10-step checklist for preparing to implement AI
Credit unions adopting AI must be strategic and thoughtful. This list offers a framework to help credit unions prepare for AI implementation that is secure, ethical, and aligned with their mission.
- Define your AI goals and governance structure
Credit unions adopting AI should have clear strategic objectives that align with their business goals — whether that’s improving risk management, modernizing member service, or gaining efficiency in operations. Before committing to new technologies, establish a cross-functional AI governance committee that includes stakeholders from compliance, data analytics, legal, technology, and business units. This group should oversee all AI use cases, maintain a model inventory, and ensure that high-risk models are reviewed regularly.
- Build AI literacy across your credit union
Successful AI adoption depends on widespread understanding. Train staff at all levels on core AI concepts like machine learning, predictive analytics, and generative AI. Credit unions adopting AI should consider offering ongoing AI literacy programs to help team members understand how AI will be used and their roles in oversight and implementation.
Identify use cases and track ROI
Prioritize high-value, low-risk pilot projects that deliver tangible benefits. Whether it’s automating document classification, enhancing fraud detection, or reducing underwriting time, each AI use case should include defined outcomes and an ROI plan. Credit unions adopting AI must continuously measure performance and adjust based on results and risk evaluations.
- Prepare for evolving regulatory expectations
Credit unions adopting AI should prepare for compliance with future expectations from the NCUA, CFPB, and other agencies. Begin by documenting AI governance activities, cybersecurity protocols, and risk assessments. Simulate internal audits to assess regulatory readiness and include AI discussions in board meetings to ensure oversight at the highest level.
- Vet and manage third-party AI vendors
Ask for detailed information on how models are trained, what data is used, and what security protocols are in place. Review contracts for audit rights, breach notification clauses, and usage restrictions. Credit unions planning to utilize AI tools from vendors must confirm that these tools are covered under the vendor’s SOC 2 report and that they comply with privacy laws like GLBA and CCPA.
- Prioritize explainability and ethical use
Document how each model is developed, trained, tested, and validated. Pay special attention to high-risk models, such as those used in credit decisions or fraud alerts. Select models that balance performance with transparency, ensure inputs and outputs are logged, and conduct regular bias audits to maintain fairness and trust.
- Strengthen data privacy and cybersecurity controls
AI adds new layers of complexity to cybersecurity. Ensure sensitive member data is encrypted and cannot be used for unauthorized model training. Ask vendors how they defend against adversarial threats such as prompt injection or model manipulation. Update your incident response plan to include new risks introduced by AI systems.
- Establish generative AI usage policies
Credit unions adopting AI should restrict the use of generative tools to institution-approved platforms and specify the types of data that can be input into these systems. Provide guidance on what constitutes appropriate use and require staff to review AI-generated content for accuracy and compliance before use in member communications or decision-making.
- Plan for member communication and transparency
Inform members when AI is being used in ways that impact them — especially in areas like credit underwriting or fraud prevention. Offer clear opt-out options where possible, and make sure members know there’s still a human in the loop. Credit unions adopting AI should also set clear service level agreements for AI-driven tools that interact directly with members.
- Invest in long-term innovation planning
AI is not a one-time investment. Create a roadmap that aligns with long-term business goals and supports responsible experimentation while maintaining regulatory compliance and ethical standards. Track the ROI of AI initiatives over time, and make adjustments based on results, risks, and changing member needs.
