The conventional wisdom surrounding artificial intelligence in financial services is that the race has already started, and anyone who is not sprinting has already fallen behind.
Three executives participating in a PYMNTS discussion offered a different view. Agentic AI may be transforming financial services, but credit unions have an opportunity to move deliberately rather than recklessly, using the technology to strengthen member relationships instead of simply replacing manual work.
For Jeremiah Lotz, senior vice president, Enterprise Data and AI, at Velera, the technology represents a shift away from administrative automation.
“This is a critical moment for credit unions,” Lotz said. “AI is moving from a back-office efficiency type of opportunity to becoming something that can support decisions and interactions and workflows in real time.”
Credit unions compete differently than many financial institutions. Their value proposition has long rested on trust, service and personal relationships. Agentic AI has the potential to reinforce those strengths, but only if institutions resist the temptation to treat automation as an end in itself.
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The opportunity extends beyond reducing costs. AI can give frontline employees better information before conversations, accelerate fraud investigations, improve dispute resolution and simplify internal research, allowing staff to spend more time with members and less time navigating administrative work.
“The goal shouldn’t be to replace the relationship aspect of it,” Lotz said. “It really should be, ‘How do we use it to scale the quality of that relationship?’”
Outcomes, Not Just Speed
Cal Al-Dhubaib, principal technologist at Rubrik, said institutions may be measuring success the wrong way.
“I am convinced that time savings is the worst measure of the value of AI,” he said.
There’s a broader challenge facing financial institutions. Automating an existing process may save labor while preserving unnecessary complexity. Agentic AI instead offers an opportunity to rethink workflows from the ground up.
A mortgage approval process illustrates the point. Rather than accelerating dozens of existing handoffs between departments, institutions can redesign those workflows entirely, introducing new escalation points where human judgment matters while eliminating unnecessary friction elsewhere.
The result should not simply be fewer hours worked. It should be better outcomes for members.
That philosophy resonated with Lotz, who said focusing on outcomes aligns naturally with the trust that credit unions have already established.
The discussion also challenged the common assumption that every institution is late to the game.
“The popular narrative right now is the AI race is off, and you’re already behind,” Al-Dhubaib said. “The reality is, we’ve only had a couple years to even start building this best practice.”
Rather than chasing competitors, leaders should first understand where AI creates value, where architectural constraints exist and where governance must intervene before systems begin making consequential decisions.
Trust Becomes the Competitive Advantage
Agentic AI introduces another complication. Existing trust does not automatically transfer to technology.
Credit unions should not assume that decades of member confidence instinctively extend to AI-enabled interactions, Lotz said.
“The biggest mistake that we could make is to assume that the trust that credit unions have already gained with their memberships just transfers automatically to any new technology,” he said.
Instead, institutions must continue earning that trust through transparency. Members should understand where AI participates, where humans remain involved, and who remains accountable when decisions affect money or personal information.
That transparency begins with employees.
Cheryl Middleton Jones, chief people officer at Velera, said organizations cannot leave workers to fill information gaps on their own.
“If we don’t start there, then people fill in the white space,” she said. “They fill in the blanks.”
Her prescription is straightforward.
“We’re introducing [AI] more for workplace enablement in order to help people do their jobs more effectively,” Jones said.
As routine work becomes automated, she said she believes communication skills, empathy, judgment and discernment become more valuable rather than less. Employees must understand when to rely on AI, when to question it and when circumstances require overriding automated recommendations.
That partnership model could ultimately improve customer experience by allowing employees to devote more attention to listening, advising and relationship building.
“Ideally, if we do it right, we’re making sure that [the] member experience becomes more personal,” she said.
The conversation also underscored the importance of communication transparency. Workers need confidence in the data AI uses, clarity about system limitations and repeated opportunities to learn rather than one-time training sessions.
Governance Before Scale
Governance should not be treated as an obstacle, Lotz said.
Responsible AI oversight should encompass data quality, model supervision, security, compliance, human review points and outcome measurement. The objective is not simply determining whether AI can perform a task, but whether it should.
That responsibility cannot belong exclusively to technology teams.
Jones said operations, compliance, people leaders and frontline managers all bring different perspectives to governance because each understands different forms of organizational risk.
Al-Dhubaib added that governance carries real costs. Monitoring AI systems, maintaining trust infrastructure and reviewing exceptions all become part of total ownership.
Those investments make thoughtful use case selection increasingly important. Institutions should focus first on areas where AI can remove friction while preserving accountability rather than attempting wholesale transformation.
The panel ultimately rejected the idea that the future belongs to whichever organization deploys AI fastest.
As financial institutions expand their use of AI, the technology itself may prove easier to implement than the cultural changes surrounding it. Credit unions have spent decades building confidence with members. Their next challenge may be ensuring that confidence extends to systems designed to act alongside people without replacing the judgment that members still expect when the stakes are highest.
Watch the full interview to hear more about:
Why agentic AI differs from traditional automation and workflow tools. Why outcomes matter more than time savings when measuring AI success. How communication and training shape employee adoption. Why governance should enable innovation instead of slowing it.For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
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