Credit Union of America Cuts Agent Turnover from 90% to 24% with Employee AI

Credit Union of America's internal knowledge was scattered across network drives, shared folders, and intranet pages, making it difficult for frontline employees to quickly find accurate answers during member interactions. Staff frequently had to search multiple systems or escalate to colleagues for help, slowing down service and creating friction for both employees and members. Agent turnover in the member support department had reached 90%, compounding the operational burden on remaining staff.

In late 2023, Credit Union of America deployed interface.ai's Employee AI to centralize internal knowledge into a single conversational interface. Employees can ask questions in plain language and instantly receive relevant policies, procedures, and operational guidance drawn from the credit union's internal documentation. The rollout was phased department by department — starting with member support teams — with an alpha testing phase involving frontline staff and a monthly incentive program to drive ongoing adoption and knowledge base refinement.

Employee AI quickly became a daily tool, with staff asking an average of 15–25 questions per day through the platform. Member satisfaction reached a score of 4.73 out of 5, reflecting the improved accuracy and consistency of frontline responses. Most notably, agent turnover in the member support department dropped from 90% to 24%, driven by reduced friction and greater employee confidence during member interactions.

• Early employee involvement in testing and phased rollout — including alpha phases and continuous feedback loops — is critical for adoption in frontline credit union environments.
• Centralizing institutional knowledge into a conversational AI tool simultaneously addresses employee retention and member satisfaction, making it a high-ROI investment.
• Incentive programs tied to usage and feedback accelerate adoption and continuously improve the AI's knowledge base over time.