Executive Summary
- AI introduces new financial statement and disclosure risks when it influences forecasts, estimates, valuations, journal entries, reconciliations, or reporting narratives.
- AI-related errors can be systematic, repeatable, and difficult to detect, making them potentially more dangerous than traditional human errors.
- CFOs remain fully responsible for financial reporting accuracy and disclosures, even when AI tools are third-party, automated, or difficult to explain.
- Regulators and investors now expect company-specific disclosure of material AI risks, not generic or boilerplate language.
- Boards and audit committees are increasingly expected to oversee AI risks as part of financial reporting integrity and enterprise risk governance.
- Private companies using AI in finance should apply public-company-style controls to reduce misstatement risk and prepare for audits, financings, and future IPO readiness.
If AI is being used anywhere in your finance, reporting, or disclosure process, staying ahead of governance and oversight expectations is critical. Ridgeway Financial Services works with CFOs and finance teams to assess AI-related financial reporting risk, design audit-ready controls, and support clear, defensible disclosures as expectations continue to evolve.
Table of Contents
- Financial Statement Risks Introduced by AI
- Defining “Material” AI Risks for SEC Disclosures
- Board and Audit Committee Oversight of AI Risks
- Applying Public-Company Controls to AI
- Examples of AI-Related Disclosures by Public Companies
- The CFO’s Role in AI Governance and Disclosure
- Bottom Line
Financial Statement Risks Introduced by AI
AI is increasingly embedded in finance functions, from forecasting and variance analysis to reconciliations, journal entry support, and disclosure drafting. While these tools can improve efficiency, they also introduce new risks to financial statement accuracy.
AI-generated outputs can be incorrect, incomplete, or confidently wrong. When flawed assumptions are embedded in models used for budgeting, valuation, revenue recognition, or loss estimates, those errors can be repeated consistently across periods. Unlike human errors, AI-driven mistakes often do not fluctuate or self-correct, making them harder to identify without deliberate review.
Over-reliance on third-party AI tools introduces additional risk. Vendor models may change behavior without notice, experience outages, or rely on data sources that are not fully understood by finance teams. Black-box models complicate auditability and make it difficult to explain how results were produced. In some cases, AI automation can collapse segregation of duties by performing multiple steps that previously acted as checks and balances.
Ultimately, responsibility does not shift to the tool. CFOs and finance leaders remain accountable for the numbers and disclosures, regardless of whether an AI system generated or influenced them.
Defining “Material” AI Risks for SEC Disclosures
AI has rapidly become a common topic in public company risk factor disclosures. Investors and regulators now expect companies to assess whether their use of AI introduces risks that are material to financial performance, operations, or reporting.
A risk is generally considered material if there is a substantial likelihood that a reasonable investor would view it as important. As AI becomes central to business models and financial processes, many AI-related risks meet this threshold. Common themes appearing in disclosures include system failures or underperformance, regulatory uncertainty, cybersecurity and fraud risks, and competitive pressure from more effective AI adoption by peers.
Regulators have emphasized that AI-related disclosures should be tailored and specific. Generic statements about AI being “new” or “evolving” are no longer sufficient. Companies are expected to explain how AI is used, what could go wrong, and how those risks could affect results. At the same time, overstating AI capabilities can create disclosure integrity issues and enforcement risk.
When AI meaningfully affects operations, financial reporting, or strategic decision-making, those risks should be evaluated carefully for inclusion in public filings.
Board and Audit Committee Oversight of AI Risks
As AI becomes more influential, boards of directors and audit committees are increasingly expected to oversee AI-related risks as part of their governance responsibilities. Many companies now explicitly describe board or committee oversight of AI in their disclosures.
Effective oversight starts with understanding where AI is used across the organization, including within finance and reporting. Boards should ask management which AI use cases are highest risk and how those risks are governed.
Audit committees play a particularly important role when AI affects financial reporting. They should understand how AI is used in forecasting, journal entries, reconciliations, controls, and disclosures, and whether internal controls have been updated accordingly. Evidence of review, validation, and exception handling becomes especially important when AI is involved.
Beyond financial reporting, boards also need visibility into broader AI risks such as data privacy, cybersecurity, regulatory compliance, and reputational exposure. Transparency and accurate public messaging are key expectations.
Applying Public-Company Controls to AI
Private companies are not subject to SOX, but AI does not eliminate the need for internal controls. When AI influences financial reporting, applying public-company discipline is a best practice.
This starts with data governance. Finance teams must ensure that AI inputs are accurate, complete, and authorized. Human review should be embedded into AI-driven processes that affect reporting, estimates, or disclosures.
Internal controls should address access, change management, and evidence retention. Companies should document where AI is used, restrict who can modify models or prompts, and retain logs of outputs and approvals. Periodic testing, such as comparing AI outputs to actual results, helps identify drift or degradation over time.
Segregation of duties remains important. AI can collapse roles if one person controls both configuration and approval, increasing risk if not offset by review and monitoring.
Examples of AI-Related Disclosures by Public Companies
Recent filings show how companies are approaching AI risk disclosure. Many highlight cybersecurity and fraud risks, noting that AI can enable more sophisticated attacks. Regulatory uncertainty is another common theme, as evolving AI and data protection laws may increase compliance costs or legal exposure.
Ethical and reputational risks appear frequently, particularly around bias, misuse of data, and public trust. Some companies specifically mention deepfakes and misinformation as emerging threats. Operational risks related to third-party AI vendors and competitive pressure from rapid AI adoption also appear regularly.
The most effective disclosures tie AI risks directly to the company’s actual systems, processes, and business model rather than relying on generic language.
The CFO’s Role in AI Governance and Disclosure
The CFO plays a central role in AI governance because AI sits at the intersection of financial reporting, controls, and strategy. CFOs are responsible for ensuring that internal controls evolve alongside AI adoption and that AI-related risks are integrated into enterprise risk management.
CFOs also oversee disclosure accuracy, coordinating across finance, legal, technology, and investor relations to ensure public statements about AI are consistent and supportable. Clear communication with the board and audit committee is essential, particularly when AI affects financial reporting or disclosures.
By balancing innovation with discipline, CFOs help organizations capture AI’s benefits without undermining trust in financial reporting.
Bottom Line
AI can accelerate finance, but it also introduces new financial reporting and disclosure risks. CFOs and boards remain accountable for accuracy, transparency, and control, regardless of whether AI tools are internal or third-party. Companies that treat AI as a governed, auditable part of their reporting infrastructure will be better positioned to meet regulatory expectations and maintain investor confidence.
FAQs
Do AI tools used in finance require disclosure?
If AI introduces risks that could reasonably influence investor decisions, those risks should be evaluated for disclosure.
What makes an AI risk material?
An AI risk is more likely to be material if it could significantly affect financial results, liquidity, compliance exposure, or reputation.
Why are boards increasingly involved in AI oversight?
AI can create enterprise-level risks, including financial reporting errors and regulatory exposure, which makes board-level oversight appropriate.
Should private companies apply public-company AI controls?
Yes. Applying public-company discipline reduces risk and prepares companies for audits, financings, and future growth.
Reviewed by YR, CPA
Senior Financial Advisor