Artificial intelligence is becoming an increasingly common part of accounting and finance operations. From automating accounts payable processes to preparing financial reports and identifying discrepancies, AI offers opportunities to improve efficiency and reduce manual work. However, as these technologies become more involved in financial processes, organizations must consider how to maintain internal controls, protect sensitive information, and satisfy audit requirements.
During a recent Controllers Council and Intuit webinar, “AI Governance for Finance: Deploying AI Tools That Survive an Audit,” Executive Director Neil Brown welcomed Genevieve Hancock, Principal Consultant and Owner of G. Hancock Advisory, and OJ Laos, Director of AI and Innovation at Sorren, to discuss practical approaches to AI governance, regulatory compliance, and audit readiness.
Controllership 2030: AI Governance Becomes a Growing Priority
To begin the discussion, Brown shared findings from the Controllers Council’s Controllership 2030: Predictions Study, highlighting how AI is expected to reshape the controller’s role.
According to the study, AI management responsibilities are projected to increase from 10% today to 34% by 2030. AI governance ranked as the leading emerging governance priority at 80%, followed by data governance at 64% and cybersecurity at 59%. Additionally, 81% identified AI proficiency as a key skill requirement for controllers by 2030.
These findings emphasize the growing responsibility of finance leaders to oversee AI adoption while maintaining effective internal controls, regulatory compliance, and accountability.
Where AI Is Delivering Value in Finance
AI is already influencing several accounting processes, particularly those involving large amounts of data, recurring calculations, and manual reconciliation.
Hancock identified accounts payable as one area where AI can provide considerable improvements. Traditional three-way matching, which compares purchase orders, invoices, and receipts, often requires extensive manual review. AI-enabled systems can assist with matching records, identifying discrepancies, and reducing administrative work.
She also discussed opportunities in financial consolidation, reporting, and board presentations, where AI can help reconcile information and identify important performance drivers.
Laos emphasized AI’s ability to connect information across systems that were not originally designed to work together. For example, AI can extract financial information from invoices and other documents, then organize it for use in accounting applications.
While these applications can improve productivity, both panelists emphasized that finance professionals remain responsible for reviewing the resulting information.
Addressing AI Governance Risks
As AI becomes more involved in accounting processes, organizations face additional concerns about accuracy, data security, regulatory compliance, and accountability.
Laos cautioned that increasingly sophisticated AI outputs can create a false sense of confidence. Financial analyses and reports may appear convincing even when they contain mistakes that require further investigation.
He also identified security concerns involving employees who use AI to develop applications or connect systems without fully understanding the associated risks. Organizations need clear policies governing which applications employees may use and how sensitive financial information is handled.
Hancock highlighted another important concern: accountability drift.
As AI assumes responsibility for preparing analyses, reconciliations, and other financial work, organizations may gradually lose clarity about who owns the resulting conclusions.
Although technology may perform much of the underlying work, an identifiable individual or team must remain responsible for reviewing and approving the final output.
Hancock also discussed shadow AI, particularly when employees use unauthorized tools or automated transactions are processed without sufficient oversight. Documented review procedures are essential for maintaining accountability and protecting confidential information.
Incorporating AI Into Existing Internal Controls
One of the central recommendations from the discussion was to build AI governance around established internal control frameworks.
Hancock recommended evaluating AI-enabled processes through existing financial reporting controls, risk assessments, accounting policies, and the COSO framework.
She described two ways organizations should assess AI: AI as a system and AI as a preparer.
When evaluating AI as a system, organizations should examine access permissions, configuration changes, monitoring procedures, model updates, and the instructions used to perform financial tasks.
When evaluating AI as a preparer, the resulting work should receive management review similar to work completed by accounting personnel. These reviews must be detailed enough to identify meaningful errors rather than simply confirming that a process was completed.
Laos supported using established frameworks and recommended exploring AI capabilities already available within approved enterprise software.
Using existing platforms can reduce additional vendor oversight, limit unnecessary data transfers, and help organizations maintain consistent security arrangements.
Maintaining Compliance with Accounting Standards
AI can assist with accounting research, technical memoranda, financial calculations, and reporting procedures, but its involvement does not change an organization’s obligations under applicable accounting standards.
Hancock emphasized that finance teams must continue to apply the appropriate reporting framework, whether US GAAP or IFRS, and independently verify information supporting financial conclusions.
This includes reviewing accounting guidance, confirming references to authoritative literature, and ensuring that cited standards have not been superseded.
She also recommended retaining human responsibility for accounting policy elections, materiality assessments, and significant management judgments.
Laos added that finance professionals should recognize the differences between general-purpose AI applications and specialized accounting tools. Regardless of the technology used, AI-generated conclusions require appropriate verification.
Preparing AI-Enabled Processes for an Audit
Audit readiness was another major focus of the webinar.
As organizations incorporate AI into reconciliations, journal entries, and financial reporting, auditors need sufficient documentation to understand how conclusions were reached.
Hancock explained that organizations should be able to reconstruct an AI-assisted process and demonstrate the evidence supporting its results.
Important documentation includes:
- The purpose and owner of the AI-enabled process
- Input data, outputs, and supporting calculations
- System configurations and relevant changes
- Management review procedures and approvals
- Exceptions identified and how they were resolved
- Evidence of human oversight and accountability
These records help auditors evaluate whether the process operated as intended and whether the resulting financial information can be relied upon.
Laos also discussed how AI could change audit preparation by simplifying document collection, analyzing larger transaction populations, and supporting more continuous monitoring.
However, as automated systems assume additional responsibilities, organizations will need reliable methods for tracking activity and identifying who authorized and reviewed financial transactions.
Getting Started with AI Governance
For organizations beginning to adopt AI, the panelists recommended starting with existing technology, established policies, and employee involvement.
Laos encouraged finance leaders to explore approved AI capabilities within applications their organizations already use, including productivity software and ERP platforms.
He also emphasized the importance of providing employees with practical tools and clear policies. When employees understand their options, organizations are better positioned to prevent unauthorized AI use.
Hancock connected successful AI adoption to familiar change management principles. She recommended involving finance, IT, legal, and other relevant stakeholders early in the implementation process.
Training is another important consideration. Hands-on learning opportunities can help employees understand how AI tools function within their responsibilities and recognize when additional review is necessary.
The panelists also discussed who should lead AI initiatives. While the appropriate leadership structure depends on organizational size and available expertise, cooperation between finance and IT remains essential.
A CFO or controller with experience managing technology implementations may be well positioned to oversee finance-related AI initiatives, with external consultants providing additional support when needed.
Final Takeaways for CFOs and Controllers
AI presents meaningful opportunities to improve accounting efficiency, strengthen financial analysis, and simplify audit preparation. However, realizing these benefits requires careful attention to governance, documentation, and internal controls.
Finance leaders should begin with established frameworks, assign clear ownership of AI-enabled processes, and apply review procedures appropriate to the financial risks involved.
As Hancock emphasized during her closing remarks: “the accountability for the financial results still belong to humans.”
As AI becomes more involved in financial reporting, maintaining that accountability will be essential for organizations seeking to improve productivity while meeting audit and compliance expectations.
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