Congratulations! Your credit union has taken the leap and implemented an artificial intelligence program. You made sure the program has defined use cases tied to business KPIs along with a defined owner.

Your program defines expected outcomes and has a governance structure tied to a risk assessment, and your employees are trained in its operation. These are all foundational elements of a successful AI program, but they’re only the starting line.

Five key processes after implementing AI

Artificial intelligence systems continue to evolve rapidly, which means your AI program cannot be static. It requires active care and oversight to ensure reliable outcomes, effective guardrails, and work output that is relevant and keeps the credit union out of potential trouble.

1) AI systems are not “set and forget.” Implement continuous production monitoring and regular independent evaluation.

Trustworthy AI depends on reliable measurements and evaluations of its work product. Therefore, every production AI system should have an ongoing measurement and monitoring plan (not just pre-launch testing), with regular independent assessments to ensure outcomes are on target.

According to NIST’s artificial intelligence RMF Playbook, a properly implemented AI program will include methods and metrics for the risks mapped, documenting what can and cannot be measured, tool security assessment (involving your internal team or employing independent assessors), documenting test, evaluation, validation and verification artifacts, and monitoring behavior of the AI in production.

Ongoing management creates a feedback loop to management and will help confirm the value of the program (ROI) and confidence that it’s not creating liability issues.

2) Implement a formal “AI Operating Model” with clearly defined owners, inventory, documentation, and decision rights.

Is your AI system a critical product for your credit union? If so, ensure that clearly defined organizational accountability, system inventories, and lifecycle documentation are in place.

Once again, we turn to NIST’s helpful AI RMF Playbook for recommendations concerning managing AI programs throughout their lifecycle:

• Maintain a living inventory (models, vendors, use cases, owners, data sources, deployment locations).
• Document clear decision rights for: approving new use cases, approving model changes, and authorizing decommissioning.
• Documentation that travels with the system: intended use, limits, test evidence, and monitoring plan.

3) Implement guardrails and test them using adversarial tactics that attempt to trip up AI outputs.

Critical AI systems should be tested (assessed) for how they fail and how they can be exploited, not just for “average accuracy.” AI “red team” testing is a foundational component of safety and security evaluations and should fit into your ongoing governance program.

This type of testing seeks to understand how AI systems can fail or be exploited to provide dangerous or unwanted outputs that, if not caugh,t could result in increased liability or financial loss to the credit union:

• Pre-release and periodic red-team exercises for high-impact systems.
• Document security evaluation outcomes.
• For “General AI,” adopt a GenAI-specific profile and risk set, which may have a lower governance standard depending on your approved use cases.

4) Expand your incident response plan to include critical AI systems

If your AI is involved in critical business functions, expand your incident response playbook to include it. Your IR plan should be expanded to include:

• Defined triggers for “serious incidents,” response roles, internal/external communications, and rollback/recovery.
• Monitoring records and technical documentation are retained as evidence. Your oversight program will be foundational evidence of your own due diligence for examiners and potential cyber insurance claims.

5) Implement a value scoreboard. Prove ROI and stop what doesn’t work.

Allocating significant resources to AI programs that lack clear business outcomes leads to solutions disconnected from real-world impact. They will consume resources without providing meaningful results.

Every AI system must have a value hypothesis, business KPIs, and a cadence to decide whether to scale up or down particular AI programs, seek specific improvements, or retire it altogether.

• KPIs include both value metrics (cost savings, revenue lift, cycle time) and risk metrics (incident rate, safety issues, bias indicators, security events).
• Independent review of outcomes vs. expectations. Consider involving internal experts not on the development team and/or independent assessors.

Monitor and update AI

Implementing AI is an important milestone, but it is not the finish line. For credit unions, the real work begins after deployment, when systems must be actively governed, measured, and improved over time. AI that is left unmanaged can quietly drift away from its intended purpose, creating risk, eroding trust, and consuming resources without delivering value.

By treating AI as a living system with clear ownership, continuous monitoring, tested guardrails, incident readiness, and measurable business outcomes, credit unions can move beyond experimentation and toward sustainable advantage. The institutions that succeed with AI will not be those that deploy the most models, but those that operate them with discipline, transparency, and accountability.