Lake Michigan Credit Union (LMCU), one of the nation’s largest mortgage lenders, has spent the past few years confronting market volatility, shifting rate cycles and rising member expectations. Those realities made it clear that its long-term growth plans could no longer depend on tactics like adding temporary workers or making incremental adjustments to heavily manual processes. The organization needed a way to scale while letting employees focus on the relationship-building work that makes credit unions popular in the first place.
While it had made early moves into automation with RPA, Noel Watts, senior vice president of Lending Operations at LMCU tells Automation Today, it took a partnership with Greenlight Consulting and UiPath, and a move to agentic automation, to realize real gains. The catalyst? An orchestration-driven model designed to blend human expertise with AI-based reasoning.
Earning Early Credibility
When Watts joined the credit union in 2019, LMCU was already a major producer of mortgage volume nationally. Yet the operation was constrained by seasonality, manual checkpoints, and the natural limits of human throughput during peak cycles. The pandemic-era surge in 2020 made the challenge clearer: LMCU jumped from roughly $3.5 billion to $5.7 billion in annual production, and no amount of contracting could keep pace with the influx.
Watts explains that the early automation discussions were driven by basic operational math: either moderate demand through pricing, or scale with technology. LMCU chose the latter. But while early RPA efforts delivered incremental gains, the transformative results didn’t surface until agentic capabilities matured.
One of the earliest use cases centered on the disclosure team that was responsible for preparing loan disclosures across all products. Employees were spending significant chunks of time completing system-required tasks that were not always relevant to the specific loan file. These tasks bogged down the workflow and diverted staff away from member-facing work.
“We implemented some automation through Greenlight with that team and in a short period of time the project was complete,” Watts explains. “And that group realized a 40 percent increase in efficiency.”
The automation evaluated all tasks associated with each loan file, determined which were unnecessary, and completed them automatically before the file reached an employee. That first automation, delivered less than four months after the partnership launched, resulted in 3,400 hours saved in the affected workflow.
Watts stresses that the goal was not workforce reduction. “Our culture is not one where we’re going to install a bunch of automation or outsource resources and significantly reduce staff. That isn’t who we are,” he says.
Instead, the credit union emphasized its longstanding “protect and preserve” philosophy: technology should safeguard employee roles and enable deeper customer engagement.
This framing created an environment that drove adoption. Staff began contributing use cases and managers gained new visibility into where bottlenecks originated.
The Technology Just Didn’t Exist Before
When it committed to the idea of an agentic journey, LMCU did not have a fully established automation team within IT. That made the partnership with Greenlight Consulting especially important.
According to Shameiz Hemani, Greenlight’s CEO, the partnership began with a focus on enabling LMCU to eventually own and manage its own automation roadmap.
“We started with conversations on what to do, how to enable LMCU, and what the priorities should be,” Hemani notes.
Much of the early work involved unwinding automations implemented by a previous software vendor that was not delivering results. From there, the teams collaborated to build a backlog, identify high-impact areas, and align business and IT leaders around shared priorities. Yet the real acceleration came as UiPath’s agentic capabilities matured.
“In the world of mortgage, we get tactical automations all the time, but the big hits really require agentic technology,” Hemani explains.
UiPath’s platform, he says, enables users like LMCU to orchestrate agents, bots, and human interventions across the full lending workflow. And he emphasizes that this technology didn’t exist in a deployable form just seven months earlier. Its arrival allowed the credit union to shift from incremental automation to a more ambitious redesign of its loan journeys.
The build cycle itself was rapid. “We’ve really been doing this for like three to four months from ideation to completing the first automation,” Hemani says. The speed reflected not only new technical capability, but also the willingness of operational teams to iterate quickly and learn how to work alongside reasoning-based agents.
A Three-Team Partnership Driving New Possibilities
Watts describes the collaboration among LMCU, Greenlight, and UiPath as a true “three-team partnership.” It was characterized by rapid iteration, hands-on support, and an unusual degree of coordination among vendor, consultant, and client.
“They didn’t try to sell us on automation projects that were going to take a year or two. They wanted us to see and experience the value right away,” Watts explains.
Hemani highlights how this partnership dynamic enabled the teams to move quickly into new territory. “We could not have had this conversation seven months ago. That’s how new this technology is,” he says. “It required true partnership and trust because everyone was learning, understanding and building at the same time.”
The impact was measurable. In the home equity area alone, LMCU identified the ability to reduce fulfillment labor from 55 hours to 10. Additional improvements—including a throughput increase of roughly 15 percent in some areas—positioned the credit union to scale without relying on seasonal hiring or contract support. Freed capacity could be reallocated to more complex workflows, while experienced staff could be moved to departments needing additional support.
For Watts, the implications extend beyond the operational benefits. “It allows you to scale at a rate that enables you as an organization to compete at a whole different level,” he says.
As other credit unions explore automation, Watts advises beginning with updated process maps, clear capacity plans, and a focus on identifying redundant, non–member-facing work. From there, organizations should seek partners who are prepared to guide them through the learning curve and help them build internal capability.
The next wave of innovation for LMCU will focus on more advanced orchestration that coordinates agents, bots, and human reviewers across the full lifecycle of each loan. As these tools mature, lenders like LMCU will move closer to real-time, always-on operations that flex with demand while keeping expertise at the center of member interactions.
