Many financial institutions are asking, “What should our board know about AI?” The answer is practical rather than technical: boards do not need to become AI experts, but they do need to understand their oversight responsibilities, the risks introduced, and how Management is governing Artificial Intelligence (AI) use across the institution.
AI has moved from a technology topic to a boardroom discussion. Whether AI is being used through Microsoft Copilot, ChatGPT, fraud detection platforms, vendor-provided tools, or embedded features within existing banking systems, financial institutions are increasingly finding that AI adoption is occurring faster than traditional governance processes can keep pace.
AI Has Become a Board-Level Governance Topic
Artificial Intelligence (AI) is no longer limited to standalone technology projects. AI capabilities are appearing in common productivity tools, cybersecurity platforms, fraud monitoring solutions, customer relationship management systems, marketing tools, vendor portals, analytics platforms, and core banking services. As a result, institutions may have AI exposure even before they have adopted a formal AI strategy.
For community banks and credit unions, the board’s first oversight priority is visibility. Management should be able to identify where AI is used, whether the use is institution-approved or vendor-provided, what data is involved, who owns the process, and how risks are evaluated. Without that visibility, AI adoption can become inconsistent across departments and difficult to control.
AI Governance is Broader than Information Technology
AI is often implemented with support from technology teams, but the risks and opportunities created by AI span the enterprise. AI may affect customer communications, lending support processes, fraud detection, employee productivity, information security, compliance monitoring, marketing, vendor relationships, and operational decision-making.
Because AI touches multiple risk areas, oversight should not rest solely with IT. The board should expect AI governance to function similarly to cybersecurity, vendor management, strategic planning, and enterprise risk management: management owns day-to-day implementation, while the board provides direction, challenge, and oversight.
Core Board Oversight Responsibilities
Directors should focus on oversight rather than operational approval of every AI use case. The following five responsibilities give boards a practical framework for evaluating whether management’s AI program is appropriately governed.
- Establishing an AI Risk Appetite
- The board should ensure management has defined the types of AI use that are acceptable, the categories of data that may be used, whether public AI tools are permitted, and the level of review required before AI capabilities are deployed. A clearly stated risk appetite helps prevent inconsistent, department-by-department adoption.
- Confirm Governance and Decision-Making
- Management should maintain a process for identifying, evaluating, approving, documenting, and monitoring AI use cases. This may be handled through an AI committee, risk committee, technology steering committee, or another governance structure with defined responsibilities.
- Oversee AI-Related Risk
- The board should confirm that AI risks are incorporated into existing risk management practices, including information security, privacy, compliance, model or output reliability, third-party risk, operational resilience, fraud, and customer impact.
- Monitor Strategic Alignment
- AI adoption should support the institution’s business strategy, not simply reflect a desire to use new technology. Management should be able to explain the business problem being addressed, expected benefits, success measures, and how each initiative aligns with institutional priorities.
- Promote Accountability
- Every approved AI use case should have an owner, documented approval path, ongoing monitoring expectations, exception reporting, and a process for escalating issues. Accountability is especially important when AI capabilities are embedded in vendor platforms or used by multiple departments.
Key AI Risks Boards Should Understand
The board does not need to review the technical design of AI models. It should, however, understand the major risk categories that management is responsible for identifying, assessing, and monitoring.
- Data leakage and privacy
- AI tools may process confidential, customer, employee, or institution data. Management should define what data may be used and ensure sensitive information is protected.
- Inaccurate or unreliable outputs
- AI-generated content can be incomplete, incorrect, or presented with unwarranted confidence. Institutions should require human review where outputs influence decisions, customer communications, or regulatory obligations.
- Bias, fairness, and customer impact
- AI-supported processes may introduce or amplify unfair outcomes if the use case, data, or decision process is not appropriately governed.
- Third-party and embedded AI
- Vendor platforms may include AI features that are enabled by default or added through product updates. Vendor due diligence should address AI capabilities, data usage, contractual protections, monitoring, and notification expectations.
- Shadow AI
- Employees may use unapproved AI tools to increase productivity, creating risk if confidential information, customer data, or internal documents are entered into public or unmanaged platforms.
- Cybersecurity and fraud
- AI may strengthen security monitoring and fraud detection, but it also enables more convincing phishing, social engineering, deepfakes, and automated attacks.
- Compliance and recordkeeping
- AI use may affect regulatory obligations, auditability, explainability, retention, complaint handling, and evidence of management review.
Practical Oversight Expectations for Directors
Boards should not rely on templated questions alone to oversee AI. Instead, directors should evaluate whether management has established an appropriate AI oversight process by looking for the following evidence:
- A current AI inventory that identifies approved tools, use cases, owners, data involved, vendors, and risk ratings.
- A written AI policy and acceptable-use standard covering approved tools, prohibited use, data handling, employee responsibilities, and escalation expectations.
- A defined review process for new AI use cases before deployment, including information security, privacy, compliance, vendor management, and business-owner review where applicable.
- Clear guidance on public AI platforms and employee use, including restrictions on entering confidential, customer, vendor, employee, or nonpublic institution data into unmanaged tools.
- AI vendor due diligence that addresses data usage, model training restrictions, contractual protections, monitoring, incident notification, and changes to embedded AI functionality.
- Periodic reporting to the board or a board committee that highlights material use, risk exceptions, incidents, remediation status, and strategic value.
- A response approach for AI-related incidents, including data leakage, unauthorized tool use, vendor issues, fraud events, inaccurate customer-impacting outputs, or compliance concerns.
Looking Ahead
AI presents meaningful opportunities for community banks and credit unions to improve efficiency, strengthen decision-making, enhance fraud detection, and support better customer and employee experiences. Those benefits are most likely to be realized when governance is established before adoption accelerates.
Boards do not need to become technical experts. They need confidence that management knows where AI is being used, has implemented appropriate controls, understands the related risks, and is managing AI in a manner consistent with the institution’s strategic priorities and risk appetite.
Looking to help your financial institution strengthen its AI governance and oversight? Contact us to schedule a consultation.