How embedded AI is changing financial reporting risk

While operational AI strategy often sits outside finance, its influence on upstream data and processes increasingly affects financial reporting. As a result, chief financial officers and controllers face a new assurance reality shaped by data quality challenges, delayed governance and unclear accountability.

Finance leaders who understand how artificial intelligence influences financial reporting risk, internal controls and audit expectations can have greater confidence in reported numbers. This requires knowing:

AI already influences financial reporting, whether finance realizes it or not

AI does not need to post journal entries or prepare financial statements to affect financial reporting. It need only influence reporting-relevant data and assumptions that flow into reported results. 

In RSM’s 2026 AI survey of middle market organizations, 86% of respondents reported that AI is already integrated into operations, either fully or partially. 

Operational AI informs reporting-relevant inputs such as forecasting models, pricing decisions, inventory management and revenue drivers. Those inputs ultimately roll up into financial reporting, even when finance teams are not directly involved. 

“When AI influences upstream data, management and auditors still need to understand where that data comes from and why it’s reliable, just as they would with human-generated data,” says Chris Blackburn, assurance partner at RSM US.

And yet, according to the survey, ownership of AI strategy most often sits outside finance. 

“CFOs need visibility into where and how it’s being used,” says Blackburn. “They don’t need to validate every outcome of the technology itself, but they are responsible for understanding whether it introduces financial reporting risk and ensuring appropriate controls are in place when it does.”

Data quality and lineage are where AI risk shows up first

In the AI survey, 34% of respondents cited “data quality/availability/lineage issues” as the greatest barriers to AI deployment. Those same weaknesses are often where financial reporting and assurance risks first become visible.

AI systems rely on large volumes of data to generate outputs that influence business decisions. When that data is incomplete, poorly governed or difficult to trace, problems can surface downstream in financial reporting. 

During the close or an audit, finance teams may be asked to explain where data originated, how it was transformed and why it can be relied upon.

Over time, weaknesses in data quality and lineage will directly affect how internal controls over financial reporting are designed, documented and evaluated. That means data governance decisions made early in AI adoption can have lasting consequences for audit readiness. 

Governance timing matters as much as governance intent

While two-thirds of survey respondents said they apply AI governance controls before pilots or production , that leaves one-third with governance following later. Delayed governance may support innovation, but it can complicate auditability, especially as AI governance is increasingly viewed through an internal control lens.

Delayed governance makes it harder to document decisions as they occur, establish clear review responsibilities and demonstrate the consistent operation of internal controls over financial reporting. These challenges become more pronounced when AI systems evolve or “learn,” increasing the need for periodic monitoring and documentation.

“Just because it came from AI doesn’t mean we can fully rely on it,” says Blackburn. “Management and auditors still have to understand where the data is coming from, how the AI was trained and what’s being done to monitor whether it’s changing over time.”

He adds, “What matters is understanding where AI is used and calibrating oversight and monitoring based on the level of financial reporting risk.”

Human judgment still anchors financial reporting

As AI influences more reporting‑relevant data, auditors will increasingly focus on how management applies judgment, oversight and documentation around AI‑generated outputs.

But even as AI accelerates processes, it often increases the volume and complexity of inputs feeding financial statements. 

Finance leaders may face new questions about how conclusions were reached, what assumptions were embedded in models and who reviewed AI-influenced outputs.

This creates pressure not just to make sound judgments, but to evidence those judgments efficiently, particularly in an audit context. Documentation, explainability and ownership of conclusions remain central to audit readiness, regardless of technology.

“AI can drive efficiency, but the real risk is overreliance,” says Blackburn. “Because the technology continues to change and isn’t perfect yet, management still needs to apply human judgment and due diligence to ensure AI-generated information is appropriate for financial reporting.” 

What CFOs can take away now

CFOs who build visibility effectively tend to focus on how AI intersects with existing finance processes rather than trying to govern the technology itself.

In practice, this often starts by mapping AI touchpoints to key reporting areas. Finance teams can anchor the exercise to known financial statement risks—such as forecasts, estimates or operational metrics used in the close—and identify where AI‑influenced outputs enter those workflows. Framing AI in terms of familiar processes helps translate enterprise activity into financial reporting context.

Visibility also improves when data questions are integrated into routine finance reviews. When AI‑influenced data is discussed in forecast or variance discussions, finance leaders naturally ask where inputs came from, whether assumptions have changed and how consistency is maintained across periods. Over time, those questions surface where data lineage or documentation may need to be strengthened.

Governance is most effective when it is layered onto existing control structures. Clarifying who reviews AI‑influenced inputs as part of management review controls—and what evidence is retained when judgments are made—helps finance stay aligned as AI systems evolve, without creating parallel processes.

Making human judgment explicit remains essential. Documenting how AI‑generated outputs were evaluated, challenged or adjusted reinforces management’s role in reaching conclusions and supports audit discussions grounded in clarity rather than technical detail.

These approaches help CFOs translate AI from a broad enterprise topic into something finance can explain, defend and stand behind, using the same disciplines that have always underpinned confidence in financial reporting.