Credit unions have a rare opportunity to leapfrog the traditional digital banking model. Just as emerging markets skipped landlines and went straight to mobile, credit unions can bypass years of incremental upgrades and move directly to AI-driven, cognitive banking. The institutions that embrace this shift now won’t just keep up—they’ll set the pace for the industry.
After nearly 25 years in digital banking, I’ve seen institutions pour billions into transformation efforts—most of which fail to deliver their intended value. Productivity at U.S. banks is declining, technology spend is rising, and only 30% of financial institutions have successfully implemented their digital strategy, according to McKinsey.
Why? There are many reasons for this, but from my observations it is a combination of the approach to digitize long established internal business processes with complex traditional technology stacks. This leads to expensive projects to deliver, as Henry Ford would say, “faster horses”. With AI reimagining customer engagement models with some amazing technology there is a great opportunity to leapfrog past traditional digital banking to a new model: cognitive banking.
The Evolution: From DIY to AI
The transition from digital banking to cognitive banking can be thought of as an evolution through three stages:
- Do-it-yourself: Members handle tasks employees once did, navigating digital platforms on their own.
- Do-it-with-me: AI-powered co-pilots assist members, making self-service more intuitive.
- Do-it-for-me: AI anticipates needs and takes action proactively.
This is the future of credit unions: moving from reactive self-service to hyper-personalized proactive service.
Digital Banking’s Core Problem: Making Customers Do the Work
Digital banking was meant to improve access, but instead, it shifted the work to customers. Institutions built interfaces that let members DIY tasks once handled by trained employees—without rethinking the process itself.
What’s worse, those employees were specialists, focused solely on delivering these services efficiently. In our attempt to enhance the customer experience, we ended up focusing on pixels instead of process. We gave customers screens to complete tasks but left them to navigate complex workflows on their own.
If you’re like me, your expectations have shifted. I use browser-based search engines less and AI-driven chat more. Instead of navigating through a menu, I type a question and get an answer. That’s what members now expect from their credit union.
Do-It-With-Me: The AI Co-Pilot Model
Generative AI provides an entirely new approach to digital banking—one that combines expert assistance with 24/7 convenience. Instead of static menus and search bars, AI-driven co-pilots engage members in natural, intuitive conversations.
Early adopters are embracing this co-pilot model, where AI assists both employees and members in real-time. Over time, as trust in AI grows, institutions can transition to full self-service AI agents.
Do-It-For-Me: The Cognitive Banking Model
The real breakthrough is predictive service. Instead of waiting for members to take action, credit unions can use AI and data to anticipate their needs and proactively assist.
Take a simple example: updating a beneficiary. I recently discovered one of my investment accounts didn’t list my wife as a beneficiary, while others did. It took me far too long to find where to update it, and I eventually had to call support.
Imagine a smarter system. It recognizes the inconsistency and sends a secure message:
“We noticed one of your accounts doesn’t have a beneficiary listed. Would you like to update it?”
With a simple “Yes” or “No”, the task is done.
This shift—from Pull/React (customer-initiated service) to Predict/Push (institution-initiated service)—drives efficiency and dramatically improves the member experience.
Overcoming the “We’re Not Ready for AI” Mindset
The biggest barrier to AI isn’t technology. It’s hesitation.
Many credit unions assume they need perfect data or a major system overhaul before they can start. That’s not true. AI doesn’t require years of cleanup or expensive upgrades to make an impact. Most credit unions already have the data they need—it’s just locked in core banking systems and scattered across platforms. The real challenge isn’t collecting data, it’s making it accessible and actionable.
This is where AI changes the game. Instead of following the traditional digital transformation playbook, credit unions have a chance to leap ahead. AI can enhance member experiences, automate operations, and improve decision-making without waiting for a full technology refresh.
Platforms like Agentforce, built on Salesforce, make this even easier. If your core system is connected to Salesforce, you already have the foundation to get started. Even with a limited dataset, AI can deliver results—streamlining account servicing, automating routine inquiries, and improving fraud detection.
Regulators are supportive. The National Credit Union Administration (NCUA) is pushing for AI-driven efficiencies, signaling that the shift isn’t optional. The institutions that start now will lead. The ones that wait will be playing catch-up.
“My priorities as Chairman include…Promoting the appropriate use of artificial intelligence (AI) as a tool for NCUA employees. One goal is enhancing productivity, but it’s also true that regulators who use technologies are more apt to understand why the regulated use them.”
The Cognitive Banking Era
Maybe this time, we finally have the technology to get digital transformation right—so McKinsey won’t be writing about failed banking strategies a few years from now.
For credit unions that feel behind, this isn’t a setback; it’s an opportunity. Just like developing nations skipped landlines and went straight to mobile, credit unions can bypass the legacy digital banking model and move directly to AI-driven, cognitive banking.
As the saying goes, “strategy is easy, execution is hard”. But when execution is driven by a clear purpose—delivering better financial services for members—it’s not just necessary. It’s energizing.
