignment between these departments, 85% of CIOs believe IT leads governance efforts, whereas 78% of CFOs say finance is in charge. Finance prioritizes accuracy and accountability, while IT focuses on scalability and execution. This disconnect can hinder governance initiatives; 32% of CFOs identify a lack of data ownership as a major obstacle. However, organizations where finance and IT are fully aligned are 5.5 times more likely to completely trust their data, turning governance from a checkbox exercise into a true foundation for AI adoption in finance.
The Path Forward for AI Adoption in Finance
As AI adoption in finance continues to transform decision-making processes, the margin for error is shrinking. Pam McIntyre, Chief Accounting Officer at OneStream, asserts that the next generation of finance leaders must prioritize data discipline, clear ownership, and robust governance to fully capitalize on AI’s potential. Building trust in data is no longer optional—it’s essential for leveraging AI effectively and safeguarding organizational success.
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