Companies Are Scaling AI on Data They Don't Trust, New Study Finds

Rhea-AI Summary

OneStream (OS) released a survey of 352 finance and IT executives (U.S., U.K., France) on May 5, 2026, revealing widespread data governance gaps undermining AI adoption. Key findings: 47% made material decisions on faulty data, 72% reported losses ≥$500k, and only 19% centralize AI inputs.

The study links Finance–IT alignment to greater data trust (5.5x) and warns that rapid AI use without governance risks amplifying errors and financial misreporting.

AI-generated analysis. Not financial advice.

Positive

• Organizations aligned Finance–IT report 5.5x greater complete data trust
• 96% of executives rate trusted data as very or extremely important
• 85% report a formal data governance program in place or underway
• AI spending forecast cited at $2 trillion for 2026

Negative

• 47% made material decisions using inaccurate, incomplete, or outdated data
• 72% report bad-data costs of $500,000 or more
• 37% report bad-data damages exceeding $1 million
• Only 19% pull most AI inputs from a single centralized enterprise system
• 61% second-guess their data at least monthly

Key Figures

AI spending forecast: $2 trillion Executives surveyed: 352 executives Decisions on bad data: 47% +5 more

8 metrics

AI spending forecast $2 trillion Global AI spending expected to surpass this in 2026

Executives surveyed 352 executives Finance and IT leaders in U.S., U.K., France; March 16–19, 2026 survey

Decisions on bad data 47% Executives who made a material decision on inaccurate or outdated financial data

Cost from bad data 72% at $500,000+ Executives saying bad data cost their organization $500,000 or more

Damages over $1M 37% Executives reporting bad-data damages exceeding $1 million

Governance AI-ready 79% Executives who believe data governance can support large-scale AI adoption

Centralized AI inputs 19% Executives pulling most AI inputs from a single centralized enterprise system

Trust with IT–Finance alignment 5.5x Higher likelihood of complete data trust when Finance and IT are fully aligned

Market Reality Check

Price: $24.00 Vol: Volume 2,655,240 vs 20-da...

normal vol

$24.00 Last Close

Volume Volume 2,655,240 vs 20-day average 2,190,585 (relative volume 1.21x) ahead of this AI governance release. normal

Technical Shares trade above the 200-day MA, at $24.00 vs 200-day MA of $21.80, after this AI-focused study release.

Peers on Argus

OS is flat pre-news while peers show mixed moves: BOX +1.2%, QLYS +4.7%, STNE -2...

OS is flat pre-news while peers show mixed moves: BOX +1.2%, QLYS +4.7%, STNE -2.34%, CALX and ZETA near unchanged. Movement appears stock-specific rather than a unified software sector reaction.

Common Catalyst Limited peer news flow today; one peer (BOX) only announced an upcoming earnings date.

Previous AI Reports

5 past events · Latest: Oct 30 (Positive)

Same Type Pattern 5 events

Date	Event	Sentiment	Move	Catalyst
Oct 30	AI strategy study	Positive	-0.3%	Study showing CFOs lead AI strategy but face scale and understanding gaps.
Oct 14	AI product launch	Positive	-0.4%	Launch of Modern Financial Close and SensibleAI-powered ESG planning tools.
May 12	AI skills study	Positive	+6.1%	Large study highlighting AI skills gaps and adoption across finance roles.
May 01	AI conference news	Positive	+3.1%	Announcement of major Splash conference focused on ushering in finance AI era.
Nov 12	AI tools preview	Positive	-2.4%	Preview of Sensible AI Library and Genesis low-code framework for finance AI.

Pattern Detected

AI-tagged news for OS often skews positive in content but has produced mixed price reactions, with more instances of short-term downside than upside despite AI leadership messaging.

Recent Company History

Recent AI-related announcements show OneStream emphasizing finance-focused AI leadership and market education. Prior AI-tag events since Nov 2024 cover product launches, AI libraries, ESG and financial close solutions, and multiple research studies on AI adoption and skills. Price reactions have been mixed, with several small declines and a few notable gains, indicating investors neither consistently reward nor punish AI news alone. Today’s governance-focused AI study fits this pattern of thought-leadership and platform positioning.

Historical Comparison

+1.2% avg move · Over the last five AI-tagged announcements, OS saw an average move of ±1.21%, with mixed reactions t...

AI

+1.2%

Average Historical Move AI

Over the last five AI-tagged announcements, OS saw an average move of ±1.21%, with mixed reactions to generally positive AI product and research news such as today’s governance-focused study.

AI-tag history shows a progression from platform capabilities (Sensible AI Library, Genesis) to large conferences and then repeated research studies framing OS as a thought leader in finance AI adoption.

Market Pulse Summary

This announcement underscores that, even as AI spend is forecast to surpass $2 trillion, many financ...

Analysis

This announcement underscores that, even as AI spend is forecast to surpass $2 trillion, many finance and IT leaders still make material decisions on bad data, often at costs above $500,000. For OneStream, it extends a pattern of AI-focused research that positions the company as a governance and finance-AI specialist. Historical AI-tag news has drawn mixed price responses, so investors may focus on how well the platform addresses fragmented systems, data ownership, and Finance–IT alignment highlighted in the study.

Key Terms

data governance, AI tools, AI-related risks, data quality rules, +4 more

8 terms

data governance technical

"examining the state of data governance across the enterprise."

Data governance is the set of rules and practices that ensure information is accurate, consistent, and used responsibly within an organization. It is like a well-organized library system that keeps track of all the books, making sure they are correct, easy to find, and used properly. For investors, strong data governance helps ensure that the information they rely on is trustworthy and decisions are based on reliable data.

AI tools technical

"those burned by bad data use the most AI tools"

AI tools are software systems that use machine learning and other artificial intelligence methods to analyze data, make predictions, automate tasks, or generate content without constant human direction. For investors, they matter because these tools can change a company's costs, productivity, competitive edge and risk profile—like upgrading from manual bookkeeping to a smart assistant that works faster and may reveal hidden opportunities or new threats to profits.

AI-related risks technical

"95% of these executives report concerns about AI-related risks"

AI-related risks are the potential harms or losses that can arise from using artificial intelligence systems, including errors, biased decisions, security breaches, regulatory penalties, or failures to perform as expected. Investors care because these risks can reduce revenue, increase costs or legal exposure, and damage reputation—much like a faulty safety system in a factory that can halt production, trigger fines, and shake customer trust.

data quality rules technical

"only half have established a consistent source of truth, implemented data quality rules"

Data quality rules are the set of checks and limits that make sure financial, operational, or regulatory data is accurate, complete and timely before it’s used. Think of them as a recipe checklist and spell‑checker for data: they flag missing entries, outliers, or inconsistent formats so analysts and automated systems don’t make decisions based on bad inputs. For investors this matters because reliable data underpins valuations, risk assessments and compliance, reducing the chance of costly mistakes or surprises.

reconciliation technical

"implemented data quality rules, or automated reconciliation across systems."

Reconciliation is the process of comparing two sets of financial records and fixing any differences so they match, like balancing a checkbook to ensure receipts agree with the bank statement. For investors, reconciliation matters because it confirms that reported numbers are accurate and complete, highlights mistakes or irregularities, and helps ensure that financial performance and cash positions used to value a company are reliable.

data ownership technical

"nearly 1 in 3 CFOs (32%) cites "lack of data ownership" as a key barrier"

Data ownership is the practical and legal control over a set of information — who can access, use, sell or delete it — similar to owning a house or a private library where you decide visitors and rules. For investors it matters because clear ownership can create a valuable asset, enable new revenue streams, reduce regulatory and breach risk, and affect a company’s valuation and future earnings potential.

AI ROI financial

"As boards demand faster execution and measurable AI ROI, a clear gap is emerging"

AI ROI measures the financial benefit a business gains from investing in artificial intelligence, comparing the extra profit or cost savings generated to the money spent on AI tools, data, and implementation. Investors care because it shows whether AI is a productive use of capital—like checking if buying a new machine pays for itself over time—and helps judge a company’s efficiency, growth potential, and risk from tech spending.

enterprise system technical

"just 19% pull the majority of their AI inputs from a single, centralized enterprise system."

An enterprise system is a large, integrated software platform that coordinates a company’s core operations—such as sales, accounting, inventory, human resources and supply chain—so different departments share one consistent set of data and processes. Investors care because these systems can boost efficiency, cut duplicated work, improve decision-making and enable growth, but they also require significant investment and carry implementation and disruption risks that affect costs and profitability.

AI-generated analysis. Not financial advice.

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Nearly half of 350+ Finance and IT Executives surveyed made major business decisions on faulty data in the past year; those burned by bad data use the most AI tools

BIRMINGHAM, Mich., May 5, 2026 /PRNewswire/ -- OneStream, the leading enterprise finance management platform that modernizes the Office of the CFO by unifying core finance and operational functions, today announced the results of a new study examining the state of data governance across the enterprise. The study, which polled over 350 senior Finance and IT executives — including CFOs, CAOs, CTOs, CIOs, Chief Digital Officers, Chief Data Officers, and Chief AI Officers across the U.S., U.K., and France — reveals how poor data practices are undermining AI adoption and driving significant financial risks to their organizations.

With AI spending forecasted to surpass $2 trillion in 2026, organizations are under mounting pressure to deploy AI at scale. Yet the research suggests the data foundation most are building on is fundamentally unstable. While nearly all executives (96%) view accurate, trusted data as very or extremely important to their organization's success, nearly half (47%) admit they have made a material business decision based on inaccurate, incomplete, or outdated financial data in the past 12 months.

The consequences are significant:

• Nearly three in four executives (72%) say bad data cost their organization $500,000 or more, with more than one-third (37%) reporting damages over $1 million.
• Downstream impacts include: more than four in 10 (44%) delayed reporting and closing, while over one-third cite lost revenue opportunities (41%), a lack of trust in automated insights (38%), and compliance issues (35%).

Despite these risks, companies are not slowing down. Executives who have made decisions on bad data are four times more likely to use ten or more AI tools than their peers. At the same time, confidence is fragile—an overwhelming 95% of these executives report concerns about AI-related risks, including flawed decisions (37%) and one in five (20%) cites financial misreporting.

"Nearly half of executives admit they've made decisions on bad data, yet AI adoption continues to accelerate. Unless companies have data they can trust, AI will only accelerate and amplify bad decisions," said Tom Shea, CEO of OneStream. "No enterprise can start from scratch to unify all its data—it's not practical. The advantage comes from aligning Finance and IT to focus on the areas where the business logic and financial context are clear, and applying AI that understands how those decisions are made. Without that, AI risks amplifying errors instead of improving outcomes."

Emerging Blind Spot: High Confidence, Low Trust in the Age of AI
As boards demand faster execution and measurable AI ROI, a clear gap is emerging between AI ambition and data readiness. Nearly four in five executives (79%) believe their data governance can support large-scale AI adoption and 85% say they have a formal program in place or underway, but that confidence breaks down in practice. Nearly two-thirds (61%) of executives second-guess their data at least once a month and over 1 in 10 (11%) question it daily.

The issue is structural. Today, just 19% pull the majority of their AI inputs from a single, centralized enterprise system. Further, only half have established a consistent source of truth, implemented data quality rules, or automated reconciliation across systems. As a result, disconnected systems and lack of integration (32%) were cited among the top barriers to effective governance. AI is often deployed on shaky data - amplifying errors rather than eliminating them.

Speed vs. Structure: Why AI Fluency Isn't Enough
The data accuracy challenges become more visible where technical fluency outpaces institutional knowledge. While AI enables faster analysis, it cannot replace the business context required to recognize when data is incomplete or misleading.

Rising leaders (ages 25–44) are heavier AI users—over four in five (82%) use three or more AI tools for decision-making, compared to 69% of more experienced peers. Yet they are also more exposed to risk: over half (51%) report making a material decision based on faulty data, versus 39% of older leaders.

The consequences are more severe as well. Younger executives are over four times more likely to report significant financial or compliance impacts (17% vs. 4% among seasoned peers), and twice as likely to cite a loss of trust in AI outputs (44% vs. 22%).

This highlights a critical gap: AI proficiency alone is not enough. Without strong governance and business context, the leaders most equipped to use these tools are also the most vulnerable to their risks.

A Shared Challenge Viewed Differently
While Finance and IT are aligned in recognizing the importance of data governance, and most (89%) executives claim Finance and IT are aligned, 85% of CIOs believe they lead data governance, while 78% of CFOs claim the lead. They also have a different perspective on data governance challenges: Finance prioritizes accuracy, context, and accountability, and IT focuses on enablement, scalability, and execution. Because of this disconnect, nearly 1 in 3 CFOs (32%) cites "lack of data ownership" as a key barrier to success.

However, when Finance and IT come together, the impact is significant: organizations that achieve complete alignment between Finance and IT are 5.5x more likely to report complete trust in their data. Closing this gap is what turns governance from a program into a true foundation for AI-driven decision-making.

"Every generation of finance leaders has had to earn trust in the numbers before they could act on them. What's different today is that AI has compressed that timeline dramatically, and the margin for error has shrunk with it," said Pam McIntyre, Chief Accounting Officer at OneStream. "The next generation of finance leaders is inheriting AI tools that can do extraordinary things, but those tools require a level of data discipline that has to be built deliberately, with clear ownership, clear definitions, and a governance structure that Finance and IT build together."

Survey Methodology

The research was conducted online in the U.S., U.K., and France by The Harris Poll on behalf of OneStream among 352 executives employed full-time as a Chief Finance Officer, Chief Accounting Officer, Chief Technology Officer, Chief Information Officer, Chief Digital Officer, Chief Data Officer, or Chief AI Officer at businesses with 100 or more employees and revenue of $50 million or more. The survey was conducted March 16–19, 2026. Raw data were not weighted and are therefore only representative of the individuals who completed the survey.

About OneStream

OneStream is how today's Finance teams can go beyond just reporting on the past and Take Finance Further by steering the business to the future. It's the leading enterprise Finance platform that unifies financial and operational data, embeds AI for better decisions and productivity and empowers the CFO to become a critical driver of business strategy and execution.

We deliver a comprehensive cloud-based platform to modernize the Office of the CFO. Our Digital Finance Cloud unifies core financial and broader operational data and processes and embeds AI for better planning and forecasting, with an extensible architecture, so customers can adopt and develop new solutions, achieving greater value as their business needs evolve.

With over 1,800 customers, including 18% of the Fortune 500, a strong ecosystem of go-to-market, implementation, and development partners and 1,600 employees, our vision is to be the operating system for modern Finance. To learn more, visit onestream.com.

Media Contact

Jaclyn Proctor
Media Relations Contact
OneStream
media@onestreamsoftware.com

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SOURCE OneStream, Inc.

FAQ

What did OneStream's May 5, 2026 (OS) survey find about decisions made on faulty data?

Nearly half of respondents admitted to material decisions on faulty data, indicating pervasive governance gaps. According to OneStream, 47% of 352 surveyed finance and IT executives acknowledged making such decisions in the past 12 months, highlighting financial and operational risk across organizations.

How large are the financial impacts reported by executives in the OneStream (OS) study?

Most executives report substantial costs tied to bad data, with many citing six-figure losses. According to OneStream, 72% say bad data cost their organization $500,000 or more, and 37% report damages exceeding $1 million.

What does OneStream (OS) say about Finance and IT alignment and data trust?

Closer Finance–IT alignment links to much higher data trust in organizations. According to OneStream, organizations with complete Finance–IT alignment are 5.5x more likely to report complete trust in their data, suggesting coordination materially improves governance outcomes.

How common is centralized data for AI inputs, per OneStream's May 2026 survey?

Centralized AI input sources are uncommon, exposing AI deployments to inconsistent data. According to OneStream, only 19% of executives pull the majority of AI inputs from a single, centralized enterprise system, leaving many projects reliant on fragmented sources.

Which executive groups are most exposed to AI risks in the OneStream (OS) findings?

Younger, AI-fluent leaders report higher exposure to faulty-data decisions and impacts. According to OneStream, leaders aged 25–44 use more AI tools and report a 51% rate of material decisions on faulty data versus 39% for older peers, with greater reported financial or compliance effects.