From Reactive to Proactive: AI Member Experience for CUs

Most credit unions still design digital experiences as a reaction to member complaints, vendor roadmaps, or exam findings. By the time changes roll out, member expectations have already shifted again.

Kent Zimmer, CEO of CU NextGen, puts it bluntly:

“Being reactive is no longer an option if you want to remain relevant.”

He’s right. In late 2025, members are comparing your mobile app and service experience to their favorite fintechs and big banks—not the credit union down the street. And those competitors are using AI, no-code tools, and automation to constantly refine every interaction.

This article, part of the AI for Credit Unions: Member-Centric Banking series, looks at how AI-driven, member-centric experiences are being built right now—drawing from the mindset and approach Kent shared about CU NextGen. We’ll walk through practical ways credit unions can shift from reactive firefighting to proactive, personalized member service at scale.

From “Fixing Problems” to Designing Member Journeys

The core shift credit unions need to make is straightforward: stop treating member experience as a series of fixes and start treating it as a designed journey. AI and no-code platforms make that shift possible without tripling your tech staff.

Historically, member experience work has often looked like this:

• A spike in call center complaints triggers a project
• A regulator comment letter prompts a new disclosure flow
• A core provider releases a new module and you “turn it on”

You’re constantly reacting, but not actually getting closer to what members want: simple, intuitive, personalized help at the exact moment they need it.

Here’s what a proactive, AI-enabled approach looks like instead:

• Member journeys mapped end-to-end (join, borrow, save, recover from hardship, change life stage)
• Data signals used in real time (spend patterns, channel usage, life events) to shape offers and outreach
• Staff and members able to request changes and see them deployed quickly using no-code tools

The reality? It’s simpler than most credit unions think. The hard part isn’t the tech; it’s committing to design around the member, not the core or the org chart.

What “Next-Generation” Member Experience Really Means

Next-generation member experience isn’t about flashy UX. It’s about combining AI, automation, and flexible platforms so your credit union can adapt as fast as member expectations change.

CU NextGen, for example, sits at the intersection of:

• No-code application development – let non-developers create and adjust workflows
• Robotic Process Automation (RPA) – automate repetitive back-office tasks
• Artificial Intelligence – power smarter decisions, routing, and personalization

That stack gives credit unions a way to do three critical things.

1. Customize Digital Experience Without Waiting on Vendors

Most credit unions are bottlenecked by vendor queues. Need a small change to a digital form or onboarding step? You might be looking at months.

No-code and low-code tools flip that equation:

• Operations, lending, and member service teams can design and adjust workflows themselves
• Simple UX changes (copy, steps, conditional questions) can be made in days, not quarters
• A/B tests become realistic for smaller teams

I’ve seen credit unions cut digital account opening abandonment by 20–30% just by:

• Removing non-essential fields
• Pre-filling data the CU already knows
• Using conditional logic to hide irrelevant questions

You don’t need a huge dev team to do this—just a platform that lets business experts actually touch the experience.

2. Automate the “Grind” So Staff Can Actually Serve Members

Here’s the thing about AI and RPA in credit unions: the biggest impact usually isn’t in some futuristic chatbot, it’s in eliminating the daily grind your staff lives with.

Think about how many of these still exist in your shop:

• Manually rekeying data between LOS, core, and imaging
• Chasing missing documents by phone and email
• Manually reviewing simple eligibility or policy rules
• Moving tickets between departments based on email content

RPA and AI can:

• Read inbound documents and route them automatically
• Validate data against policies and flag only exceptions
• Trigger member notifications when a status changes
• Update multiple systems from a single action

When CU NextGen and similar platforms do this well, the result is simple: frontline staff stop playing traffic cop and start acting like trusted advisors.

3. Use AI to Make Every Interaction Feel 1:1

Member-centric banking with AI isn’t about “creepy” surveillance. It’s about using the data members already trust you with to make their lives easier.

Concrete uses of AI that credit unions are implementing now:

• Next-best-action recommendations in online banking: pay down high-interest debt, set up savings rules, or refinance
• AI-powered chat for routine questions that integrates with your knowledge base and core data
• Risk and fraud signals that let you intervene early when behavior looks unusual
• Intelligent routing that sends complex issues to the right specialist the first time

Done right, members experience:

• Fewer repetitive questions
• Faster resolutions
• More timely, relevant offers

And your teams get better context when a member calls, instead of starting from zero.

From Reactive to Proactive: A Simple Roadmap

You don’t need a five-year digital transformation master plan. You need a 12–18 month roadmap that moves you steadily from reactive changes to proactive, AI-informed design.

Here’s a practical approach that works for most credit unions.

Step 1: Pick One High-Impact Journey

Start where friction is most obvious. Good candidates:

• Consumer loan origination
• Credit card onboarding
• New member account opening
• Collections and hardship support

Ask two questions:

1. Where are members abandoning or complaining?
2. Where is staff time getting chewed up on manual steps?

That’s your starting point.

Step 2: Map the Current Experience and Data

Walk through the journey like a member. Literally click every step.

Document:

• Every screen, form, and authentication step
• Every handoff between systems or departments
• Every manual decision a person has to make

Then layer in data:

• Abandonment rates by step
• Average handle time for related calls or tickets
• Common error codes or exception reasons

This isn’t busywork. It’s how you identify where AI, no-code, and automation will actually matter.

Step 3: Introduce Automation and AI Where It Counts

Now prioritize 3–5 specific improvements. For example:

• Use no-code forms to shorten the application and hide irrelevant questions
• Deploy RPA bots to push approved applications to the core and imaging
• Add an AI assistant in the flow to answer “What does this mean?” questions
• Use AI-based decisioning (within policy) for clean, low-risk applications

The goal: reduce friction for the member and remove drudgery for staff in the same set of changes.

Step 4: Measure, Learn, Repeat

Member-centric AI isn’t “set it and forget it.” It’s a continuous loop:

• Ship targeted improvements quickly
• Monitor completion rates, NPS, call volume, and staff time
• Adjust flows with no-code tools based on real usage data

Within six months, you can have a noticeably better experience—and a better engine for future changes.

Member-Centric AI That Reflects Credit Union Values

A lot of credit union leaders quietly worry that AI will make their institution feel more like a faceless bank. That only happens when AI is used to optimize the institution, not the member.

The better way: use AI to deliver on traditional credit union values at scale.

Here’s what that looks like.

AI-Supported, Not AI-Driven Decisions

For areas like lending and collections, AI should:

• Surface insights (propensity to repay, early risk indicators)
• Highlight members who might need human outreach
• Automate documentation and analysis

But final decisions and member conversations stay human, guided by policy and empathy.

Transparent, Fair, and Explainable Models

Credit unions have a responsibility to:

• Avoid black-box decisioning they can’t explain
• Regularly test models for bias and disparate impact
• Offer clear, understandable reasons for adverse actions

If your team can’t explain why an AI model recommended something in plain language, it’s the wrong model for a credit union.

Member Trust as a Core Metric

AI for credit unions should be judged not just on ROI, but on trust indicators:

• Complaint rates on digital channels
• Member satisfaction with problem resolution
• Adoption of self-service tools without increased confusion

Member-centric banking means you’re optimizing for long-term relationships, not just short-term conversions.

Getting Started: Questions Every CU Leader Should Ask

If you’re serious about moving from reactive to proactive member experience with AI, here are a few tough questions to bring to your next leadership meeting:

1. Where are members explicitly telling us we’re hard to work with?
2. Which processes still depend on rekeying, swivel-chair work, or manual routing?
3. What’s the smallest member journey where AI, RPA, and no-code could make a visible difference within 6–9 months?
4. Do we have a partner or CUSO that can help us build once and reuse across the organization?
5. How will we measure “member-centric” outcomes, not just volume and cost?

There’s a better way to approach digital transformation than waiting for the next crisis or vendor upgrade. Leaders like Kent Zimmer and organizations like CU NextGen show that credit unions can adopt AI and automation in a way that feels personal, local, and aligned with the movement’s values.

For this AI for Credit Unions: Member-Centric Banking series, the playbook is clear: start small, focus on member journeys, use AI to amplify your people, and keep your institution firmly in the driver’s seat.

If your team is still mostly reacting to issues instead of shaping the experience, now’s the moment to change that. Your members are already comparing you to whatever app is on their home screen—and they’re not waiting.