Artificial intelligence is increasingly becoming part of the credit union technology landscape. It may be embedded in a vendor platform, supporting an employee’s research or helping a team streamline an administrative task.
The most important question is no longer whether a credit union is using AI in some form, but a question of whether or not that use is deliberate.
“The conversation is becoming less about whether a particular vendor has AI and more about whether AI can solve a meaningful business problem for the credit union,” says Pete Major, Vice President, Fintech & Digital Solutions for MDT.
Across MDT’s client community, AI conversations are becoming more practical. Credit union leaders are moving beyond broad questions about what the technology can do and asking where it could create meaningful value, how it should be governed, and whether their organizations are prepared to use it responsibly.
This shift, from curiosity to operational consideration, is meaningful. But it does not mean that every institution is moving at the same pace—or should follow the same path.
Moving Beyond Isolated Experimentation
Many credit unions have begun using large language models, establishing AI policies, or exploring AI-enabled capabilities within existing systems. However, we still see that activity concentrated among a few employees or departments. One team may be testing a tool while the rest of the organization has limited visibility into how it is being used, or how it could support their work.
“Many credit unions have made a large language model available, but employees are still asking how to use it productively in their day-to-day work,” says Dylan Misajlovski, Consultant, Project & Consulting Solutions for MDT. “The challenge is moving from access to meaningful application across the organization.”
That challenge extends well beyond the credit union industry. According to Gallup’s 2026 workplace research, 65% of employees at organizations implementing AI said it had improved their productivity and efficiency. Yet only about one in 10 strongly agreed that AI had transformed how work gets done across their organization.
Bridging that gap—moving from individual productivity gains to sustainable adoption at scale—requires a broader organizational view.
Leadership, operations, IT, information security, compliance, and risk all have roles to play, and so do the employees who best understand the affected processes. The key is working in alignment. Together, they must identify the problem they are trying to solve, determine what responsible use looks like, and establish how results will be reviewed.
This alignment becomes especially important as more technology providers incorporate AI into their products and platforms. Misajlovski cautions that a solution labeled “AI” is not automatically the right answer. Credit unions should look past the terminology and ask: Does this capability address a genuine need? Can we manage it appropriately? What value should it create for our employees, members, or organization?
Finding the Right Problem Before Choosing the Technology
The pressure to become more efficient is real. Credit union teams are managing growing workloads, staffing constraints, and increasingly complex operational responsibilities. At a smaller institution, one executive may perform many different roles. Reducing repetitive work can free up valuable capacity for strategic decision-making, problem-solving, and member relationships.
Still, not every inefficient process calls for AI.
Some challenges may be better addressed through traditional automation, clearer procedures, employee training, or process redesign. A manual workflow should not be automated simply because it is manual; an inefficient or poorly understood process can remain inefficient after technology is applied.
“AI can help analyze spreadsheet data or provide a starting point for a presentation or business plan,” Misajlovski explains. “It can save an employee from starting with a blank page, but human expertise is still needed to review and customize the final product.”
That is why process documentation and thoughtful evaluation matter. Before selecting a solution, a credit union should understand how the work is performed today, where friction occurs, and what an improved outcome would look like. Even a test that does not succeed can provide useful insight by clarifying where AI is and is not a good fit.
The institution’s identity should also guide its choices. For example, a credit union built around personal, human service may use AI extensively behind the scenes to support employees while taking a more selective approach to member-facing applications. The goal is not to use AI everywhere. It is to apply it where it strengthens the credit union’s strategy and service model.
Keeping Employees at the Center
Employee involvement is one of the clearest dividing lines between technology that is merely introduced and technology that is meaningfully adopted.
Employees may be concerned that AI will diminish their responsibilities or eventually replace their roles. Leaders should address those concerns honestly and explain where the organization sees AI fitting into their work. In many cases, its most immediate value is as an assistant—helping analyze information, organize a starting point, or reduce time spent on repetitive administrative tasks.
“AI can help reduce the time employees spend on tasks that are not adding the greatest value, allowing them to focus their time and expertise where they matter most,” Misajlovski adds.
That does not eliminate the need for expertise. It increases its importance. Employees also need training to use AI effectively while supporting compliance, accuracy, and responsible decision-making.
AI-generated information can be incomplete or incorrect. Employees still need to question the output, apply institutional knowledge, and make the final judgment. Human review is especially critical when work affects members, regulatory obligations, or organizational risk.
Training, therefore, must go beyond showing employees how to enter a prompt. Teams need opportunities to experiment in a controlled environment, understand limitations, recognize when sensitive information should not be shared, and learn where human approval belongs within a workflow. The employees closest to a given process can also help identify pain points and shape the future state. People are more likely to embrace change when they have helped author it.
Defining Meaningful Progress
Over the next 12 months, responsible progress will not be measured by the number of AI tools a credit union adopts. A better measure will be whether leaders have created a shared understanding, selected a manageable starting point, and connected that effort to a real organizational priority.
Start small. Learn how the technology works at a practical level. Study how peers are approaching it. Identify one process where improvement would matter, and establish the guardrails and human oversight needed to explore it responsibly.
AI is moving quickly, but meaningful transformation does not come from chasing every new capability. It comes from aligning people, processes, and technology around the work that matters most.
That is how experimentation begins to become strategy—and how strategy becomes sustainable progress.