Podcast Summary: CIO Leadership Live

Episode: TruStone Tech Execs Share AI-Driven Credit Union Transformation
Date: October 1, 2025
Host: Lucas Merian (A)
Guests: Gary Jeter (B) – Chief Technology Officer, TruStone Financial Credit Union
Meka Tao (C) – Vice President of IT Development and Global Planning & Delivery

Episode Overview

This episode explores TruStone Financial Credit Union’s digital transformation journey, with a particular focus on how the organization leveraged artificial intelligence (AI) following a major merger. CTO Gary Jeter and VP Meka Tao share their hands-on experiences tackling system integration, innovating with AI and generative models, regulatory and data challenges, internal culture shifts, and how these changes are driving member and employee experiences at one of Minnesota’s largest credit unions.

Key Discussion Points & Insights

1. Merger-Driven Modernization and Tech Integration

[00:05 – 02:41]

• TruStone merged with Firefly Credit Union in the biggest “merger of equals” at that time.
  • “We took it from the approach of best in breed as far as the technology.” — Gary Jeter [00:51]
• Integration blended 60% TruStone and 40% Firefly tech stacks.
• Migration highlights:
  • Moved core financial platform to a private cloud.
  • Brought in an enterprise data team (previously only at Firefly).
  • Adopted iPaaS for system integration and reusable connectors.
  • Prioritized security improvements and agility.

2. Digital Banking Evolution: Mobile and Fraud Features

[02:41 – 04:34]

• Majority (55%) of members now prefer digital/online banking.
• Roadmap for customer experience:
  • Launching advanced card controls with new alerts/notifications.
  • Modernizing loan origination for “three-minute account opening, two-minute loan.”
  • Integrating fraud detection directly into core systems for near real-time alerts.
  • “We just integrated our fraud solution within the core...bringing some of those near real-time transactions so that our fraud and BSA department can tackle trending suspicious activities.” — Meka Tao [04:11]
• Emphasis on reducing friction and enhanced experience.

3. AI for Fraud, Security, and Generative Applications

[04:34 – 07:11]

• Current anti-fraud tools utilize vendor-embedded machine learning.
• Exploring use cases for generative AI (GenAI):
  • Actively piloting GenAI for frontline staff knowledge:
    • All policies/procedures searchable via a ChatGPT-like experience.
    • “From a 15 minute [wait]… down to under three minutes… that’s a 5x improvement.” — Meka Tao [06:00]
• AI reduces knowledge retrieval friction and enhances both employee adoption and member service.

4. Data Quality, Cleansing, and LLM Implementation

[08:00 – 09:51]

• Deployed LLM on existing policy documents, accepting initial lower data quality to gain speed.
• Employed “thumbs up/thumbs down” feedback to iterate and cleanse:
  • Launched with 23% data quality, reaching 92%.
  • “Rather than waiting for perfection, we just got it out there.” — Gary Jeter [09:16]
• Moved all policy content from legacy intranet to the LLM tool—now the sole source for frontline staff.

5. Change Management and AI Education

[10:32 – 13:00]

• Executive/board education was the first step:
  • Explained GenAI fundamentals early to top leadership.
• Created GenAI employee courses; they rapidly became the most popular internal training.
• Developed digital maturity assessments to pinpoint skill/tech gaps.
• Loosened AI tool access (ChatGPT, Gemini, NotebookLM, Claude AI) with compliance guardrails:
  • “We have monitoring… to make sure they don’t put any PII.” — Gary Jeter [11:00]
• Change management is constant—focusing on upskilling and maturity, not just tech shifts.

6. Regulation, Security, and Compliance

[13:00 – 16:17]

• AI regulation is a “work in progress”:
  • Inputs and outputs can be assured, but “what’s happening between that black box, that’s the part… you can’t explain.” — Meka Tao [13:56]
• Growing regulatory flexibility:
  • Credit union oversight is “tech-leaning forward,” allowing experimentation under careful legal/compliance review.
• Close partnership with vendor and legal teams to stay compliant (especially with chatbot/chat services).

7. AI Costs and Control

[16:17 – 17:54]

• Used Senso AI (CUSO) at a fixed cost for policy/procedure chatbot.
• As new data-use LLM functions grow, cost management (especially for prompt-heavy tools) remains unresolved:
  • “We have not figured out the cost thing yet. So we’re…that’s going to be a big aspect.” — Gary Jeter [17:02]

8. Addressing Skills Gaps and AI-Assisted Development

[17:54 – 20:05]

• Not experiencing acute skills shortages, but recognize possible future challenges.
• AI-powered dev tools (like Cursor, Gemini API) speed up work and reduce grunt tasks.
• Hackathons and experimentation help foster learning and rapid prototyping.
• “If anything, this [AI] is just going to make folks’ jobs easier.” — Gary Jeter [18:34]

9. Favorite Tech Gadgets & Personal AI Use

[20:12 – 21:46]

• Gary’s favorite: ChatGPT voice for on-the-go brainstorming during commutes.
• Meka’s favorite: NotebookLM for deep research, summaries, and document podcasting.
  • “NotebookLM…if you wanted to consume a document, it makes it into a podcast.” — Gary Jeter [21:28]

Notable Quotes and Memorable Moments

• On policy knowledge management with GenAI:
  “It’s gone down to under three minutes or a lot faster. I mean, that’s a 5x improvement.” — Meka Tao [06:00]

• On launching with imperfect data:
  “Rather than waiting for perfection, we just got it out there and now it’s sitting at about 92% data quality.” — Gary Jeter [09:16]

• On regulation’s black box challenge:
  “[Regulators] can understand the input of data going in and the output of it. But what’s happening between that black box, that’s the part…you can’t explain.” — Meka Tao [13:56]

• On ChatGPT as a daily assistant:
  “It’s like a brainstorming companion on my very own joyful commute.” — Gary Jeter [20:54]

Timestamps for Key Segments

• [00:05] — Introductions and merger context
• [02:41] — Digital/mobile apps and fraud features
• [04:34] — AI for fraud and knowledge management
• [08:00] — Launching LLM policy tool and data cleansing
• [10:32] — Change management, education, and upskilling
• [13:00] — Regulation and compliance challenges
• [16:17] — Managing cloud-based AI costs
• [17:54] — Skills gaps and AI-assisted software development
• [20:12] — Personal favorite tech tools

Summary

TruStone Financial’s executive team candidly discusses bringing AI into highly regulated credit union operations. Their pragmatic, value-driven approach—using generative AI to expedite knowledge, embracing imperfect launches, iterative cleansing, hands-on training, and close vendor collaboration—demonstrates how digital transformation is about both people and platforms. With regulatory issues still evolving and costs an “art of the possible,” the credit union continues to focus on speed, member experience, and bridging the skills and change management divide, all while keeping a sense of humor and curiosity about the latest tech.