Why We Brought AI Leadership In-House at BCU Financial

The Question We Couldn’t Avoid

Like most credit unions, we at BCU Financial have been watching the AI conversation move very quickly. Our members are starting to expect the kinds of experiences AI enables. Our staff are already experimenting with tools like CoPilot on their own. Our vendors are all rolling out new AI capabilities we need to make sense of. And other financial institutions are making it a core part of their offering and way of working.

The question for us wasn’t whether to engage with AI. It was how to do it in a way that fits a credit union: member-first, risk-aware, and grounded in our cooperative values.

Why Remote Training Wasn’t the Answer

Our first instinct was to look at the available AI training programs and send a few of our management team through them. But the more we thought about it, the less that felt right.

Sending one or two people at a time to a remote course creates isolated champions. They come back energized, with new vocabulary and new ideas, but the rest of the leadership team hasn’t been on the same journey. The momentum doesn’t transfer. And the conversations that need to happen, about governance, about vendors, about use cases, about risk, can’t really happen when only a few people in the room share the context.

We wanted our whole leadership team to move forward together. That meant bringing the experience in-house, to us, and shaping it around our actual priorities.

Choosing a Bespoke Coaching Journey

We partnered with Nelson Lee and The General Consulting Company (The GCC) to design something tailored specifically to BCU. This wasn’t a prescribed curriculum pulled off a shelf, it was a bespoke AI coaching journey built around the questions we were actually wrestling with: our banking system conversion, our vendor decisions, our data, our members.

A few things made this approach work for us:

Sessions delivered in-house, to the whole leadership team together. We built a shared understanding and a shared vocabulary at the same time, which meant decisions moved faster afterward.
Working on real BCU problems, not hypothetical case studies. Every session connected to something we were already trying to figure out.
Practical artifacts we could use right away. Not slideware. Things that landed in our hands and got put to work.
Ongoing advisory access as new questions and vendor conversations came up.

What We’ve Actually Built

I want to be specific about this, because I think abstract AI talk is part of what makes the sector cautious. Here’s what the engagement has produced for us so far:

1. Plain-English Access to Our Banking Platform Documentation

We are in the middle of a major banking system conversion, which comes with thousands of pages of manuals, configuration guides, and reference documents. Navigating all of that page by page wasn’t realistic for our staff.

What made The GCC engagement different is that they didn’t just hand us a tool. Nelson and his team showed our internal people how to build this capability themselves, inside our own Microsoft and Copilot ecosystem, using tools we already own. They walked us through what was possible, demonstrated the approach, and then pushed our team to take it the rest of the way. Our internal staff learned the mechanics, internalized the approach, and made it real.

The result: frontline employees can now ask plain-English questions of thousands of pages of documentation and get accurate, sourced answers back in seconds. What used to be a needle-in-a-haystack search is now a conversation, and more importantly, the capability lives with our own team.

2. Starting to Explore Natural-Language Analytics for Non-Technical Staff

Another area we’ve begun exploring through the engagement is how to give non-technical team members the ability to ask real business questions of our own data, without having to wait on an analyst or a pre-built report. The kinds of questions our staff ask every day, like:

“How many people visited the Hamilton branch on Tuesday and how does that compare to the same day last month? How does that impact our staffing levels?”
“What products are our newest members taking?”
“Is there a correlation between particular behaviours and members taking (or leaving) a product?”
“Which of our customers fall into our high-risk category, and why?”

We’re early in this conversation, working through what’s possible, what the data readiness requirements look like, and how to do it responsibly. But even starting the discussion has shifted how our team thinks about what our data can do for us, and what we can put into the hands of the people closest to our members.

3. An AI Policy Template We Could Adapt

Every credit union needs a clear internal position on how staff can and cannot use AI tools. The GCC gave us a policy template as a starting point, which we’ve tailored to our own risk appetite and governance structure. That saved us weeks of blank-page work.

4. Vendor Evaluation Questions

Our existing CX and core vendors are all rolling out AI features. The GCC gave us a structured set of questions to ask those vendors, so that conversations about AI capabilities become substantive evaluations rather than marketing demos. It’s changed the dynamic of those meetings.

What We’ve Learned So Far

A few reflections from where we sit today:

A leadership team that has been through the journey together makes AI decisions faster and with more confidence than one where knowledge is pocketed in a few individuals.
The shift in internal conversation has been noticeable. Less anxiety and speculation about AI, more grounded discussion of specific opportunities and risks. And there’s an eagerness to find further leverage.
Having tangible tools in staff hands, not just frameworks on a slide, is what actually builds belief that AI is practical, not hypothetical.
Our people are asking better questions of our vendors, and getting better answers.
The impacts of these solutions will be seen quickly and the business value of AI will become more tangible in the weeks ahead.

What I’d Say to Other Credit Unions

If you’re thinking about how to approach AI in your own credit union, here’s what I’d offer based on our experience:

Move the whole leadership team at once. Sending individuals off one by one will not produce organizational momentum. Whatever you do, do it together.

Bespoke beats prescribed. Your core system, your members, your risk profile, your strategic plan. These are specific to you. The engagement that works for you should be too.

Insist on tangible artifacts. A working tool. A usable policy. A set of questions for your vendors. If an AI engagement doesn’t leave something concrete behind, it was a seminar, not a transformation.

Keep members at the centre of every AI conversation. The question is never “what can AI do?” It’s “how does this serve our members and stay true to who we are as a credit union?”

We’re early in this journey, and we don’t pretend to have all the answers. But we feel confident we’re asking the right questions, as a team, and that we have a partner in The GCC helping us get to good answers faster.

If any of this resonates with your credit union’s own thinking, I’d welcome the conversation.

Jim Kozack is Chief Operating Officer at BCU Financial. BCU Financial’s AI Leadership engagement is delivered in partnership with Nelson Lee and The General Consulting Company.

BCU Financial is a modern, Ontario-based financial cooperative providing banking, insurance, and wealth management services to members, with a longstanding focus on serving the Ukrainian-Canadian community. Through personalized advice, digital banking, and local branch service, BCU Financial supports the financial well-being and prosperity of the communities it serves.