OpenAI recently, during awebinarshowcased how its finance team is leveraging artificial intelligence to reshape the finance function. Although the presentation focused on more efficient workflows and new forms of collaboration, participants repeatedly submitted governance-related questions during the Q&A session, which the hosts never directly addressed.

The silence was striking. Attendees asked how ChatGPT Work addresses data governance, internal controls, agent oversight, and security challenges—core concerns for finance organizations adopting AI. However, OpenAI's presenters spent nearly all of the presentation and Q&A time discussing forecasting models, data platforms, workflow automation, and the company's newly launchedSites feature。

These questions reflect one of thebiggest obstaclesfacing enterprise AI adoption in finance. Before automating forecasts or accelerating month-end close, many finance leaders first want to clarify more fundamental questions: Where is data stored? Who has access? How are controls enforced? Do workflows comply with SOX requirements and existing IT general controls?

However, these concerns were not addressed by the webinar hosts—OpenAI's Director of Product Finance Kyle Kober, Head of Product Finance Stephanie Struck, and data scientist and technology team member Jackson Wang.

Guardrails First, Excellence Later

Throughout the webinar, Kober, Struck, and Wang argued that AI is fundamentally changing how finance teams operate. They demonstrated how OpenAI uses ChatGPT Work to automate its own forecasting, reconcile financial models, build interactive dashboards, and enhance finance team collaboration.

Describing how AI makes forecasting "more interesting"—enabling teams to collaborate on real-time scenarios rather than working "in isolation" in spreadsheets—Wang joked, "Folks, let's make forecasting great again."

However, while the presenters focused on making finance faster and more interactive, the webinar's chat revealed a very different discussion. As they watched the demos, attendees asked how AI could fit into finance's existing control environment before being integrated into forecasting processes. These questions were never directly answered; instead, the Q&A discussion centered on topics like OpenAI's talent needs, data architecture, system connectivity, build-versus-buy strategies, and workflow automation.

The conversation also repeatedly returned to the data quality issues that AI systems depend on. Attendees asked how to build a trusted single source of truth, using governed datasets as the foundation for AI-driven finance processes. These questions echoa common view among CFOs: reliable outputs begin with reliable financial information.

This discussion aligns withan Intuit survey previously reported by CFO.com. That survey found that 70% of finance leaders lack a single trusted source for critical business data; more than half (57%) said that delays in financial visibility over the past six months caused their organizations to miss time-sensitive strategic opportunities.

Whether a true single source of truth is achievable remains a topic of debate.Gartner arguesthat organizations should instead pursue a "good enough version of the truth." The questions submitted during the OpenAI webinar suggest that this issue remains a top consideration for finance leaders evaluating how AI fits into financial reporting and decision-making.

Limited Mention of Governance

To some extent, the webinar did briefly touch on governance. Kober described ChatGPT Work as "governed and auditable, with controls to lock down spend and usage." Later in the presentation, the presenters also discussed admin-controlled access to Sites and the use of approved "golden tables" as trusted data sources for AI applications.

The webinar's discussion aligns with what CFO.com has heard from finance leaders throughout the year.Previous reporting foundthat organizations are increasing AI governance investments, assigning internal audit teams to test AI models, and investing in data architecture before scaling AI across finance. Meanwhile,broader industry surveysfind that relatively few organizations believe they have the governance maturity needed to scale AI effectively.

The questions submitted during the webinar show that finance leaders have reached a consensus on one of the most pressing issues of the digital age: if AI is destined to be part of finance teams, who is responsible for the data it uses, thecosts it incurs, and the systems that govern its operation?

AI Governance Concerns Intensify

The questions submitted during the OpenAI webinar also reflectbroader concerns revealed by a recent Deloitte survey. That survey showed that although 93% of large organizations are using AI across multiple key business functions, only 40% of CFOs said they are "very confident" in their organization's AI governance framework.

The survey also found that governance remains one of the biggest challenges facing finance leaders. More than half of CFOs cited a lack of governance authority as a barrier to building an effective enterprise-wide AI governance framework, while the most common concern was balancing the pressure to deploy AI quickly with the need to manage risk.

The concerns in Deloitte's research were fully reflected in the OpenAI webinar: as presenters demonstrated how AI automates forecasting and generates executive dashboards, attendees repeatedly steered the conversation toward governance, internal controls, and data integrity. These questions suggest that, for many finance leaders, confidence in AI capabilities depends not only on the technology itself, but also on whether the guardrails around it are in place.