Workiva: AI Errors Reach Boardrooms as Data Flaws hit Trust

A sharp divide has emerged between executive faith in AI and the reality of enterprise deployments. Although 84% of surveyed business leaders report confidence in the accuracy of AI outputs without human oversight, internal audits have caught AI errors that reached external audiences or board members at 26% of organisations.

The findings from Workiva’s 2026 Midyear Executive Benchmark Survey highlight a growing operational risk for finance leaders. 

Rapid digital adoption is threatening to outpace the governance frameworks designed to supervise automated tools, leaving financial reporting and market disclosures vulnerable to unverified information.

Speaking to Finance Chief exclusively, Junko Swain, CAO of Workiva notes: “Workiva's Midyear Executive Benchmark report surfaces a clear pattern: as leaders deepen their partnership with AI, their confidence grows, but so does their awareness of its risks. In fact, 26 % of executives said internal AI audits have detected errors that reached external audiences or the board. 

“This figure demonstrates that AI errors aren't theoretical risks. Human oversight and data lineage are baseline requirements. This is especially true in fields like internal and external financial reporting, accounting processes and sustainability disclosure, where errors carry regulatory, reputational and legal consequences.

“When those errors do get caught, it's often because an experienced professional noticed something that didn't look right. That's valuable, but it's not a system. A governance model that depends on someone happening to spot the problem is a governance model that will eventually miss one.”

Establishing control over data quality remains essential to maintaining operational agility and preventing reputational damage.

"Confidence in AI without control over data quality is a liability, not a strategy,” notes Barbara Larson, Chief Financial Officer at Workiva.

 “CFOs need platforms that connect AI to trusted, auditable data so every output is one they can verify and every disclosure is one they can defend. 

“Getting this right is about more than avoiding errors. Business leaders can move faster and embed AI deeper into their operations when they trust what their systems produce. That's a real competitive edge”.

Flawed data stalls workflow adoption

The study indicates that underlying data quality remains the primary obstacle to expanding enterprise AI. A substantial majority of executives acknowledge that weak data architecture prevents them from scaling automated tools across key business functions.

According to the data, 27% of leaders state that poor data quality has significantly blocked AI deployment within core workflows. A further 71% report that data deficiencies have exerted at least a moderate negative impact on AI usage across financial and sustainability reporting. Only 11% of executives believe their organisation's current data quality is sufficient for AI application.

Junko continues: “Poor data generates wrong answers, and often wrong answers that look entirely credible.

“I believe leaders are expressing confidence in the controls and review processes around it. That's a reasonable starting point, but only if those controls actually exist and are operating effectively. In too many organisations, AI adoption has outpaced the governance framework, and no single person is accountable for closing that gap. 

“Without clear ownership, it's too easy for the hard questions to go unasked: who validated this output? Where did the data come from? Is this auditable? 

“For finance leaders, confidence in AI must extend all the way down to the data layer. That means knowing precisely where data originated and whether it can withstand scrutiny.”

This lack of reliable data extends beyond internal processes to external market relations. The survey revealed that 89% of institutional investors are concerned about the accuracy of AI-generated content in corporate disclosures. 

As financial reporting teams face increased scrutiny, organisations that fail to address data deficiencies risk damaging stakeholder credibility and falling behind competitors.

Demand for governance infrastructure

To mitigate these risks, chief financial officers and risk teams are calling for specialised supporting infrastructure to oversee increasingly autonomous agents. Rather than relying solely on broad AI models, leaders are prioritising tools that ensure traceability, governance and continuous audit capability.

Survey respondents outlined several essential infrastructure requirements: 55% cited platforms to manage autonomous agents and automated workflows, 49% pointed to traditional systems of record such as general ledgers, and 45% highlighted software that enables full auditability.

Workiva’s 2026 midyear benchmark survey gathered responses from 2,272 finance, risk and sustainability professionals, including 847 C-level executives across North America, Latin America, Europe and the Asia Pacific region. 

The study also evaluated feedback from 367 institutional investors based in North America and the United Kingdom.